Journal of Hospital Medicine

You are signing over to a colleague on the COVID-19 inpatient hospital ward. You are stressed after having failed to reach the chief medical resident who did not respond despite repeated texts. You think about mentioning this apparent professional lapse to your colleague. You pause, however, because you are uncertain about the appropriate norm, hesitant around finding the right words, and unsure about a mutual feeling of camaraderie.

Lay and scientific perspectives about gossip diverge widely. Lay definitions of gossip generally include malicious, salacious, immoral, trivial, or unfair comments that attack someone else’s reputation. Scientific definitions of gossip, in contrast, also include neutral or positive social information intended to align group dynamics.¹ The common feature of both is that the named individual is not present to hear about themselves.² A further commonality is that gossip involves informal assessments loaded with subjective judgments, unlike professional comments about patients from clinicians providing care. In contrast to stereotype remarks, gossip focuses on a specific person and not a group.

Gossip is widespread. A recent study in nonhospital settings suggests nearly all adults engage in gossip during normal interactions, averaging 52 minutes on a typical day.³ Most gossip is neutral (74%) rather than negative or positive. The content usually (92%) concerns relationships, and the typical person identified (82%) is an acquaintance. Some of the potential benefits include conveying information for social learning, defining what is socially acceptable, or promoting personal connections. Men and women gossip to nearly the same degree.⁴ Indeed, evolutionary theory suggests gossip is not deviant behavior and arises even in small hunter-gatherer communities.⁵Social psychology science provides some insights on fundamental principles of gossip that may be relevant to clinicians in medicine.⁶ In this article, we review three important findings from social psychology science relevant to team cooperation, the specific transmitter, and the individual receiver (Table 1). Clinicians working in groups may benefit from recognizing the prosocial function of healthy gossip and avoiding the antisocial adverse effects of harmful gossip.⁷ At a time when work-related conversations have radically shifted online,⁸ hospitalists need to be aware of positives and pitfalls of gossip to help provide effective medical care and avoid adverse events.

JHMVol16No11_Redelme06341117e_t1.JPG

Large team endeavors often require social signals to coordinate people.⁹ Gossip helps groups establish reputations, monitor their members, deter antisocial behavior, and protect newcomers from exploitation.¹⁰ Sharing social information can also indirectly promote cooperation because individuals place a high value on their own reputations and want to avoid embarrassment.^11,12 The absence of gossip, in contrast, may lead individuals to be oblivious to team expectations and fail to do their fair share. A lack of gossip, in particular, may add to inefficiencies during the COVID-19 pandemic since exchanging gossip seems to feel awkward over email or other digital channels (albeit a chat function for side conversations in virtual meetings is a partial substitute).^13,14

A paradigm for testing the positive effects of gossip involves a trust game where participants consider making small contributions for later rewards in recurrent rounds of cooperation.^15-17 In one online study of volunteers, for example, individuals contributed to a group account and gained rewards equal to a doubling of total contributions shared over everyone equally (even those contributing nothing).¹⁸ Half the experiments allowed participants to send notes about other participants, whereas the other experiments allowed no such “gossip.” As predicted, gossip increased the proportion contributed (40% vs 32%, P = .020) and average total reward (64 vs 56, P = .002). In this and other studies of healthy volunteers, gossip builds trust and increases gains for the entire group.^19-22

Effective medical practice inside hospitals often involves constructive gossip for pointers on how to behave (eg, how quickly to reply to a text message from the ward pharmacist). The blend of objective facts with subjective opinion provides a compelling message otherwise lacking from institutional guidelines or directives on how not to behave (eg, how quickly to complete an annual report with an arbitrary deadline). Gossip is the antithesis of a cursory interaction between strangers and is also less awkward than open flattery or public ridicule that may occur when the third person is in earshot.²³ Even negative social comparisons can be constructive to listeners since people want to know how to avoid bad gossip about themselves in a world with changing morality.²⁴

Gossip can provide a distinct emotional benefit for the gossiper as a form of self-expression, an exercise of justice, and a validation of one’s perspective.²⁵ Consider, for example, witnessing an antisocial act that leads to subsequent feelings of unfairness yet having no way to communicate personal dissatisfaction. Similarly, expressing prosocial gossip may help relieve some of the annoyance after a hassle (eg, talking with a friend after encountering a new onerous hospital protocol). The sharing of gossip might also help bolster solidarity after an offense (eg, talking with a friend on how to deal with another warning from health records).²⁶ In contrast, lost opportunities to gossip about unfairness could be exacerbating the social isolation and emotional distress of the COVID-19 pandemic.^27,28

A rigorous example of the emotional benefits of expressing gossip involves undergraduates witnessing staged behavior under laboratory conditions where one actor appeared to exploit the generosity of another actor.²⁹ By random assignment, half the participants had an opportunity to gossip, and the other half had no such opportunity. All participants reacted to the antisocial behavior by feeling frustrated (self-report survey scale of 0-100, where higher scores indicate worse frustration). Importantly, almost all chose to engage in gossip when feasible, and those who had the opportunity to gossip experienced more relief than those who had no opportunity (absolute improvement in frustration scores, 9.69 vs 0.16; P < .01). Evidentially, engaging in prosocial gossip can sometimes provide solace.

Sharing gossip might strengthen social bonds, bolster self-esteem, promote personal power, elicit reciprocal favors, or telegraph the presence of a larger network of personal connections. Gossip is cheap and efficient compared with peer-sanctioning or formal sanctioning to control behavior.³⁰ Airing grievances through gossip may also solve some social dilemmas more easily than channeling messages through institutional reporting structures or formal performance reviews. Gossip has another advantage of raising delicate comparative judgments without the discomfort of direct confrontation (eg, defining the appropriate level of detail for a case presentation is perhaps best done by identifying those who are judged too verbose).³¹

People tend to enjoy listening to gossip despite the uneven quality where some comments are more valuable than others. The receiver, therefore, faces an irregular payoff similar to random intermittent reinforcement. Ironically, random intermittent reinforcement can be particularly addictive when compared with steady rewards with predictable payoffs. This includes cases where gossip conveys good news that helps elevate, inspire, or motivate the receiver. The thirst for more gossip may partially explain why receivers keep seeking gossip despite knowing the material may be unimportant. The shortfall of enticing gossip might also be another factor adding to a feeling of loneliness that prevails widely during the COVID-19 pandemic.^32,33

Classic research on reinforcement includes experiments examining operant conditioning for creating addiction.^34,35 An important distinction is the contrast between random reinforcement (eg, variable reward akin to gambling on a roulette wheel) and consistent reinforcement (eg, regular pay akin to a steady salary each week). In a study of pigeons trained to peck a lever for food, for example, random reinforcement resulted in twice the response compared with consistent reinforcement (despite an equalized total amount of food received).³⁶ Moreover, random reinforcement was hard to extinguish, and the behavior continued long after all food ended. In general, random compared with consistent reinforcement tended to cause a more intense and persistent change of behavior.

The inconsistent quality makes the prospect of new, exciting gossip seem nearly impossible to resist; indeed, gossip from any source is surprisingly tantalizing. Moreover, the validity of gossip is rarely challenged, unlike the typical norm of lively thoughtful debate that surrounds new ideas (eg, whether to prescribe a novel medication).² Gossip, of course, can also lead to a positive thrill where, for example, a recipient subsequently feels emboldened with passionate enthusiasm to relay the point to others. This means that spreading inaccurate characterizations may be particularly destructive for a listener who is gullible or easily provoked.³⁷ Conversely, gossip can also lead to anxiety about future uncertainties.³⁸

This perspective summarizes positive and negative features of gossip drawn from social psychology science on a normally hidden activity. The main benefits in medical care are to support team communication, the specific transmitter, and the individual receiver. Some specific gains are to enhance team cooperation, deter exploitation, signal trust, and convey codes of conduct. Sharing gossip might also promote honest dialogue, foster friendships, facilitate reciprocity, and curtail excessive use of force by a dominant individual. Listening to gossip possibly also reduces loneliness, affirms an innate desire for inclusion, and provides a way to share insights. Of course, gossip has downsides from direct or indirect adverse effects that merit attention and mitigation (Table 2).

JHMVol16No11_Redelme06341117e_t2.JPG

A large direct downside of gossip is in propagating damaging misinformation that harms individuals.²⁴ Toxic gossip can wreck relationships, hurt feelings, violate privacy, and manipulate others. Malicious gossip may become further accentuated because of groupthink, polarization, or selfish biases.³⁹ Presumably, these downsides of gossip are sufficiently infrequent because regular people spend substantial time, attention, and effort engaging in gossip.³ In society, healthy gossip that propagates positive information goes by synonyms having a less negative connotation, including socializing, networking, chatting, schmoozing, friendly banter, small talk, and scuttlebutt. The net benefits must be real since one person is often both a transmitter and a receiver of gossip over time.

Another large direct limitation of gossip is that it can magnify social inequities by allowing some people but not others to access hidden information. In essence, receiving gossip is a privilege that is not universally available within a community and depends on social capital.⁴⁰ Gossip helps strengthen personal bonds, so marginalized individuals can become further disempowered by not receiving gossip. Social exclusion is painful when different individuals realize they are left out of gossip circles. In summary, gossip can provide an unfair advantage because it allows only some people to learn what is going on behind their backs (eg, different hospitalists within the same institution may have differing circles of friendships for different professional advantages).

Gossip is a way to communicate priorities and regulate behavior. Without interpersonal comparisons, clinicians might find themselves adrift in a complex, difficult, and mysterious medical world. Listening to intelligent gossip can also be an effective way to learn lessons that are otherwise difficult to grasp (eg, an impolite comment may be more easily recognized in someone else than in yourself).⁴¹ Perhaps this explains why hospital executives gossip about physicians and vice versa.⁴² Healthy gossip tends to be positive or neutral (not malicious or negative), propagates accurate information (not hurtful falsehoods), and corrects social inequities (not worsening unearned privileges).⁴³ We suggest that a careful practice of healthy gossip may help regulate trust, enhance social bonding, shape how people feel working together, and promote collective benefit.

Your colleague spontaneously comments that the chief medical resident is away because of a death in the family. In turn, you realize you were unaware of this personal nuance because the point was unmentioned in the (virtual) staff meeting last week. You thank your colleague for tactfully relaying the point. You also secretly wonder what other interpersonal details you might be missing during the COVID-19 pandemic.

The authors thank Cindy Kao, Fizza Manzoor, Sheharyar Raza, Lee Ross, Miriam Shuchman, and William Silverstein for helpful suggestions on specific points.

You are signing over to a colleague on the COVID-19 inpatient hospital ward. You are stressed after having failed to reach the chief medical resident who did not respond despite repeated texts. You think about mentioning this apparent professional lapse to your colleague. You pause, however, because you are uncertain about the appropriate norm, hesitant around finding the right words, and unsure about a mutual feeling of camaraderie.

Lay and scientific perspectives about gossip diverge widely. Lay definitions of gossip generally include malicious, salacious, immoral, trivial, or unfair comments that attack someone else’s reputation. Scientific definitions of gossip, in contrast, also include neutral or positive social information intended to align group dynamics.¹ The common feature of both is that the named individual is not present to hear about themselves.² A further commonality is that gossip involves informal assessments loaded with subjective judgments, unlike professional comments about patients from clinicians providing care. In contrast to stereotype remarks, gossip focuses on a specific person and not a group.

Gossip is widespread. A recent study in nonhospital settings suggests nearly all adults engage in gossip during normal interactions, averaging 52 minutes on a typical day.³ Most gossip is neutral (74%) rather than negative or positive. The content usually (92%) concerns relationships, and the typical person identified (82%) is an acquaintance. Some of the potential benefits include conveying information for social learning, defining what is socially acceptable, or promoting personal connections. Men and women gossip to nearly the same degree.⁴ Indeed, evolutionary theory suggests gossip is not deviant behavior and arises even in small hunter-gatherer communities.⁵Social psychology science provides some insights on fundamental principles of gossip that may be relevant to clinicians in medicine.⁶ In this article, we review three important findings from social psychology science relevant to team cooperation, the specific transmitter, and the individual receiver (Table 1). Clinicians working in groups may benefit from recognizing the prosocial function of healthy gossip and avoiding the antisocial adverse effects of harmful gossip.⁷ At a time when work-related conversations have radically shifted online,⁸ hospitalists need to be aware of positives and pitfalls of gossip to help provide effective medical care and avoid adverse events.

JHMVol16No11_Redelme06341117e_t1.JPG

Large team endeavors often require social signals to coordinate people.⁹ Gossip helps groups establish reputations, monitor their members, deter antisocial behavior, and protect newcomers from exploitation.¹⁰ Sharing social information can also indirectly promote cooperation because individuals place a high value on their own reputations and want to avoid embarrassment.^11,12 The absence of gossip, in contrast, may lead individuals to be oblivious to team expectations and fail to do their fair share. A lack of gossip, in particular, may add to inefficiencies during the COVID-19 pandemic since exchanging gossip seems to feel awkward over email or other digital channels (albeit a chat function for side conversations in virtual meetings is a partial substitute).^13,14

A paradigm for testing the positive effects of gossip involves a trust game where participants consider making small contributions for later rewards in recurrent rounds of cooperation.^15-17 In one online study of volunteers, for example, individuals contributed to a group account and gained rewards equal to a doubling of total contributions shared over everyone equally (even those contributing nothing).¹⁸ Half the experiments allowed participants to send notes about other participants, whereas the other experiments allowed no such “gossip.” As predicted, gossip increased the proportion contributed (40% vs 32%, P = .020) and average total reward (64 vs 56, P = .002). In this and other studies of healthy volunteers, gossip builds trust and increases gains for the entire group.^19-22

Effective medical practice inside hospitals often involves constructive gossip for pointers on how to behave (eg, how quickly to reply to a text message from the ward pharmacist). The blend of objective facts with subjective opinion provides a compelling message otherwise lacking from institutional guidelines or directives on how not to behave (eg, how quickly to complete an annual report with an arbitrary deadline). Gossip is the antithesis of a cursory interaction between strangers and is also less awkward than open flattery or public ridicule that may occur when the third person is in earshot.²³ Even negative social comparisons can be constructive to listeners since people want to know how to avoid bad gossip about themselves in a world with changing morality.²⁴

Gossip can provide a distinct emotional benefit for the gossiper as a form of self-expression, an exercise of justice, and a validation of one’s perspective.²⁵ Consider, for example, witnessing an antisocial act that leads to subsequent feelings of unfairness yet having no way to communicate personal dissatisfaction. Similarly, expressing prosocial gossip may help relieve some of the annoyance after a hassle (eg, talking with a friend after encountering a new onerous hospital protocol). The sharing of gossip might also help bolster solidarity after an offense (eg, talking with a friend on how to deal with another warning from health records).²⁶ In contrast, lost opportunities to gossip about unfairness could be exacerbating the social isolation and emotional distress of the COVID-19 pandemic.^27,28

A rigorous example of the emotional benefits of expressing gossip involves undergraduates witnessing staged behavior under laboratory conditions where one actor appeared to exploit the generosity of another actor.²⁹ By random assignment, half the participants had an opportunity to gossip, and the other half had no such opportunity. All participants reacted to the antisocial behavior by feeling frustrated (self-report survey scale of 0-100, where higher scores indicate worse frustration). Importantly, almost all chose to engage in gossip when feasible, and those who had the opportunity to gossip experienced more relief than those who had no opportunity (absolute improvement in frustration scores, 9.69 vs 0.16; P < .01). Evidentially, engaging in prosocial gossip can sometimes provide solace.

Sharing gossip might strengthen social bonds, bolster self-esteem, promote personal power, elicit reciprocal favors, or telegraph the presence of a larger network of personal connections. Gossip is cheap and efficient compared with peer-sanctioning or formal sanctioning to control behavior.³⁰ Airing grievances through gossip may also solve some social dilemmas more easily than channeling messages through institutional reporting structures or formal performance reviews. Gossip has another advantage of raising delicate comparative judgments without the discomfort of direct confrontation (eg, defining the appropriate level of detail for a case presentation is perhaps best done by identifying those who are judged too verbose).³¹

People tend to enjoy listening to gossip despite the uneven quality where some comments are more valuable than others. The receiver, therefore, faces an irregular payoff similar to random intermittent reinforcement. Ironically, random intermittent reinforcement can be particularly addictive when compared with steady rewards with predictable payoffs. This includes cases where gossip conveys good news that helps elevate, inspire, or motivate the receiver. The thirst for more gossip may partially explain why receivers keep seeking gossip despite knowing the material may be unimportant. The shortfall of enticing gossip might also be another factor adding to a feeling of loneliness that prevails widely during the COVID-19 pandemic.^32,33

Classic research on reinforcement includes experiments examining operant conditioning for creating addiction.^34,35 An important distinction is the contrast between random reinforcement (eg, variable reward akin to gambling on a roulette wheel) and consistent reinforcement (eg, regular pay akin to a steady salary each week). In a study of pigeons trained to peck a lever for food, for example, random reinforcement resulted in twice the response compared with consistent reinforcement (despite an equalized total amount of food received).³⁶ Moreover, random reinforcement was hard to extinguish, and the behavior continued long after all food ended. In general, random compared with consistent reinforcement tended to cause a more intense and persistent change of behavior.

The inconsistent quality makes the prospect of new, exciting gossip seem nearly impossible to resist; indeed, gossip from any source is surprisingly tantalizing. Moreover, the validity of gossip is rarely challenged, unlike the typical norm of lively thoughtful debate that surrounds new ideas (eg, whether to prescribe a novel medication).² Gossip, of course, can also lead to a positive thrill where, for example, a recipient subsequently feels emboldened with passionate enthusiasm to relay the point to others. This means that spreading inaccurate characterizations may be particularly destructive for a listener who is gullible or easily provoked.³⁷ Conversely, gossip can also lead to anxiety about future uncertainties.³⁸

This perspective summarizes positive and negative features of gossip drawn from social psychology science on a normally hidden activity. The main benefits in medical care are to support team communication, the specific transmitter, and the individual receiver. Some specific gains are to enhance team cooperation, deter exploitation, signal trust, and convey codes of conduct. Sharing gossip might also promote honest dialogue, foster friendships, facilitate reciprocity, and curtail excessive use of force by a dominant individual. Listening to gossip possibly also reduces loneliness, affirms an innate desire for inclusion, and provides a way to share insights. Of course, gossip has downsides from direct or indirect adverse effects that merit attention and mitigation (Table 2).

JHMVol16No11_Redelme06341117e_t2.JPG

A large direct downside of gossip is in propagating damaging misinformation that harms individuals.²⁴ Toxic gossip can wreck relationships, hurt feelings, violate privacy, and manipulate others. Malicious gossip may become further accentuated because of groupthink, polarization, or selfish biases.³⁹ Presumably, these downsides of gossip are sufficiently infrequent because regular people spend substantial time, attention, and effort engaging in gossip.³ In society, healthy gossip that propagates positive information goes by synonyms having a less negative connotation, including socializing, networking, chatting, schmoozing, friendly banter, small talk, and scuttlebutt. The net benefits must be real since one person is often both a transmitter and a receiver of gossip over time.

Another large direct limitation of gossip is that it can magnify social inequities by allowing some people but not others to access hidden information. In essence, receiving gossip is a privilege that is not universally available within a community and depends on social capital.⁴⁰ Gossip helps strengthen personal bonds, so marginalized individuals can become further disempowered by not receiving gossip. Social exclusion is painful when different individuals realize they are left out of gossip circles. In summary, gossip can provide an unfair advantage because it allows only some people to learn what is going on behind their backs (eg, different hospitalists within the same institution may have differing circles of friendships for different professional advantages).

Gossip is a way to communicate priorities and regulate behavior. Without interpersonal comparisons, clinicians might find themselves adrift in a complex, difficult, and mysterious medical world. Listening to intelligent gossip can also be an effective way to learn lessons that are otherwise difficult to grasp (eg, an impolite comment may be more easily recognized in someone else than in yourself).⁴¹ Perhaps this explains why hospital executives gossip about physicians and vice versa.⁴² Healthy gossip tends to be positive or neutral (not malicious or negative), propagates accurate information (not hurtful falsehoods), and corrects social inequities (not worsening unearned privileges).⁴³ We suggest that a careful practice of healthy gossip may help regulate trust, enhance social bonding, shape how people feel working together, and promote collective benefit.

Your colleague spontaneously comments that the chief medical resident is away because of a death in the family. In turn, you realize you were unaware of this personal nuance because the point was unmentioned in the (virtual) staff meeting last week. You thank your colleague for tactfully relaying the point. You also secretly wonder what other interpersonal details you might be missing during the COVID-19 pandemic.

The authors thank Cindy Kao, Fizza Manzoor, Sheharyar Raza, Lee Ross, Miriam Shuchman, and William Silverstein for helpful suggestions on specific points.

You are signing over to a colleague on the COVID-19 inpatient hospital ward. You are stressed after having failed to reach the chief medical resident who did not respond despite repeated texts. You think about mentioning this apparent professional lapse to your colleague. You pause, however, because you are uncertain about the appropriate norm, hesitant around finding the right words, and unsure about a mutual feeling of camaraderie.

Lay and scientific perspectives about gossip diverge widely. Lay definitions of gossip generally include malicious, salacious, immoral, trivial, or unfair comments that attack someone else’s reputation. Scientific definitions of gossip, in contrast, also include neutral or positive social information intended to align group dynamics.¹ The common feature of both is that the named individual is not present to hear about themselves.² A further commonality is that gossip involves informal assessments loaded with subjective judgments, unlike professional comments about patients from clinicians providing care. In contrast to stereotype remarks, gossip focuses on a specific person and not a group.

Gossip is widespread. A recent study in nonhospital settings suggests nearly all adults engage in gossip during normal interactions, averaging 52 minutes on a typical day.³ Most gossip is neutral (74%) rather than negative or positive. The content usually (92%) concerns relationships, and the typical person identified (82%) is an acquaintance. Some of the potential benefits include conveying information for social learning, defining what is socially acceptable, or promoting personal connections. Men and women gossip to nearly the same degree.⁴ Indeed, evolutionary theory suggests gossip is not deviant behavior and arises even in small hunter-gatherer communities.⁵Social psychology science provides some insights on fundamental principles of gossip that may be relevant to clinicians in medicine.⁶ In this article, we review three important findings from social psychology science relevant to team cooperation, the specific transmitter, and the individual receiver (Table 1). Clinicians working in groups may benefit from recognizing the prosocial function of healthy gossip and avoiding the antisocial adverse effects of harmful gossip.⁷ At a time when work-related conversations have radically shifted online,⁸ hospitalists need to be aware of positives and pitfalls of gossip to help provide effective medical care and avoid adverse events.

JHMVol16No11_Redelme06341117e_t1.JPG

Large team endeavors often require social signals to coordinate people.⁹ Gossip helps groups establish reputations, monitor their members, deter antisocial behavior, and protect newcomers from exploitation.¹⁰ Sharing social information can also indirectly promote cooperation because individuals place a high value on their own reputations and want to avoid embarrassment.^11,12 The absence of gossip, in contrast, may lead individuals to be oblivious to team expectations and fail to do their fair share. A lack of gossip, in particular, may add to inefficiencies during the COVID-19 pandemic since exchanging gossip seems to feel awkward over email or other digital channels (albeit a chat function for side conversations in virtual meetings is a partial substitute).^13,14

A paradigm for testing the positive effects of gossip involves a trust game where participants consider making small contributions for later rewards in recurrent rounds of cooperation.^15-17 In one online study of volunteers, for example, individuals contributed to a group account and gained rewards equal to a doubling of total contributions shared over everyone equally (even those contributing nothing).¹⁸ Half the experiments allowed participants to send notes about other participants, whereas the other experiments allowed no such “gossip.” As predicted, gossip increased the proportion contributed (40% vs 32%, P = .020) and average total reward (64 vs 56, P = .002). In this and other studies of healthy volunteers, gossip builds trust and increases gains for the entire group.^19-22

Effective medical practice inside hospitals often involves constructive gossip for pointers on how to behave (eg, how quickly to reply to a text message from the ward pharmacist). The blend of objective facts with subjective opinion provides a compelling message otherwise lacking from institutional guidelines or directives on how not to behave (eg, how quickly to complete an annual report with an arbitrary deadline). Gossip is the antithesis of a cursory interaction between strangers and is also less awkward than open flattery or public ridicule that may occur when the third person is in earshot.²³ Even negative social comparisons can be constructive to listeners since people want to know how to avoid bad gossip about themselves in a world with changing morality.²⁴

Gossip can provide a distinct emotional benefit for the gossiper as a form of self-expression, an exercise of justice, and a validation of one’s perspective.²⁵ Consider, for example, witnessing an antisocial act that leads to subsequent feelings of unfairness yet having no way to communicate personal dissatisfaction. Similarly, expressing prosocial gossip may help relieve some of the annoyance after a hassle (eg, talking with a friend after encountering a new onerous hospital protocol). The sharing of gossip might also help bolster solidarity after an offense (eg, talking with a friend on how to deal with another warning from health records).²⁶ In contrast, lost opportunities to gossip about unfairness could be exacerbating the social isolation and emotional distress of the COVID-19 pandemic.^27,28

A rigorous example of the emotional benefits of expressing gossip involves undergraduates witnessing staged behavior under laboratory conditions where one actor appeared to exploit the generosity of another actor.²⁹ By random assignment, half the participants had an opportunity to gossip, and the other half had no such opportunity. All participants reacted to the antisocial behavior by feeling frustrated (self-report survey scale of 0-100, where higher scores indicate worse frustration). Importantly, almost all chose to engage in gossip when feasible, and those who had the opportunity to gossip experienced more relief than those who had no opportunity (absolute improvement in frustration scores, 9.69 vs 0.16; P < .01). Evidentially, engaging in prosocial gossip can sometimes provide solace.

Sharing gossip might strengthen social bonds, bolster self-esteem, promote personal power, elicit reciprocal favors, or telegraph the presence of a larger network of personal connections. Gossip is cheap and efficient compared with peer-sanctioning or formal sanctioning to control behavior.³⁰ Airing grievances through gossip may also solve some social dilemmas more easily than channeling messages through institutional reporting structures or formal performance reviews. Gossip has another advantage of raising delicate comparative judgments without the discomfort of direct confrontation (eg, defining the appropriate level of detail for a case presentation is perhaps best done by identifying those who are judged too verbose).³¹

People tend to enjoy listening to gossip despite the uneven quality where some comments are more valuable than others. The receiver, therefore, faces an irregular payoff similar to random intermittent reinforcement. Ironically, random intermittent reinforcement can be particularly addictive when compared with steady rewards with predictable payoffs. This includes cases where gossip conveys good news that helps elevate, inspire, or motivate the receiver. The thirst for more gossip may partially explain why receivers keep seeking gossip despite knowing the material may be unimportant. The shortfall of enticing gossip might also be another factor adding to a feeling of loneliness that prevails widely during the COVID-19 pandemic.^32,33

Classic research on reinforcement includes experiments examining operant conditioning for creating addiction.^34,35 An important distinction is the contrast between random reinforcement (eg, variable reward akin to gambling on a roulette wheel) and consistent reinforcement (eg, regular pay akin to a steady salary each week). In a study of pigeons trained to peck a lever for food, for example, random reinforcement resulted in twice the response compared with consistent reinforcement (despite an equalized total amount of food received).³⁶ Moreover, random reinforcement was hard to extinguish, and the behavior continued long after all food ended. In general, random compared with consistent reinforcement tended to cause a more intense and persistent change of behavior.

The inconsistent quality makes the prospect of new, exciting gossip seem nearly impossible to resist; indeed, gossip from any source is surprisingly tantalizing. Moreover, the validity of gossip is rarely challenged, unlike the typical norm of lively thoughtful debate that surrounds new ideas (eg, whether to prescribe a novel medication).² Gossip, of course, can also lead to a positive thrill where, for example, a recipient subsequently feels emboldened with passionate enthusiasm to relay the point to others. This means that spreading inaccurate characterizations may be particularly destructive for a listener who is gullible or easily provoked.³⁷ Conversely, gossip can also lead to anxiety about future uncertainties.³⁸

This perspective summarizes positive and negative features of gossip drawn from social psychology science on a normally hidden activity. The main benefits in medical care are to support team communication, the specific transmitter, and the individual receiver. Some specific gains are to enhance team cooperation, deter exploitation, signal trust, and convey codes of conduct. Sharing gossip might also promote honest dialogue, foster friendships, facilitate reciprocity, and curtail excessive use of force by a dominant individual. Listening to gossip possibly also reduces loneliness, affirms an innate desire for inclusion, and provides a way to share insights. Of course, gossip has downsides from direct or indirect adverse effects that merit attention and mitigation (Table 2).

JHMVol16No11_Redelme06341117e_t2.JPG

A large direct downside of gossip is in propagating damaging misinformation that harms individuals.²⁴ Toxic gossip can wreck relationships, hurt feelings, violate privacy, and manipulate others. Malicious gossip may become further accentuated because of groupthink, polarization, or selfish biases.³⁹ Presumably, these downsides of gossip are sufficiently infrequent because regular people spend substantial time, attention, and effort engaging in gossip.³ In society, healthy gossip that propagates positive information goes by synonyms having a less negative connotation, including socializing, networking, chatting, schmoozing, friendly banter, small talk, and scuttlebutt. The net benefits must be real since one person is often both a transmitter and a receiver of gossip over time.

Another large direct limitation of gossip is that it can magnify social inequities by allowing some people but not others to access hidden information. In essence, receiving gossip is a privilege that is not universally available within a community and depends on social capital.⁴⁰ Gossip helps strengthen personal bonds, so marginalized individuals can become further disempowered by not receiving gossip. Social exclusion is painful when different individuals realize they are left out of gossip circles. In summary, gossip can provide an unfair advantage because it allows only some people to learn what is going on behind their backs (eg, different hospitalists within the same institution may have differing circles of friendships for different professional advantages).

Gossip is a way to communicate priorities and regulate behavior. Without interpersonal comparisons, clinicians might find themselves adrift in a complex, difficult, and mysterious medical world. Listening to intelligent gossip can also be an effective way to learn lessons that are otherwise difficult to grasp (eg, an impolite comment may be more easily recognized in someone else than in yourself).⁴¹ Perhaps this explains why hospital executives gossip about physicians and vice versa.⁴² Healthy gossip tends to be positive or neutral (not malicious or negative), propagates accurate information (not hurtful falsehoods), and corrects social inequities (not worsening unearned privileges).⁴³ We suggest that a careful practice of healthy gossip may help regulate trust, enhance social bonding, shape how people feel working together, and promote collective benefit.

Your colleague spontaneously comments that the chief medical resident is away because of a death in the family. In turn, you realize you were unaware of this personal nuance because the point was unmentioned in the (virtual) staff meeting last week. You thank your colleague for tactfully relaying the point. You also secretly wonder what other interpersonal details you might be missing during the COVID-19 pandemic.

The authors thank Cindy Kao, Fizza Manzoor, Sheharyar Raza, Lee Ross, Miriam Shuchman, and William Silverstein for helpful suggestions on specific points.

Inspired by the ABIM Foundation’s Choosing Wisely^® campaign, the “Things We Do for No Reason^™” (TWDFNR) series reviews practices that have become common parts of hospital care but may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent clear-cut conclusions or clinical practice standards but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion.

A 70-year-old woman presented to the emergency department (ED) with diffuse abdominal pain, nausea, and vomiting with normal liver function tests and lipase. Computed tomography (CT) of the abdomen and pelvis with intravenous contrast revealed no acute intraabdominal pathology except for an incidentally noted, mildly enlarged but nondistended gallbladder without evident cholelithiasis, pericholecystic fluid, or gallbladder wall edema. The hospitalist orders an abdominal ultrasound to evaluate for acute biliary pathology potentially missed by CT. 

Guidelines and expert opinion recommend an “ultrasound-first” approach when patients present with right upper quadrant (RUQ) abdominal pain or pelvic pain of suspected gynecologic origin.^1-3 When evaluating suspected biliary disease, experts recommend beginning with ultrasonography based on the speed of obtaining results, absence of radiation exposure, reduced cost, and good diagnostic accuracy.¹ Ultrasound has superior sensitivity, of 98%,⁴ in identifying radiolucent gallstones, compared to CT’s 79% sensitivity.⁵ Ultrasonography also differentiates gallbladder sludge from cholelithiasis, evaluates the extrahepatic and intrahepatic bile ducts, and can identify alternate causes of RUQ pain.^1,3 Since ultrasound has important advantages, a negative initial CT may lead the clinician to consider an ultrasound to evaluate for gallbladder diseases.

Additionally, ultrasound provides improved anatomic detail of pelvic structures when diagnosing endometrial or ovarian pathology² and improves diagnostic accuracy when the initial CT reveals an abnormal pelvic finding (eg, defining an enlarged ovary on CT as ovarian torsion, a cyst, or an adnexal mass).⁶ While CT excludes emergent surgical diagnoses, ultrasound may add value in elucidating a cause of the pain, even when urgent surgical management is not necessary.⁷

Many providers believe that a CT lacks sensitivity for acute biliary or pelvic pathology and will order an ultrasound to avoid missing an important diagnosis.⁷ Within 6 months at a single center, clinicians ordered 614 abdominal ultrasounds within 72 hours of an abdominal CT; 227 of these orders were to evaluate the gallbladder. Clinicians documented a discussion with a radiologist in only 19% of cases.⁸

While ultrasound is more sensitive for detecting gallstones, the data do not indicate that it is more sensitive than CT for detecting acute cholecystitis. Abdominal ultrasound has a sensitivity for the diagnosis of acute cholecystitis of 81%, with a specificity of 83%,⁹ while CT has a comparable 85% to 94%^9,10 sensitivity and specificity ranging from 59% to 99%.^9,11 A recent study using more stringent radiographic criteria (two or more abnormal features) for diagnosing acute cholecystitis found ultrasound and CT had near equivalent sensitivities of 61% and 55%, respectively.¹² Even with these stringent criteria, CT had a negative predictive value of 90% and approached 95% when applying a less strict (one feature) criterion.¹² As a result, an abdominal ultrasound will rarely diagnose cholecystitis after a normal CT.

A 2020 study evaluated the diagnostic yield and clinical impact of ordering an abdominal or pelvic ultrasound within 24 hours of a negative abdominal CT.⁷It found that only 3/132 (2.3%) of abdominal ultrasounds ordered after a negative CT revealed acute pathology potentially requiring surgery. Only one of these three patients (1/132) required surgical intervention for confirmed acute cholecystitis.⁷ The follow-up abdominal ultrasound identified asymptomatic gallstones in 9/132 (6.8%) and gallbladder polyps in 4/132 (3.0%) of cases.⁷ Selective use of ultrasound after CT for patients with clinically worsening or progressive RUQ pain will avoid missing a “can’t miss” diagnosis and reduce low-yield testing for a majority of patients.

As with abdominal CT and ultrasound, the recommendation for an initial pelvic ultrasound when evaluating female pelvic pain also stems from the reduced cost, absence of radiation exposure, and superior anatomic visualization of the pelvic organs when compared with pelvic CT.^2,13 However, as with the results of studies investigating the use of abdominal ultrasound after negative CT, a study of pelvic ultrasound after a negative CT revealed that only 4/126 (3.2%) follow-up ultrasounds had an abnormal finding not identified on CT.¹³ Pelvic ultrasound found four endometrial abnormalities that did not alter acute management.¹³ Notably, in 58% of the cases, the indication for ordering the subsequent ultrasound was “rule out ovarian torsion.” However, CT almost always finds a morphologically abnormal ovary in the case of torsion.⁶ One study and literature review found that all 28 patients studied and all 85 patients from previous studies with proven ovarian torsion had either an adnexal mass or an enlarged ovary on pelvic CT.⁶ Harfouch et al found that 0 out of 199 pelvic ultrasounds ordered after a negative CT revealed acute surgical pathology, but pelvic ultrasound did identify nonsurgical uterine and ovarian abnormalities.⁷ In conclusion, when clinicians order CT as the first study to diagnose acute, surgical biliary or gynecologic causes of pain, follow-up ultrasound has a low probability of affecting diagnosis or management if the CT is normal.

The previous discussion only applies if hospitalists order an ultrasound within 24 to 48 hours of the initial CT. Time and clinical course are critical diagnostic tools during an admission for abdominal pain. Consider pelvic or abdominal ultrasound based on guideline recommendations if a patient develops new or evolving RUQ or pelvic pain.^1,2 The rationale for obtaining the initial negative CT may no longer apply, and the clinician must consider the changing characteristics of the patient’s symptoms. For example, initial CT imaging may miss cholelithiasis in a patient presenting for biliary colic. Under observation, the patient may develop acute cholecystitis, potentially requiring an abdominal ultrasound. Also, the data for pelvic ultrasound apply to a normal CT of the abdomen and pelvis. Ultrasound may help to further evaluate indeterminate findings present on initial CT or if recommended by radiology.

When the hospitalist assumes care for a patient with abdominal pain and a negative CT, appropriate next steps include taking time to reexamine the differential diagnosis, repeating the history and physical, and communicating directly with a radiologist. These steps ensure the highest diagnostic yield and the lowest cost and help prevent diagnostic error arising from anchoring on the initial negative ED evaluation. Prior research demonstrates that the initial history alone can lead to the correct diagnosis in up to 76% of cases of abdominal pain.¹⁴ If repeat evaluation determines that additional imaging is necessary, the American College of Radiology provides evidence-based guidelines to help clinicians determine the correct imaging test based on the clinical situation (Appendix Table).^1,2 For example, an equivocal ultrasound or CT exam with continued suspicion for acute cholecystitis or an alternate diagnosis, such as acalculous cholecystitis or choledocholithiasis, merits alternative tests with improved sensitivity and specificity profiles (Tc 99 m hepatobiliary iminodiacetic acid scan, also known as cholescintigraphy, for cholecystitis and acalculous cholecystitis, or magnetic resonance cholangiopancreatography for choledocholithiasis).¹

Remember to communicate with the radiologist to rule out “can’t miss” diagnoses, increase mutual understanding of the radiographic test characteristics for specific disease processes, and improve the radiologist’s understanding of the patient’s history and clinical question.¹⁵ Collaboration with the radiologist can also determine the need for follow-up imaging and its timing. One single-center study found that surgeons’ diagnostic impression and management changed in 35/100 (35%) cases after an in-person review with the radiologist.¹⁵ Observing patients in the hospital with a nondiagnostic initial evaluation but concerning clinical features often allows for either a trial of cure or for the disease process to “declare itself.”¹⁴ This allows clinicians to target additional testing to a specific diagnosis and avoid reflexive ordering of additional radiographic studies.

• Order an ultrasound for initial imaging of RUQ and female pelvic pain.
• Do not reflexively order an ultrasound within 24 to 48 hours of a negative CT scan to pursue biliary or pelvic pathology.
• Only order repeat abdominal imaging if clinical circumstances evolve or discussions with a radiologist conclude it will answer a more specific diagnostic question.

In our clinical scenario involving a patient with diffuse abdominal pain and a negative CT, the hospitalist should reevaluate the history, exam, and differential diagnosis before pursuing further diagnostic imaging. Based on the evidence presented, CT has similar diagnostic accuracy to ultrasound for biliary and gynecologic pathologies necessitating urgent surgical management (eg, acute cholecystitis, ovarian torsion), and a follow-up ultrasound adds little. If the utility of imaging remains in question, hospitalist consultation with a radiologist can clarify whether prior imaging answered the clinical question and the diagnostic utility of repeat abdominal imaging. With thoughtful reevaluation of the history and physical, and communication with radiology, hospitalists can reduce unnecessary, low-yield imaging and reduce healthcare costs when evaluating patients with abdominal pain.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason^™”? Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason^™” topics by emailing TWDFNR@hospitalmedicine.org

Inspired by the ABIM Foundation’s Choosing Wisely^® campaign, the “Things We Do for No Reason^™” (TWDFNR) series reviews practices that have become common parts of hospital care but may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent clear-cut conclusions or clinical practice standards but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion.

A 70-year-old woman presented to the emergency department (ED) with diffuse abdominal pain, nausea, and vomiting with normal liver function tests and lipase. Computed tomography (CT) of the abdomen and pelvis with intravenous contrast revealed no acute intraabdominal pathology except for an incidentally noted, mildly enlarged but nondistended gallbladder without evident cholelithiasis, pericholecystic fluid, or gallbladder wall edema. The hospitalist orders an abdominal ultrasound to evaluate for acute biliary pathology potentially missed by CT. 

Guidelines and expert opinion recommend an “ultrasound-first” approach when patients present with right upper quadrant (RUQ) abdominal pain or pelvic pain of suspected gynecologic origin.^1-3 When evaluating suspected biliary disease, experts recommend beginning with ultrasonography based on the speed of obtaining results, absence of radiation exposure, reduced cost, and good diagnostic accuracy.¹ Ultrasound has superior sensitivity, of 98%,⁴ in identifying radiolucent gallstones, compared to CT’s 79% sensitivity.⁵ Ultrasonography also differentiates gallbladder sludge from cholelithiasis, evaluates the extrahepatic and intrahepatic bile ducts, and can identify alternate causes of RUQ pain.^1,3 Since ultrasound has important advantages, a negative initial CT may lead the clinician to consider an ultrasound to evaluate for gallbladder diseases.

Additionally, ultrasound provides improved anatomic detail of pelvic structures when diagnosing endometrial or ovarian pathology² and improves diagnostic accuracy when the initial CT reveals an abnormal pelvic finding (eg, defining an enlarged ovary on CT as ovarian torsion, a cyst, or an adnexal mass).⁶ While CT excludes emergent surgical diagnoses, ultrasound may add value in elucidating a cause of the pain, even when urgent surgical management is not necessary.⁷

Many providers believe that a CT lacks sensitivity for acute biliary or pelvic pathology and will order an ultrasound to avoid missing an important diagnosis.⁷ Within 6 months at a single center, clinicians ordered 614 abdominal ultrasounds within 72 hours of an abdominal CT; 227 of these orders were to evaluate the gallbladder. Clinicians documented a discussion with a radiologist in only 19% of cases.⁸

While ultrasound is more sensitive for detecting gallstones, the data do not indicate that it is more sensitive than CT for detecting acute cholecystitis. Abdominal ultrasound has a sensitivity for the diagnosis of acute cholecystitis of 81%, with a specificity of 83%,⁹ while CT has a comparable 85% to 94%^9,10 sensitivity and specificity ranging from 59% to 99%.^9,11 A recent study using more stringent radiographic criteria (two or more abnormal features) for diagnosing acute cholecystitis found ultrasound and CT had near equivalent sensitivities of 61% and 55%, respectively.¹² Even with these stringent criteria, CT had a negative predictive value of 90% and approached 95% when applying a less strict (one feature) criterion.¹² As a result, an abdominal ultrasound will rarely diagnose cholecystitis after a normal CT.

A 2020 study evaluated the diagnostic yield and clinical impact of ordering an abdominal or pelvic ultrasound within 24 hours of a negative abdominal CT.⁷It found that only 3/132 (2.3%) of abdominal ultrasounds ordered after a negative CT revealed acute pathology potentially requiring surgery. Only one of these three patients (1/132) required surgical intervention for confirmed acute cholecystitis.⁷ The follow-up abdominal ultrasound identified asymptomatic gallstones in 9/132 (6.8%) and gallbladder polyps in 4/132 (3.0%) of cases.⁷ Selective use of ultrasound after CT for patients with clinically worsening or progressive RUQ pain will avoid missing a “can’t miss” diagnosis and reduce low-yield testing for a majority of patients.

As with abdominal CT and ultrasound, the recommendation for an initial pelvic ultrasound when evaluating female pelvic pain also stems from the reduced cost, absence of radiation exposure, and superior anatomic visualization of the pelvic organs when compared with pelvic CT.^2,13 However, as with the results of studies investigating the use of abdominal ultrasound after negative CT, a study of pelvic ultrasound after a negative CT revealed that only 4/126 (3.2%) follow-up ultrasounds had an abnormal finding not identified on CT.¹³ Pelvic ultrasound found four endometrial abnormalities that did not alter acute management.¹³ Notably, in 58% of the cases, the indication for ordering the subsequent ultrasound was “rule out ovarian torsion.” However, CT almost always finds a morphologically abnormal ovary in the case of torsion.⁶ One study and literature review found that all 28 patients studied and all 85 patients from previous studies with proven ovarian torsion had either an adnexal mass or an enlarged ovary on pelvic CT.⁶ Harfouch et al found that 0 out of 199 pelvic ultrasounds ordered after a negative CT revealed acute surgical pathology, but pelvic ultrasound did identify nonsurgical uterine and ovarian abnormalities.⁷ In conclusion, when clinicians order CT as the first study to diagnose acute, surgical biliary or gynecologic causes of pain, follow-up ultrasound has a low probability of affecting diagnosis or management if the CT is normal.

The previous discussion only applies if hospitalists order an ultrasound within 24 to 48 hours of the initial CT. Time and clinical course are critical diagnostic tools during an admission for abdominal pain. Consider pelvic or abdominal ultrasound based on guideline recommendations if a patient develops new or evolving RUQ or pelvic pain.^1,2 The rationale for obtaining the initial negative CT may no longer apply, and the clinician must consider the changing characteristics of the patient’s symptoms. For example, initial CT imaging may miss cholelithiasis in a patient presenting for biliary colic. Under observation, the patient may develop acute cholecystitis, potentially requiring an abdominal ultrasound. Also, the data for pelvic ultrasound apply to a normal CT of the abdomen and pelvis. Ultrasound may help to further evaluate indeterminate findings present on initial CT or if recommended by radiology.

When the hospitalist assumes care for a patient with abdominal pain and a negative CT, appropriate next steps include taking time to reexamine the differential diagnosis, repeating the history and physical, and communicating directly with a radiologist. These steps ensure the highest diagnostic yield and the lowest cost and help prevent diagnostic error arising from anchoring on the initial negative ED evaluation. Prior research demonstrates that the initial history alone can lead to the correct diagnosis in up to 76% of cases of abdominal pain.¹⁴ If repeat evaluation determines that additional imaging is necessary, the American College of Radiology provides evidence-based guidelines to help clinicians determine the correct imaging test based on the clinical situation (Appendix Table).^1,2 For example, an equivocal ultrasound or CT exam with continued suspicion for acute cholecystitis or an alternate diagnosis, such as acalculous cholecystitis or choledocholithiasis, merits alternative tests with improved sensitivity and specificity profiles (Tc 99 m hepatobiliary iminodiacetic acid scan, also known as cholescintigraphy, for cholecystitis and acalculous cholecystitis, or magnetic resonance cholangiopancreatography for choledocholithiasis).¹

Remember to communicate with the radiologist to rule out “can’t miss” diagnoses, increase mutual understanding of the radiographic test characteristics for specific disease processes, and improve the radiologist’s understanding of the patient’s history and clinical question.¹⁵ Collaboration with the radiologist can also determine the need for follow-up imaging and its timing. One single-center study found that surgeons’ diagnostic impression and management changed in 35/100 (35%) cases after an in-person review with the radiologist.¹⁵ Observing patients in the hospital with a nondiagnostic initial evaluation but concerning clinical features often allows for either a trial of cure or for the disease process to “declare itself.”¹⁴ This allows clinicians to target additional testing to a specific diagnosis and avoid reflexive ordering of additional radiographic studies.

• Order an ultrasound for initial imaging of RUQ and female pelvic pain.
• Do not reflexively order an ultrasound within 24 to 48 hours of a negative CT scan to pursue biliary or pelvic pathology.
• Only order repeat abdominal imaging if clinical circumstances evolve or discussions with a radiologist conclude it will answer a more specific diagnostic question.

In our clinical scenario involving a patient with diffuse abdominal pain and a negative CT, the hospitalist should reevaluate the history, exam, and differential diagnosis before pursuing further diagnostic imaging. Based on the evidence presented, CT has similar diagnostic accuracy to ultrasound for biliary and gynecologic pathologies necessitating urgent surgical management (eg, acute cholecystitis, ovarian torsion), and a follow-up ultrasound adds little. If the utility of imaging remains in question, hospitalist consultation with a radiologist can clarify whether prior imaging answered the clinical question and the diagnostic utility of repeat abdominal imaging. With thoughtful reevaluation of the history and physical, and communication with radiology, hospitalists can reduce unnecessary, low-yield imaging and reduce healthcare costs when evaluating patients with abdominal pain.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason^™”? Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason^™” topics by emailing TWDFNR@hospitalmedicine.org

Inspired by the ABIM Foundation’s Choosing Wisely^® campaign, the “Things We Do for No Reason^™” (TWDFNR) series reviews practices that have become common parts of hospital care but may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent clear-cut conclusions or clinical practice standards but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion.

A 70-year-old woman presented to the emergency department (ED) with diffuse abdominal pain, nausea, and vomiting with normal liver function tests and lipase. Computed tomography (CT) of the abdomen and pelvis with intravenous contrast revealed no acute intraabdominal pathology except for an incidentally noted, mildly enlarged but nondistended gallbladder without evident cholelithiasis, pericholecystic fluid, or gallbladder wall edema. The hospitalist orders an abdominal ultrasound to evaluate for acute biliary pathology potentially missed by CT. 

Guidelines and expert opinion recommend an “ultrasound-first” approach when patients present with right upper quadrant (RUQ) abdominal pain or pelvic pain of suspected gynecologic origin.^1-3 When evaluating suspected biliary disease, experts recommend beginning with ultrasonography based on the speed of obtaining results, absence of radiation exposure, reduced cost, and good diagnostic accuracy.¹ Ultrasound has superior sensitivity, of 98%,⁴ in identifying radiolucent gallstones, compared to CT’s 79% sensitivity.⁵ Ultrasonography also differentiates gallbladder sludge from cholelithiasis, evaluates the extrahepatic and intrahepatic bile ducts, and can identify alternate causes of RUQ pain.^1,3 Since ultrasound has important advantages, a negative initial CT may lead the clinician to consider an ultrasound to evaluate for gallbladder diseases.

Additionally, ultrasound provides improved anatomic detail of pelvic structures when diagnosing endometrial or ovarian pathology² and improves diagnostic accuracy when the initial CT reveals an abnormal pelvic finding (eg, defining an enlarged ovary on CT as ovarian torsion, a cyst, or an adnexal mass).⁶ While CT excludes emergent surgical diagnoses, ultrasound may add value in elucidating a cause of the pain, even when urgent surgical management is not necessary.⁷

Many providers believe that a CT lacks sensitivity for acute biliary or pelvic pathology and will order an ultrasound to avoid missing an important diagnosis.⁷ Within 6 months at a single center, clinicians ordered 614 abdominal ultrasounds within 72 hours of an abdominal CT; 227 of these orders were to evaluate the gallbladder. Clinicians documented a discussion with a radiologist in only 19% of cases.⁸

While ultrasound is more sensitive for detecting gallstones, the data do not indicate that it is more sensitive than CT for detecting acute cholecystitis. Abdominal ultrasound has a sensitivity for the diagnosis of acute cholecystitis of 81%, with a specificity of 83%,⁹ while CT has a comparable 85% to 94%^9,10 sensitivity and specificity ranging from 59% to 99%.^9,11 A recent study using more stringent radiographic criteria (two or more abnormal features) for diagnosing acute cholecystitis found ultrasound and CT had near equivalent sensitivities of 61% and 55%, respectively.¹² Even with these stringent criteria, CT had a negative predictive value of 90% and approached 95% when applying a less strict (one feature) criterion.¹² As a result, an abdominal ultrasound will rarely diagnose cholecystitis after a normal CT.

A 2020 study evaluated the diagnostic yield and clinical impact of ordering an abdominal or pelvic ultrasound within 24 hours of a negative abdominal CT.⁷It found that only 3/132 (2.3%) of abdominal ultrasounds ordered after a negative CT revealed acute pathology potentially requiring surgery. Only one of these three patients (1/132) required surgical intervention for confirmed acute cholecystitis.⁷ The follow-up abdominal ultrasound identified asymptomatic gallstones in 9/132 (6.8%) and gallbladder polyps in 4/132 (3.0%) of cases.⁷ Selective use of ultrasound after CT for patients with clinically worsening or progressive RUQ pain will avoid missing a “can’t miss” diagnosis and reduce low-yield testing for a majority of patients.

As with abdominal CT and ultrasound, the recommendation for an initial pelvic ultrasound when evaluating female pelvic pain also stems from the reduced cost, absence of radiation exposure, and superior anatomic visualization of the pelvic organs when compared with pelvic CT.^2,13 However, as with the results of studies investigating the use of abdominal ultrasound after negative CT, a study of pelvic ultrasound after a negative CT revealed that only 4/126 (3.2%) follow-up ultrasounds had an abnormal finding not identified on CT.¹³ Pelvic ultrasound found four endometrial abnormalities that did not alter acute management.¹³ Notably, in 58% of the cases, the indication for ordering the subsequent ultrasound was “rule out ovarian torsion.” However, CT almost always finds a morphologically abnormal ovary in the case of torsion.⁶ One study and literature review found that all 28 patients studied and all 85 patients from previous studies with proven ovarian torsion had either an adnexal mass or an enlarged ovary on pelvic CT.⁶ Harfouch et al found that 0 out of 199 pelvic ultrasounds ordered after a negative CT revealed acute surgical pathology, but pelvic ultrasound did identify nonsurgical uterine and ovarian abnormalities.⁷ In conclusion, when clinicians order CT as the first study to diagnose acute, surgical biliary or gynecologic causes of pain, follow-up ultrasound has a low probability of affecting diagnosis or management if the CT is normal.

The previous discussion only applies if hospitalists order an ultrasound within 24 to 48 hours of the initial CT. Time and clinical course are critical diagnostic tools during an admission for abdominal pain. Consider pelvic or abdominal ultrasound based on guideline recommendations if a patient develops new or evolving RUQ or pelvic pain.^1,2 The rationale for obtaining the initial negative CT may no longer apply, and the clinician must consider the changing characteristics of the patient’s symptoms. For example, initial CT imaging may miss cholelithiasis in a patient presenting for biliary colic. Under observation, the patient may develop acute cholecystitis, potentially requiring an abdominal ultrasound. Also, the data for pelvic ultrasound apply to a normal CT of the abdomen and pelvis. Ultrasound may help to further evaluate indeterminate findings present on initial CT or if recommended by radiology.

When the hospitalist assumes care for a patient with abdominal pain and a negative CT, appropriate next steps include taking time to reexamine the differential diagnosis, repeating the history and physical, and communicating directly with a radiologist. These steps ensure the highest diagnostic yield and the lowest cost and help prevent diagnostic error arising from anchoring on the initial negative ED evaluation. Prior research demonstrates that the initial history alone can lead to the correct diagnosis in up to 76% of cases of abdominal pain.¹⁴ If repeat evaluation determines that additional imaging is necessary, the American College of Radiology provides evidence-based guidelines to help clinicians determine the correct imaging test based on the clinical situation (Appendix Table).^1,2 For example, an equivocal ultrasound or CT exam with continued suspicion for acute cholecystitis or an alternate diagnosis, such as acalculous cholecystitis or choledocholithiasis, merits alternative tests with improved sensitivity and specificity profiles (Tc 99 m hepatobiliary iminodiacetic acid scan, also known as cholescintigraphy, for cholecystitis and acalculous cholecystitis, or magnetic resonance cholangiopancreatography for choledocholithiasis).¹

Remember to communicate with the radiologist to rule out “can’t miss” diagnoses, increase mutual understanding of the radiographic test characteristics for specific disease processes, and improve the radiologist’s understanding of the patient’s history and clinical question.¹⁵ Collaboration with the radiologist can also determine the need for follow-up imaging and its timing. One single-center study found that surgeons’ diagnostic impression and management changed in 35/100 (35%) cases after an in-person review with the radiologist.¹⁵ Observing patients in the hospital with a nondiagnostic initial evaluation but concerning clinical features often allows for either a trial of cure or for the disease process to “declare itself.”¹⁴ This allows clinicians to target additional testing to a specific diagnosis and avoid reflexive ordering of additional radiographic studies.

• Order an ultrasound for initial imaging of RUQ and female pelvic pain.
• Do not reflexively order an ultrasound within 24 to 48 hours of a negative CT scan to pursue biliary or pelvic pathology.
• Only order repeat abdominal imaging if clinical circumstances evolve or discussions with a radiologist conclude it will answer a more specific diagnostic question.

In our clinical scenario involving a patient with diffuse abdominal pain and a negative CT, the hospitalist should reevaluate the history, exam, and differential diagnosis before pursuing further diagnostic imaging. Based on the evidence presented, CT has similar diagnostic accuracy to ultrasound for biliary and gynecologic pathologies necessitating urgent surgical management (eg, acute cholecystitis, ovarian torsion), and a follow-up ultrasound adds little. If the utility of imaging remains in question, hospitalist consultation with a radiologist can clarify whether prior imaging answered the clinical question and the diagnostic utility of repeat abdominal imaging. With thoughtful reevaluation of the history and physical, and communication with radiology, hospitalists can reduce unnecessary, low-yield imaging and reduce healthcare costs when evaluating patients with abdominal pain.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason^™”? Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason^™” topics by emailing TWDFNR@hospitalmedicine.org

Upper gastrointestinal bleeding (UGIB) is defined as a bleed originating from the esophagus, stomach, or duodenum. Approximately 80% of patients with UGIB presenting to the emergency department are admitted to the hospital, accounting for more than 200,000 hospital admissions and 4000 in-hospital deaths per year.¹ In this article, we highlight 9 of the 16 recommendations from the 2021 American College of Gastroenterology (ACG) guidelines that are most pertinent to the hospitalist, presented in sections corresponding to the stages of inpatient clinical management.

Initial Triage

Recommendation 1. Patients with UGIB presenting to the emergency department who are classified as very low risk, defined as a risk assessment score with ≤1% false-negative rate for the outcome of hospital-based intervention or death (ie, Glasgow-Blatchford score of 0-1), should be discharged with outpatient follow-up rather than admitted to the hospital (conditional recommendation, very-low-quality evidence). The Glasgow-Blatchford score is an effective risk-assessment tool that can classify patients at high risk for death or needing a hospital-based intervention (eg, endoscopy or blood transfusion) with a sensitivity of 99%.² Triage decisions should incorporate other patient factors, such as age, comorbidities, and reliability of close follow-up after discharge.

Pre-endoscopy Management

Recommendation 2. A restrictive threshold for red blood cell transfusion of 7 g/dL is recommended for patients with UGIB (conditional recommendation, low-quality evidence) as it appears to reduce death and further bleeding.³ It is reasonable to transfuse patients with preexisting cardiovascular disease whose hemoglobin is below 8 g/dL. For patients who are exsanguinating with hemodynamic instability, it is reasonable to transfuse before the hemoglobin reaches 7 g/dL.

Recommendation 3. An infusion of erythromycin is recommended before endoscopy in patients with UGIB (conditional recommendation, very-low-quality evidence). Erythromycin (250 mg intravenously [IV]) improves endoscopic visualization and diagnostic accuracy by moving the blood and clot out of the upper GI tract. A meta-analysis showed a reduction of need for repeat endoscopy (odds ratio [OR], 0.51; 95% CI, 0.34-0.77) and length of hospitalization (mean difference, –1.75 d).⁴

Recommendation 4. There is no consensus for or against pre-endoscopic proton pump inhibitor (PPI) therapy for patients with UGIB, owing to overall limited available data.

Recommendation 5. Patients hospitalized for UGIB should undergo endoscopy within 24 hours of presentation (conditional recommendation, very-low-quality evidence). Performing endoscopy within 24 hours, rather than 12 hours, of presentation demonstrated a potential trend toward decreased length of stay, mortality, and need for surgery. The potential harm in performing earlier endoscopy was attributed to inadequate resuscitation and insufficient optimization of active comorbidities.

Post-endoscopy Management

Recommendation 6. High-dose PPI therapy should be given for 3 days after successful endoscopic hemostatic therapy of a bleeding ulcer (strong recommendation, moderate- to high-quality evidence). When compared with placebo, there is an absolute risk reduction of 3% in mortality and 10% in further bleeding when administering continuous (80 mg bolus with 8 mg/h infusion) or intermittent high-dose PPI therapy (80 mg bolus with 40 mg 2-4 times daily thereafter) for 3 days after endoscopic therapy.^5,6 Cost and ease of administration should be considered when choosing between intermittent or continuous PPI therapy. Oral PPI therapy may be appropriate for patients who are able to tolerate oral intake (no nausea, vomiting, dysphagia, or somnolence).

Recommendation 7. High-risk patients (defined as a Rockall score of ≥6 ) with UGIB due to ulcers who received endoscopic hemostatic therapy followed by short-term high-dose PPI therapy in hospital should be continued on twice-daily PPI therapy until 2 weeks after index endoscopy (conditional recommendation, low-quality evidence). A randomized controlled trial of high-risk patients showed significantly lower recurrence of bleeding with twice-daily vs once daily PPI.⁷ It remains uncertain whether patients benefit from PPI therapy beyond 4 weeks.

Rebleeding Management

Recommendation 8. Patients with recurrent bleeding after endoscopic therapy for a bleeding ulcer should undergo repeat endoscopic therapy rather than surgery or transcatheter arterial embolization (TAE) (conditional recommendation, low-quality evidence for comparison with surgery, very-low-quality evidence for comparison with TAE). In a small randomized controlled trial of repeat endoscopy vs surgery in patients with rebleeding after initial successful endoscopic treatment, there were more subsequent bleeding episodes in the repeat endoscopy group, but no significant difference in mortality and length of stay.⁸ The repeat endoscopy group had fewer complications, though, and a successful treatment rate of 75%. Because of the lack of high-quality studies in support of TAE and the known safety and efficacy of repeat endoscopy, repeat endoscopy is preferred over TAE for recurrent UGIB.

Recommendation 9. Patients with bleeding ulcers who have failed repeat endoscopic therapy should be treated with TAE (conditional recommendation, very-low-quality evidence). Based on a meta-analysis, when comparing TAE with surgery in patients with UGIB who fail endoscopic therapy, overall mortality was the same, and TAE patients had fewer complications and shorter hospital stays despite having a higher risk of further bleeding.⁹

The guidelines were formulated by panel members with input from the ACG Practice Parameters Committee using the population, intervention, comparator, and outcome (PICO) format to frame each question. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was used to assess the strength of the recommendation and the quality of evidence.

Most of the recommendations are conditional and/or based on low-quality or very-low-quality evidence. Although randomized control trials were sought, observational studies were sometimes included when randomized controlled trials were lacking. The literature review process appeared to focus on the primary outcome of further bleeding, which, although critical in patients with UGIB, could have limited the scope of evidence used in making the recommendations. It was stated that studies identified as relevant to the panel members or authors were considered for review without mentioning any standardized approach. The composition of the panel members was not discussed, and it is uncertain whether the guidelines underwent any formal peer-review process. Furthermore, although competing interests were declared, the panel did not discuss how conflicts were managed and what potential impact they had in the guideline recommendations. Finally, some of the recommendations (eg, TAE) will depend on local expertise and may not be available at all medical centers.

Further study is needed to address the integration of risk-assessment tools into electronic health records to assist with timely decisions on managing patients with acute UGIB, to clarify the role for pre-endoscopic PPI therapy, and to specify fluid resuscitation and blood pressure goals in patients with more severe bleeding episodes and determine whether a subset of patients might benefit from very-early endoscopy (the 2012 ACG guidelines suggested that endoscopy within 12 hours may be considered in patients with high-risk clinical features such as hemodynamic instability or cirrhosis).

Glasgow-Blatchford Score (https://www.mdcalc.com/glasgow-blatchford-bleeding-score-gbs)

Rockall Score (https://www.mdcalc.com/rockall-score-upper-gi-bleeding-pre-endoscopy)

Upper gastrointestinal bleeding (UGIB) is defined as a bleed originating from the esophagus, stomach, or duodenum. Approximately 80% of patients with UGIB presenting to the emergency department are admitted to the hospital, accounting for more than 200,000 hospital admissions and 4000 in-hospital deaths per year.¹ In this article, we highlight 9 of the 16 recommendations from the 2021 American College of Gastroenterology (ACG) guidelines that are most pertinent to the hospitalist, presented in sections corresponding to the stages of inpatient clinical management.

Initial Triage

Recommendation 1. Patients with UGIB presenting to the emergency department who are classified as very low risk, defined as a risk assessment score with ≤1% false-negative rate for the outcome of hospital-based intervention or death (ie, Glasgow-Blatchford score of 0-1), should be discharged with outpatient follow-up rather than admitted to the hospital (conditional recommendation, very-low-quality evidence). The Glasgow-Blatchford score is an effective risk-assessment tool that can classify patients at high risk for death or needing a hospital-based intervention (eg, endoscopy or blood transfusion) with a sensitivity of 99%.² Triage decisions should incorporate other patient factors, such as age, comorbidities, and reliability of close follow-up after discharge.

Pre-endoscopy Management

Recommendation 2. A restrictive threshold for red blood cell transfusion of 7 g/dL is recommended for patients with UGIB (conditional recommendation, low-quality evidence) as it appears to reduce death and further bleeding.³ It is reasonable to transfuse patients with preexisting cardiovascular disease whose hemoglobin is below 8 g/dL. For patients who are exsanguinating with hemodynamic instability, it is reasonable to transfuse before the hemoglobin reaches 7 g/dL.

Recommendation 3. An infusion of erythromycin is recommended before endoscopy in patients with UGIB (conditional recommendation, very-low-quality evidence). Erythromycin (250 mg intravenously [IV]) improves endoscopic visualization and diagnostic accuracy by moving the blood and clot out of the upper GI tract. A meta-analysis showed a reduction of need for repeat endoscopy (odds ratio [OR], 0.51; 95% CI, 0.34-0.77) and length of hospitalization (mean difference, –1.75 d).⁴

Recommendation 4. There is no consensus for or against pre-endoscopic proton pump inhibitor (PPI) therapy for patients with UGIB, owing to overall limited available data.

Recommendation 5. Patients hospitalized for UGIB should undergo endoscopy within 24 hours of presentation (conditional recommendation, very-low-quality evidence). Performing endoscopy within 24 hours, rather than 12 hours, of presentation demonstrated a potential trend toward decreased length of stay, mortality, and need for surgery. The potential harm in performing earlier endoscopy was attributed to inadequate resuscitation and insufficient optimization of active comorbidities.

Post-endoscopy Management

Recommendation 6. High-dose PPI therapy should be given for 3 days after successful endoscopic hemostatic therapy of a bleeding ulcer (strong recommendation, moderate- to high-quality evidence). When compared with placebo, there is an absolute risk reduction of 3% in mortality and 10% in further bleeding when administering continuous (80 mg bolus with 8 mg/h infusion) or intermittent high-dose PPI therapy (80 mg bolus with 40 mg 2-4 times daily thereafter) for 3 days after endoscopic therapy.^5,6 Cost and ease of administration should be considered when choosing between intermittent or continuous PPI therapy. Oral PPI therapy may be appropriate for patients who are able to tolerate oral intake (no nausea, vomiting, dysphagia, or somnolence).

Recommendation 7. High-risk patients (defined as a Rockall score of ≥6 ) with UGIB due to ulcers who received endoscopic hemostatic therapy followed by short-term high-dose PPI therapy in hospital should be continued on twice-daily PPI therapy until 2 weeks after index endoscopy (conditional recommendation, low-quality evidence). A randomized controlled trial of high-risk patients showed significantly lower recurrence of bleeding with twice-daily vs once daily PPI.⁷ It remains uncertain whether patients benefit from PPI therapy beyond 4 weeks.

Rebleeding Management

Recommendation 8. Patients with recurrent bleeding after endoscopic therapy for a bleeding ulcer should undergo repeat endoscopic therapy rather than surgery or transcatheter arterial embolization (TAE) (conditional recommendation, low-quality evidence for comparison with surgery, very-low-quality evidence for comparison with TAE). In a small randomized controlled trial of repeat endoscopy vs surgery in patients with rebleeding after initial successful endoscopic treatment, there were more subsequent bleeding episodes in the repeat endoscopy group, but no significant difference in mortality and length of stay.⁸ The repeat endoscopy group had fewer complications, though, and a successful treatment rate of 75%. Because of the lack of high-quality studies in support of TAE and the known safety and efficacy of repeat endoscopy, repeat endoscopy is preferred over TAE for recurrent UGIB.

Recommendation 9. Patients with bleeding ulcers who have failed repeat endoscopic therapy should be treated with TAE (conditional recommendation, very-low-quality evidence). Based on a meta-analysis, when comparing TAE with surgery in patients with UGIB who fail endoscopic therapy, overall mortality was the same, and TAE patients had fewer complications and shorter hospital stays despite having a higher risk of further bleeding.⁹

The guidelines were formulated by panel members with input from the ACG Practice Parameters Committee using the population, intervention, comparator, and outcome (PICO) format to frame each question. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was used to assess the strength of the recommendation and the quality of evidence.

Most of the recommendations are conditional and/or based on low-quality or very-low-quality evidence. Although randomized control trials were sought, observational studies were sometimes included when randomized controlled trials were lacking. The literature review process appeared to focus on the primary outcome of further bleeding, which, although critical in patients with UGIB, could have limited the scope of evidence used in making the recommendations. It was stated that studies identified as relevant to the panel members or authors were considered for review without mentioning any standardized approach. The composition of the panel members was not discussed, and it is uncertain whether the guidelines underwent any formal peer-review process. Furthermore, although competing interests were declared, the panel did not discuss how conflicts were managed and what potential impact they had in the guideline recommendations. Finally, some of the recommendations (eg, TAE) will depend on local expertise and may not be available at all medical centers.

Further study is needed to address the integration of risk-assessment tools into electronic health records to assist with timely decisions on managing patients with acute UGIB, to clarify the role for pre-endoscopic PPI therapy, and to specify fluid resuscitation and blood pressure goals in patients with more severe bleeding episodes and determine whether a subset of patients might benefit from very-early endoscopy (the 2012 ACG guidelines suggested that endoscopy within 12 hours may be considered in patients with high-risk clinical features such as hemodynamic instability or cirrhosis).

Glasgow-Blatchford Score (https://www.mdcalc.com/glasgow-blatchford-bleeding-score-gbs)

Rockall Score (https://www.mdcalc.com/rockall-score-upper-gi-bleeding-pre-endoscopy)

Upper gastrointestinal bleeding (UGIB) is defined as a bleed originating from the esophagus, stomach, or duodenum. Approximately 80% of patients with UGIB presenting to the emergency department are admitted to the hospital, accounting for more than 200,000 hospital admissions and 4000 in-hospital deaths per year.¹ In this article, we highlight 9 of the 16 recommendations from the 2021 American College of Gastroenterology (ACG) guidelines that are most pertinent to the hospitalist, presented in sections corresponding to the stages of inpatient clinical management.

Initial Triage

Recommendation 1. Patients with UGIB presenting to the emergency department who are classified as very low risk, defined as a risk assessment score with ≤1% false-negative rate for the outcome of hospital-based intervention or death (ie, Glasgow-Blatchford score of 0-1), should be discharged with outpatient follow-up rather than admitted to the hospital (conditional recommendation, very-low-quality evidence). The Glasgow-Blatchford score is an effective risk-assessment tool that can classify patients at high risk for death or needing a hospital-based intervention (eg, endoscopy or blood transfusion) with a sensitivity of 99%.² Triage decisions should incorporate other patient factors, such as age, comorbidities, and reliability of close follow-up after discharge.

Pre-endoscopy Management

Recommendation 2. A restrictive threshold for red blood cell transfusion of 7 g/dL is recommended for patients with UGIB (conditional recommendation, low-quality evidence) as it appears to reduce death and further bleeding.³ It is reasonable to transfuse patients with preexisting cardiovascular disease whose hemoglobin is below 8 g/dL. For patients who are exsanguinating with hemodynamic instability, it is reasonable to transfuse before the hemoglobin reaches 7 g/dL.

Recommendation 3. An infusion of erythromycin is recommended before endoscopy in patients with UGIB (conditional recommendation, very-low-quality evidence). Erythromycin (250 mg intravenously [IV]) improves endoscopic visualization and diagnostic accuracy by moving the blood and clot out of the upper GI tract. A meta-analysis showed a reduction of need for repeat endoscopy (odds ratio [OR], 0.51; 95% CI, 0.34-0.77) and length of hospitalization (mean difference, –1.75 d).⁴

Recommendation 4. There is no consensus for or against pre-endoscopic proton pump inhibitor (PPI) therapy for patients with UGIB, owing to overall limited available data.

Recommendation 5. Patients hospitalized for UGIB should undergo endoscopy within 24 hours of presentation (conditional recommendation, very-low-quality evidence). Performing endoscopy within 24 hours, rather than 12 hours, of presentation demonstrated a potential trend toward decreased length of stay, mortality, and need for surgery. The potential harm in performing earlier endoscopy was attributed to inadequate resuscitation and insufficient optimization of active comorbidities.

Post-endoscopy Management

Recommendation 6. High-dose PPI therapy should be given for 3 days after successful endoscopic hemostatic therapy of a bleeding ulcer (strong recommendation, moderate- to high-quality evidence). When compared with placebo, there is an absolute risk reduction of 3% in mortality and 10% in further bleeding when administering continuous (80 mg bolus with 8 mg/h infusion) or intermittent high-dose PPI therapy (80 mg bolus with 40 mg 2-4 times daily thereafter) for 3 days after endoscopic therapy.^5,6 Cost and ease of administration should be considered when choosing between intermittent or continuous PPI therapy. Oral PPI therapy may be appropriate for patients who are able to tolerate oral intake (no nausea, vomiting, dysphagia, or somnolence).

Recommendation 7. High-risk patients (defined as a Rockall score of ≥6 ) with UGIB due to ulcers who received endoscopic hemostatic therapy followed by short-term high-dose PPI therapy in hospital should be continued on twice-daily PPI therapy until 2 weeks after index endoscopy (conditional recommendation, low-quality evidence). A randomized controlled trial of high-risk patients showed significantly lower recurrence of bleeding with twice-daily vs once daily PPI.⁷ It remains uncertain whether patients benefit from PPI therapy beyond 4 weeks.

Rebleeding Management

Recommendation 8. Patients with recurrent bleeding after endoscopic therapy for a bleeding ulcer should undergo repeat endoscopic therapy rather than surgery or transcatheter arterial embolization (TAE) (conditional recommendation, low-quality evidence for comparison with surgery, very-low-quality evidence for comparison with TAE). In a small randomized controlled trial of repeat endoscopy vs surgery in patients with rebleeding after initial successful endoscopic treatment, there were more subsequent bleeding episodes in the repeat endoscopy group, but no significant difference in mortality and length of stay.⁸ The repeat endoscopy group had fewer complications, though, and a successful treatment rate of 75%. Because of the lack of high-quality studies in support of TAE and the known safety and efficacy of repeat endoscopy, repeat endoscopy is preferred over TAE for recurrent UGIB.

Recommendation 9. Patients with bleeding ulcers who have failed repeat endoscopic therapy should be treated with TAE (conditional recommendation, very-low-quality evidence). Based on a meta-analysis, when comparing TAE with surgery in patients with UGIB who fail endoscopic therapy, overall mortality was the same, and TAE patients had fewer complications and shorter hospital stays despite having a higher risk of further bleeding.⁹

The guidelines were formulated by panel members with input from the ACG Practice Parameters Committee using the population, intervention, comparator, and outcome (PICO) format to frame each question. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was used to assess the strength of the recommendation and the quality of evidence.

Most of the recommendations are conditional and/or based on low-quality or very-low-quality evidence. Although randomized control trials were sought, observational studies were sometimes included when randomized controlled trials were lacking. The literature review process appeared to focus on the primary outcome of further bleeding, which, although critical in patients with UGIB, could have limited the scope of evidence used in making the recommendations. It was stated that studies identified as relevant to the panel members or authors were considered for review without mentioning any standardized approach. The composition of the panel members was not discussed, and it is uncertain whether the guidelines underwent any formal peer-review process. Furthermore, although competing interests were declared, the panel did not discuss how conflicts were managed and what potential impact they had in the guideline recommendations. Finally, some of the recommendations (eg, TAE) will depend on local expertise and may not be available at all medical centers.

Further study is needed to address the integration of risk-assessment tools into electronic health records to assist with timely decisions on managing patients with acute UGIB, to clarify the role for pre-endoscopic PPI therapy, and to specify fluid resuscitation and blood pressure goals in patients with more severe bleeding episodes and determine whether a subset of patients might benefit from very-early endoscopy (the 2012 ACG guidelines suggested that endoscopy within 12 hours may be considered in patients with high-risk clinical features such as hemodynamic instability or cirrhosis).

Glasgow-Blatchford Score (https://www.mdcalc.com/glasgow-blatchford-bleeding-score-gbs)

Rockall Score (https://www.mdcalc.com/rockall-score-upper-gi-bleeding-pre-endoscopy)

Events requiring communication among and within teams are vulnerable points in patient care in hospital medicine, with communication failures representing important contributors to adverse events.^1-4 Consultations and handoffs are exceptionally common inpatient practices, yet training in these practices is variable across educational and practice domains.^5,6 Advanced inpatient communication-skills training requires an effective, feasible, and scalable format. Simulation-based bootcamps can effectively support clinical skills training, often in procedural domains, and have been increasingly utilized for communication skills.^7,8 We previously described the development and implementation of an in-person bootcamp for training and feedback in consultation and handoff communication.^5,8

As hospitalist leaders grapple with how to systematically support and assess essential clinical skills, the COVID-19 pandemic has presented another impetus to rethink current processes. The rapid shift to virtual activities met immediate needs of the pandemic, but also inspired creativity in applying new methodologies to improve teaching strategies and implementation long-term.^9,10 One such strategy, telesimulation, offers a way to continue simulation-based training limited by the need for physical distancing.¹⁰ Furthermore, recent calls to study the efficacy of virtual bootcamp structures have acknowledged potential benefits, even outside of the pandemic.¹¹

The primary objective of this feasibility study was to convert our previously described consultation and handoff bootcamp to a telesimulation bootcamp (TBC), preserving rigorous performance evaluation and opportunities for skills-based feedback. We additionally compared evaluation between virtual and in-person formats to understand the utility of telesimulation for bootcamp-based clinical education moving forward.

Setting and Participants

The TBC occurred in June 2020 during the University of Chicago institution-wide graduate medical education (GME) orientation; 130 interns entering 13 residency programs participated. The comparison group was 128 interns who underwent the traditional University of Chicago GME orientation “Advanced Communication Skills Bootcamp” (ACSBC) in 2019.^5,8

Program Description

To develop TBC, we adapted observed structured clinical experiences (OSCEs) created for ACSBC. Until 2020, ACSBC included three in-person OSCEs: (1) requesting a consultation; (2) conducting handoffs; and (3) acquiring informed consent. COVID-19 necessitated conversion of ACSBC to virtual in June 2020. For this, we selected the consultation and handoff OSCEs, as these skills require near-universal and immediate application in clinical practice. Additionally, they required only trained facilitators (TFs), whereas informed consent required standardized patients. Hospitalist and emergency medicine faculty were recruited as TFs; 7 of 12 TFs were hospitalists. Each OSCE had two parts: an asynchronous, mandatory training module and a clinical simulation. For TBC, we adapted the simulations, previously separate experiences, into a 20-minute combined handoff/consultation telesimulation using the Zoom^® video platform. Interns were paired with one TF who served as both standardized consultant (for one mock case) and handoff receiver (for three mock cases, including the consultation case). TFs rated intern performance and provided feedback.

TBC occurred on June 17 and 18, 2020. Interns were emailed asynchronous modules on June 1, and mock cases and instructions on June 12. When TBC began, GME staff proctors oriented interns in the Zoom^® platform. Proctors placed TFs into private breakout rooms into which interns rotated through 20-minute timeslots. Faculty received copies of all TBC materials for review (Appendix 1) and underwent Zoom^®-based training 1 to 2 weeks prior.

We evaluated TBC using several methods: (1) consultation and handoff skills performance measured by two validated checklists^5,8; (2) survey of intern self-reported preparedness to practice consultations and handoffs; and (3) survey of intern satisfaction. Surveys were administered both immediately post bootcamp (Appendix 2) and 8 weeks into internship (Appendix 3). Skills performance checklists were a 12-item consultation checklist⁵ and 6-item handoff checklist.⁸ The handoff checklist was modified to remove activities impossible to assess virtually (ie, orienting sign-outs in a shared space) and to add a three-level rating scale of “outstanding,” “satisfactory,” and “needs improvement.” This was done based on feedback from ACSBC to allow more nuanced feedback for interns. A rating of “outstanding” was used to define successful completion of the item (Appendix 1). Interns rated preparedness and satisfaction on 5-point Likert-type items. All measures were compared to the 2019 in-person ACSBC cohort.

Data Analysis

Stata 16.1 (StataCorp LP) was used for analysis. We dichotomized preparedness and satisfaction scores, defining ratings of “4” or “5” as “prepared” or “satisfied.” As previously described,⁵ we created a composite score averaging both checklist scores for each intern. We normalized this score by rater to a z score (mean, 0; SD, 1) to account for rater differences. “Poor” and “outstanding” performances were defined as z scores below and above 1 SD, respectively. Fisher’s exact test was used to compare proportions, and Pearson correlation test to correlate z scores. The University of Chicago Institutional Review Board granted exemption.

All 130 entering interns participated in TBC. Internal medicine (IM) was the largest specialty (n = 37), followed by pediatrics (n = 22), emergency medicine (EM) (n = 16), and anesthesiology (n = 12). The remaining 9 programs ranged from 2 to 10 interns per program. The 128 interns in ACSBC were similar, including 40 IM, 23 pediatrics, 14 EM, and 12 anesthesia interns, with 2 to 10 interns in remaining programs.

TBC skills performance evaluations were compared to ACSBC (Table 1). The TBC intern cohort’s consultation performance was the same or better than the ACSBC intern cohort’s. For handoffs, TBC interns completed significantly fewer checklist items compared to ACSBC. Performance in each exercise was moderately correlated (r = 0.39, P < .05). For z scores, 14 TBC interns (10.8%) had “outstanding” and 15 (11.6%) had “poor” performances, compared to ACSBC interns with 7 (5.5%) “outstanding” and 10 (7.81%) “poor” performances (P = .15).

JHMVol16No11_Carter08601117e_t1.JPG

All 130 interns (100%) completed the immediate post-TBC survey. Overall, TBC satisfaction was comparable to ACSBC, and significantly improved for satisfaction with performance (Table 2). Compared to ACSBC, TBC interns felt more prepared for simulation and handoff clinical practice. Nearly all interns would recommend TBC (99% vs 96% of ACSBC interns, P = 0.28), and 99% felt the software used for the simulation ran smoothly.

JHMVol16No11_Carter08601117e_t2.JPG

The 8-week post-TBC survey had a response rate of 88% (115/130); 69% of interns reported conducting more effective handoffs due to TBC, and 79% felt confident in handoff skills. Similarly, 73% felt more effective at calling consultations, and 75% reported retained knowledge of consultation frameworks taught during TBC. Additionally, 71% of interns reported that TBC helped identify areas for self-directed improvement. There were no significant differences in 8-week postsurvey ratings between ACSBC and TBC.

In converting the advanced communication skills bootcamp from an in-person to a virtual format, telesimulation was well-received by interns and rated similarly to in-person bootcamp in most respects. Nearly all interns agreed the experience was realistic, provided useful feedback, and prepared them for clinical practice. Although we shifted to virtual out of necessity, our results demonstrate a high-quality, streamlined bootcamp experience that was less labor-intensive for interns, staff, and faculty. Telesimulation may represent an effective strategy beyond the COVID-19 pandemic to increase ease of administration and scale the use of bootcamps in supporting advanced clinical skill training for hospital-based practice.

TBC interns felt better prepared for simulation and more satisfied with their performance than ACSBC interns, potentially due to the revised format. The mock cases were adapted and consolidated for TBC, such that the handoff and consultation simulations shared a common case, whereas previously they were separate. Thus, intern preparation for TBC required familiarity with fewer overall cases. Ultimately, TBC maintained the quality of training but required review of less information.

In comparing performance, TBC interns were rated as well or better during consultation simulation compared to ASCBC, but handoffs were rated lower. This was likely due to the change in the handoff checklist from a dichotomous to a three-level rating scale. This change was made after receiving feedback from ACSBC TFs that a rating scale allowing for more nuance was needed to provide adequate feedback to interns. Although we defined handoff item completion for TBC interns as being rated “outstanding,” if the top two rankings, “outstanding” and “satisfactory,” are dichotomized to reflect completion, TBC handoff performance is equivalent or better than ACSBC. TF recruitment additionally differed between TBC and ACSBC cohorts. In ACSBC, resident physicians served as handoff TFs, whereas only faculty were recruited for TBC. Faculty were primarily clinically active hospitalists, whose expertise in handoffs may resulted in more stringent performance ratings, contributing to differences seen.

Hospitalist groups require clinicians to be immediately proficient in essential communication skills like consultation and handoffs, potentially requiring just-in-time training and feedback for large cohorts.¹² Bootcamps can meet this need but require participation and time investment by many faculty members, staff, and administrators.^5,8 Combining TBC into one virtual handoff/consultation simulation required recruitment and training of 50% fewer TFs and reduced administrative burden. ACSBC consultation simulations were high-fidelity but resource-heavy, requiring reliable two-way telephones with reliable connections and separate spaces for simulation and feedback.⁵ Conversely, TBC only required consultations to be “called” via audio-only Zoom^® discussion, then both individuals turned on cameras for feedback. The slight decrease in perceived fidelity was certainly outweighed by ease of administration. TBC’s more efficient and less labor-intensive format is an appealing strategy for hospitalist groups looking to train up clinicians, including those operating across multiple or geographically distant sites.

Our study has limitations. It occurred with one group of learners at a single site with consistent consultation and handoff communication practices, which may not be the case elsewhere. Our comparison group was a separate cohort, and groups were not randomized; thus, differences seen may reflect inherent dissimilarities in these groups. Changes to the handoff checklist rating scale between 2019 and 2020 additionally may limit the direct comparison of handoff performance between cohorts. While overall fewer resources were required, TBC implementation did require time and institutional support, along with full virtual platform capability without user or time limitations. Our preparedness outcomes were self-reported without direct measurement of clinical performance, which is an area for future work.

We describe a feasible implementation of an adapted telesimulation communication bootcamp, with comparison to a previous in-person cohort’s skills performance and satisfaction. While COVID-19 has made the future of in-person training activities uncertain, it also served as a catalyst for educational innovation that may be sustained beyond the pandemic. Although developed out of necessity, the telesimulation communication bootcamp was effective and well-received. Telesimulation represents an opportunity for hospital medicine groups to implement advanced communication skills training and assessment in a more efficient, flexible, and potentially preferable way, even after the pandemic ends.

The authors thank the staff at the University of Chicago Office of Graduate Medical Education and the UChicago Medicine Simulation Center.

Events requiring communication among and within teams are vulnerable points in patient care in hospital medicine, with communication failures representing important contributors to adverse events.^1-4 Consultations and handoffs are exceptionally common inpatient practices, yet training in these practices is variable across educational and practice domains.^5,6 Advanced inpatient communication-skills training requires an effective, feasible, and scalable format. Simulation-based bootcamps can effectively support clinical skills training, often in procedural domains, and have been increasingly utilized for communication skills.^7,8 We previously described the development and implementation of an in-person bootcamp for training and feedback in consultation and handoff communication.^5,8

As hospitalist leaders grapple with how to systematically support and assess essential clinical skills, the COVID-19 pandemic has presented another impetus to rethink current processes. The rapid shift to virtual activities met immediate needs of the pandemic, but also inspired creativity in applying new methodologies to improve teaching strategies and implementation long-term.^9,10 One such strategy, telesimulation, offers a way to continue simulation-based training limited by the need for physical distancing.¹⁰ Furthermore, recent calls to study the efficacy of virtual bootcamp structures have acknowledged potential benefits, even outside of the pandemic.¹¹

The primary objective of this feasibility study was to convert our previously described consultation and handoff bootcamp to a telesimulation bootcamp (TBC), preserving rigorous performance evaluation and opportunities for skills-based feedback. We additionally compared evaluation between virtual and in-person formats to understand the utility of telesimulation for bootcamp-based clinical education moving forward.

Setting and Participants

The TBC occurred in June 2020 during the University of Chicago institution-wide graduate medical education (GME) orientation; 130 interns entering 13 residency programs participated. The comparison group was 128 interns who underwent the traditional University of Chicago GME orientation “Advanced Communication Skills Bootcamp” (ACSBC) in 2019.^5,8

Program Description

To develop TBC, we adapted observed structured clinical experiences (OSCEs) created for ACSBC. Until 2020, ACSBC included three in-person OSCEs: (1) requesting a consultation; (2) conducting handoffs; and (3) acquiring informed consent. COVID-19 necessitated conversion of ACSBC to virtual in June 2020. For this, we selected the consultation and handoff OSCEs, as these skills require near-universal and immediate application in clinical practice. Additionally, they required only trained facilitators (TFs), whereas informed consent required standardized patients. Hospitalist and emergency medicine faculty were recruited as TFs; 7 of 12 TFs were hospitalists. Each OSCE had two parts: an asynchronous, mandatory training module and a clinical simulation. For TBC, we adapted the simulations, previously separate experiences, into a 20-minute combined handoff/consultation telesimulation using the Zoom^® video platform. Interns were paired with one TF who served as both standardized consultant (for one mock case) and handoff receiver (for three mock cases, including the consultation case). TFs rated intern performance and provided feedback.

TBC occurred on June 17 and 18, 2020. Interns were emailed asynchronous modules on June 1, and mock cases and instructions on June 12. When TBC began, GME staff proctors oriented interns in the Zoom^® platform. Proctors placed TFs into private breakout rooms into which interns rotated through 20-minute timeslots. Faculty received copies of all TBC materials for review (Appendix 1) and underwent Zoom^®-based training 1 to 2 weeks prior.

We evaluated TBC using several methods: (1) consultation and handoff skills performance measured by two validated checklists^5,8; (2) survey of intern self-reported preparedness to practice consultations and handoffs; and (3) survey of intern satisfaction. Surveys were administered both immediately post bootcamp (Appendix 2) and 8 weeks into internship (Appendix 3). Skills performance checklists were a 12-item consultation checklist⁵ and 6-item handoff checklist.⁸ The handoff checklist was modified to remove activities impossible to assess virtually (ie, orienting sign-outs in a shared space) and to add a three-level rating scale of “outstanding,” “satisfactory,” and “needs improvement.” This was done based on feedback from ACSBC to allow more nuanced feedback for interns. A rating of “outstanding” was used to define successful completion of the item (Appendix 1). Interns rated preparedness and satisfaction on 5-point Likert-type items. All measures were compared to the 2019 in-person ACSBC cohort.

Data Analysis

Stata 16.1 (StataCorp LP) was used for analysis. We dichotomized preparedness and satisfaction scores, defining ratings of “4” or “5” as “prepared” or “satisfied.” As previously described,⁵ we created a composite score averaging both checklist scores for each intern. We normalized this score by rater to a z score (mean, 0; SD, 1) to account for rater differences. “Poor” and “outstanding” performances were defined as z scores below and above 1 SD, respectively. Fisher’s exact test was used to compare proportions, and Pearson correlation test to correlate z scores. The University of Chicago Institutional Review Board granted exemption.

All 130 entering interns participated in TBC. Internal medicine (IM) was the largest specialty (n = 37), followed by pediatrics (n = 22), emergency medicine (EM) (n = 16), and anesthesiology (n = 12). The remaining 9 programs ranged from 2 to 10 interns per program. The 128 interns in ACSBC were similar, including 40 IM, 23 pediatrics, 14 EM, and 12 anesthesia interns, with 2 to 10 interns in remaining programs.

TBC skills performance evaluations were compared to ACSBC (Table 1). The TBC intern cohort’s consultation performance was the same or better than the ACSBC intern cohort’s. For handoffs, TBC interns completed significantly fewer checklist items compared to ACSBC. Performance in each exercise was moderately correlated (r = 0.39, P < .05). For z scores, 14 TBC interns (10.8%) had “outstanding” and 15 (11.6%) had “poor” performances, compared to ACSBC interns with 7 (5.5%) “outstanding” and 10 (7.81%) “poor” performances (P = .15).

JHMVol16No11_Carter08601117e_t1.JPG

All 130 interns (100%) completed the immediate post-TBC survey. Overall, TBC satisfaction was comparable to ACSBC, and significantly improved for satisfaction with performance (Table 2). Compared to ACSBC, TBC interns felt more prepared for simulation and handoff clinical practice. Nearly all interns would recommend TBC (99% vs 96% of ACSBC interns, P = 0.28), and 99% felt the software used for the simulation ran smoothly.

JHMVol16No11_Carter08601117e_t2.JPG

The 8-week post-TBC survey had a response rate of 88% (115/130); 69% of interns reported conducting more effective handoffs due to TBC, and 79% felt confident in handoff skills. Similarly, 73% felt more effective at calling consultations, and 75% reported retained knowledge of consultation frameworks taught during TBC. Additionally, 71% of interns reported that TBC helped identify areas for self-directed improvement. There were no significant differences in 8-week postsurvey ratings between ACSBC and TBC.

In converting the advanced communication skills bootcamp from an in-person to a virtual format, telesimulation was well-received by interns and rated similarly to in-person bootcamp in most respects. Nearly all interns agreed the experience was realistic, provided useful feedback, and prepared them for clinical practice. Although we shifted to virtual out of necessity, our results demonstrate a high-quality, streamlined bootcamp experience that was less labor-intensive for interns, staff, and faculty. Telesimulation may represent an effective strategy beyond the COVID-19 pandemic to increase ease of administration and scale the use of bootcamps in supporting advanced clinical skill training for hospital-based practice.

TBC interns felt better prepared for simulation and more satisfied with their performance than ACSBC interns, potentially due to the revised format. The mock cases were adapted and consolidated for TBC, such that the handoff and consultation simulations shared a common case, whereas previously they were separate. Thus, intern preparation for TBC required familiarity with fewer overall cases. Ultimately, TBC maintained the quality of training but required review of less information.

In comparing performance, TBC interns were rated as well or better during consultation simulation compared to ASCBC, but handoffs were rated lower. This was likely due to the change in the handoff checklist from a dichotomous to a three-level rating scale. This change was made after receiving feedback from ACSBC TFs that a rating scale allowing for more nuance was needed to provide adequate feedback to interns. Although we defined handoff item completion for TBC interns as being rated “outstanding,” if the top two rankings, “outstanding” and “satisfactory,” are dichotomized to reflect completion, TBC handoff performance is equivalent or better than ACSBC. TF recruitment additionally differed between TBC and ACSBC cohorts. In ACSBC, resident physicians served as handoff TFs, whereas only faculty were recruited for TBC. Faculty were primarily clinically active hospitalists, whose expertise in handoffs may resulted in more stringent performance ratings, contributing to differences seen.

Hospitalist groups require clinicians to be immediately proficient in essential communication skills like consultation and handoffs, potentially requiring just-in-time training and feedback for large cohorts.¹² Bootcamps can meet this need but require participation and time investment by many faculty members, staff, and administrators.^5,8 Combining TBC into one virtual handoff/consultation simulation required recruitment and training of 50% fewer TFs and reduced administrative burden. ACSBC consultation simulations were high-fidelity but resource-heavy, requiring reliable two-way telephones with reliable connections and separate spaces for simulation and feedback.⁵ Conversely, TBC only required consultations to be “called” via audio-only Zoom^® discussion, then both individuals turned on cameras for feedback. The slight decrease in perceived fidelity was certainly outweighed by ease of administration. TBC’s more efficient and less labor-intensive format is an appealing strategy for hospitalist groups looking to train up clinicians, including those operating across multiple or geographically distant sites.

Our study has limitations. It occurred with one group of learners at a single site with consistent consultation and handoff communication practices, which may not be the case elsewhere. Our comparison group was a separate cohort, and groups were not randomized; thus, differences seen may reflect inherent dissimilarities in these groups. Changes to the handoff checklist rating scale between 2019 and 2020 additionally may limit the direct comparison of handoff performance between cohorts. While overall fewer resources were required, TBC implementation did require time and institutional support, along with full virtual platform capability without user or time limitations. Our preparedness outcomes were self-reported without direct measurement of clinical performance, which is an area for future work.

We describe a feasible implementation of an adapted telesimulation communication bootcamp, with comparison to a previous in-person cohort’s skills performance and satisfaction. While COVID-19 has made the future of in-person training activities uncertain, it also served as a catalyst for educational innovation that may be sustained beyond the pandemic. Although developed out of necessity, the telesimulation communication bootcamp was effective and well-received. Telesimulation represents an opportunity for hospital medicine groups to implement advanced communication skills training and assessment in a more efficient, flexible, and potentially preferable way, even after the pandemic ends.

The authors thank the staff at the University of Chicago Office of Graduate Medical Education and the UChicago Medicine Simulation Center.

Events requiring communication among and within teams are vulnerable points in patient care in hospital medicine, with communication failures representing important contributors to adverse events.^1-4 Consultations and handoffs are exceptionally common inpatient practices, yet training in these practices is variable across educational and practice domains.^5,6 Advanced inpatient communication-skills training requires an effective, feasible, and scalable format. Simulation-based bootcamps can effectively support clinical skills training, often in procedural domains, and have been increasingly utilized for communication skills.^7,8 We previously described the development and implementation of an in-person bootcamp for training and feedback in consultation and handoff communication.^5,8

As hospitalist leaders grapple with how to systematically support and assess essential clinical skills, the COVID-19 pandemic has presented another impetus to rethink current processes. The rapid shift to virtual activities met immediate needs of the pandemic, but also inspired creativity in applying new methodologies to improve teaching strategies and implementation long-term.^9,10 One such strategy, telesimulation, offers a way to continue simulation-based training limited by the need for physical distancing.¹⁰ Furthermore, recent calls to study the efficacy of virtual bootcamp structures have acknowledged potential benefits, even outside of the pandemic.¹¹

The primary objective of this feasibility study was to convert our previously described consultation and handoff bootcamp to a telesimulation bootcamp (TBC), preserving rigorous performance evaluation and opportunities for skills-based feedback. We additionally compared evaluation between virtual and in-person formats to understand the utility of telesimulation for bootcamp-based clinical education moving forward.

Setting and Participants

The TBC occurred in June 2020 during the University of Chicago institution-wide graduate medical education (GME) orientation; 130 interns entering 13 residency programs participated. The comparison group was 128 interns who underwent the traditional University of Chicago GME orientation “Advanced Communication Skills Bootcamp” (ACSBC) in 2019.^5,8

Program Description

To develop TBC, we adapted observed structured clinical experiences (OSCEs) created for ACSBC. Until 2020, ACSBC included three in-person OSCEs: (1) requesting a consultation; (2) conducting handoffs; and (3) acquiring informed consent. COVID-19 necessitated conversion of ACSBC to virtual in June 2020. For this, we selected the consultation and handoff OSCEs, as these skills require near-universal and immediate application in clinical practice. Additionally, they required only trained facilitators (TFs), whereas informed consent required standardized patients. Hospitalist and emergency medicine faculty were recruited as TFs; 7 of 12 TFs were hospitalists. Each OSCE had two parts: an asynchronous, mandatory training module and a clinical simulation. For TBC, we adapted the simulations, previously separate experiences, into a 20-minute combined handoff/consultation telesimulation using the Zoom^® video platform. Interns were paired with one TF who served as both standardized consultant (for one mock case) and handoff receiver (for three mock cases, including the consultation case). TFs rated intern performance and provided feedback.

TBC occurred on June 17 and 18, 2020. Interns were emailed asynchronous modules on June 1, and mock cases and instructions on June 12. When TBC began, GME staff proctors oriented interns in the Zoom^® platform. Proctors placed TFs into private breakout rooms into which interns rotated through 20-minute timeslots. Faculty received copies of all TBC materials for review (Appendix 1) and underwent Zoom^®-based training 1 to 2 weeks prior.

We evaluated TBC using several methods: (1) consultation and handoff skills performance measured by two validated checklists^5,8; (2) survey of intern self-reported preparedness to practice consultations and handoffs; and (3) survey of intern satisfaction. Surveys were administered both immediately post bootcamp (Appendix 2) and 8 weeks into internship (Appendix 3). Skills performance checklists were a 12-item consultation checklist⁵ and 6-item handoff checklist.⁸ The handoff checklist was modified to remove activities impossible to assess virtually (ie, orienting sign-outs in a shared space) and to add a three-level rating scale of “outstanding,” “satisfactory,” and “needs improvement.” This was done based on feedback from ACSBC to allow more nuanced feedback for interns. A rating of “outstanding” was used to define successful completion of the item (Appendix 1). Interns rated preparedness and satisfaction on 5-point Likert-type items. All measures were compared to the 2019 in-person ACSBC cohort.

Data Analysis

Stata 16.1 (StataCorp LP) was used for analysis. We dichotomized preparedness and satisfaction scores, defining ratings of “4” or “5” as “prepared” or “satisfied.” As previously described,⁵ we created a composite score averaging both checklist scores for each intern. We normalized this score by rater to a z score (mean, 0; SD, 1) to account for rater differences. “Poor” and “outstanding” performances were defined as z scores below and above 1 SD, respectively. Fisher’s exact test was used to compare proportions, and Pearson correlation test to correlate z scores. The University of Chicago Institutional Review Board granted exemption.

All 130 entering interns participated in TBC. Internal medicine (IM) was the largest specialty (n = 37), followed by pediatrics (n = 22), emergency medicine (EM) (n = 16), and anesthesiology (n = 12). The remaining 9 programs ranged from 2 to 10 interns per program. The 128 interns in ACSBC were similar, including 40 IM, 23 pediatrics, 14 EM, and 12 anesthesia interns, with 2 to 10 interns in remaining programs.

TBC skills performance evaluations were compared to ACSBC (Table 1). The TBC intern cohort’s consultation performance was the same or better than the ACSBC intern cohort’s. For handoffs, TBC interns completed significantly fewer checklist items compared to ACSBC. Performance in each exercise was moderately correlated (r = 0.39, P < .05). For z scores, 14 TBC interns (10.8%) had “outstanding” and 15 (11.6%) had “poor” performances, compared to ACSBC interns with 7 (5.5%) “outstanding” and 10 (7.81%) “poor” performances (P = .15).

JHMVol16No11_Carter08601117e_t1.JPG

All 130 interns (100%) completed the immediate post-TBC survey. Overall, TBC satisfaction was comparable to ACSBC, and significantly improved for satisfaction with performance (Table 2). Compared to ACSBC, TBC interns felt more prepared for simulation and handoff clinical practice. Nearly all interns would recommend TBC (99% vs 96% of ACSBC interns, P = 0.28), and 99% felt the software used for the simulation ran smoothly.

JHMVol16No11_Carter08601117e_t2.JPG

The 8-week post-TBC survey had a response rate of 88% (115/130); 69% of interns reported conducting more effective handoffs due to TBC, and 79% felt confident in handoff skills. Similarly, 73% felt more effective at calling consultations, and 75% reported retained knowledge of consultation frameworks taught during TBC. Additionally, 71% of interns reported that TBC helped identify areas for self-directed improvement. There were no significant differences in 8-week postsurvey ratings between ACSBC and TBC.

In converting the advanced communication skills bootcamp from an in-person to a virtual format, telesimulation was well-received by interns and rated similarly to in-person bootcamp in most respects. Nearly all interns agreed the experience was realistic, provided useful feedback, and prepared them for clinical practice. Although we shifted to virtual out of necessity, our results demonstrate a high-quality, streamlined bootcamp experience that was less labor-intensive for interns, staff, and faculty. Telesimulation may represent an effective strategy beyond the COVID-19 pandemic to increase ease of administration and scale the use of bootcamps in supporting advanced clinical skill training for hospital-based practice.

TBC interns felt better prepared for simulation and more satisfied with their performance than ACSBC interns, potentially due to the revised format. The mock cases were adapted and consolidated for TBC, such that the handoff and consultation simulations shared a common case, whereas previously they were separate. Thus, intern preparation for TBC required familiarity with fewer overall cases. Ultimately, TBC maintained the quality of training but required review of less information.

In comparing performance, TBC interns were rated as well or better during consultation simulation compared to ASCBC, but handoffs were rated lower. This was likely due to the change in the handoff checklist from a dichotomous to a three-level rating scale. This change was made after receiving feedback from ACSBC TFs that a rating scale allowing for more nuance was needed to provide adequate feedback to interns. Although we defined handoff item completion for TBC interns as being rated “outstanding,” if the top two rankings, “outstanding” and “satisfactory,” are dichotomized to reflect completion, TBC handoff performance is equivalent or better than ACSBC. TF recruitment additionally differed between TBC and ACSBC cohorts. In ACSBC, resident physicians served as handoff TFs, whereas only faculty were recruited for TBC. Faculty were primarily clinically active hospitalists, whose expertise in handoffs may resulted in more stringent performance ratings, contributing to differences seen.

Hospitalist groups require clinicians to be immediately proficient in essential communication skills like consultation and handoffs, potentially requiring just-in-time training and feedback for large cohorts.¹² Bootcamps can meet this need but require participation and time investment by many faculty members, staff, and administrators.^5,8 Combining TBC into one virtual handoff/consultation simulation required recruitment and training of 50% fewer TFs and reduced administrative burden. ACSBC consultation simulations were high-fidelity but resource-heavy, requiring reliable two-way telephones with reliable connections and separate spaces for simulation and feedback.⁵ Conversely, TBC only required consultations to be “called” via audio-only Zoom^® discussion, then both individuals turned on cameras for feedback. The slight decrease in perceived fidelity was certainly outweighed by ease of administration. TBC’s more efficient and less labor-intensive format is an appealing strategy for hospitalist groups looking to train up clinicians, including those operating across multiple or geographically distant sites.

Our study has limitations. It occurred with one group of learners at a single site with consistent consultation and handoff communication practices, which may not be the case elsewhere. Our comparison group was a separate cohort, and groups were not randomized; thus, differences seen may reflect inherent dissimilarities in these groups. Changes to the handoff checklist rating scale between 2019 and 2020 additionally may limit the direct comparison of handoff performance between cohorts. While overall fewer resources were required, TBC implementation did require time and institutional support, along with full virtual platform capability without user or time limitations. Our preparedness outcomes were self-reported without direct measurement of clinical performance, which is an area for future work.

We describe a feasible implementation of an adapted telesimulation communication bootcamp, with comparison to a previous in-person cohort’s skills performance and satisfaction. While COVID-19 has made the future of in-person training activities uncertain, it also served as a catalyst for educational innovation that may be sustained beyond the pandemic. Although developed out of necessity, the telesimulation communication bootcamp was effective and well-received. Telesimulation represents an opportunity for hospital medicine groups to implement advanced communication skills training and assessment in a more efficient, flexible, and potentially preferable way, even after the pandemic ends.

The authors thank the staff at the University of Chicago Office of Graduate Medical Education and the UChicago Medicine Simulation Center.

Practice guidelines discourage continuous pulse oximetry (SpO₂) monitoring of patients with bronchiolitis who are not receiving supplemental oxygen.^1,2 Overuse of SpO₂ monitoring in this patient population has been associated with increased length of stay, unnecessary oxygen therapy, and excess hospital costs, without measurable patient benefit.^3-5 In spite of this evidence base and expert guidance, nearly half of the more than 100,000 infants admitted for bronchiolitis each year receive excess SpO₂ monitoring.^6,7

Bronchiolitis guidelines suggest that guideline-discordant SpO₂ monitoring may result in excess alarms, which disrupt families’ sleep and engender alarm fatigue among staff.¹ Pediatric nurses receive up to 155 alarms per monitored patient per day.^8,9 Frequent alarms are associated with slower nurse response times^10,11 and increased nurse subjective workload.¹²The rate of excess alarms occurring during guideline-discordant, continuously SpO₂ monitored time, compared to the rate of alarms occurring during guideline-concordant (intermittently measured SpO₂) time, has not been evaluated. The magnitude of this difference in alarm rates, if such a difference exists, will inform prioritization of guideline-discordant continuous SpO₂ measurement de-implementation. The objective of this study was to quantify the alarm burden associated with excess SpO₂ monitoring of bronchiolitis patients not receiving supplemental oxygen.

Cohort

We retrospectively evaluated SpO₂ monitoring patterns and alarm rates of children 0 to 24 months old admitted to a general pediatrics service at a tertiary care children’s hospital. We included patients who had a discharge diagnosis of bronchiolitis (International Classification of Diseases, Tenth Revision codes J45x, T17.2x, T17.3x, T17.4x, T17.5x, T17.8x, T17.9x, A37xx, J04x, J05x, J05.1x, J69.0x, J69.1x, J69.8x) between November 24, 2019, and January 21, 2020, the period of time during which alarm data and monitor data were concurrently available for analysis. In order to conservatively assure applicability of clinical practice guidelines, we excluded patients with discharge diagnoses that included other respiratory conditions (eg, reactive airway disease), patients with complex chronic conditions (CCC) as defined by the CCC version 2 classification system,¹³ and patients with intensive care unit (ICU) stays during the admission.

Time

Flowsheet data detailing nursing respiratory assessments were extracted from the electronic health record (EHR) database (Clarity, Epic Systems). Using previously validated methodology,¹⁴ we identified minutes during which patients received supplemental oxygen or high-flow nasal cannula (supplemental oxygen) based on the documented fraction of inspired oxygen (FiO₂), flow rate, and support devices. We then identified the final discontinuation of respiratory support during the hospital admission, and censored the 60 minutes after final discontinuation of supplemental oxygen based upon recent monitoring guidelines.² Minutes up to an hour after supplemental oxygen discontinuation were classified as receiving supplemental oxygen and not included in our analysis. Only minutes between the end of the censored period and hospital discharge were used in the analysis. For patients who never received respiratory support during the admission, we censored the first 60 minutes of the admission and analyzed the remainder of their stay.

SpO₂ Monitoring

We used device-integrated, physiologic-monitor, vital sign data sent each minute from the General Electric monitor network to the EHR to identify minutes during which patients were connected to physiologic monitors and transmitting signals from SpO₂ sensors. We extracted minute-level SpO₂ data from the hospital clinical data warehouse (CDW). Minutes in which SpO₂ data were present were classified as “monitored,” an approach previously validated using in-person observation.¹⁴

To categorize time as “not receiving supplemental oxygen and continuously monitored (guideline-discordant monitoring),” or “not receiving supplemental oxygen and not continuously monitored (guideline-concordant intermittent measurement),” we evaluated the percent of minutes within an hour during which the patient received SpO₂ monitoring and applied an a priori conservative rule. Hours during which ≥90% of minutes had SpO₂ monitoring data were classified as “continuously monitored.” Hours during which ≤10% of minutes had SpO₂ monitoring data were classified as “intermittently measured.” Hours during which 11% to 89% of minutes included monitor data were excluded from further analysis. The number of continuously monitored hours was tabulated for each patient. The median number of continuously monitored hours was computed; results were stratified by prior receipt of respiratory support.

Alarm Counts

Minute-level monitor alarm counts (the absolute number of abnormal vital signs that triggered a monitor to alarm) were extracted from the CDW. Alarm counts were tabulated for each patient hour. For each patient, the alarm rate (total number of alarms divided by time) was computed for continuously monitored and intermittently measured time. Results were stratified by prior receipt of respiratory support.

The study was reviewed by the institutional review board and determined to meet exemption criteria.

Our cohort included 201 admissions by 197 unique patients (Table). We evaluated 4402 hours that occurred ≥60 minutes following final discontinuation of supplemental oxygen, the time period during which guidelines discourage routine use of continuous SpO₂ monitoring. This represented a median of 19 hours (interquartile range [IQR], 14-25) per admission. We excluded 474 hours (11%) that could not be classified as either continuously or intermittently measured.

JHMVol16No11_Rasooly08251117e_t1.JPG

During time ≥60 minutes following discontinuation of supplemental oxygen, our cohort experienced 1537 hours of guideline-discordant continuous monitoring, a median of 6 hours (IQR, 3-12) per admission. Patients experienced a median of 12 hours (IQR, 5-17) of intermittent measurement. Among patients who received supplemental oxygen, 91% experienced guideline-discordant continuous SpO₂ monitoring, as compared to 68% of patients who did not receive supplemental oxygen. Among those who received guideline-discordant continuous SpO₂ monitoring, the duration of this monitoring did not differ significantly between those who had received supplemental oxygen during the admission and those who had not.

During classifiable time ≥60 minutes following discontinuation of supplemental oxygen, our cohort experienced 14,371 alarms; 77% (11,101) of these alarms were generated during periods of guideline-discordant continuous monitoring. The median hourly alarm rate during these periods of guideline-discordant continuous monitoring was 6.7 alarms per hour (IQR, 2.1-12.3), representing a median of 35 (IQR, 10-81) additional alarms per patient. During periods of guideline-concordant intermittent measurement, the median hourly alarm rate was 0.5 (IQR, 0.1-0.8), with a median of 5 (IQR, 1-13) alarms per patient.

Those who received supplemental oxygen earlier in the admission had higher alarm rates during continuously monitored time (7.3 per hour [IQR, 2.7-12.7]) than patients who had not received supplemental oxygen (3.3 per hour [IQR, 0.6-11.8]), likely reflecting clinical differences between these patient populations. The most frequent alarm type among continuously monitored patients who had previously received supplemental oxygen was “SpO₂ low.”

Across 4402 patient hours, guideline-discordant continuous SpO₂ monitoring of patients with bronchiolitis resulted in 11,101 alarms, at a rate of approximately 1 additional alarm every 9 minutes. Patients in our cohort received a median of 6 hours of guideline-discordant monitoring, which imposes a significant alarm burden that is potentially modifiable using targeted reduction strategies.¹⁵

Transient, self-resolved hypoxemia is a common feature of bronchiolitis and likely of little clinical consequence.¹⁶ Therefore, this rate of hypoxemia alarms is not unexpected. Though we evaluated only the period of time following final discontinuation of respiratory support, this finding is in keeping with the literature associating excess physiologic monitoring of patients with bronchiolitis with unnecessary oxygen therapy and increased length of stay,^3-5 largely because clinicians may feel compelled to respond to hypoxemia alarms with either supplemental oxygen or longer monitoring.

Our findings must be contextualized in light of the limitations of our approach. We did not evaluate nurse workload associated with guideline-discordant continuous SpO₂ monitoring. Prior work conducted by our lab has demonstrated that when nurses experience more than 40 alarms within a 2-hour period, their measured subjective workload increases to a degree associated with missing important tasks, threatening the quality and safety of the care they deliver.^12,17 Given that nurses care for multiple patients, it is likely that the excess alarms introduced by guideline-discordant continuous monitoring contribute to increased nurse workload and alarm fatigue.

Similarly, we could not evaluate whether the alarms nurses experienced were actionable. Although our lab has previously reported that ≥99% of alarms occurring on non-ICU pediatric wards are nonactionable,^10,11 it is possible that some of the alarms during guideline-discordant monitoring periods required action. However, it is unlikely that any life-sustaining actions were taken because (1) we only evaluated time >60 minutes after final discontinuation of supplemental oxygen, so by definition none of these alarms required treatment with supplemental oxygen, and (2) none of the patients we included received ICU care during their admission.

The avoidable alarm burden identified in our analysis suggests that eliminating continuous SpO₂ monitoring overuse in bronchiolitis will likely reduce nurses’ workload and alarm fatigue in hospital settings that care for children with bronchiolitis.

Practice guidelines discourage continuous pulse oximetry (SpO₂) monitoring of patients with bronchiolitis who are not receiving supplemental oxygen.^1,2 Overuse of SpO₂ monitoring in this patient population has been associated with increased length of stay, unnecessary oxygen therapy, and excess hospital costs, without measurable patient benefit.^3-5 In spite of this evidence base and expert guidance, nearly half of the more than 100,000 infants admitted for bronchiolitis each year receive excess SpO₂ monitoring.^6,7

Bronchiolitis guidelines suggest that guideline-discordant SpO₂ monitoring may result in excess alarms, which disrupt families’ sleep and engender alarm fatigue among staff.¹ Pediatric nurses receive up to 155 alarms per monitored patient per day.^8,9 Frequent alarms are associated with slower nurse response times^10,11 and increased nurse subjective workload.¹²The rate of excess alarms occurring during guideline-discordant, continuously SpO₂ monitored time, compared to the rate of alarms occurring during guideline-concordant (intermittently measured SpO₂) time, has not been evaluated. The magnitude of this difference in alarm rates, if such a difference exists, will inform prioritization of guideline-discordant continuous SpO₂ measurement de-implementation. The objective of this study was to quantify the alarm burden associated with excess SpO₂ monitoring of bronchiolitis patients not receiving supplemental oxygen.

Cohort

We retrospectively evaluated SpO₂ monitoring patterns and alarm rates of children 0 to 24 months old admitted to a general pediatrics service at a tertiary care children’s hospital. We included patients who had a discharge diagnosis of bronchiolitis (International Classification of Diseases, Tenth Revision codes J45x, T17.2x, T17.3x, T17.4x, T17.5x, T17.8x, T17.9x, A37xx, J04x, J05x, J05.1x, J69.0x, J69.1x, J69.8x) between November 24, 2019, and January 21, 2020, the period of time during which alarm data and monitor data were concurrently available for analysis. In order to conservatively assure applicability of clinical practice guidelines, we excluded patients with discharge diagnoses that included other respiratory conditions (eg, reactive airway disease), patients with complex chronic conditions (CCC) as defined by the CCC version 2 classification system,¹³ and patients with intensive care unit (ICU) stays during the admission.

Time

Flowsheet data detailing nursing respiratory assessments were extracted from the electronic health record (EHR) database (Clarity, Epic Systems). Using previously validated methodology,¹⁴ we identified minutes during which patients received supplemental oxygen or high-flow nasal cannula (supplemental oxygen) based on the documented fraction of inspired oxygen (FiO₂), flow rate, and support devices. We then identified the final discontinuation of respiratory support during the hospital admission, and censored the 60 minutes after final discontinuation of supplemental oxygen based upon recent monitoring guidelines.² Minutes up to an hour after supplemental oxygen discontinuation were classified as receiving supplemental oxygen and not included in our analysis. Only minutes between the end of the censored period and hospital discharge were used in the analysis. For patients who never received respiratory support during the admission, we censored the first 60 minutes of the admission and analyzed the remainder of their stay.

SpO₂ Monitoring

We used device-integrated, physiologic-monitor, vital sign data sent each minute from the General Electric monitor network to the EHR to identify minutes during which patients were connected to physiologic monitors and transmitting signals from SpO₂ sensors. We extracted minute-level SpO₂ data from the hospital clinical data warehouse (CDW). Minutes in which SpO₂ data were present were classified as “monitored,” an approach previously validated using in-person observation.¹⁴

To categorize time as “not receiving supplemental oxygen and continuously monitored (guideline-discordant monitoring),” or “not receiving supplemental oxygen and not continuously monitored (guideline-concordant intermittent measurement),” we evaluated the percent of minutes within an hour during which the patient received SpO₂ monitoring and applied an a priori conservative rule. Hours during which ≥90% of minutes had SpO₂ monitoring data were classified as “continuously monitored.” Hours during which ≤10% of minutes had SpO₂ monitoring data were classified as “intermittently measured.” Hours during which 11% to 89% of minutes included monitor data were excluded from further analysis. The number of continuously monitored hours was tabulated for each patient. The median number of continuously monitored hours was computed; results were stratified by prior receipt of respiratory support.

Alarm Counts

Minute-level monitor alarm counts (the absolute number of abnormal vital signs that triggered a monitor to alarm) were extracted from the CDW. Alarm counts were tabulated for each patient hour. For each patient, the alarm rate (total number of alarms divided by time) was computed for continuously monitored and intermittently measured time. Results were stratified by prior receipt of respiratory support.

The study was reviewed by the institutional review board and determined to meet exemption criteria.

Our cohort included 201 admissions by 197 unique patients (Table). We evaluated 4402 hours that occurred ≥60 minutes following final discontinuation of supplemental oxygen, the time period during which guidelines discourage routine use of continuous SpO₂ monitoring. This represented a median of 19 hours (interquartile range [IQR], 14-25) per admission. We excluded 474 hours (11%) that could not be classified as either continuously or intermittently measured.

JHMVol16No11_Rasooly08251117e_t1.JPG

During time ≥60 minutes following discontinuation of supplemental oxygen, our cohort experienced 1537 hours of guideline-discordant continuous monitoring, a median of 6 hours (IQR, 3-12) per admission. Patients experienced a median of 12 hours (IQR, 5-17) of intermittent measurement. Among patients who received supplemental oxygen, 91% experienced guideline-discordant continuous SpO₂ monitoring, as compared to 68% of patients who did not receive supplemental oxygen. Among those who received guideline-discordant continuous SpO₂ monitoring, the duration of this monitoring did not differ significantly between those who had received supplemental oxygen during the admission and those who had not.

During classifiable time ≥60 minutes following discontinuation of supplemental oxygen, our cohort experienced 14,371 alarms; 77% (11,101) of these alarms were generated during periods of guideline-discordant continuous monitoring. The median hourly alarm rate during these periods of guideline-discordant continuous monitoring was 6.7 alarms per hour (IQR, 2.1-12.3), representing a median of 35 (IQR, 10-81) additional alarms per patient. During periods of guideline-concordant intermittent measurement, the median hourly alarm rate was 0.5 (IQR, 0.1-0.8), with a median of 5 (IQR, 1-13) alarms per patient.

Those who received supplemental oxygen earlier in the admission had higher alarm rates during continuously monitored time (7.3 per hour [IQR, 2.7-12.7]) than patients who had not received supplemental oxygen (3.3 per hour [IQR, 0.6-11.8]), likely reflecting clinical differences between these patient populations. The most frequent alarm type among continuously monitored patients who had previously received supplemental oxygen was “SpO₂ low.”

Across 4402 patient hours, guideline-discordant continuous SpO₂ monitoring of patients with bronchiolitis resulted in 11,101 alarms, at a rate of approximately 1 additional alarm every 9 minutes. Patients in our cohort received a median of 6 hours of guideline-discordant monitoring, which imposes a significant alarm burden that is potentially modifiable using targeted reduction strategies.¹⁵

Transient, self-resolved hypoxemia is a common feature of bronchiolitis and likely of little clinical consequence.¹⁶ Therefore, this rate of hypoxemia alarms is not unexpected. Though we evaluated only the period of time following final discontinuation of respiratory support, this finding is in keeping with the literature associating excess physiologic monitoring of patients with bronchiolitis with unnecessary oxygen therapy and increased length of stay,^3-5 largely because clinicians may feel compelled to respond to hypoxemia alarms with either supplemental oxygen or longer monitoring.

Our findings must be contextualized in light of the limitations of our approach. We did not evaluate nurse workload associated with guideline-discordant continuous SpO₂ monitoring. Prior work conducted by our lab has demonstrated that when nurses experience more than 40 alarms within a 2-hour period, their measured subjective workload increases to a degree associated with missing important tasks, threatening the quality and safety of the care they deliver.^12,17 Given that nurses care for multiple patients, it is likely that the excess alarms introduced by guideline-discordant continuous monitoring contribute to increased nurse workload and alarm fatigue.

Similarly, we could not evaluate whether the alarms nurses experienced were actionable. Although our lab has previously reported that ≥99% of alarms occurring on non-ICU pediatric wards are nonactionable,^10,11 it is possible that some of the alarms during guideline-discordant monitoring periods required action. However, it is unlikely that any life-sustaining actions were taken because (1) we only evaluated time >60 minutes after final discontinuation of supplemental oxygen, so by definition none of these alarms required treatment with supplemental oxygen, and (2) none of the patients we included received ICU care during their admission.

The avoidable alarm burden identified in our analysis suggests that eliminating continuous SpO₂ monitoring overuse in bronchiolitis will likely reduce nurses’ workload and alarm fatigue in hospital settings that care for children with bronchiolitis.

Practice guidelines discourage continuous pulse oximetry (SpO₂) monitoring of patients with bronchiolitis who are not receiving supplemental oxygen.^1,2 Overuse of SpO₂ monitoring in this patient population has been associated with increased length of stay, unnecessary oxygen therapy, and excess hospital costs, without measurable patient benefit.^3-5 In spite of this evidence base and expert guidance, nearly half of the more than 100,000 infants admitted for bronchiolitis each year receive excess SpO₂ monitoring.^6,7

Bronchiolitis guidelines suggest that guideline-discordant SpO₂ monitoring may result in excess alarms, which disrupt families’ sleep and engender alarm fatigue among staff.¹ Pediatric nurses receive up to 155 alarms per monitored patient per day.^8,9 Frequent alarms are associated with slower nurse response times^10,11 and increased nurse subjective workload.¹²The rate of excess alarms occurring during guideline-discordant, continuously SpO₂ monitored time, compared to the rate of alarms occurring during guideline-concordant (intermittently measured SpO₂) time, has not been evaluated. The magnitude of this difference in alarm rates, if such a difference exists, will inform prioritization of guideline-discordant continuous SpO₂ measurement de-implementation. The objective of this study was to quantify the alarm burden associated with excess SpO₂ monitoring of bronchiolitis patients not receiving supplemental oxygen.

Cohort

We retrospectively evaluated SpO₂ monitoring patterns and alarm rates of children 0 to 24 months old admitted to a general pediatrics service at a tertiary care children’s hospital. We included patients who had a discharge diagnosis of bronchiolitis (International Classification of Diseases, Tenth Revision codes J45x, T17.2x, T17.3x, T17.4x, T17.5x, T17.8x, T17.9x, A37xx, J04x, J05x, J05.1x, J69.0x, J69.1x, J69.8x) between November 24, 2019, and January 21, 2020, the period of time during which alarm data and monitor data were concurrently available for analysis. In order to conservatively assure applicability of clinical practice guidelines, we excluded patients with discharge diagnoses that included other respiratory conditions (eg, reactive airway disease), patients with complex chronic conditions (CCC) as defined by the CCC version 2 classification system,¹³ and patients with intensive care unit (ICU) stays during the admission.

Time

Flowsheet data detailing nursing respiratory assessments were extracted from the electronic health record (EHR) database (Clarity, Epic Systems). Using previously validated methodology,¹⁴ we identified minutes during which patients received supplemental oxygen or high-flow nasal cannula (supplemental oxygen) based on the documented fraction of inspired oxygen (FiO₂), flow rate, and support devices. We then identified the final discontinuation of respiratory support during the hospital admission, and censored the 60 minutes after final discontinuation of supplemental oxygen based upon recent monitoring guidelines.² Minutes up to an hour after supplemental oxygen discontinuation were classified as receiving supplemental oxygen and not included in our analysis. Only minutes between the end of the censored period and hospital discharge were used in the analysis. For patients who never received respiratory support during the admission, we censored the first 60 minutes of the admission and analyzed the remainder of their stay.

SpO₂ Monitoring

We used device-integrated, physiologic-monitor, vital sign data sent each minute from the General Electric monitor network to the EHR to identify minutes during which patients were connected to physiologic monitors and transmitting signals from SpO₂ sensors. We extracted minute-level SpO₂ data from the hospital clinical data warehouse (CDW). Minutes in which SpO₂ data were present were classified as “monitored,” an approach previously validated using in-person observation.¹⁴

To categorize time as “not receiving supplemental oxygen and continuously monitored (guideline-discordant monitoring),” or “not receiving supplemental oxygen and not continuously monitored (guideline-concordant intermittent measurement),” we evaluated the percent of minutes within an hour during which the patient received SpO₂ monitoring and applied an a priori conservative rule. Hours during which ≥90% of minutes had SpO₂ monitoring data were classified as “continuously monitored.” Hours during which ≤10% of minutes had SpO₂ monitoring data were classified as “intermittently measured.” Hours during which 11% to 89% of minutes included monitor data were excluded from further analysis. The number of continuously monitored hours was tabulated for each patient. The median number of continuously monitored hours was computed; results were stratified by prior receipt of respiratory support.

Alarm Counts

Minute-level monitor alarm counts (the absolute number of abnormal vital signs that triggered a monitor to alarm) were extracted from the CDW. Alarm counts were tabulated for each patient hour. For each patient, the alarm rate (total number of alarms divided by time) was computed for continuously monitored and intermittently measured time. Results were stratified by prior receipt of respiratory support.

The study was reviewed by the institutional review board and determined to meet exemption criteria.

Our cohort included 201 admissions by 197 unique patients (Table). We evaluated 4402 hours that occurred ≥60 minutes following final discontinuation of supplemental oxygen, the time period during which guidelines discourage routine use of continuous SpO₂ monitoring. This represented a median of 19 hours (interquartile range [IQR], 14-25) per admission. We excluded 474 hours (11%) that could not be classified as either continuously or intermittently measured.

JHMVol16No11_Rasooly08251117e_t1.JPG

During time ≥60 minutes following discontinuation of supplemental oxygen, our cohort experienced 1537 hours of guideline-discordant continuous monitoring, a median of 6 hours (IQR, 3-12) per admission. Patients experienced a median of 12 hours (IQR, 5-17) of intermittent measurement. Among patients who received supplemental oxygen, 91% experienced guideline-discordant continuous SpO₂ monitoring, as compared to 68% of patients who did not receive supplemental oxygen. Among those who received guideline-discordant continuous SpO₂ monitoring, the duration of this monitoring did not differ significantly between those who had received supplemental oxygen during the admission and those who had not.

During classifiable time ≥60 minutes following discontinuation of supplemental oxygen, our cohort experienced 14,371 alarms; 77% (11,101) of these alarms were generated during periods of guideline-discordant continuous monitoring. The median hourly alarm rate during these periods of guideline-discordant continuous monitoring was 6.7 alarms per hour (IQR, 2.1-12.3), representing a median of 35 (IQR, 10-81) additional alarms per patient. During periods of guideline-concordant intermittent measurement, the median hourly alarm rate was 0.5 (IQR, 0.1-0.8), with a median of 5 (IQR, 1-13) alarms per patient.

Those who received supplemental oxygen earlier in the admission had higher alarm rates during continuously monitored time (7.3 per hour [IQR, 2.7-12.7]) than patients who had not received supplemental oxygen (3.3 per hour [IQR, 0.6-11.8]), likely reflecting clinical differences between these patient populations. The most frequent alarm type among continuously monitored patients who had previously received supplemental oxygen was “SpO₂ low.”

Across 4402 patient hours, guideline-discordant continuous SpO₂ monitoring of patients with bronchiolitis resulted in 11,101 alarms, at a rate of approximately 1 additional alarm every 9 minutes. Patients in our cohort received a median of 6 hours of guideline-discordant monitoring, which imposes a significant alarm burden that is potentially modifiable using targeted reduction strategies.¹⁵

Transient, self-resolved hypoxemia is a common feature of bronchiolitis and likely of little clinical consequence.¹⁶ Therefore, this rate of hypoxemia alarms is not unexpected. Though we evaluated only the period of time following final discontinuation of respiratory support, this finding is in keeping with the literature associating excess physiologic monitoring of patients with bronchiolitis with unnecessary oxygen therapy and increased length of stay,^3-5 largely because clinicians may feel compelled to respond to hypoxemia alarms with either supplemental oxygen or longer monitoring.

Our findings must be contextualized in light of the limitations of our approach. We did not evaluate nurse workload associated with guideline-discordant continuous SpO₂ monitoring. Prior work conducted by our lab has demonstrated that when nurses experience more than 40 alarms within a 2-hour period, their measured subjective workload increases to a degree associated with missing important tasks, threatening the quality and safety of the care they deliver.^12,17 Given that nurses care for multiple patients, it is likely that the excess alarms introduced by guideline-discordant continuous monitoring contribute to increased nurse workload and alarm fatigue.

Similarly, we could not evaluate whether the alarms nurses experienced were actionable. Although our lab has previously reported that ≥99% of alarms occurring on non-ICU pediatric wards are nonactionable,^10,11 it is possible that some of the alarms during guideline-discordant monitoring periods required action. However, it is unlikely that any life-sustaining actions were taken because (1) we only evaluated time >60 minutes after final discontinuation of supplemental oxygen, so by definition none of these alarms required treatment with supplemental oxygen, and (2) none of the patients we included received ICU care during their admission.

The avoidable alarm burden identified in our analysis suggests that eliminating continuous SpO₂ monitoring overuse in bronchiolitis will likely reduce nurses’ workload and alarm fatigue in hospital settings that care for children with bronchiolitis.

Transition out of the hospital is a vulnerable time for older adults. Medications, particularly opioids, are a common cause of adverse events during this transitionary period.^1,2 For hospitalized patients with acute noncancer pain that necessitates opioid treatment, guidelines recommend using short-acting, rather than long-acting, opioids.^3,4 Long-acting opioids have a longer duration of action but also have a significantly elevated risk of unintentional overdose and morbidity compared to short-acting opioids, even when total daily dosing is identical.^5,6 This risk is highest in the first 2 weeks following initial prescription.^7,8

Despite the recent decrease in overall prescription of opioids,⁹ a small but significant proportion continue to be prescribed as long-acting formulations.^10-12 We sought to understand the incidence of, and patient characteristics associated with, long-acting opioid initiation following hospital discharge among opioid-naïve older adults.

We examined the 20% random sample of US Medicare beneficiaries ≥65 years old who were hospitalized in 2016 and continuously enrolled in Parts A, B, and D for 1 year prior and 1 month following discharge, excluding beneficiaries with cancer or hospice care, those transferred from or discharged to a care facility, and those who had filled a prescription for an opioid within 90 days prior to hospitalization. We identified beneficiaries with a Part D claim for an opioid, excluding methadone and buprenorphine, within 7 days of discharge. We compared beneficiaries with at least one claim for a long-acting opioid (including extended-release formulations) within 7 days of hospital discharge to those with short-acting opioid claims only.

We used a multivariable, generalized estimating equation to determine patient-level factors independently associated with prescription of any long-acting opioids. We selected characteristics that we hypothesized to be associated with new opioid prescription, based on clinical experience and previous literature, including sociodemographics, patient clinical characteristics such as a modified Elixhauser index (a composite index of nearly 30 comorbidities, excluding cancer),¹³ substance use-related factors, co-prescribed medications, and hospitalization-related factors. The latter included being hospitalized for a medical vs surgical reason, defined based on diagnosis-related group (DRG), primary diagnosis, and primary procedure, grouped using the Agency for Healthcare Research and Quality Clinical Classification System¹⁴ (Table 1).

JHMVol16No11_Deshpan09371117e_t1.JPG

We conducted a sensitivity analysis, excluding beneficiaries with high-dose long-term opioid use in the year before hospitalization.

Of 258,193 hospitalizations meeting eligibility criteria, 47,945 (18.6%) had an opioid claim within 7 days of discharge and comprised our analytic cohort (see the Appendix Figure for the study consort diagram), including 47,003 (18.2%) with short-acting opioids only and 942 (0.4%) with at least one claim for long-acting opioids, of whom 817 received both short- and long-acting opioids (Table 1).

Beneficiaries with long-acting opioid claims were more likely to be younger (ages 65-69 and 70-74 years) and White than those with claims for short-acting opioids only. They had a lower mean number of Elixhauser comorbidities but a higher prevalence of mental health conditions, including anxiety disorders and opioid use disorder, as well as a higher prevalence of previous high-dose long-term opioid use (occurring more than 90 days prior to hospitalization). They were more likely to have been hospitalized for a procedural rather than a medical reason, with 770 of the 942 (81.7%) beneficiaries receiving long-acting opioids having been hospitalized for a procedural reason (based on DRG). They were also more likely to have benzodiazepine co-prescription.

Factors independently associated with receipt of long-acting opioids compared to short-acting opioids only included younger age, having been admitted for a musculoskeletal problem, and presence of known risk factors for opioid-related adverse events, including anxiety disorders, opioid use disorder, prior long-term high-dose opioid use, and benzodiazepine co-prescription (Table 2). After excluding 33 beneficiaries with previous high-dose long-term opioid use in the year before hospitalization, associations were unchanged (Appendix Table).

JHMVol16No11_Deshpan09371117e_t2.JPG

Among a nationally representative sample of opioid-naïve Medicare beneficiaries without cancer, almost 20% filled a new opioid prescription within 7 days of hospital discharge. While prescription of long-acting opioids was uncommon, 81.7% who were prescribed a long-acting opioid had a procedural reason for hospitalization, raising concern since postoperative pain is typically acute and limited. Beneficiaries started on long-acting opioids more frequently had risk factors for opioid-related adverse events, including history of opioid use disorder and benzodiazepine co-prescription. With nearly three-quarters of patients with a long-acting opioid claim having been hospitalized for musculoskeletal disorders or orthopedic procedures, this population represents a key target for quality improvement interventions.

This is the first analysis describing the incidence and factors associated with long-acting opioid receipt shortly after hospital discharge among Medicare beneficiaries. Given that our data predate the publication of the Society of Hospital Medicine’s consensus statement on safe opioid prescribing in hospitalized patients,³ it is possible that there have been changes to prescribing patterns since 2016 that we are unable to characterize with our data. We are also limited by an inability to determine the appropriateness of any individual long-acting opioid prescription, though previous research has shown that long-acting opioids are frequently inappropriately initiated in older adults.¹⁵ Finally, our findings may not be generalizable to non-Medicare populations.

While long-acting opioid initiation following hospitalization is uncommon, these medications are most often prescribed to individuals with pain that is typically of limited duration and those at high risk for harm. Our findings highlight potential targets for systems-based solutions to improve guideline-concordant prescribing of long-acting opioids.

Transition out of the hospital is a vulnerable time for older adults. Medications, particularly opioids, are a common cause of adverse events during this transitionary period.^1,2 For hospitalized patients with acute noncancer pain that necessitates opioid treatment, guidelines recommend using short-acting, rather than long-acting, opioids.^3,4 Long-acting opioids have a longer duration of action but also have a significantly elevated risk of unintentional overdose and morbidity compared to short-acting opioids, even when total daily dosing is identical.^5,6 This risk is highest in the first 2 weeks following initial prescription.^7,8

Despite the recent decrease in overall prescription of opioids,⁹ a small but significant proportion continue to be prescribed as long-acting formulations.^10-12 We sought to understand the incidence of, and patient characteristics associated with, long-acting opioid initiation following hospital discharge among opioid-naïve older adults.

We examined the 20% random sample of US Medicare beneficiaries ≥65 years old who were hospitalized in 2016 and continuously enrolled in Parts A, B, and D for 1 year prior and 1 month following discharge, excluding beneficiaries with cancer or hospice care, those transferred from or discharged to a care facility, and those who had filled a prescription for an opioid within 90 days prior to hospitalization. We identified beneficiaries with a Part D claim for an opioid, excluding methadone and buprenorphine, within 7 days of discharge. We compared beneficiaries with at least one claim for a long-acting opioid (including extended-release formulations) within 7 days of hospital discharge to those with short-acting opioid claims only.

We used a multivariable, generalized estimating equation to determine patient-level factors independently associated with prescription of any long-acting opioids. We selected characteristics that we hypothesized to be associated with new opioid prescription, based on clinical experience and previous literature, including sociodemographics, patient clinical characteristics such as a modified Elixhauser index (a composite index of nearly 30 comorbidities, excluding cancer),¹³ substance use-related factors, co-prescribed medications, and hospitalization-related factors. The latter included being hospitalized for a medical vs surgical reason, defined based on diagnosis-related group (DRG), primary diagnosis, and primary procedure, grouped using the Agency for Healthcare Research and Quality Clinical Classification System¹⁴ (Table 1).

JHMVol16No11_Deshpan09371117e_t1.JPG

We conducted a sensitivity analysis, excluding beneficiaries with high-dose long-term opioid use in the year before hospitalization.

Of 258,193 hospitalizations meeting eligibility criteria, 47,945 (18.6%) had an opioid claim within 7 days of discharge and comprised our analytic cohort (see the Appendix Figure for the study consort diagram), including 47,003 (18.2%) with short-acting opioids only and 942 (0.4%) with at least one claim for long-acting opioids, of whom 817 received both short- and long-acting opioids (Table 1).

Beneficiaries with long-acting opioid claims were more likely to be younger (ages 65-69 and 70-74 years) and White than those with claims for short-acting opioids only. They had a lower mean number of Elixhauser comorbidities but a higher prevalence of mental health conditions, including anxiety disorders and opioid use disorder, as well as a higher prevalence of previous high-dose long-term opioid use (occurring more than 90 days prior to hospitalization). They were more likely to have been hospitalized for a procedural rather than a medical reason, with 770 of the 942 (81.7%) beneficiaries receiving long-acting opioids having been hospitalized for a procedural reason (based on DRG). They were also more likely to have benzodiazepine co-prescription.

Factors independently associated with receipt of long-acting opioids compared to short-acting opioids only included younger age, having been admitted for a musculoskeletal problem, and presence of known risk factors for opioid-related adverse events, including anxiety disorders, opioid use disorder, prior long-term high-dose opioid use, and benzodiazepine co-prescription (Table 2). After excluding 33 beneficiaries with previous high-dose long-term opioid use in the year before hospitalization, associations were unchanged (Appendix Table).

JHMVol16No11_Deshpan09371117e_t2.JPG

Among a nationally representative sample of opioid-naïve Medicare beneficiaries without cancer, almost 20% filled a new opioid prescription within 7 days of hospital discharge. While prescription of long-acting opioids was uncommon, 81.7% who were prescribed a long-acting opioid had a procedural reason for hospitalization, raising concern since postoperative pain is typically acute and limited. Beneficiaries started on long-acting opioids more frequently had risk factors for opioid-related adverse events, including history of opioid use disorder and benzodiazepine co-prescription. With nearly three-quarters of patients with a long-acting opioid claim having been hospitalized for musculoskeletal disorders or orthopedic procedures, this population represents a key target for quality improvement interventions.

This is the first analysis describing the incidence and factors associated with long-acting opioid receipt shortly after hospital discharge among Medicare beneficiaries. Given that our data predate the publication of the Society of Hospital Medicine’s consensus statement on safe opioid prescribing in hospitalized patients,³ it is possible that there have been changes to prescribing patterns since 2016 that we are unable to characterize with our data. We are also limited by an inability to determine the appropriateness of any individual long-acting opioid prescription, though previous research has shown that long-acting opioids are frequently inappropriately initiated in older adults.¹⁵ Finally, our findings may not be generalizable to non-Medicare populations.

While long-acting opioid initiation following hospitalization is uncommon, these medications are most often prescribed to individuals with pain that is typically of limited duration and those at high risk for harm. Our findings highlight potential targets for systems-based solutions to improve guideline-concordant prescribing of long-acting opioids.

Transition out of the hospital is a vulnerable time for older adults. Medications, particularly opioids, are a common cause of adverse events during this transitionary period.^1,2 For hospitalized patients with acute noncancer pain that necessitates opioid treatment, guidelines recommend using short-acting, rather than long-acting, opioids.^3,4 Long-acting opioids have a longer duration of action but also have a significantly elevated risk of unintentional overdose and morbidity compared to short-acting opioids, even when total daily dosing is identical.^5,6 This risk is highest in the first 2 weeks following initial prescription.^7,8

Despite the recent decrease in overall prescription of opioids,⁹ a small but significant proportion continue to be prescribed as long-acting formulations.^10-12 We sought to understand the incidence of, and patient characteristics associated with, long-acting opioid initiation following hospital discharge among opioid-naïve older adults.

We examined the 20% random sample of US Medicare beneficiaries ≥65 years old who were hospitalized in 2016 and continuously enrolled in Parts A, B, and D for 1 year prior and 1 month following discharge, excluding beneficiaries with cancer or hospice care, those transferred from or discharged to a care facility, and those who had filled a prescription for an opioid within 90 days prior to hospitalization. We identified beneficiaries with a Part D claim for an opioid, excluding methadone and buprenorphine, within 7 days of discharge. We compared beneficiaries with at least one claim for a long-acting opioid (including extended-release formulations) within 7 days of hospital discharge to those with short-acting opioid claims only.

We used a multivariable, generalized estimating equation to determine patient-level factors independently associated with prescription of any long-acting opioids. We selected characteristics that we hypothesized to be associated with new opioid prescription, based on clinical experience and previous literature, including sociodemographics, patient clinical characteristics such as a modified Elixhauser index (a composite index of nearly 30 comorbidities, excluding cancer),¹³ substance use-related factors, co-prescribed medications, and hospitalization-related factors. The latter included being hospitalized for a medical vs surgical reason, defined based on diagnosis-related group (DRG), primary diagnosis, and primary procedure, grouped using the Agency for Healthcare Research and Quality Clinical Classification System¹⁴ (Table 1).

JHMVol16No11_Deshpan09371117e_t1.JPG

We conducted a sensitivity analysis, excluding beneficiaries with high-dose long-term opioid use in the year before hospitalization.

Of 258,193 hospitalizations meeting eligibility criteria, 47,945 (18.6%) had an opioid claim within 7 days of discharge and comprised our analytic cohort (see the Appendix Figure for the study consort diagram), including 47,003 (18.2%) with short-acting opioids only and 942 (0.4%) with at least one claim for long-acting opioids, of whom 817 received both short- and long-acting opioids (Table 1).

Beneficiaries with long-acting opioid claims were more likely to be younger (ages 65-69 and 70-74 years) and White than those with claims for short-acting opioids only. They had a lower mean number of Elixhauser comorbidities but a higher prevalence of mental health conditions, including anxiety disorders and opioid use disorder, as well as a higher prevalence of previous high-dose long-term opioid use (occurring more than 90 days prior to hospitalization). They were more likely to have been hospitalized for a procedural rather than a medical reason, with 770 of the 942 (81.7%) beneficiaries receiving long-acting opioids having been hospitalized for a procedural reason (based on DRG). They were also more likely to have benzodiazepine co-prescription.

Factors independently associated with receipt of long-acting opioids compared to short-acting opioids only included younger age, having been admitted for a musculoskeletal problem, and presence of known risk factors for opioid-related adverse events, including anxiety disorders, opioid use disorder, prior long-term high-dose opioid use, and benzodiazepine co-prescription (Table 2). After excluding 33 beneficiaries with previous high-dose long-term opioid use in the year before hospitalization, associations were unchanged (Appendix Table).

JHMVol16No11_Deshpan09371117e_t2.JPG

Among a nationally representative sample of opioid-naïve Medicare beneficiaries without cancer, almost 20% filled a new opioid prescription within 7 days of hospital discharge. While prescription of long-acting opioids was uncommon, 81.7% who were prescribed a long-acting opioid had a procedural reason for hospitalization, raising concern since postoperative pain is typically acute and limited. Beneficiaries started on long-acting opioids more frequently had risk factors for opioid-related adverse events, including history of opioid use disorder and benzodiazepine co-prescription. With nearly three-quarters of patients with a long-acting opioid claim having been hospitalized for musculoskeletal disorders or orthopedic procedures, this population represents a key target for quality improvement interventions.

This is the first analysis describing the incidence and factors associated with long-acting opioid receipt shortly after hospital discharge among Medicare beneficiaries. Given that our data predate the publication of the Society of Hospital Medicine’s consensus statement on safe opioid prescribing in hospitalized patients,³ it is possible that there have been changes to prescribing patterns since 2016 that we are unable to characterize with our data. We are also limited by an inability to determine the appropriateness of any individual long-acting opioid prescription, though previous research has shown that long-acting opioids are frequently inappropriately initiated in older adults.¹⁵ Finally, our findings may not be generalizable to non-Medicare populations.

While long-acting opioid initiation following hospitalization is uncommon, these medications are most often prescribed to individuals with pain that is typically of limited duration and those at high risk for harm. Our findings highlight potential targets for systems-based solutions to improve guideline-concordant prescribing of long-acting opioids.

Bundled payments represent one of the most prominent value-based payment arrangements nationwide. Under this payment approach, hospitals assume responsibility for quality and costs across discrete episodes of care. Hospitals that maintain quality while achieving cost reductions are eligible for financial incentives, whereas those that do not are subject to financial penalties.

To date, the largest completed bundled payment program nationwide is Medicare’s Bundled Payments for Care Improvement (BPCI) initiative. Among four different participation models in BPCI, hospital enrollment was greatest in Model 2, in which episodes spanned from hospitalization through 90 days of post–acute care. The overall results from BPCI Model 2 have been positive: hospitals participating in both common surgical episodes, such as joint replacement surgery, and medical episodes, such as acute myocardial infarction (AMI) and congestive heart failure (CHF), have demonstrated long-term financial savings with stable quality performance.^1,2

Safety net hospitals that disproportionately serve low-income patients may fare differently than other hospitals under bundled payment models. At baseline, these hospitals typically have fewer financial resources, which may limit their ability to implement measures to standardize care during hospitalization (eg, clinical pathways) or after discharge (eg, postdischarge programs and other strategies to reduce readmissions).³ Efforts to redesign care may be further complicated by greater clinical complexity and social and structural determinants of health among patients seeking care at safety net hospitals. Given the well-known interactions between social determinants and health conditions, these factors are highly relevant for patients hospitalized at safety net hospitals for acute medical events or exacerbations of chronic conditions.

Existing evidence has shown that safety net hospitals have not performed as well as other hospitals in other value-based reforms.^4-8 In the context of bundled payments for joint replacement surgery, safety net hospitals have been less likely to achieve financial savings but more likely to receive penalties.^9-11 Moreover, the savings achieved by safety net hospitals have been smaller than those achieved by non–safety net hospitals.¹²

Despite these concerning findings, there are few data about how safety net hospitals have fared under bundled payments for common medical conditions. To address this critical knowledge gap, we evaluated the effect of hospital safety net status on the association between BPCI Model 2 participation and changes in outcomes for medical condition episodes.

This study was approved by the University of Pennsylvania Institutional Review Board with a waiver of informed consent.

Data

We used 100% Medicare claims data from 2011 to 2016 for patients receiving care at hospitals participating in BPCI Model 2 for one of four common medical condition episodes: AMI, pneumonia, CHF, and chronic obstructive pulmonary disease (COPD). A 20% random national sample was used for patients hospitalized at nonparticipant hospitals. Publicly available data from the Centers for Medicare & Medicaid Services (CMS) were used to identify hospital enrollment in BPCI Model 2, while data from the 2017 CMS Impact File were used to quantify each hospital’s disproportionate patient percentage (DPP), which reflects the proportion of Medicaid and low-income Medicare beneficiaries served and determines a hospital’s eligibility to earn disproportionate share hospital payments.

Data from the 2011 American Hospital Association Annual Survey were used to capture hospital characteristics, such as number of beds, teaching status, and profit status, while data from the Medicare provider of service, beneficiary summary, and accountable care organization files were used to capture additional hospital characteristics and market characteristics, such as population size and Medicare Advantage penetration. The Medicare Provider Enrollment, Chain, and Ownership System file was used to identify and remove BPCI episodes from physician group practices. State-level data about area deprivation index—a census tract–based measure that incorporates factors such as income, education, employment, and housing quality to describe socioeconomic disadvantage among neighborhoods—were used to define socioeconomically disadvantaged areas as those in the top 20% of area deprivation index statewide.¹³ Markets were defined using hospital referral regions.¹⁴

Study Periods and Hospital Groups

Our analysis spanned the period between January 1, 2011, and December 31, 2016. We separated this period into a baseline period (January 2011–September 2013) prior to the start of BPCI and a subsequent BPCI period (October 2013–December 2016).

We defined any hospitals participating in BPCI Model 2 across this period for any of the four included medical condition episodes as BPCI hospitals. Because hospitals were able to enter or exit BPCI over time, and enrollment data were provided by CMS as quarterly participation files, we were able to identify dates of entry into or exit from BPCI over time by hospital-condition pairs. Hospitals were considered BPCI hospitals until the end of the study period, regardless of subsequent exit.

We defined non-BPCI hospitals as those that never participated in the program and had 10 or more admissions in the BPCI period for the included medical condition episodes. We used this approach to minimize potential bias arising from BPCI entry and exit over time.

Across both BPCI and non-BPCI hospital groups, we followed prior methods and defined safety net hospitals based on a hospital’s DPP.¹⁵ Specifically, safety net hospitals were those in the top quartile of DPP among all hospitals nationwide, and hospitals in the other three quartiles were defined as non–safety net hospitals.^9,12

Study Sample and Episode Construction

Our study sample included Medicare fee-for-service beneficiaries admitted to BPCI and non-BPCI hospitals for any of the four medical conditions of interest. We adhered to BPCI program rules, which defined each episode type based on a set of Medicare Severity Diagnosis Related Group (MS-DRG) codes (eg, myocardial infarction episodes were defined as MS-DRGs 280-282). From this sample, we excluded beneficiaries with end-stage renal disease or insurance coverage through Medicare Advantage, as well as beneficiaries who died during the index hospital admission, had any non–Inpatient Prospective Payment System claims, or lacked continuous primary Medicare fee-for-service coverage either during the episode or in the 12 months preceding it.

We constructed 90-day medical condition episodes that began with hospital admission and spanned 90 days after hospital discharge. To avoid bias arising from CMS rules related to precedence (rules for handling how overlapping episodes are assigned to hospitals), we followed prior methods and constructed naturally occurring episodes by assigning overlapping ones to the earlier hospital admission.^2,16 From this set of episodes, we identified those for AMI, CHF, COPD, and pneumonia.

Exposure and Covariate Variables

Our study exposure was the interaction between hospital safety net status and hospital BPCI participation, which captured whether the association between BPCI participation and outcomes varied by safety net status (eg, whether differential changes in an outcome related to BPCI participation were different for safety net and non–safety net hospitals in the program). BPCI participation was defined using a time-varying indicator of BPCI participation to distinguish between episodes occurring under the program (ie, after a hospital began participating) or before participation in it. Covariates were chosen based on prior studies and included patient variables such as age, sex, Elixhauser comorbidities, frailty, and Medicare/Medicaid dual-eligibility status.^17-23 Additionally, our analysis included market variables such as population size and Medicare Advantage penetration.

Outcome Variables

The prespecified primary study outcome was standardized 90-day postdischarge spending. This outcome was chosen owing to the lack of variation in standardized index hospitalization spending given the MS-DRG system and prior work suggesting that bundled payment participants instead targeted changes to postdischarge utilization and spending.² Secondary outcomes included 90-day unplanned readmission rates, 90-day postdischarge mortality rates, discharge to institutional post–acute care providers (defined as either skilled nursing facilities [SNFs] or inpatient rehabilitation facilities), discharge home with home health agency services, and—among patients discharged to SNFs—SNF length of stay (LOS), measured in number of days.

Statistical Analysis

We described the characteristics of patients and hospitals in our samples. In adjusted analyses, we used a series of difference-in-differences (DID) generalized linear models to conduct a heterogeneity analysis evaluating whether the relationship between hospital BPCI participation and medical condition episode outcomes varied based on hospital safety net status.

In these models, the DID estimator was a time-varying indicator of hospital BPCI participation (equal to 1 for episodes occurring during the BPCI period at BPCI hospitals after they initiated participation; 0 otherwise) together with hospital and quarter-time fixed effects. To examine differences in the association between BPCI and episode outcomes by hospital safety net status—that is, whether there was heterogeneity in the outcome changes between safety net and non–safety net hospitals participating in BPCI—our models also included an interaction term between hospital safety net status and the time-varying BPCI participation term (Appendix Methods). In this approach, BPCI safety net and BPCI non–safety net hospitals were compared with non-BPCI hospitals as the comparison group. The comparisons were chosen to yield the most policy-salient findings, since Medicare evaluated hospitals in BPCI, whether safety net or not, by comparing their performance to nonparticipating hospitals, whether safety net or not.

All models controlled for patient and time-varying market characteristics and included hospital fixed effects (to account for time-invariant hospital market characteristics) and MS-DRG fixed effects. All outcomes were evaluated using models with identity links and normal distributions (ie, ordinary least squares). These variables and models were applied to data from the baseline period to examine consistency with the parallel trends assumption. Overall, Wald tests did not indicate divergent baseline period trends in outcomes between BPCI and non-BPCI hospitals (Appendix Figure 1) or BPCI safety net versus BPCI non–safety net hospitals (Appendix Figure 2).

We conducted sensitivity analyses to evaluate the robustness of our results. First, instead of comparing differential changes at BPCI safety net vs BPCI non–safety net hospitals (ie, evaluating safety net status among BPCI hospitals), we evaluated changes at BPCI safety net vs non-BPCI safety net hospitals compared with changes at BPCI non–safety net vs non-BPCI non–safety net hospitals (ie, marginal differences in the changes associated with BPCI participation among safety net vs non–safety net hospitals). Because safety net hospitals in BPCI were compared with nonparticipating safety net hospitals, and non–safety net hospitals in BPCI were compared with nonparticipating non–safety net hospitals, this set of analyses helped address potential concerns about unobservable differences between safety net and non–safety net organizations and their potential impact on our findings.

Second, we used an alternative, BPCI-specific definition for safety net hospitals: instead of defining safety net status based on all hospitals nationwide, we defined it only among BPCI hospitals (safety net hospitals defined as those in the top quartile of DPP among all BPCI hospitals) and non-BPCI hospitals (safety net hospitals defined as those in the top quartile of DPP among all non-BPCI hospitals). Third, we repeated our main analyses using models with standard errors clustered at the hospital level and without hospital fixed effects. Fourth, we repeated analysis using models with alternative nonlinear link functions and outcome distributions and without hospital fixed effects.

Statistical tests were two-tailed and considered significant at α = .05 for the primary outcome. Statistical analyses were conducted using SAS 9.4 (SAS Institute, Inc.).

Our sample consisted of 3066 hospitals nationwide that collectively provided medical condition episode care to a total of 1,611,848 Medicare fee-for-service beneficiaries. This sample included 238 BPCI hospitals and 2769 non-BPCI hospitals (Table 1, Appendix Table 1).

JHMVol16No11_Liao03601117e_t1.JPG

Among BPCI hospitals, 63 were safety net and 175 were non–safety net hospitals. Compared with non–safety net hospitals, safety net hospitals tended to be larger and were more likely to be urban teaching hospitals. Safety net hospitals also tended to be located in areas with larger populations, more low-income individuals, and greater Medicare Advantage penetration.

In both the baseline and BPCI periods, there were differences in several characteristics for patients admitted to safety net vs non–safety net hospitals (Table 2; Appendix Table 2). Among BPCI hospitals, in both periods, patients admitted at safety net hospitals were younger and more likely to be Black, be Medicare/Medicaid dual eligible, and report having a disability than patients admitted to non–safety net hospitals. Patients admitted to safety net hospitals were also more likely to reside in socioeconomically disadvantaged areas.

JHMVol16No11_Liao03601117e_t2.JPG

Safety Net Status Among BPCI Hospitals

In the baseline period (Appendix Table 3), postdischarge spending was slightly greater among patients admitted to BPCI safety net hospitals ($18,817) than those admitted to BPCI non–safety net hospitals ($18,335). There were also small differences in secondary outcomes between the BPCI safety net and non−safety net groups.

In adjusted analyses evaluating heterogeneity in the effect of BPCI participation between safety net and non–safety net hospitals (Figure 1), differential changes in postdischarge spending between baseline and BPCI participation periods did not differ between safety net and non–safety net hospitals participating in BPCI (aDID, $40; 95% CI, –$254 to $335; P = .79).

JHMVol16No11_Liao03601117e_f1.JPG

JHMVol16No11_Liao03601117e_f2.JPG

Sensitivity Analysis

Analyses of BPCI participation among safety net vs non–safety net hospitals nationwide yielded results that were similar to those from our main analyses (Appendix Figures 4, 5, and 6). Compared with BPCI participation among non–safety net hospitals, participation among safety net hospitals was associated with a differential increase from baseline to BPCI periods in discharge to institutional post–acute care providers (aDID, 1.07 percentage points; 95% CI, 0.47-1.67 percentage points; P < .001), but no differential changes between baseline and BPCI periods in postdischarge spending (aDID, –$199;95% CI, –$461 to $63; P = .14), SNF LOS (aDID, –0.22 days; 95% CI, –0.54 to 0.09 days; P = .16), or other secondary outcomes.

Replicating our main analyses using an alternative, BPCI-specific definition of safety net hospitals yielded similar results overall (Appendix Table 4; Appendix Figures 7, 8, and 9). There were no differential changes between baseline and BPCI periods in postdischarge spending between BPCI safety net and BPCI non–safety net hospitals (aDID, $111; 95% CI, –$189 to $411; P = .47). Results for secondary outcomes were also qualitatively similar to results from main analyses, with the exception that among BPCI hospitals, safety net hospitals had a differentially higher SNF LOS than non–safety net hospitals between baseline and BPCI periods (aDID, 0.38 days; 95% CI, 0.02-0.74 days; P = .04).

Compared with results from our main analysis, findings were qualitatively similar overall in analyses using models with hospital-clustered standard errors and without hospital fixed effects (Appendix Figures 10, 11, and 12) as well as models with alternative link functions and outcome distributions and without hospital fixed effects (Appendix Figures 13, 14, and 15).

This analysis builds on prior work by evaluating how hospital safety net status affected the known association between bundled payment participation and decreased spending and stable quality for medical condition episodes. Although safety net status did not appear to affect those relationships, it did affect the relationship between participation and post–acute care utilization. These results have three main implications.

First, our results suggest that policymakers should continue engaging safety net hospitals in medical condition bundled payments while monitoring for unintended consequences. Our findings with regard to spending provide some reassurance that safety net hospitals can potentially achieve savings while maintaining quality under bundled payments, similar to other types of hospitals. However, the differences in patient populations and post–acute care utilization patterns suggest that policymakers should continue to carefully monitor for disparities based on hospital safety net status and consider implementing measures that have been used in other payment reforms to support safety net organizations. Such measures could involve providing customized technical assistance or evaluating performance using “peer groups” that compare performance among safety net hospitals alone rather than among all hospitals.^24,25

Second, our findings underscore potential challenges that safety net hospitals may face when attempting to redesign care. For instance, among hospitals accepting bundled payments for medical conditions, successful strategies in BPCI have often included maintaining the proportion of patients discharged to institutional post–acute care providers while reducing SNF LOS.² However, in our study, discharge to institutional post–acute care providers actually increased among safety net hospitals relative to other hospitals while SNF LOS did not decrease. Additionally, while other hospitals in bundled payments have exhibited differentially greater discharge home with home health services, we found that safety net hospitals did not. These represent areas for future work, particularly because little is known about how safety net hospitals coordinate post–acute care (eg, the extent to which safety net hospitals integrate with post–acute care providers or coordinate home-based care for vulnerable patient populations).

Third, study results offer insight into potential challenges to practice changes. Compared with other hospitals, safety net hospitals in our analysis provided medical condition episode care to more Black, Medicare/Medicaid dual-eligible, and disabled patients, as well as individuals living in socioeconomically disadvantaged areas. Collectively, these groups may face more challenging socioeconomic circumstances or existing disparities. The combination of these factors and limited financial resources at safety net hospitals could complicate their ability to manage transitions of care after hospitalization by shifting discharge away from high-intensity institutional post–acute care facilities.

Our analysis has limitations. First, given the observational study design, findings are subject to residual confounding and selection bias. For instance, findings related to post–acute care utilization could have been influenced by unobservable changes in market supply and other factors. However, we mitigated these risks using a quasi-experimental methodology that also directly accounted for multiple patient, hospital, and market characteristics and also used fixed effects to account for unobserved heterogeneity. Second, in studying BPCI Model 2, we evaluated one model within one bundled payment program. However, BPCI Model 2 encompassed a wide range of medical conditions, and both this scope and program design have served as the direct basis for subsequent bundled payment models, such as the ongoing BPCI Advanced and other forthcoming programs.²⁶ Third, while our analysis evaluated multiple aspects of patient complexity, individuals may be “high risk” owing to several clinical and social determinants. Future work should evaluate different features of patient risk and how they affect outcomes under payment models such as bundled payments.

Safety net status appeared to affect the relationship between bundled payment participation and post–acute care utilization, but not episode spending. These findings suggest that policymakers could support safety net hospitals within bundled payment programs and consider safety net status when evaluating them.

Bundled payments represent one of the most prominent value-based payment arrangements nationwide. Under this payment approach, hospitals assume responsibility for quality and costs across discrete episodes of care. Hospitals that maintain quality while achieving cost reductions are eligible for financial incentives, whereas those that do not are subject to financial penalties.

To date, the largest completed bundled payment program nationwide is Medicare’s Bundled Payments for Care Improvement (BPCI) initiative. Among four different participation models in BPCI, hospital enrollment was greatest in Model 2, in which episodes spanned from hospitalization through 90 days of post–acute care. The overall results from BPCI Model 2 have been positive: hospitals participating in both common surgical episodes, such as joint replacement surgery, and medical episodes, such as acute myocardial infarction (AMI) and congestive heart failure (CHF), have demonstrated long-term financial savings with stable quality performance.^1,2

Safety net hospitals that disproportionately serve low-income patients may fare differently than other hospitals under bundled payment models. At baseline, these hospitals typically have fewer financial resources, which may limit their ability to implement measures to standardize care during hospitalization (eg, clinical pathways) or after discharge (eg, postdischarge programs and other strategies to reduce readmissions).³ Efforts to redesign care may be further complicated by greater clinical complexity and social and structural determinants of health among patients seeking care at safety net hospitals. Given the well-known interactions between social determinants and health conditions, these factors are highly relevant for patients hospitalized at safety net hospitals for acute medical events or exacerbations of chronic conditions.

Existing evidence has shown that safety net hospitals have not performed as well as other hospitals in other value-based reforms.^4-8 In the context of bundled payments for joint replacement surgery, safety net hospitals have been less likely to achieve financial savings but more likely to receive penalties.^9-11 Moreover, the savings achieved by safety net hospitals have been smaller than those achieved by non–safety net hospitals.¹²

Despite these concerning findings, there are few data about how safety net hospitals have fared under bundled payments for common medical conditions. To address this critical knowledge gap, we evaluated the effect of hospital safety net status on the association between BPCI Model 2 participation and changes in outcomes for medical condition episodes.

This study was approved by the University of Pennsylvania Institutional Review Board with a waiver of informed consent.

Data

We used 100% Medicare claims data from 2011 to 2016 for patients receiving care at hospitals participating in BPCI Model 2 for one of four common medical condition episodes: AMI, pneumonia, CHF, and chronic obstructive pulmonary disease (COPD). A 20% random national sample was used for patients hospitalized at nonparticipant hospitals. Publicly available data from the Centers for Medicare & Medicaid Services (CMS) were used to identify hospital enrollment in BPCI Model 2, while data from the 2017 CMS Impact File were used to quantify each hospital’s disproportionate patient percentage (DPP), which reflects the proportion of Medicaid and low-income Medicare beneficiaries served and determines a hospital’s eligibility to earn disproportionate share hospital payments.

Data from the 2011 American Hospital Association Annual Survey were used to capture hospital characteristics, such as number of beds, teaching status, and profit status, while data from the Medicare provider of service, beneficiary summary, and accountable care organization files were used to capture additional hospital characteristics and market characteristics, such as population size and Medicare Advantage penetration. The Medicare Provider Enrollment, Chain, and Ownership System file was used to identify and remove BPCI episodes from physician group practices. State-level data about area deprivation index—a census tract–based measure that incorporates factors such as income, education, employment, and housing quality to describe socioeconomic disadvantage among neighborhoods—were used to define socioeconomically disadvantaged areas as those in the top 20% of area deprivation index statewide.¹³ Markets were defined using hospital referral regions.¹⁴

Study Periods and Hospital Groups

Our analysis spanned the period between January 1, 2011, and December 31, 2016. We separated this period into a baseline period (January 2011–September 2013) prior to the start of BPCI and a subsequent BPCI period (October 2013–December 2016).

We defined any hospitals participating in BPCI Model 2 across this period for any of the four included medical condition episodes as BPCI hospitals. Because hospitals were able to enter or exit BPCI over time, and enrollment data were provided by CMS as quarterly participation files, we were able to identify dates of entry into or exit from BPCI over time by hospital-condition pairs. Hospitals were considered BPCI hospitals until the end of the study period, regardless of subsequent exit.

We defined non-BPCI hospitals as those that never participated in the program and had 10 or more admissions in the BPCI period for the included medical condition episodes. We used this approach to minimize potential bias arising from BPCI entry and exit over time.

Across both BPCI and non-BPCI hospital groups, we followed prior methods and defined safety net hospitals based on a hospital’s DPP.¹⁵ Specifically, safety net hospitals were those in the top quartile of DPP among all hospitals nationwide, and hospitals in the other three quartiles were defined as non–safety net hospitals.^9,12

Study Sample and Episode Construction

Our study sample included Medicare fee-for-service beneficiaries admitted to BPCI and non-BPCI hospitals for any of the four medical conditions of interest. We adhered to BPCI program rules, which defined each episode type based on a set of Medicare Severity Diagnosis Related Group (MS-DRG) codes (eg, myocardial infarction episodes were defined as MS-DRGs 280-282). From this sample, we excluded beneficiaries with end-stage renal disease or insurance coverage through Medicare Advantage, as well as beneficiaries who died during the index hospital admission, had any non–Inpatient Prospective Payment System claims, or lacked continuous primary Medicare fee-for-service coverage either during the episode or in the 12 months preceding it.

We constructed 90-day medical condition episodes that began with hospital admission and spanned 90 days after hospital discharge. To avoid bias arising from CMS rules related to precedence (rules for handling how overlapping episodes are assigned to hospitals), we followed prior methods and constructed naturally occurring episodes by assigning overlapping ones to the earlier hospital admission.^2,16 From this set of episodes, we identified those for AMI, CHF, COPD, and pneumonia.

Exposure and Covariate Variables

Our study exposure was the interaction between hospital safety net status and hospital BPCI participation, which captured whether the association between BPCI participation and outcomes varied by safety net status (eg, whether differential changes in an outcome related to BPCI participation were different for safety net and non–safety net hospitals in the program). BPCI participation was defined using a time-varying indicator of BPCI participation to distinguish between episodes occurring under the program (ie, after a hospital began participating) or before participation in it. Covariates were chosen based on prior studies and included patient variables such as age, sex, Elixhauser comorbidities, frailty, and Medicare/Medicaid dual-eligibility status.^17-23 Additionally, our analysis included market variables such as population size and Medicare Advantage penetration.

Outcome Variables

The prespecified primary study outcome was standardized 90-day postdischarge spending. This outcome was chosen owing to the lack of variation in standardized index hospitalization spending given the MS-DRG system and prior work suggesting that bundled payment participants instead targeted changes to postdischarge utilization and spending.² Secondary outcomes included 90-day unplanned readmission rates, 90-day postdischarge mortality rates, discharge to institutional post–acute care providers (defined as either skilled nursing facilities [SNFs] or inpatient rehabilitation facilities), discharge home with home health agency services, and—among patients discharged to SNFs—SNF length of stay (LOS), measured in number of days.

Statistical Analysis

We described the characteristics of patients and hospitals in our samples. In adjusted analyses, we used a series of difference-in-differences (DID) generalized linear models to conduct a heterogeneity analysis evaluating whether the relationship between hospital BPCI participation and medical condition episode outcomes varied based on hospital safety net status.

In these models, the DID estimator was a time-varying indicator of hospital BPCI participation (equal to 1 for episodes occurring during the BPCI period at BPCI hospitals after they initiated participation; 0 otherwise) together with hospital and quarter-time fixed effects. To examine differences in the association between BPCI and episode outcomes by hospital safety net status—that is, whether there was heterogeneity in the outcome changes between safety net and non–safety net hospitals participating in BPCI—our models also included an interaction term between hospital safety net status and the time-varying BPCI participation term (Appendix Methods). In this approach, BPCI safety net and BPCI non–safety net hospitals were compared with non-BPCI hospitals as the comparison group. The comparisons were chosen to yield the most policy-salient findings, since Medicare evaluated hospitals in BPCI, whether safety net or not, by comparing their performance to nonparticipating hospitals, whether safety net or not.

All models controlled for patient and time-varying market characteristics and included hospital fixed effects (to account for time-invariant hospital market characteristics) and MS-DRG fixed effects. All outcomes were evaluated using models with identity links and normal distributions (ie, ordinary least squares). These variables and models were applied to data from the baseline period to examine consistency with the parallel trends assumption. Overall, Wald tests did not indicate divergent baseline period trends in outcomes between BPCI and non-BPCI hospitals (Appendix Figure 1) or BPCI safety net versus BPCI non–safety net hospitals (Appendix Figure 2).

We conducted sensitivity analyses to evaluate the robustness of our results. First, instead of comparing differential changes at BPCI safety net vs BPCI non–safety net hospitals (ie, evaluating safety net status among BPCI hospitals), we evaluated changes at BPCI safety net vs non-BPCI safety net hospitals compared with changes at BPCI non–safety net vs non-BPCI non–safety net hospitals (ie, marginal differences in the changes associated with BPCI participation among safety net vs non–safety net hospitals). Because safety net hospitals in BPCI were compared with nonparticipating safety net hospitals, and non–safety net hospitals in BPCI were compared with nonparticipating non–safety net hospitals, this set of analyses helped address potential concerns about unobservable differences between safety net and non–safety net organizations and their potential impact on our findings.

Second, we used an alternative, BPCI-specific definition for safety net hospitals: instead of defining safety net status based on all hospitals nationwide, we defined it only among BPCI hospitals (safety net hospitals defined as those in the top quartile of DPP among all BPCI hospitals) and non-BPCI hospitals (safety net hospitals defined as those in the top quartile of DPP among all non-BPCI hospitals). Third, we repeated our main analyses using models with standard errors clustered at the hospital level and without hospital fixed effects. Fourth, we repeated analysis using models with alternative nonlinear link functions and outcome distributions and without hospital fixed effects.

Statistical tests were two-tailed and considered significant at α = .05 for the primary outcome. Statistical analyses were conducted using SAS 9.4 (SAS Institute, Inc.).

Our sample consisted of 3066 hospitals nationwide that collectively provided medical condition episode care to a total of 1,611,848 Medicare fee-for-service beneficiaries. This sample included 238 BPCI hospitals and 2769 non-BPCI hospitals (Table 1, Appendix Table 1).

JHMVol16No11_Liao03601117e_t1.JPG

Among BPCI hospitals, 63 were safety net and 175 were non–safety net hospitals. Compared with non–safety net hospitals, safety net hospitals tended to be larger and were more likely to be urban teaching hospitals. Safety net hospitals also tended to be located in areas with larger populations, more low-income individuals, and greater Medicare Advantage penetration.

In both the baseline and BPCI periods, there were differences in several characteristics for patients admitted to safety net vs non–safety net hospitals (Table 2; Appendix Table 2). Among BPCI hospitals, in both periods, patients admitted at safety net hospitals were younger and more likely to be Black, be Medicare/Medicaid dual eligible, and report having a disability than patients admitted to non–safety net hospitals. Patients admitted to safety net hospitals were also more likely to reside in socioeconomically disadvantaged areas.

JHMVol16No11_Liao03601117e_t2.JPG

Safety Net Status Among BPCI Hospitals

In the baseline period (Appendix Table 3), postdischarge spending was slightly greater among patients admitted to BPCI safety net hospitals ($18,817) than those admitted to BPCI non–safety net hospitals ($18,335). There were also small differences in secondary outcomes between the BPCI safety net and non−safety net groups.

In adjusted analyses evaluating heterogeneity in the effect of BPCI participation between safety net and non–safety net hospitals (Figure 1), differential changes in postdischarge spending between baseline and BPCI participation periods did not differ between safety net and non–safety net hospitals participating in BPCI (aDID, $40; 95% CI, –$254 to $335; P = .79).

JHMVol16No11_Liao03601117e_f1.JPG

JHMVol16No11_Liao03601117e_f2.JPG

Sensitivity Analysis

Analyses of BPCI participation among safety net vs non–safety net hospitals nationwide yielded results that were similar to those from our main analyses (Appendix Figures 4, 5, and 6). Compared with BPCI participation among non–safety net hospitals, participation among safety net hospitals was associated with a differential increase from baseline to BPCI periods in discharge to institutional post–acute care providers (aDID, 1.07 percentage points; 95% CI, 0.47-1.67 percentage points; P < .001), but no differential changes between baseline and BPCI periods in postdischarge spending (aDID, –$199;95% CI, –$461 to $63; P = .14), SNF LOS (aDID, –0.22 days; 95% CI, –0.54 to 0.09 days; P = .16), or other secondary outcomes.

Replicating our main analyses using an alternative, BPCI-specific definition of safety net hospitals yielded similar results overall (Appendix Table 4; Appendix Figures 7, 8, and 9). There were no differential changes between baseline and BPCI periods in postdischarge spending between BPCI safety net and BPCI non–safety net hospitals (aDID, $111; 95% CI, –$189 to $411; P = .47). Results for secondary outcomes were also qualitatively similar to results from main analyses, with the exception that among BPCI hospitals, safety net hospitals had a differentially higher SNF LOS than non–safety net hospitals between baseline and BPCI periods (aDID, 0.38 days; 95% CI, 0.02-0.74 days; P = .04).

Compared with results from our main analysis, findings were qualitatively similar overall in analyses using models with hospital-clustered standard errors and without hospital fixed effects (Appendix Figures 10, 11, and 12) as well as models with alternative link functions and outcome distributions and without hospital fixed effects (Appendix Figures 13, 14, and 15).

This analysis builds on prior work by evaluating how hospital safety net status affected the known association between bundled payment participation and decreased spending and stable quality for medical condition episodes. Although safety net status did not appear to affect those relationships, it did affect the relationship between participation and post–acute care utilization. These results have three main implications.

First, our results suggest that policymakers should continue engaging safety net hospitals in medical condition bundled payments while monitoring for unintended consequences. Our findings with regard to spending provide some reassurance that safety net hospitals can potentially achieve savings while maintaining quality under bundled payments, similar to other types of hospitals. However, the differences in patient populations and post–acute care utilization patterns suggest that policymakers should continue to carefully monitor for disparities based on hospital safety net status and consider implementing measures that have been used in other payment reforms to support safety net organizations. Such measures could involve providing customized technical assistance or evaluating performance using “peer groups” that compare performance among safety net hospitals alone rather than among all hospitals.^24,25

Second, our findings underscore potential challenges that safety net hospitals may face when attempting to redesign care. For instance, among hospitals accepting bundled payments for medical conditions, successful strategies in BPCI have often included maintaining the proportion of patients discharged to institutional post–acute care providers while reducing SNF LOS.² However, in our study, discharge to institutional post–acute care providers actually increased among safety net hospitals relative to other hospitals while SNF LOS did not decrease. Additionally, while other hospitals in bundled payments have exhibited differentially greater discharge home with home health services, we found that safety net hospitals did not. These represent areas for future work, particularly because little is known about how safety net hospitals coordinate post–acute care (eg, the extent to which safety net hospitals integrate with post–acute care providers or coordinate home-based care for vulnerable patient populations).

Third, study results offer insight into potential challenges to practice changes. Compared with other hospitals, safety net hospitals in our analysis provided medical condition episode care to more Black, Medicare/Medicaid dual-eligible, and disabled patients, as well as individuals living in socioeconomically disadvantaged areas. Collectively, these groups may face more challenging socioeconomic circumstances or existing disparities. The combination of these factors and limited financial resources at safety net hospitals could complicate their ability to manage transitions of care after hospitalization by shifting discharge away from high-intensity institutional post–acute care facilities.

Our analysis has limitations. First, given the observational study design, findings are subject to residual confounding and selection bias. For instance, findings related to post–acute care utilization could have been influenced by unobservable changes in market supply and other factors. However, we mitigated these risks using a quasi-experimental methodology that also directly accounted for multiple patient, hospital, and market characteristics and also used fixed effects to account for unobserved heterogeneity. Second, in studying BPCI Model 2, we evaluated one model within one bundled payment program. However, BPCI Model 2 encompassed a wide range of medical conditions, and both this scope and program design have served as the direct basis for subsequent bundled payment models, such as the ongoing BPCI Advanced and other forthcoming programs.²⁶ Third, while our analysis evaluated multiple aspects of patient complexity, individuals may be “high risk” owing to several clinical and social determinants. Future work should evaluate different features of patient risk and how they affect outcomes under payment models such as bundled payments.

Safety net status appeared to affect the relationship between bundled payment participation and post–acute care utilization, but not episode spending. These findings suggest that policymakers could support safety net hospitals within bundled payment programs and consider safety net status when evaluating them.

Bundled payments represent one of the most prominent value-based payment arrangements nationwide. Under this payment approach, hospitals assume responsibility for quality and costs across discrete episodes of care. Hospitals that maintain quality while achieving cost reductions are eligible for financial incentives, whereas those that do not are subject to financial penalties.

To date, the largest completed bundled payment program nationwide is Medicare’s Bundled Payments for Care Improvement (BPCI) initiative. Among four different participation models in BPCI, hospital enrollment was greatest in Model 2, in which episodes spanned from hospitalization through 90 days of post–acute care. The overall results from BPCI Model 2 have been positive: hospitals participating in both common surgical episodes, such as joint replacement surgery, and medical episodes, such as acute myocardial infarction (AMI) and congestive heart failure (CHF), have demonstrated long-term financial savings with stable quality performance.^1,2

Safety net hospitals that disproportionately serve low-income patients may fare differently than other hospitals under bundled payment models. At baseline, these hospitals typically have fewer financial resources, which may limit their ability to implement measures to standardize care during hospitalization (eg, clinical pathways) or after discharge (eg, postdischarge programs and other strategies to reduce readmissions).³ Efforts to redesign care may be further complicated by greater clinical complexity and social and structural determinants of health among patients seeking care at safety net hospitals. Given the well-known interactions between social determinants and health conditions, these factors are highly relevant for patients hospitalized at safety net hospitals for acute medical events or exacerbations of chronic conditions.

Existing evidence has shown that safety net hospitals have not performed as well as other hospitals in other value-based reforms.^4-8 In the context of bundled payments for joint replacement surgery, safety net hospitals have been less likely to achieve financial savings but more likely to receive penalties.^9-11 Moreover, the savings achieved by safety net hospitals have been smaller than those achieved by non–safety net hospitals.¹²

Despite these concerning findings, there are few data about how safety net hospitals have fared under bundled payments for common medical conditions. To address this critical knowledge gap, we evaluated the effect of hospital safety net status on the association between BPCI Model 2 participation and changes in outcomes for medical condition episodes.

This study was approved by the University of Pennsylvania Institutional Review Board with a waiver of informed consent.

Data

We used 100% Medicare claims data from 2011 to 2016 for patients receiving care at hospitals participating in BPCI Model 2 for one of four common medical condition episodes: AMI, pneumonia, CHF, and chronic obstructive pulmonary disease (COPD). A 20% random national sample was used for patients hospitalized at nonparticipant hospitals. Publicly available data from the Centers for Medicare & Medicaid Services (CMS) were used to identify hospital enrollment in BPCI Model 2, while data from the 2017 CMS Impact File were used to quantify each hospital’s disproportionate patient percentage (DPP), which reflects the proportion of Medicaid and low-income Medicare beneficiaries served and determines a hospital’s eligibility to earn disproportionate share hospital payments.

Data from the 2011 American Hospital Association Annual Survey were used to capture hospital characteristics, such as number of beds, teaching status, and profit status, while data from the Medicare provider of service, beneficiary summary, and accountable care organization files were used to capture additional hospital characteristics and market characteristics, such as population size and Medicare Advantage penetration. The Medicare Provider Enrollment, Chain, and Ownership System file was used to identify and remove BPCI episodes from physician group practices. State-level data about area deprivation index—a census tract–based measure that incorporates factors such as income, education, employment, and housing quality to describe socioeconomic disadvantage among neighborhoods—were used to define socioeconomically disadvantaged areas as those in the top 20% of area deprivation index statewide.¹³ Markets were defined using hospital referral regions.¹⁴

Study Periods and Hospital Groups

Our analysis spanned the period between January 1, 2011, and December 31, 2016. We separated this period into a baseline period (January 2011–September 2013) prior to the start of BPCI and a subsequent BPCI period (October 2013–December 2016).

We defined any hospitals participating in BPCI Model 2 across this period for any of the four included medical condition episodes as BPCI hospitals. Because hospitals were able to enter or exit BPCI over time, and enrollment data were provided by CMS as quarterly participation files, we were able to identify dates of entry into or exit from BPCI over time by hospital-condition pairs. Hospitals were considered BPCI hospitals until the end of the study period, regardless of subsequent exit.

We defined non-BPCI hospitals as those that never participated in the program and had 10 or more admissions in the BPCI period for the included medical condition episodes. We used this approach to minimize potential bias arising from BPCI entry and exit over time.

Across both BPCI and non-BPCI hospital groups, we followed prior methods and defined safety net hospitals based on a hospital’s DPP.¹⁵ Specifically, safety net hospitals were those in the top quartile of DPP among all hospitals nationwide, and hospitals in the other three quartiles were defined as non–safety net hospitals.^9,12

Study Sample and Episode Construction

Our study sample included Medicare fee-for-service beneficiaries admitted to BPCI and non-BPCI hospitals for any of the four medical conditions of interest. We adhered to BPCI program rules, which defined each episode type based on a set of Medicare Severity Diagnosis Related Group (MS-DRG) codes (eg, myocardial infarction episodes were defined as MS-DRGs 280-282). From this sample, we excluded beneficiaries with end-stage renal disease or insurance coverage through Medicare Advantage, as well as beneficiaries who died during the index hospital admission, had any non–Inpatient Prospective Payment System claims, or lacked continuous primary Medicare fee-for-service coverage either during the episode or in the 12 months preceding it.

We constructed 90-day medical condition episodes that began with hospital admission and spanned 90 days after hospital discharge. To avoid bias arising from CMS rules related to precedence (rules for handling how overlapping episodes are assigned to hospitals), we followed prior methods and constructed naturally occurring episodes by assigning overlapping ones to the earlier hospital admission.^2,16 From this set of episodes, we identified those for AMI, CHF, COPD, and pneumonia.

Exposure and Covariate Variables

Our study exposure was the interaction between hospital safety net status and hospital BPCI participation, which captured whether the association between BPCI participation and outcomes varied by safety net status (eg, whether differential changes in an outcome related to BPCI participation were different for safety net and non–safety net hospitals in the program). BPCI participation was defined using a time-varying indicator of BPCI participation to distinguish between episodes occurring under the program (ie, after a hospital began participating) or before participation in it. Covariates were chosen based on prior studies and included patient variables such as age, sex, Elixhauser comorbidities, frailty, and Medicare/Medicaid dual-eligibility status.^17-23 Additionally, our analysis included market variables such as population size and Medicare Advantage penetration.

Outcome Variables

The prespecified primary study outcome was standardized 90-day postdischarge spending. This outcome was chosen owing to the lack of variation in standardized index hospitalization spending given the MS-DRG system and prior work suggesting that bundled payment participants instead targeted changes to postdischarge utilization and spending.² Secondary outcomes included 90-day unplanned readmission rates, 90-day postdischarge mortality rates, discharge to institutional post–acute care providers (defined as either skilled nursing facilities [SNFs] or inpatient rehabilitation facilities), discharge home with home health agency services, and—among patients discharged to SNFs—SNF length of stay (LOS), measured in number of days.

Statistical Analysis

We described the characteristics of patients and hospitals in our samples. In adjusted analyses, we used a series of difference-in-differences (DID) generalized linear models to conduct a heterogeneity analysis evaluating whether the relationship between hospital BPCI participation and medical condition episode outcomes varied based on hospital safety net status.

In these models, the DID estimator was a time-varying indicator of hospital BPCI participation (equal to 1 for episodes occurring during the BPCI period at BPCI hospitals after they initiated participation; 0 otherwise) together with hospital and quarter-time fixed effects. To examine differences in the association between BPCI and episode outcomes by hospital safety net status—that is, whether there was heterogeneity in the outcome changes between safety net and non–safety net hospitals participating in BPCI—our models also included an interaction term between hospital safety net status and the time-varying BPCI participation term (Appendix Methods). In this approach, BPCI safety net and BPCI non–safety net hospitals were compared with non-BPCI hospitals as the comparison group. The comparisons were chosen to yield the most policy-salient findings, since Medicare evaluated hospitals in BPCI, whether safety net or not, by comparing their performance to nonparticipating hospitals, whether safety net or not.

All models controlled for patient and time-varying market characteristics and included hospital fixed effects (to account for time-invariant hospital market characteristics) and MS-DRG fixed effects. All outcomes were evaluated using models with identity links and normal distributions (ie, ordinary least squares). These variables and models were applied to data from the baseline period to examine consistency with the parallel trends assumption. Overall, Wald tests did not indicate divergent baseline period trends in outcomes between BPCI and non-BPCI hospitals (Appendix Figure 1) or BPCI safety net versus BPCI non–safety net hospitals (Appendix Figure 2).

We conducted sensitivity analyses to evaluate the robustness of our results. First, instead of comparing differential changes at BPCI safety net vs BPCI non–safety net hospitals (ie, evaluating safety net status among BPCI hospitals), we evaluated changes at BPCI safety net vs non-BPCI safety net hospitals compared with changes at BPCI non–safety net vs non-BPCI non–safety net hospitals (ie, marginal differences in the changes associated with BPCI participation among safety net vs non–safety net hospitals). Because safety net hospitals in BPCI were compared with nonparticipating safety net hospitals, and non–safety net hospitals in BPCI were compared with nonparticipating non–safety net hospitals, this set of analyses helped address potential concerns about unobservable differences between safety net and non–safety net organizations and their potential impact on our findings.

Second, we used an alternative, BPCI-specific definition for safety net hospitals: instead of defining safety net status based on all hospitals nationwide, we defined it only among BPCI hospitals (safety net hospitals defined as those in the top quartile of DPP among all BPCI hospitals) and non-BPCI hospitals (safety net hospitals defined as those in the top quartile of DPP among all non-BPCI hospitals). Third, we repeated our main analyses using models with standard errors clustered at the hospital level and without hospital fixed effects. Fourth, we repeated analysis using models with alternative nonlinear link functions and outcome distributions and without hospital fixed effects.

Statistical tests were two-tailed and considered significant at α = .05 for the primary outcome. Statistical analyses were conducted using SAS 9.4 (SAS Institute, Inc.).

Our sample consisted of 3066 hospitals nationwide that collectively provided medical condition episode care to a total of 1,611,848 Medicare fee-for-service beneficiaries. This sample included 238 BPCI hospitals and 2769 non-BPCI hospitals (Table 1, Appendix Table 1).

JHMVol16No11_Liao03601117e_t1.JPG

Among BPCI hospitals, 63 were safety net and 175 were non–safety net hospitals. Compared with non–safety net hospitals, safety net hospitals tended to be larger and were more likely to be urban teaching hospitals. Safety net hospitals also tended to be located in areas with larger populations, more low-income individuals, and greater Medicare Advantage penetration.

In both the baseline and BPCI periods, there were differences in several characteristics for patients admitted to safety net vs non–safety net hospitals (Table 2; Appendix Table 2). Among BPCI hospitals, in both periods, patients admitted at safety net hospitals were younger and more likely to be Black, be Medicare/Medicaid dual eligible, and report having a disability than patients admitted to non–safety net hospitals. Patients admitted to safety net hospitals were also more likely to reside in socioeconomically disadvantaged areas.

JHMVol16No11_Liao03601117e_t2.JPG

Safety Net Status Among BPCI Hospitals

In the baseline period (Appendix Table 3), postdischarge spending was slightly greater among patients admitted to BPCI safety net hospitals ($18,817) than those admitted to BPCI non–safety net hospitals ($18,335). There were also small differences in secondary outcomes between the BPCI safety net and non−safety net groups.

In adjusted analyses evaluating heterogeneity in the effect of BPCI participation between safety net and non–safety net hospitals (Figure 1), differential changes in postdischarge spending between baseline and BPCI participation periods did not differ between safety net and non–safety net hospitals participating in BPCI (aDID, $40; 95% CI, –$254 to $335; P = .79).

JHMVol16No11_Liao03601117e_f1.JPG

JHMVol16No11_Liao03601117e_f2.JPG

Sensitivity Analysis

Analyses of BPCI participation among safety net vs non–safety net hospitals nationwide yielded results that were similar to those from our main analyses (Appendix Figures 4, 5, and 6). Compared with BPCI participation among non–safety net hospitals, participation among safety net hospitals was associated with a differential increase from baseline to BPCI periods in discharge to institutional post–acute care providers (aDID, 1.07 percentage points; 95% CI, 0.47-1.67 percentage points; P < .001), but no differential changes between baseline and BPCI periods in postdischarge spending (aDID, –$199;95% CI, –$461 to $63; P = .14), SNF LOS (aDID, –0.22 days; 95% CI, –0.54 to 0.09 days; P = .16), or other secondary outcomes.

Replicating our main analyses using an alternative, BPCI-specific definition of safety net hospitals yielded similar results overall (Appendix Table 4; Appendix Figures 7, 8, and 9). There were no differential changes between baseline and BPCI periods in postdischarge spending between BPCI safety net and BPCI non–safety net hospitals (aDID, $111; 95% CI, –$189 to $411; P = .47). Results for secondary outcomes were also qualitatively similar to results from main analyses, with the exception that among BPCI hospitals, safety net hospitals had a differentially higher SNF LOS than non–safety net hospitals between baseline and BPCI periods (aDID, 0.38 days; 95% CI, 0.02-0.74 days; P = .04).

Compared with results from our main analysis, findings were qualitatively similar overall in analyses using models with hospital-clustered standard errors and without hospital fixed effects (Appendix Figures 10, 11, and 12) as well as models with alternative link functions and outcome distributions and without hospital fixed effects (Appendix Figures 13, 14, and 15).

This analysis builds on prior work by evaluating how hospital safety net status affected the known association between bundled payment participation and decreased spending and stable quality for medical condition episodes. Although safety net status did not appear to affect those relationships, it did affect the relationship between participation and post–acute care utilization. These results have three main implications.

First, our results suggest that policymakers should continue engaging safety net hospitals in medical condition bundled payments while monitoring for unintended consequences. Our findings with regard to spending provide some reassurance that safety net hospitals can potentially achieve savings while maintaining quality under bundled payments, similar to other types of hospitals. However, the differences in patient populations and post–acute care utilization patterns suggest that policymakers should continue to carefully monitor for disparities based on hospital safety net status and consider implementing measures that have been used in other payment reforms to support safety net organizations. Such measures could involve providing customized technical assistance or evaluating performance using “peer groups” that compare performance among safety net hospitals alone rather than among all hospitals.^24,25

Second, our findings underscore potential challenges that safety net hospitals may face when attempting to redesign care. For instance, among hospitals accepting bundled payments for medical conditions, successful strategies in BPCI have often included maintaining the proportion of patients discharged to institutional post–acute care providers while reducing SNF LOS.² However, in our study, discharge to institutional post–acute care providers actually increased among safety net hospitals relative to other hospitals while SNF LOS did not decrease. Additionally, while other hospitals in bundled payments have exhibited differentially greater discharge home with home health services, we found that safety net hospitals did not. These represent areas for future work, particularly because little is known about how safety net hospitals coordinate post–acute care (eg, the extent to which safety net hospitals integrate with post–acute care providers or coordinate home-based care for vulnerable patient populations).

Third, study results offer insight into potential challenges to practice changes. Compared with other hospitals, safety net hospitals in our analysis provided medical condition episode care to more Black, Medicare/Medicaid dual-eligible, and disabled patients, as well as individuals living in socioeconomically disadvantaged areas. Collectively, these groups may face more challenging socioeconomic circumstances or existing disparities. The combination of these factors and limited financial resources at safety net hospitals could complicate their ability to manage transitions of care after hospitalization by shifting discharge away from high-intensity institutional post–acute care facilities.

Our analysis has limitations. First, given the observational study design, findings are subject to residual confounding and selection bias. For instance, findings related to post–acute care utilization could have been influenced by unobservable changes in market supply and other factors. However, we mitigated these risks using a quasi-experimental methodology that also directly accounted for multiple patient, hospital, and market characteristics and also used fixed effects to account for unobserved heterogeneity. Second, in studying BPCI Model 2, we evaluated one model within one bundled payment program. However, BPCI Model 2 encompassed a wide range of medical conditions, and both this scope and program design have served as the direct basis for subsequent bundled payment models, such as the ongoing BPCI Advanced and other forthcoming programs.²⁶ Third, while our analysis evaluated multiple aspects of patient complexity, individuals may be “high risk” owing to several clinical and social determinants. Future work should evaluate different features of patient risk and how they affect outcomes under payment models such as bundled payments.

Safety net status appeared to affect the relationship between bundled payment participation and post–acute care utilization, but not episode spending. These findings suggest that policymakers could support safety net hospitals within bundled payment programs and consider safety net status when evaluating them.

Patients whose primary language is not English and who have a limited ability to read, speak, write, or understand English experience worse healthcare access than their English-speaking counterparts, highlighted by fewer healthcare visits and filled prescription medications.¹ These patients with limited English proficiency (LEP) face additional barriers to quality healthcare during the COVID-19 pandemic, including lower rates of telehealth use,² lower rates of COVID-19 testing,³ and challenges with implementing high-quality interpretation.⁴ As a result of such long-standing disparities, healthcare policy has focused on improving access to language-concordant care.

The Civil Rights Act of 1964 and Department of Health and Human Services (HHS) regulations require recipients of federal financial assistance to provide reasonable access to programs, services, and activities to persons with LEP. Section 1557 of the Affordable Care Act extends Title VI nondiscrimination standards to the entire healthcare system, including insurers and health plans. In 2016, the Obama administration implemented Section 1557 through regulations that clarified and expanded language accessibility standards, although several years later the Trump administration sought to weaken the rule’s protections.

Although the requirement to make healthcare accessible to patients with LEP is unequivocal, “reasonable access” provides clinicians who accept federal funds with flexibility in how they deliver language access services. Differing interpretations of what is considered “reasonable” drives variation in how and when medical facilities provide interpreter services. This results in inconsistency of services provided across care settings and decreased availability of language-concordant care. For example, less than one-third of outpatient providers regularly use qualified interpreters when seeing patients with LEP.⁵ Furthermore, only about 69% of hospitals offer language access services.⁶ Clinician underutilization of interpreters for patients with LEP results in poor patient satisfaction and worse health outcomes.⁷ In light of the Biden administration’s commitment to civil rights and healthcare access, we outline a roadmap of actions that this administration must take to ensure access to basic communication needs and improve health equity.

The Trump-era rules loosened the requirement that care providers notify patients with LEP of their rights to language services and provide instructions on how to access these services. These rules also allowed providers to replace video-based interpreter services with audio-based services, which disproportionately impacts patients in rural areas, who rely on high-quality video interpretation to facilitate telehealth visits, especially during the ongoing COVID-19 pandemic, which has increased patient reliance on telehealth infrastructure for primary healthcare access. The Trump administration weakened both the standards for ensuring adequate access to language assistance services and the compliance tests used to assess whether healthcare organizations have met those standards. The revised regulations deem certain healthcare services effectively exempt from interpreter standards if the projected number of encountered patients with LEP falls below preset minimums and a healthcare entity considers the cost of compliance onerous.⁸ The Trump administration justified these changes as a cost-savings matter, but the suboptimal care resulting from these changes will likely offset any savings.

To restore a strong commitment to language access, the HHS Office for Civil Rights, which the Biden administration has targeted for new investments in fiscal year 2022, should reestablish the HHS Language Access Steering Committee. This committee maintains criteria that guide covered health entities in developing language access compliance plans. Maintaining such plans should become a basic element of the revised Section 1557 compliance rules and should also become a core feature of the standards applicable to Joint Commission–accredited healthcare organizations. In addition, the Center for Medicare and Medicaid Innovation, whose mission is to identify, test, and implement major improvements in healthcare quality and efficiency, could undertake a special project to identify and incentivize adoption of the most effective language access innovations for incorporation into language access plans.

Section 1557, as well as federal standards governing the conditions of participation in federal healthcare programs, should ensure that covered entities report on interpreter use and associated patient health outcomes for patients with LEP. Overall compliance measurement and reporting in connection with language access is a matter of basic health equity. Currently, any individual who believes they have experienced discrimination based on language can report a potential violation for federal investigation. But an individual remedy is insufficient because it cannot ensure the types of systemic changes essential to overcome decades of structural exclusion and achieve broader health equity. Further, barriers from digital literacy gaps and fear of legal repercussions, such as deportation, hamper individual reporting efforts. Any policy focused on improving language access use should apply to all patients, regardless of immigration status.

Ultimately, there is little benefit to imposing standards without a concomitant assurance of the resources needed to achieve full adoption and ongoing compliance. For this reason, a commitment to language access must be accompanied by payment reforms that enable Medicare and Medicaid providers to embrace this vital feature of accessible healthcare by recognizing interpreter costs as part of the clinical encounter and care management. Covered entities could use these resources either to strengthen their own staffing or contract with third-party interpreter services organizations. Currently, the Centers for Medicare & Medicaid Services (CMS) allow states to claim federal matching funds for language assistance services provided to Medicaid enrollees, though rates are dependent on how service claims are categorized. State Medicaid programs can facilitate the provision of such services by optimizing reimbursements for provider organizations under CMS policy.

The Merit-based Incentive Payment System (MIPS) provides an opportunity to incorporate the provision of interpreter services into quality measure reporting. Such efforts could improve health equity and address long-standing needs for research into how language barriers affect healthcare outcomes. Given that analyses of inaugural MIPS data revealed that safety-net practices were more likely to receive lower composite scores, additional scoring flexibility under pay-for-performance schemes (rather than strict penalties) may be necessary to ensure the solvency of safety net practices that disproportionately care for patients with LEP.⁹ Here, CMMI can play a critical role in expanding the use of patient-facing resources by designing new alternative payment models that reward participants for providing high-quality language concordant care.

The COVID-19 pandemic has exacerbated disparities in care for patients with LEP and even starker disparities among immigrant communities and patients of color. These disparities will only worsen if regulations aimed at improving access to language access services are not reinstated and improved. Failing to focus on healthcare access for patients with LEP hurts individual patient health and public health, as we have seen through lower rates of testing and vaccination in communities of color during this pandemic. The Biden administration can put healthcare on a more equitable pathway by expanding and strengthening language access as a core feature of healthcare, as a matter of both civil rights and health care quality.

The authors thank Jocelyn Samuels, JD, and Sara Rosenbaum, JD, for comments and guidance on an earlier draft of this article.

Patients whose primary language is not English and who have a limited ability to read, speak, write, or understand English experience worse healthcare access than their English-speaking counterparts, highlighted by fewer healthcare visits and filled prescription medications.¹ These patients with limited English proficiency (LEP) face additional barriers to quality healthcare during the COVID-19 pandemic, including lower rates of telehealth use,² lower rates of COVID-19 testing,³ and challenges with implementing high-quality interpretation.⁴ As a result of such long-standing disparities, healthcare policy has focused on improving access to language-concordant care.

The Civil Rights Act of 1964 and Department of Health and Human Services (HHS) regulations require recipients of federal financial assistance to provide reasonable access to programs, services, and activities to persons with LEP. Section 1557 of the Affordable Care Act extends Title VI nondiscrimination standards to the entire healthcare system, including insurers and health plans. In 2016, the Obama administration implemented Section 1557 through regulations that clarified and expanded language accessibility standards, although several years later the Trump administration sought to weaken the rule’s protections.

Although the requirement to make healthcare accessible to patients with LEP is unequivocal, “reasonable access” provides clinicians who accept federal funds with flexibility in how they deliver language access services. Differing interpretations of what is considered “reasonable” drives variation in how and when medical facilities provide interpreter services. This results in inconsistency of services provided across care settings and decreased availability of language-concordant care. For example, less than one-third of outpatient providers regularly use qualified interpreters when seeing patients with LEP.⁵ Furthermore, only about 69% of hospitals offer language access services.⁶ Clinician underutilization of interpreters for patients with LEP results in poor patient satisfaction and worse health outcomes.⁷ In light of the Biden administration’s commitment to civil rights and healthcare access, we outline a roadmap of actions that this administration must take to ensure access to basic communication needs and improve health equity.

The Trump-era rules loosened the requirement that care providers notify patients with LEP of their rights to language services and provide instructions on how to access these services. These rules also allowed providers to replace video-based interpreter services with audio-based services, which disproportionately impacts patients in rural areas, who rely on high-quality video interpretation to facilitate telehealth visits, especially during the ongoing COVID-19 pandemic, which has increased patient reliance on telehealth infrastructure for primary healthcare access. The Trump administration weakened both the standards for ensuring adequate access to language assistance services and the compliance tests used to assess whether healthcare organizations have met those standards. The revised regulations deem certain healthcare services effectively exempt from interpreter standards if the projected number of encountered patients with LEP falls below preset minimums and a healthcare entity considers the cost of compliance onerous.⁸ The Trump administration justified these changes as a cost-savings matter, but the suboptimal care resulting from these changes will likely offset any savings.

To restore a strong commitment to language access, the HHS Office for Civil Rights, which the Biden administration has targeted for new investments in fiscal year 2022, should reestablish the HHS Language Access Steering Committee. This committee maintains criteria that guide covered health entities in developing language access compliance plans. Maintaining such plans should become a basic element of the revised Section 1557 compliance rules and should also become a core feature of the standards applicable to Joint Commission–accredited healthcare organizations. In addition, the Center for Medicare and Medicaid Innovation, whose mission is to identify, test, and implement major improvements in healthcare quality and efficiency, could undertake a special project to identify and incentivize adoption of the most effective language access innovations for incorporation into language access plans.

Section 1557, as well as federal standards governing the conditions of participation in federal healthcare programs, should ensure that covered entities report on interpreter use and associated patient health outcomes for patients with LEP. Overall compliance measurement and reporting in connection with language access is a matter of basic health equity. Currently, any individual who believes they have experienced discrimination based on language can report a potential violation for federal investigation. But an individual remedy is insufficient because it cannot ensure the types of systemic changes essential to overcome decades of structural exclusion and achieve broader health equity. Further, barriers from digital literacy gaps and fear of legal repercussions, such as deportation, hamper individual reporting efforts. Any policy focused on improving language access use should apply to all patients, regardless of immigration status.

Ultimately, there is little benefit to imposing standards without a concomitant assurance of the resources needed to achieve full adoption and ongoing compliance. For this reason, a commitment to language access must be accompanied by payment reforms that enable Medicare and Medicaid providers to embrace this vital feature of accessible healthcare by recognizing interpreter costs as part of the clinical encounter and care management. Covered entities could use these resources either to strengthen their own staffing or contract with third-party interpreter services organizations. Currently, the Centers for Medicare & Medicaid Services (CMS) allow states to claim federal matching funds for language assistance services provided to Medicaid enrollees, though rates are dependent on how service claims are categorized. State Medicaid programs can facilitate the provision of such services by optimizing reimbursements for provider organizations under CMS policy.

The Merit-based Incentive Payment System (MIPS) provides an opportunity to incorporate the provision of interpreter services into quality measure reporting. Such efforts could improve health equity and address long-standing needs for research into how language barriers affect healthcare outcomes. Given that analyses of inaugural MIPS data revealed that safety-net practices were more likely to receive lower composite scores, additional scoring flexibility under pay-for-performance schemes (rather than strict penalties) may be necessary to ensure the solvency of safety net practices that disproportionately care for patients with LEP.⁹ Here, CMMI can play a critical role in expanding the use of patient-facing resources by designing new alternative payment models that reward participants for providing high-quality language concordant care.

The COVID-19 pandemic has exacerbated disparities in care for patients with LEP and even starker disparities among immigrant communities and patients of color. These disparities will only worsen if regulations aimed at improving access to language access services are not reinstated and improved. Failing to focus on healthcare access for patients with LEP hurts individual patient health and public health, as we have seen through lower rates of testing and vaccination in communities of color during this pandemic. The Biden administration can put healthcare on a more equitable pathway by expanding and strengthening language access as a core feature of healthcare, as a matter of both civil rights and health care quality.

The authors thank Jocelyn Samuels, JD, and Sara Rosenbaum, JD, for comments and guidance on an earlier draft of this article.

Patients whose primary language is not English and who have a limited ability to read, speak, write, or understand English experience worse healthcare access than their English-speaking counterparts, highlighted by fewer healthcare visits and filled prescription medications.¹ These patients with limited English proficiency (LEP) face additional barriers to quality healthcare during the COVID-19 pandemic, including lower rates of telehealth use,² lower rates of COVID-19 testing,³ and challenges with implementing high-quality interpretation.⁴ As a result of such long-standing disparities, healthcare policy has focused on improving access to language-concordant care.

The Civil Rights Act of 1964 and Department of Health and Human Services (HHS) regulations require recipients of federal financial assistance to provide reasonable access to programs, services, and activities to persons with LEP. Section 1557 of the Affordable Care Act extends Title VI nondiscrimination standards to the entire healthcare system, including insurers and health plans. In 2016, the Obama administration implemented Section 1557 through regulations that clarified and expanded language accessibility standards, although several years later the Trump administration sought to weaken the rule’s protections.

Although the requirement to make healthcare accessible to patients with LEP is unequivocal, “reasonable access” provides clinicians who accept federal funds with flexibility in how they deliver language access services. Differing interpretations of what is considered “reasonable” drives variation in how and when medical facilities provide interpreter services. This results in inconsistency of services provided across care settings and decreased availability of language-concordant care. For example, less than one-third of outpatient providers regularly use qualified interpreters when seeing patients with LEP.⁵ Furthermore, only about 69% of hospitals offer language access services.⁶ Clinician underutilization of interpreters for patients with LEP results in poor patient satisfaction and worse health outcomes.⁷ In light of the Biden administration’s commitment to civil rights and healthcare access, we outline a roadmap of actions that this administration must take to ensure access to basic communication needs and improve health equity.

The Trump-era rules loosened the requirement that care providers notify patients with LEP of their rights to language services and provide instructions on how to access these services. These rules also allowed providers to replace video-based interpreter services with audio-based services, which disproportionately impacts patients in rural areas, who rely on high-quality video interpretation to facilitate telehealth visits, especially during the ongoing COVID-19 pandemic, which has increased patient reliance on telehealth infrastructure for primary healthcare access. The Trump administration weakened both the standards for ensuring adequate access to language assistance services and the compliance tests used to assess whether healthcare organizations have met those standards. The revised regulations deem certain healthcare services effectively exempt from interpreter standards if the projected number of encountered patients with LEP falls below preset minimums and a healthcare entity considers the cost of compliance onerous.⁸ The Trump administration justified these changes as a cost-savings matter, but the suboptimal care resulting from these changes will likely offset any savings.

To restore a strong commitment to language access, the HHS Office for Civil Rights, which the Biden administration has targeted for new investments in fiscal year 2022, should reestablish the HHS Language Access Steering Committee. This committee maintains criteria that guide covered health entities in developing language access compliance plans. Maintaining such plans should become a basic element of the revised Section 1557 compliance rules and should also become a core feature of the standards applicable to Joint Commission–accredited healthcare organizations. In addition, the Center for Medicare and Medicaid Innovation, whose mission is to identify, test, and implement major improvements in healthcare quality and efficiency, could undertake a special project to identify and incentivize adoption of the most effective language access innovations for incorporation into language access plans.

Section 1557, as well as federal standards governing the conditions of participation in federal healthcare programs, should ensure that covered entities report on interpreter use and associated patient health outcomes for patients with LEP. Overall compliance measurement and reporting in connection with language access is a matter of basic health equity. Currently, any individual who believes they have experienced discrimination based on language can report a potential violation for federal investigation. But an individual remedy is insufficient because it cannot ensure the types of systemic changes essential to overcome decades of structural exclusion and achieve broader health equity. Further, barriers from digital literacy gaps and fear of legal repercussions, such as deportation, hamper individual reporting efforts. Any policy focused on improving language access use should apply to all patients, regardless of immigration status.

Ultimately, there is little benefit to imposing standards without a concomitant assurance of the resources needed to achieve full adoption and ongoing compliance. For this reason, a commitment to language access must be accompanied by payment reforms that enable Medicare and Medicaid providers to embrace this vital feature of accessible healthcare by recognizing interpreter costs as part of the clinical encounter and care management. Covered entities could use these resources either to strengthen their own staffing or contract with third-party interpreter services organizations. Currently, the Centers for Medicare & Medicaid Services (CMS) allow states to claim federal matching funds for language assistance services provided to Medicaid enrollees, though rates are dependent on how service claims are categorized. State Medicaid programs can facilitate the provision of such services by optimizing reimbursements for provider organizations under CMS policy.

The Merit-based Incentive Payment System (MIPS) provides an opportunity to incorporate the provision of interpreter services into quality measure reporting. Such efforts could improve health equity and address long-standing needs for research into how language barriers affect healthcare outcomes. Given that analyses of inaugural MIPS data revealed that safety-net practices were more likely to receive lower composite scores, additional scoring flexibility under pay-for-performance schemes (rather than strict penalties) may be necessary to ensure the solvency of safety net practices that disproportionately care for patients with LEP.⁹ Here, CMMI can play a critical role in expanding the use of patient-facing resources by designing new alternative payment models that reward participants for providing high-quality language concordant care.

The COVID-19 pandemic has exacerbated disparities in care for patients with LEP and even starker disparities among immigrant communities and patients of color. These disparities will only worsen if regulations aimed at improving access to language access services are not reinstated and improved. Failing to focus on healthcare access for patients with LEP hurts individual patient health and public health, as we have seen through lower rates of testing and vaccination in communities of color during this pandemic. The Biden administration can put healthcare on a more equitable pathway by expanding and strengthening language access as a core feature of healthcare, as a matter of both civil rights and health care quality.

The authors thank Jocelyn Samuels, JD, and Sara Rosenbaum, JD, for comments and guidance on an earlier draft of this article.

The collective struggle felt by healthcare workers simultaneously learning about and caring for patients impacted by SARS-CoV2 infections throughout 2020 was physically and emotionally exhausting. The majority of us had never experienced a global pandemic. Beyond our work in the professional arena of ambulatory practices and hospitals, we also felt the soul-crushing impact of the pandemic in every other aspect of our lives. Preexisting health disparities were amplified by COVID-19. Some of the most affected communities also bore the weight of an additional tsunami of ongoing racial injustice.¹ And as healthcare workers, we did our best to process and navigate it all while trying to avoid burnout—as well as being infected with COVID-19 ourselves. When the news of the highly effective vaccines against SARS-CoV2 receiving emergency use authorization broke late in 2020, it felt like a light at the end of a very dark tunnel.

In the weeks preceding wide availability of the vaccines, it became apparent that significant numbers of people lacked confidence in the vaccines. Given the disproportionate impact of COVID-19 on racial minorities, much of the discussion centered around “vaccine hesitancy” in these communities. Reasons such as historical mistrust, belief in conspiracy theories, and misinformation emerged as the leading explanations.² Campaigns and educational programs targeting Black Americans were quickly developed to counter this widely distributed narrative.

Vaccine uptake also became politicized, which created additional challenges. As schools and businesses reopened, the voices of those opposing pandemic mitigation mandates such as masking and vaccination were highlighted by media outlets. And though a large movement of individuals who had opted against vaccines existed well before the pandemic, with few exceptions, that number had never been great enough to impact public health to this extent.³ This primarily nonminority group of unvaccinated individuals also morphed into another monolithic identity: the “anti-vaxxer.”

The lion’s share of discussions around vaccine uptake centered on these two groups: the “vaccine hesitant” minority and the “anti-vaxxer.” The perspectives and frustration around these two stereotypical unvaccinated groups were underscored in journals and the lay press. But those working in communities and in direct care came into contact with countless COVID-19-positive patients who were unvaccinated and fell into neither of these categories. There was a large swath of vulnerable people who still had unanswered questions and mistrust in the medical system standing in their way. Awareness of health disparities among racial minorities is something that was discussed among providers, but it was something experienced and felt by patients daily in regard to so much more than just COVID-19.

With broader access to vaccines through retail, community-based, and clinical facilities, more patients who desired vaccination had the opportunity. After an initial rise in vaccine uptake, the numbers plateaued. But what remained was the repetitive messaging and sustained focus directed toward Black people and their “vaccine hesitancy.”

Grady Memorial Hospital, a public safety net hospital in Atlanta, serves a predominantly Black and uninsured patient population. We found that a “FAQ” approach with a narrow range of hypothetical ideas about unvaccinated minorities clashed with the reality of what we encountered in clinical environments and the community. While misinformation did appear to be prevalent, we appreciated that the context and level of conviction were heterogenous. We appreciated that each individual conversation could reveal something new to us about that unique patient and their personal concerns about vaccination. As time moved forward, it became clear that there was no playbook for any group, especially for historically disadvantaged communities. Importantly, it was recognized that attempts to anticipate what may be a person’s barrier to vaccination often worked to further erode trust. However, when we focused on creating a space for dialogue, we found we were able to move beyond information-sharing and instead were able to co-construct interpretations of information and co-create solutions that matched patients’ values and lived experiences.⁴ Through dialogue, we were better able to be transparent about our own experiences, which ultimately facilitated authentic conversations with patients.

In September 2021, we approached our hospital leadership with a patient-centered strategy aimed at providing our patients, staff, and visitors a psychologically safe place to discuss vaccine-related concerns without judgment. With their support, we set up a table in the busiest part of our hospital atrium between the information desk and vaccine-administration site. Beside it was a folding board sign with an image and these words:

“Still unsure about being vaccinated? Let’s talk about it.”

We aptly called the area the “No Judgment Zone.”

The No Judgment Zone is collaboratively staffed in 1- to 2-hour voluntary increments by physician faculty and resident physicians at Emory University School of Medicine and Morehouse School of Medicine. Our goal is to increase patient trust by honoring individual vaccine-related concerns without shame or ridicule. We also work to increase patient trust by being transparent around our own experiences with COVID-19; by sharing our own journeys, concerns, and challenges, we are better able to engage in meaningful dialogue. Also, recognizing the power of logistical barriers, in addition to answering questions, we offer physical assistance with check-in, forms, and escorts to our administration areas. The numbers of unique visits have varied from day to day, but the impact of each individual encounter cannot be overstated.

Here, we describe our approach to interactions at the No Judgment Zone. These are the instructions offered to our volunteers. Though we offer some explicit examples, each talking point is designed to open the door to a patient-centered individual dialogue. We believe that these strategies can be applied to clinical settings as well as any conversation surrounding vaccination with those who have not yet decided to be vaccinated.

No Labels

Try to think of all who are not yet vaccinated as “on a spectrum of deliberation” about their decision—not “hesitant” or “anti-vaxxer.”

Step 1: Gratitude

• “Thank you for stopping to talk to us today.”
• “I appreciate you taking the time.”
• “Before we start—I’m glad you’re here. Thanks.”

Step 2: Determine Where They Are

• Has the person you’re speaking with been vaccinated yet?
• If no, ask: “On a scale of 0 to 10—zero being “I will never get vaccinated under any circumstances” and 10 being ‘I will definitely get vaccinated’—what number would you give yourself?”
• If the person is a firm zero: “Is there anything I might be able to share with you or tell you about that might move you away from that perspective?”
• If the answer is NO: “It sounds like you’ve thought a lot about this and are no longer deliberating about whether you will be vaccinated. If you find yourself considering it, come back to talk with us, okay?” We are not here to debate or argue. We also need to avail ourselves to those who are open to changing their mind.
• If they say anything other than zero, move to an open-ended question about #WhatsYourWhy.

Step 3: #WhatsYourWhy

• “What would you say has been your main reason for not getting vaccinated yet?”
• “Tell me what has stood in the way of you getting vaccinated.”
• Remember: Assume nothing. It may have nothing to do with misinformation, fear, or perceived threat. It could be logistics or many other things. You will not know unless you ask.
• Providers should feel encouraged to also share their why as well and the reasons they encouraged their parents/kids/loved ones to get vaccinated. Making it personal can help establish connection and be more compelling.

Step 4: Listen Completely

• Give full eye contact. Slow all body movements. Use facilitative gestures to let the person know you are listening.
• Do not plan what you wish to say next.
• Limit reactions to misinformation. Shame and judgment can be subtle. Be mindful.
• Repeat the concern back if you are not sure or want to confirm that you’ve heard correctly.
• Ask questions for clarity if you aren’t sure.

Step 5: Affirm All Concerns and Find Common Ground

• “I can only imagine how scary it must be to take a shot that you believe could cause you to not be able to have babies.”
• “You aren’t alone. That’s a concern that many of my patients have had, too. May I share some information about that with you?”
• “When I first heard about the vaccine, I worried it was too new, too. Can I share what I learned?”

Step 6: Provide Factual Information

• Without excessive medical jargon, offer factual information aimed at each concern or question. Probe to be certain your patient understands through a teach-back or question.
• If you are unsure about the answer to their question, admit that you don’t know. You can also ask a colleague or the attending with you. Another option is to call someone or say “Let’s pull this up together.” Then share your answer.
• It is okay to acknowledge that the healthcare system has not and does not always do right by minority populations, especially Black people. Use that as a pivot to why these truths make vaccination that much more important
• Have FAQ information sheets available. Confirm that the patient is comfortable with the information sheet by asking.

Step 7: Offer to Help Them Get Vaccinated Today

• “Would you like me to help you get vaccinated today?”
• “What can I do to assist you with getting vaccinated? Is today a good day?”
• Escort those who agree to the registration area.
• Affirm those plans to get vaccinated or those who feel closer to getting vaccinated after speaking with you.

Step 8: Gratitude

• Close with gratitude and an affirmation.
• “I’m so glad you took the time to talk with us today. You didn’t have to stop.”
• “Feel free to come back to talk to us if you think of any more questions. I’m grateful that you stopped.”
• We are planting seeds. Do not feel pressure to get a person to say yes. Our secret sauce is kindness, respect, and empathy.
• We do not think of our unvaccinated community members as “hesitant.” We approach all as if they are on a spectrum of deliberation.

Step 9: Reflect

• Understand the importance of your service and the potential impact each encounter has.
• Recognize the unique lived experiences of individual patients and how this may impact their deliberation process. While there is urgency and we may feel frustrated, the ultimate goal is to engender trust through respectful interactions.
• Pause for moments of quiet gratitude and self-check-ins.

Just as SARS-CoV2 spreads from one person to many, we recognize that information—factual and otherwise—has the potential to move quickly as well. It is important to realize that providing an opportunity for people to ask questions or receive clarification and confirmation in a safe space is critical. The No Judgement Zone, as the name indicates, offers this opportunity. The conversations that we have in this space are valuable to those who are still considering the vaccine as an option for themselves. The trust required for such conversations is less about the transmission of information and more about the social act of engaging in bidirectional dialogue. The foundation upon which trust is built is consistent trustworthy actions. One such action is respectful communication without shame or ridicule. Another is our willingness to be transparent about our own concerns, experiences, and journeys. Assumptions based upon single-story narratives of the unvaccinated—particularly those from historically marginalized groups—fracture an already fragile confidence in medical authorities.

While we understand that mitigating the ongoing spread of the virus and getting more people vaccinated will call for more than just individual conversations, we believe that respecting the unique perspectives of community members is an equally critical piece to moving forward. Throughout a healthcare worker’s typical day, we work to create personal moments of connection with patients among the immense bustle of other work that has to be done. Initiatives like this one have a focused intentionality behind creating space for patients to feel heard that is not only helpful for vaccine uptake and addressing mistrust, but can also be restorative for providers as well.

The collective struggle felt by healthcare workers simultaneously learning about and caring for patients impacted by SARS-CoV2 infections throughout 2020 was physically and emotionally exhausting. The majority of us had never experienced a global pandemic. Beyond our work in the professional arena of ambulatory practices and hospitals, we also felt the soul-crushing impact of the pandemic in every other aspect of our lives. Preexisting health disparities were amplified by COVID-19. Some of the most affected communities also bore the weight of an additional tsunami of ongoing racial injustice.¹ And as healthcare workers, we did our best to process and navigate it all while trying to avoid burnout—as well as being infected with COVID-19 ourselves. When the news of the highly effective vaccines against SARS-CoV2 receiving emergency use authorization broke late in 2020, it felt like a light at the end of a very dark tunnel.

In the weeks preceding wide availability of the vaccines, it became apparent that significant numbers of people lacked confidence in the vaccines. Given the disproportionate impact of COVID-19 on racial minorities, much of the discussion centered around “vaccine hesitancy” in these communities. Reasons such as historical mistrust, belief in conspiracy theories, and misinformation emerged as the leading explanations.² Campaigns and educational programs targeting Black Americans were quickly developed to counter this widely distributed narrative.

Vaccine uptake also became politicized, which created additional challenges. As schools and businesses reopened, the voices of those opposing pandemic mitigation mandates such as masking and vaccination were highlighted by media outlets. And though a large movement of individuals who had opted against vaccines existed well before the pandemic, with few exceptions, that number had never been great enough to impact public health to this extent.³ This primarily nonminority group of unvaccinated individuals also morphed into another monolithic identity: the “anti-vaxxer.”

The lion’s share of discussions around vaccine uptake centered on these two groups: the “vaccine hesitant” minority and the “anti-vaxxer.” The perspectives and frustration around these two stereotypical unvaccinated groups were underscored in journals and the lay press. But those working in communities and in direct care came into contact with countless COVID-19-positive patients who were unvaccinated and fell into neither of these categories. There was a large swath of vulnerable people who still had unanswered questions and mistrust in the medical system standing in their way. Awareness of health disparities among racial minorities is something that was discussed among providers, but it was something experienced and felt by patients daily in regard to so much more than just COVID-19.

With broader access to vaccines through retail, community-based, and clinical facilities, more patients who desired vaccination had the opportunity. After an initial rise in vaccine uptake, the numbers plateaued. But what remained was the repetitive messaging and sustained focus directed toward Black people and their “vaccine hesitancy.”

Grady Memorial Hospital, a public safety net hospital in Atlanta, serves a predominantly Black and uninsured patient population. We found that a “FAQ” approach with a narrow range of hypothetical ideas about unvaccinated minorities clashed with the reality of what we encountered in clinical environments and the community. While misinformation did appear to be prevalent, we appreciated that the context and level of conviction were heterogenous. We appreciated that each individual conversation could reveal something new to us about that unique patient and their personal concerns about vaccination. As time moved forward, it became clear that there was no playbook for any group, especially for historically disadvantaged communities. Importantly, it was recognized that attempts to anticipate what may be a person’s barrier to vaccination often worked to further erode trust. However, when we focused on creating a space for dialogue, we found we were able to move beyond information-sharing and instead were able to co-construct interpretations of information and co-create solutions that matched patients’ values and lived experiences.⁴ Through dialogue, we were better able to be transparent about our own experiences, which ultimately facilitated authentic conversations with patients.

In September 2021, we approached our hospital leadership with a patient-centered strategy aimed at providing our patients, staff, and visitors a psychologically safe place to discuss vaccine-related concerns without judgment. With their support, we set up a table in the busiest part of our hospital atrium between the information desk and vaccine-administration site. Beside it was a folding board sign with an image and these words:

“Still unsure about being vaccinated? Let’s talk about it.”

We aptly called the area the “No Judgment Zone.”

The No Judgment Zone is collaboratively staffed in 1- to 2-hour voluntary increments by physician faculty and resident physicians at Emory University School of Medicine and Morehouse School of Medicine. Our goal is to increase patient trust by honoring individual vaccine-related concerns without shame or ridicule. We also work to increase patient trust by being transparent around our own experiences with COVID-19; by sharing our own journeys, concerns, and challenges, we are better able to engage in meaningful dialogue. Also, recognizing the power of logistical barriers, in addition to answering questions, we offer physical assistance with check-in, forms, and escorts to our administration areas. The numbers of unique visits have varied from day to day, but the impact of each individual encounter cannot be overstated.

Here, we describe our approach to interactions at the No Judgment Zone. These are the instructions offered to our volunteers. Though we offer some explicit examples, each talking point is designed to open the door to a patient-centered individual dialogue. We believe that these strategies can be applied to clinical settings as well as any conversation surrounding vaccination with those who have not yet decided to be vaccinated.

No Labels

Try to think of all who are not yet vaccinated as “on a spectrum of deliberation” about their decision—not “hesitant” or “anti-vaxxer.”

Step 1: Gratitude

• “Thank you for stopping to talk to us today.”
• “I appreciate you taking the time.”
• “Before we start—I’m glad you’re here. Thanks.”

Step 2: Determine Where They Are

• Has the person you’re speaking with been vaccinated yet?
• If no, ask: “On a scale of 0 to 10—zero being “I will never get vaccinated under any circumstances” and 10 being ‘I will definitely get vaccinated’—what number would you give yourself?”
• If the person is a firm zero: “Is there anything I might be able to share with you or tell you about that might move you away from that perspective?”
• If the answer is NO: “It sounds like you’ve thought a lot about this and are no longer deliberating about whether you will be vaccinated. If you find yourself considering it, come back to talk with us, okay?” We are not here to debate or argue. We also need to avail ourselves to those who are open to changing their mind.
• If they say anything other than zero, move to an open-ended question about #WhatsYourWhy.

Step 3: #WhatsYourWhy

• “What would you say has been your main reason for not getting vaccinated yet?”
• “Tell me what has stood in the way of you getting vaccinated.”
• Remember: Assume nothing. It may have nothing to do with misinformation, fear, or perceived threat. It could be logistics or many other things. You will not know unless you ask.
• Providers should feel encouraged to also share their why as well and the reasons they encouraged their parents/kids/loved ones to get vaccinated. Making it personal can help establish connection and be more compelling.

Step 4: Listen Completely

• Give full eye contact. Slow all body movements. Use facilitative gestures to let the person know you are listening.
• Do not plan what you wish to say next.
• Limit reactions to misinformation. Shame and judgment can be subtle. Be mindful.
• Repeat the concern back if you are not sure or want to confirm that you’ve heard correctly.
• Ask questions for clarity if you aren’t sure.

Step 5: Affirm All Concerns and Find Common Ground

• “I can only imagine how scary it must be to take a shot that you believe could cause you to not be able to have babies.”
• “You aren’t alone. That’s a concern that many of my patients have had, too. May I share some information about that with you?”
• “When I first heard about the vaccine, I worried it was too new, too. Can I share what I learned?”

Step 6: Provide Factual Information

• Without excessive medical jargon, offer factual information aimed at each concern or question. Probe to be certain your patient understands through a teach-back or question.
• If you are unsure about the answer to their question, admit that you don’t know. You can also ask a colleague or the attending with you. Another option is to call someone or say “Let’s pull this up together.” Then share your answer.
• It is okay to acknowledge that the healthcare system has not and does not always do right by minority populations, especially Black people. Use that as a pivot to why these truths make vaccination that much more important
• Have FAQ information sheets available. Confirm that the patient is comfortable with the information sheet by asking.

Step 7: Offer to Help Them Get Vaccinated Today

• “Would you like me to help you get vaccinated today?”
• “What can I do to assist you with getting vaccinated? Is today a good day?”
• Escort those who agree to the registration area.
• Affirm those plans to get vaccinated or those who feel closer to getting vaccinated after speaking with you.

Step 8: Gratitude

• Close with gratitude and an affirmation.
• “I’m so glad you took the time to talk with us today. You didn’t have to stop.”
• “Feel free to come back to talk to us if you think of any more questions. I’m grateful that you stopped.”
• We are planting seeds. Do not feel pressure to get a person to say yes. Our secret sauce is kindness, respect, and empathy.
• We do not think of our unvaccinated community members as “hesitant.” We approach all as if they are on a spectrum of deliberation.

Step 9: Reflect

• Understand the importance of your service and the potential impact each encounter has.
• Recognize the unique lived experiences of individual patients and how this may impact their deliberation process. While there is urgency and we may feel frustrated, the ultimate goal is to engender trust through respectful interactions.
• Pause for moments of quiet gratitude and self-check-ins.

Just as SARS-CoV2 spreads from one person to many, we recognize that information—factual and otherwise—has the potential to move quickly as well. It is important to realize that providing an opportunity for people to ask questions or receive clarification and confirmation in a safe space is critical. The No Judgement Zone, as the name indicates, offers this opportunity. The conversations that we have in this space are valuable to those who are still considering the vaccine as an option for themselves. The trust required for such conversations is less about the transmission of information and more about the social act of engaging in bidirectional dialogue. The foundation upon which trust is built is consistent trustworthy actions. One such action is respectful communication without shame or ridicule. Another is our willingness to be transparent about our own concerns, experiences, and journeys. Assumptions based upon single-story narratives of the unvaccinated—particularly those from historically marginalized groups—fracture an already fragile confidence in medical authorities.

While we understand that mitigating the ongoing spread of the virus and getting more people vaccinated will call for more than just individual conversations, we believe that respecting the unique perspectives of community members is an equally critical piece to moving forward. Throughout a healthcare worker’s typical day, we work to create personal moments of connection with patients among the immense bustle of other work that has to be done. Initiatives like this one have a focused intentionality behind creating space for patients to feel heard that is not only helpful for vaccine uptake and addressing mistrust, but can also be restorative for providers as well.

The collective struggle felt by healthcare workers simultaneously learning about and caring for patients impacted by SARS-CoV2 infections throughout 2020 was physically and emotionally exhausting. The majority of us had never experienced a global pandemic. Beyond our work in the professional arena of ambulatory practices and hospitals, we also felt the soul-crushing impact of the pandemic in every other aspect of our lives. Preexisting health disparities were amplified by COVID-19. Some of the most affected communities also bore the weight of an additional tsunami of ongoing racial injustice.¹ And as healthcare workers, we did our best to process and navigate it all while trying to avoid burnout—as well as being infected with COVID-19 ourselves. When the news of the highly effective vaccines against SARS-CoV2 receiving emergency use authorization broke late in 2020, it felt like a light at the end of a very dark tunnel.

In the weeks preceding wide availability of the vaccines, it became apparent that significant numbers of people lacked confidence in the vaccines. Given the disproportionate impact of COVID-19 on racial minorities, much of the discussion centered around “vaccine hesitancy” in these communities. Reasons such as historical mistrust, belief in conspiracy theories, and misinformation emerged as the leading explanations.² Campaigns and educational programs targeting Black Americans were quickly developed to counter this widely distributed narrative.

Vaccine uptake also became politicized, which created additional challenges. As schools and businesses reopened, the voices of those opposing pandemic mitigation mandates such as masking and vaccination were highlighted by media outlets. And though a large movement of individuals who had opted against vaccines existed well before the pandemic, with few exceptions, that number had never been great enough to impact public health to this extent.³ This primarily nonminority group of unvaccinated individuals also morphed into another monolithic identity: the “anti-vaxxer.”

The lion’s share of discussions around vaccine uptake centered on these two groups: the “vaccine hesitant” minority and the “anti-vaxxer.” The perspectives and frustration around these two stereotypical unvaccinated groups were underscored in journals and the lay press. But those working in communities and in direct care came into contact with countless COVID-19-positive patients who were unvaccinated and fell into neither of these categories. There was a large swath of vulnerable people who still had unanswered questions and mistrust in the medical system standing in their way. Awareness of health disparities among racial minorities is something that was discussed among providers, but it was something experienced and felt by patients daily in regard to so much more than just COVID-19.

With broader access to vaccines through retail, community-based, and clinical facilities, more patients who desired vaccination had the opportunity. After an initial rise in vaccine uptake, the numbers plateaued. But what remained was the repetitive messaging and sustained focus directed toward Black people and their “vaccine hesitancy.”

Grady Memorial Hospital, a public safety net hospital in Atlanta, serves a predominantly Black and uninsured patient population. We found that a “FAQ” approach with a narrow range of hypothetical ideas about unvaccinated minorities clashed with the reality of what we encountered in clinical environments and the community. While misinformation did appear to be prevalent, we appreciated that the context and level of conviction were heterogenous. We appreciated that each individual conversation could reveal something new to us about that unique patient and their personal concerns about vaccination. As time moved forward, it became clear that there was no playbook for any group, especially for historically disadvantaged communities. Importantly, it was recognized that attempts to anticipate what may be a person’s barrier to vaccination often worked to further erode trust. However, when we focused on creating a space for dialogue, we found we were able to move beyond information-sharing and instead were able to co-construct interpretations of information and co-create solutions that matched patients’ values and lived experiences.⁴ Through dialogue, we were better able to be transparent about our own experiences, which ultimately facilitated authentic conversations with patients.

In September 2021, we approached our hospital leadership with a patient-centered strategy aimed at providing our patients, staff, and visitors a psychologically safe place to discuss vaccine-related concerns without judgment. With their support, we set up a table in the busiest part of our hospital atrium between the information desk and vaccine-administration site. Beside it was a folding board sign with an image and these words:

“Still unsure about being vaccinated? Let’s talk about it.”

We aptly called the area the “No Judgment Zone.”

The No Judgment Zone is collaboratively staffed in 1- to 2-hour voluntary increments by physician faculty and resident physicians at Emory University School of Medicine and Morehouse School of Medicine. Our goal is to increase patient trust by honoring individual vaccine-related concerns without shame or ridicule. We also work to increase patient trust by being transparent around our own experiences with COVID-19; by sharing our own journeys, concerns, and challenges, we are better able to engage in meaningful dialogue. Also, recognizing the power of logistical barriers, in addition to answering questions, we offer physical assistance with check-in, forms, and escorts to our administration areas. The numbers of unique visits have varied from day to day, but the impact of each individual encounter cannot be overstated.

Here, we describe our approach to interactions at the No Judgment Zone. These are the instructions offered to our volunteers. Though we offer some explicit examples, each talking point is designed to open the door to a patient-centered individual dialogue. We believe that these strategies can be applied to clinical settings as well as any conversation surrounding vaccination with those who have not yet decided to be vaccinated.

No Labels

Try to think of all who are not yet vaccinated as “on a spectrum of deliberation” about their decision—not “hesitant” or “anti-vaxxer.”

Step 1: Gratitude

• “Thank you for stopping to talk to us today.”
• “I appreciate you taking the time.”
• “Before we start—I’m glad you’re here. Thanks.”

Step 2: Determine Where They Are

• Has the person you’re speaking with been vaccinated yet?
• If no, ask: “On a scale of 0 to 10—zero being “I will never get vaccinated under any circumstances” and 10 being ‘I will definitely get vaccinated’—what number would you give yourself?”
• If the person is a firm zero: “Is there anything I might be able to share with you or tell you about that might move you away from that perspective?”
• If the answer is NO: “It sounds like you’ve thought a lot about this and are no longer deliberating about whether you will be vaccinated. If you find yourself considering it, come back to talk with us, okay?” We are not here to debate or argue. We also need to avail ourselves to those who are open to changing their mind.
• If they say anything other than zero, move to an open-ended question about #WhatsYourWhy.

Step 3: #WhatsYourWhy

• “What would you say has been your main reason for not getting vaccinated yet?”
• “Tell me what has stood in the way of you getting vaccinated.”
• Remember: Assume nothing. It may have nothing to do with misinformation, fear, or perceived threat. It could be logistics or many other things. You will not know unless you ask.
• Providers should feel encouraged to also share their why as well and the reasons they encouraged their parents/kids/loved ones to get vaccinated. Making it personal can help establish connection and be more compelling.

Step 4: Listen Completely

• Give full eye contact. Slow all body movements. Use facilitative gestures to let the person know you are listening.
• Do not plan what you wish to say next.
• Limit reactions to misinformation. Shame and judgment can be subtle. Be mindful.
• Repeat the concern back if you are not sure or want to confirm that you’ve heard correctly.
• Ask questions for clarity if you aren’t sure.

Step 5: Affirm All Concerns and Find Common Ground

• “I can only imagine how scary it must be to take a shot that you believe could cause you to not be able to have babies.”
• “You aren’t alone. That’s a concern that many of my patients have had, too. May I share some information about that with you?”
• “When I first heard about the vaccine, I worried it was too new, too. Can I share what I learned?”

Step 6: Provide Factual Information

• Without excessive medical jargon, offer factual information aimed at each concern or question. Probe to be certain your patient understands through a teach-back or question.
• If you are unsure about the answer to their question, admit that you don’t know. You can also ask a colleague or the attending with you. Another option is to call someone or say “Let’s pull this up together.” Then share your answer.
• It is okay to acknowledge that the healthcare system has not and does not always do right by minority populations, especially Black people. Use that as a pivot to why these truths make vaccination that much more important
• Have FAQ information sheets available. Confirm that the patient is comfortable with the information sheet by asking.

Step 7: Offer to Help Them Get Vaccinated Today

• “Would you like me to help you get vaccinated today?”
• “What can I do to assist you with getting vaccinated? Is today a good day?”
• Escort those who agree to the registration area.
• Affirm those plans to get vaccinated or those who feel closer to getting vaccinated after speaking with you.

Step 8: Gratitude

• Close with gratitude and an affirmation.
• “I’m so glad you took the time to talk with us today. You didn’t have to stop.”
• “Feel free to come back to talk to us if you think of any more questions. I’m grateful that you stopped.”
• We are planting seeds. Do not feel pressure to get a person to say yes. Our secret sauce is kindness, respect, and empathy.
• We do not think of our unvaccinated community members as “hesitant.” We approach all as if they are on a spectrum of deliberation.

Step 9: Reflect

• Understand the importance of your service and the potential impact each encounter has.
• Recognize the unique lived experiences of individual patients and how this may impact their deliberation process. While there is urgency and we may feel frustrated, the ultimate goal is to engender trust through respectful interactions.
• Pause for moments of quiet gratitude and self-check-ins.

Just as SARS-CoV2 spreads from one person to many, we recognize that information—factual and otherwise—has the potential to move quickly as well. It is important to realize that providing an opportunity for people to ask questions or receive clarification and confirmation in a safe space is critical. The No Judgement Zone, as the name indicates, offers this opportunity. The conversations that we have in this space are valuable to those who are still considering the vaccine as an option for themselves. The trust required for such conversations is less about the transmission of information and more about the social act of engaging in bidirectional dialogue. The foundation upon which trust is built is consistent trustworthy actions. One such action is respectful communication without shame or ridicule. Another is our willingness to be transparent about our own concerns, experiences, and journeys. Assumptions based upon single-story narratives of the unvaccinated—particularly those from historically marginalized groups—fracture an already fragile confidence in medical authorities.

While we understand that mitigating the ongoing spread of the virus and getting more people vaccinated will call for more than just individual conversations, we believe that respecting the unique perspectives of community members is an equally critical piece to moving forward. Throughout a healthcare worker’s typical day, we work to create personal moments of connection with patients among the immense bustle of other work that has to be done. Initiatives like this one have a focused intentionality behind creating space for patients to feel heard that is not only helpful for vaccine uptake and addressing mistrust, but can also be restorative for providers as well.

Patients in the United States continue to experience rising out-of-pocket medical costs, with little access to the price information they desire when making decisions regarding medical care.¹ The Centers for Medicare & Medicaid Services (CMS) has taken steps toward transparency by requiring hospitals to publish price information.² In this issue of the Journal of Hospital Medicine, White and Liao³ break down the new rule, and we further discuss how this policy affects patients, hospitals, and hospitalists.

The new CMS rule requires hospitals to publish the prices of 300 “shoppable” services, including those negotiated with different payors. The rule standardizes how this information is displayed and accessed, with a daily penalty for facilities that fail to comply. Clinics and ambulatory surgical centers are currently excluded, as are facility and ancillary fees, such as those billed by pathology or anesthesiology. As White and Liao point out, a limitation for hospitalists is that this rule will only affect orders for the outpatient setting at discharge. In addition, this rule separates cost from quality. Although quality data are publicly available via CMS, price data are posted directly by hospitals, making a true value assessment difficult. To strengthen the rule, White and Liao recommend the following: increasing the financial penalty for noncompliance; aggregating data centrally to allow for comparisons; adding quality data to cost; expanding included sites and types of services; and adding common additional fees to the service price.

The larger question is whether patients will use these data in the manner intended. Previous studies have found a paradoxical relationship between patients’ expressed desire to compare prices for medical services vs documented low levels of price-shopping behavior. Mehrotra et al¹ found that lack of access to data as well as loyalty to providers were significant barriers to using price data effectively. The CMS rule increases access to the price information patients desire but cannot find. However, it is unclear whether available prices will be sufficient to change behaviors given that, aside from those with no insurance and those with high-deductible plans, most patients are fairly removed from the actual cost of service.

This rule may have a larger, unexpected impact on hospitals and access to care. Sharing price data could increase pressure on facilities to merge with larger systems in order to obtain more favorable rates via increased negotiating power. Hospitals that serve poorer communities may not be attractive merger candidates for large systems and could be left out of the push toward consolidation. Charging higher prices for the same services could lead to hospital closures or cuts in resources, potentially exacerbating health inequities for underserved populations.

On the provider end, it is unlikely that price transparency will influence resource utilization. Mummadi et al⁴ found that displaying price information in the electronic health record did not significantly influence physician ordering behavior. For hospitalists today, the emphasis on “high-value care” is already an important consideration when utilizing healthcare resources, considering the Accreditation Council for Graduate Medical Education (ACGME) requirements for residency, restrictive insurance protocols, and guidelines such as the ACR Appropriateness Criteria and the American Board of Internal Medicine’s Choosing Wisely^® campaign. Outside of extremes, separate cost data likely will not make a difference in provider ordering practices.

Although the information from this rule may not cause dramatic practice change, it will allow us to help our patients by providing those interested in price-shopping with data. This policy represents a large step toward a more transparent healthcare system, though it may have limited impact on overall healthcare costs.

Patients in the United States continue to experience rising out-of-pocket medical costs, with little access to the price information they desire when making decisions regarding medical care.¹ The Centers for Medicare & Medicaid Services (CMS) has taken steps toward transparency by requiring hospitals to publish price information.² In this issue of the Journal of Hospital Medicine, White and Liao³ break down the new rule, and we further discuss how this policy affects patients, hospitals, and hospitalists.

The new CMS rule requires hospitals to publish the prices of 300 “shoppable” services, including those negotiated with different payors. The rule standardizes how this information is displayed and accessed, with a daily penalty for facilities that fail to comply. Clinics and ambulatory surgical centers are currently excluded, as are facility and ancillary fees, such as those billed by pathology or anesthesiology. As White and Liao point out, a limitation for hospitalists is that this rule will only affect orders for the outpatient setting at discharge. In addition, this rule separates cost from quality. Although quality data are publicly available via CMS, price data are posted directly by hospitals, making a true value assessment difficult. To strengthen the rule, White and Liao recommend the following: increasing the financial penalty for noncompliance; aggregating data centrally to allow for comparisons; adding quality data to cost; expanding included sites and types of services; and adding common additional fees to the service price.

The larger question is whether patients will use these data in the manner intended. Previous studies have found a paradoxical relationship between patients’ expressed desire to compare prices for medical services vs documented low levels of price-shopping behavior. Mehrotra et al¹ found that lack of access to data as well as loyalty to providers were significant barriers to using price data effectively. The CMS rule increases access to the price information patients desire but cannot find. However, it is unclear whether available prices will be sufficient to change behaviors given that, aside from those with no insurance and those with high-deductible plans, most patients are fairly removed from the actual cost of service.

This rule may have a larger, unexpected impact on hospitals and access to care. Sharing price data could increase pressure on facilities to merge with larger systems in order to obtain more favorable rates via increased negotiating power. Hospitals that serve poorer communities may not be attractive merger candidates for large systems and could be left out of the push toward consolidation. Charging higher prices for the same services could lead to hospital closures or cuts in resources, potentially exacerbating health inequities for underserved populations.

On the provider end, it is unlikely that price transparency will influence resource utilization. Mummadi et al⁴ found that displaying price information in the electronic health record did not significantly influence physician ordering behavior. For hospitalists today, the emphasis on “high-value care” is already an important consideration when utilizing healthcare resources, considering the Accreditation Council for Graduate Medical Education (ACGME) requirements for residency, restrictive insurance protocols, and guidelines such as the ACR Appropriateness Criteria and the American Board of Internal Medicine’s Choosing Wisely^® campaign. Outside of extremes, separate cost data likely will not make a difference in provider ordering practices.

Although the information from this rule may not cause dramatic practice change, it will allow us to help our patients by providing those interested in price-shopping with data. This policy represents a large step toward a more transparent healthcare system, though it may have limited impact on overall healthcare costs.

Patients in the United States continue to experience rising out-of-pocket medical costs, with little access to the price information they desire when making decisions regarding medical care.¹ The Centers for Medicare & Medicaid Services (CMS) has taken steps toward transparency by requiring hospitals to publish price information.² In this issue of the Journal of Hospital Medicine, White and Liao³ break down the new rule, and we further discuss how this policy affects patients, hospitals, and hospitalists.

The new CMS rule requires hospitals to publish the prices of 300 “shoppable” services, including those negotiated with different payors. The rule standardizes how this information is displayed and accessed, with a daily penalty for facilities that fail to comply. Clinics and ambulatory surgical centers are currently excluded, as are facility and ancillary fees, such as those billed by pathology or anesthesiology. As White and Liao point out, a limitation for hospitalists is that this rule will only affect orders for the outpatient setting at discharge. In addition, this rule separates cost from quality. Although quality data are publicly available via CMS, price data are posted directly by hospitals, making a true value assessment difficult. To strengthen the rule, White and Liao recommend the following: increasing the financial penalty for noncompliance; aggregating data centrally to allow for comparisons; adding quality data to cost; expanding included sites and types of services; and adding common additional fees to the service price.

The larger question is whether patients will use these data in the manner intended. Previous studies have found a paradoxical relationship between patients’ expressed desire to compare prices for medical services vs documented low levels of price-shopping behavior. Mehrotra et al¹ found that lack of access to data as well as loyalty to providers were significant barriers to using price data effectively. The CMS rule increases access to the price information patients desire but cannot find. However, it is unclear whether available prices will be sufficient to change behaviors given that, aside from those with no insurance and those with high-deductible plans, most patients are fairly removed from the actual cost of service.

This rule may have a larger, unexpected impact on hospitals and access to care. Sharing price data could increase pressure on facilities to merge with larger systems in order to obtain more favorable rates via increased negotiating power. Hospitals that serve poorer communities may not be attractive merger candidates for large systems and could be left out of the push toward consolidation. Charging higher prices for the same services could lead to hospital closures or cuts in resources, potentially exacerbating health inequities for underserved populations.

On the provider end, it is unlikely that price transparency will influence resource utilization. Mummadi et al⁴ found that displaying price information in the electronic health record did not significantly influence physician ordering behavior. For hospitalists today, the emphasis on “high-value care” is already an important consideration when utilizing healthcare resources, considering the Accreditation Council for Graduate Medical Education (ACGME) requirements for residency, restrictive insurance protocols, and guidelines such as the ACR Appropriateness Criteria and the American Board of Internal Medicine’s Choosing Wisely^® campaign. Outside of extremes, separate cost data likely will not make a difference in provider ordering practices.

Although the information from this rule may not cause dramatic practice change, it will allow us to help our patients by providing those interested in price-shopping with data. This policy represents a large step toward a more transparent healthcare system, though it may have limited impact on overall healthcare costs.