Christine M. White, MD, MAT

Timely and reliable communication of important data between hospital‐based physicians and primary care physicians is critical for prevention of medical adverse events.[1, 2] Extrapolation from high‐performance organizations outside of medicine suggests that verbal communication is an important component of patient handoffs.[3, 4] Though the Joint Commission does not mandate verbal communication during handoffs per se, stipulating instead that handoff participants have an opportunity to ask and respond to questions,[5] there is some evidence that primary care providers prefer verbal handoffs at least for certain patients such as those with medical complexity.[6] Verbal communication offers the receiver the opportunity to ask questions, but in practice, 2‐way verbal communication is often difficult to achieve at hospital discharge.

At our institution, hospital medicine (HM) physicians serve as the primary inpatient providers for nearly 90% of all general pediatric admissions. When the HM service was established, primary care physicians (PCPs) and HM physicians together agreed upon an expectation for verbal, physician‐to‐physician communication at the time of discharge. Discharge communication is provided by either residents or attendings depending on the facility. A telephone operator service called Physician Priority Link (PPL) was made available to facilitate this communication. The PPL service is staffed 24/7 by operators whose only responsibilities are to connect providers inside and outside the institution. By utilizing this service, PCPs could respond in a nonemergent fashion to discharge phone calls.

Over the last several years, PCPs have observed high variation in the reliability of discharge communication phone calls. A review of PPL phone records in 2009 showed that only 52% of HM discharges had a record of a call initiated to the PCP on the day of discharge. The overall goal of this improvement project was to improve the completion of verbal handoffs from HM physicians (residents or attendings) to PCPs. The specific aim of the project was to increase the proportion of completed verbal handoffs from on‐call residents or attendings to PCPs within 24 hours of discharge to more than 90% within 18 months.

METHODS

Planning the Intervention

The project was championed by physicians on the HM service and supported by a chief resident, PPL administrators, and 2 information technology analysts.

At the onset of the project, the team mapped the process for completing a discharge call to the PCPs, conducted a modified failure mode and effects analysis,[7, 8] and examined the key drivers used to prioritize interventions (Figure 1). Through the modified failure modes effect analysis, the team was able to identify system issues that led to unsuccessful communication: failure of call initiation, absence of an identified PCP, long wait times on hold, failure of PCP to call back, and failure of the call to be documented. These failure modes informed the key drivers to achieving the study aim. Figure 2 depicts the final key drivers, which were revised through testing and learning.

Figure 1

Figure 2

Interventions to Standardization the Communication Process

To facilitate initiation of calls to PCPs at hospital discharge, the improvement team created a standard process using the PPL service (Figure 3). All patients discharged from the HM service were included in the process. Discharging physicians (who were usually but not always residents, depending on the facility), were instructed to call the PPL operator at the time of discharge. The PPL operator would then page the patient's PCP. It was the responsibility of the discharging physician to identify a PCP prior to discharge. Instances where no PCP was identified were counted as process failures because no phone call could be made. The expectation for the PCPs was that they would return the page within 20 minutes. PPL operators would then page back to the discharging physician to connect the 2 parties with the expectation that the discharging physician respond within 2 to 4 minutes to the PPL operator's page. Standardization of all calls through PPL allowed efficient tracking of incomplete calls and operators to reattempt calls that were not completed. This process also shifted the burden of following up on incomplete calls to PPL. The use of PPL to make the connection also allowed the physician to complete other work while awaiting a call back from the PCP.

Figure 3

RESULTS

From July 2011 to March 2014, there were 6313 discharges from the HM service. The process measure (percentage of calls initiated) improved from 50% to 97% after 4 interventions (Figure 4). Data for the outcome measure (percentage of calls completed) were collected starting in August 2012, shortly after linking the EHR discharge order to the discharge call. Over the first 8 weeks, our median was 80%, which increased to a median of 93% (Figure 5). These results were sustained for 18 months.

Figure 4

Figure 5

Several key interventions were identified that were critical to achievement of our goal. Standardization of the communication process through PPL was temporally associated with a shift in the median rate of call initiation from 52% to 72%. Use of the discharge order to initiate discharge communication was associated with an increase from 72% to 97%. Finally, the percentage of completed verbal handoffs increased to more than 93% following batching of phone calls to PCPs at specific times during the day.

DISCUSSION

We used improvement and reliability science methods to implement a successful process for improving verbal handoffs from HM physicians to PCPs within 24 hours of discharge to 93%. This result has been sustained for 18 months.

Utilization of the PPL call center for flexible call facilitation along with support for data analysis and leveraging the EHR to automate the process increased reliability, leading to rapid improvement. Prior to mandating the use of PPL to connect discharging physicians with PCPs, the exact rate of successful handoffs in our institution was not known. We do know, however, that only 52% of calls were initiated, so clearly a large gap was present prior to our improvement work. Data collection from the PPL system was automated so that accurate, timely, and sustainable data could be provided, greatly aiding improvement efforts. Flexibility in call‐back timing was also crucial, because coordinating the availability of PCPs and discharging physicians is often challenging. The EHR‐initiated process for discharge communication was a key intervention, and improvement of our process measure to 97% performance was associated with this implementation. Two final interventions: (1) assignment of responsibility for communication to a team pager held by a designated resident and (2) batching of calls to specific times streamlined the EHR‐initiated process and were associated with achievement of our main outcome goal of >90% completed verbal communication.

There are several reports of successful interventions to improve receipt or content of discharge summaries by PCPs following hospital discharge available in the literature.[14, 15, 16, 17, 18, 19, 20] Recently, Shen et al. reported on the success of a multisite improvement collaborative involving pediatric hospitalist programs at community hospitals whose aim was to improve the timely documentation of communication directed at PCPs.[21] In their report, all 7 hospital sites that participated in the collaborative for more than 4 months were able to demonstrate substantial improvement in documentation of some form of communication directed at PCPs (whether by e‐mail, fax, or telephone call), from a baseline of approximately 50% to more than 90%. A limitation of their study was that they were unable to document whether PCPs had received any information or by what method. A recent survey of PCPs by Sheu et al. indicated that for many discharges, information in addition to that present in the EHR was desirable to ensure a safe transition of care.[6] Two‐way communication, such as with a phone call, allows for senders to verify information receipt and for receivers to ask questions to ensure complete information. To our knowledge, there have been no previous reports describing processes for improving verbal communication between hospitalist services and PCPs at discharge.

It may be that use of the call system allowed PCPs to return phone calls regarding discharges at convenient stopping points in their day while allowing discharging physicians to initiate a call without having to wait on hold. Interestingly, though we anticipated the need for additional PPL resources during the course of this improvement, the final process was efficient enough that PPL did not require additional staffing to accommodate the higher call volume.

A key insight during our implementation was that relying on the EHR to initiate every discharge communication created disruption of resident workflow due to disregard of patient, resident, and PCP factors. This was reflected by the improvement in call initiation (our process measure) following this intervention, whereas at the same time call completion (our outcome measure) remained below goal. To achieve our goal of completing verbal communication required a process that was highly reliable yet flexible enough to allow discharging physicians to complete the call in the unpredictable environment of inpatient care. Ultimately, this was achieved by allowing discharging physicians to initiate the process when convenient, and allowing for the EHR‐initiated process to function as a backup strategy to identify and mitigate failures of initiation.

An important limitation of our study was the lack of PCPs on the improvement team, likely making the success of the project more difficult than it might have been. For example, during the study we did not measure the time PCPs spent on hold or how many reattempts were needed to complete the communication loop. Immediately following the completion of our study, it became apparent that physicians returning calls for our own institution's primary care clinic were experiencing regular workflow interruptions and occasional hold times more than 20 minutes, necessitating ongoing further work to determine the root causes and solutions to these problems. Though this work is ongoing, average PCP hold times measured from a sample of call reviews in 2013 to 2014 was 3 minutes and 15 seconds.

This study has several other limitations. We were unable to account for phone calls to PCPs initiated outside of the new process. It may be that PCPs were called more than 52% of the time at baseline due to noncompliance with the new protocol. Also, we only have data for call completion starting after implementation of the link between the discharge order and the discharge phone call, making the baseline appear artificially high and precluding any analysis of how earlier interventions affected our outcome metric. Communication with PCPs should ideally occur prior to discharge. An important limitation of our process is that calls could occur several hours after discharge between an on‐call resident and an on‐call outpatient physician rather than between the PCP and the discharging resident, limiting appropriate information exchange. Though verbal discharge communication is a desirable goal for many reasons, the current project did not focus on the quality of the call or the information that was transmitted to the PCP. Additionally, direct attending‐to‐attending communication may be valuable with medically or socially complex discharges, but we did not have a process to facilitate this. We also did not measure what effect our new process had on outcomes such as quality of patient and family transition from hospital or physician satisfaction. The existence of programs similar to our PPL subspecialty referral line may be limited to large institutions. However, it should be noted that although some internal resource reallocation was necessary within PPL, no actual staffing increases were required despite a large increase in call volume. It may be that any hospital operator system could be adapted for this purpose with modest additional resources. Finally, although our EHR system is widely utilized, there are many competing systems in the market, and our intervention required utilization of EHR capabilities that may not be present in all systems. However, our EHR intervention utilized existing functionality and did not require modification of the system.

This project focused on discharge phone calls to primary care physicians for patients hospitalized on the hospital medicine service. Because communication with the PCP should ideally occur prior to discharge, future work will include identifying a more proximal trigger than the discharge order to which to link the EHR trigger for discharge communication. Other next steps to improve handoff effectiveness and optimize the efficiency of our process include identifying essential information that should be transmitted to the primary care physician at the time of the phone call, developing processes to ensure communication of this information, measuring PCP satisfaction with this communication, and measuring the impact on patient outcomes. Finally, though expert opinion indicates that verbal handoffs may have safety advantages over nonverbal handoffs, studies comparing the safety and efficacy of verbal versus nonverbal handoffs at patient discharge are lacking. Studies establishing the relative efficacy and safety of verbal versus nonverbal handoffs at hospital discharge are needed. Knowledge gained from these activities could inform future projects centered on the spread of the process to other hospital services and/or other hospitals.

CONCLUSION

We increased the percentage of calls initiated to PCPs at patient discharge from 52% to 97% and the percentage of calls completed between HM physicians and PCPs to 93% through the use of a standardized discharge communication process coupled with a basic EHR messaging functionality. The results of this study may be of interest for further testing and adaptation for any institution with an electronic healthcare system.

Disclosure: Nothing to report.

Timely and reliable communication of important data between hospital‐based physicians and primary care physicians is critical for prevention of medical adverse events.[1, 2] Extrapolation from high‐performance organizations outside of medicine suggests that verbal communication is an important component of patient handoffs.[3, 4] Though the Joint Commission does not mandate verbal communication during handoffs per se, stipulating instead that handoff participants have an opportunity to ask and respond to questions,[5] there is some evidence that primary care providers prefer verbal handoffs at least for certain patients such as those with medical complexity.[6] Verbal communication offers the receiver the opportunity to ask questions, but in practice, 2‐way verbal communication is often difficult to achieve at hospital discharge.

At our institution, hospital medicine (HM) physicians serve as the primary inpatient providers for nearly 90% of all general pediatric admissions. When the HM service was established, primary care physicians (PCPs) and HM physicians together agreed upon an expectation for verbal, physician‐to‐physician communication at the time of discharge. Discharge communication is provided by either residents or attendings depending on the facility. A telephone operator service called Physician Priority Link (PPL) was made available to facilitate this communication. The PPL service is staffed 24/7 by operators whose only responsibilities are to connect providers inside and outside the institution. By utilizing this service, PCPs could respond in a nonemergent fashion to discharge phone calls.

Over the last several years, PCPs have observed high variation in the reliability of discharge communication phone calls. A review of PPL phone records in 2009 showed that only 52% of HM discharges had a record of a call initiated to the PCP on the day of discharge. The overall goal of this improvement project was to improve the completion of verbal handoffs from HM physicians (residents or attendings) to PCPs. The specific aim of the project was to increase the proportion of completed verbal handoffs from on‐call residents or attendings to PCPs within 24 hours of discharge to more than 90% within 18 months.

METHODS

Planning the Intervention

The project was championed by physicians on the HM service and supported by a chief resident, PPL administrators, and 2 information technology analysts.

At the onset of the project, the team mapped the process for completing a discharge call to the PCPs, conducted a modified failure mode and effects analysis,[7, 8] and examined the key drivers used to prioritize interventions (Figure 1). Through the modified failure modes effect analysis, the team was able to identify system issues that led to unsuccessful communication: failure of call initiation, absence of an identified PCP, long wait times on hold, failure of PCP to call back, and failure of the call to be documented. These failure modes informed the key drivers to achieving the study aim. Figure 2 depicts the final key drivers, which were revised through testing and learning.

Figure 1

Figure 2

Interventions to Standardization the Communication Process

To facilitate initiation of calls to PCPs at hospital discharge, the improvement team created a standard process using the PPL service (Figure 3). All patients discharged from the HM service were included in the process. Discharging physicians (who were usually but not always residents, depending on the facility), were instructed to call the PPL operator at the time of discharge. The PPL operator would then page the patient's PCP. It was the responsibility of the discharging physician to identify a PCP prior to discharge. Instances where no PCP was identified were counted as process failures because no phone call could be made. The expectation for the PCPs was that they would return the page within 20 minutes. PPL operators would then page back to the discharging physician to connect the 2 parties with the expectation that the discharging physician respond within 2 to 4 minutes to the PPL operator's page. Standardization of all calls through PPL allowed efficient tracking of incomplete calls and operators to reattempt calls that were not completed. This process also shifted the burden of following up on incomplete calls to PPL. The use of PPL to make the connection also allowed the physician to complete other work while awaiting a call back from the PCP.

Figure 3

RESULTS

From July 2011 to March 2014, there were 6313 discharges from the HM service. The process measure (percentage of calls initiated) improved from 50% to 97% after 4 interventions (Figure 4). Data for the outcome measure (percentage of calls completed) were collected starting in August 2012, shortly after linking the EHR discharge order to the discharge call. Over the first 8 weeks, our median was 80%, which increased to a median of 93% (Figure 5). These results were sustained for 18 months.

Figure 4

Figure 5

Several key interventions were identified that were critical to achievement of our goal. Standardization of the communication process through PPL was temporally associated with a shift in the median rate of call initiation from 52% to 72%. Use of the discharge order to initiate discharge communication was associated with an increase from 72% to 97%. Finally, the percentage of completed verbal handoffs increased to more than 93% following batching of phone calls to PCPs at specific times during the day.

DISCUSSION

We used improvement and reliability science methods to implement a successful process for improving verbal handoffs from HM physicians to PCPs within 24 hours of discharge to 93%. This result has been sustained for 18 months.

Utilization of the PPL call center for flexible call facilitation along with support for data analysis and leveraging the EHR to automate the process increased reliability, leading to rapid improvement. Prior to mandating the use of PPL to connect discharging physicians with PCPs, the exact rate of successful handoffs in our institution was not known. We do know, however, that only 52% of calls were initiated, so clearly a large gap was present prior to our improvement work. Data collection from the PPL system was automated so that accurate, timely, and sustainable data could be provided, greatly aiding improvement efforts. Flexibility in call‐back timing was also crucial, because coordinating the availability of PCPs and discharging physicians is often challenging. The EHR‐initiated process for discharge communication was a key intervention, and improvement of our process measure to 97% performance was associated with this implementation. Two final interventions: (1) assignment of responsibility for communication to a team pager held by a designated resident and (2) batching of calls to specific times streamlined the EHR‐initiated process and were associated with achievement of our main outcome goal of >90% completed verbal communication.

There are several reports of successful interventions to improve receipt or content of discharge summaries by PCPs following hospital discharge available in the literature.[14, 15, 16, 17, 18, 19, 20] Recently, Shen et al. reported on the success of a multisite improvement collaborative involving pediatric hospitalist programs at community hospitals whose aim was to improve the timely documentation of communication directed at PCPs.[21] In their report, all 7 hospital sites that participated in the collaborative for more than 4 months were able to demonstrate substantial improvement in documentation of some form of communication directed at PCPs (whether by e‐mail, fax, or telephone call), from a baseline of approximately 50% to more than 90%. A limitation of their study was that they were unable to document whether PCPs had received any information or by what method. A recent survey of PCPs by Sheu et al. indicated that for many discharges, information in addition to that present in the EHR was desirable to ensure a safe transition of care.[6] Two‐way communication, such as with a phone call, allows for senders to verify information receipt and for receivers to ask questions to ensure complete information. To our knowledge, there have been no previous reports describing processes for improving verbal communication between hospitalist services and PCPs at discharge.

It may be that use of the call system allowed PCPs to return phone calls regarding discharges at convenient stopping points in their day while allowing discharging physicians to initiate a call without having to wait on hold. Interestingly, though we anticipated the need for additional PPL resources during the course of this improvement, the final process was efficient enough that PPL did not require additional staffing to accommodate the higher call volume.

A key insight during our implementation was that relying on the EHR to initiate every discharge communication created disruption of resident workflow due to disregard of patient, resident, and PCP factors. This was reflected by the improvement in call initiation (our process measure) following this intervention, whereas at the same time call completion (our outcome measure) remained below goal. To achieve our goal of completing verbal communication required a process that was highly reliable yet flexible enough to allow discharging physicians to complete the call in the unpredictable environment of inpatient care. Ultimately, this was achieved by allowing discharging physicians to initiate the process when convenient, and allowing for the EHR‐initiated process to function as a backup strategy to identify and mitigate failures of initiation.

An important limitation of our study was the lack of PCPs on the improvement team, likely making the success of the project more difficult than it might have been. For example, during the study we did not measure the time PCPs spent on hold or how many reattempts were needed to complete the communication loop. Immediately following the completion of our study, it became apparent that physicians returning calls for our own institution's primary care clinic were experiencing regular workflow interruptions and occasional hold times more than 20 minutes, necessitating ongoing further work to determine the root causes and solutions to these problems. Though this work is ongoing, average PCP hold times measured from a sample of call reviews in 2013 to 2014 was 3 minutes and 15 seconds.

This study has several other limitations. We were unable to account for phone calls to PCPs initiated outside of the new process. It may be that PCPs were called more than 52% of the time at baseline due to noncompliance with the new protocol. Also, we only have data for call completion starting after implementation of the link between the discharge order and the discharge phone call, making the baseline appear artificially high and precluding any analysis of how earlier interventions affected our outcome metric. Communication with PCPs should ideally occur prior to discharge. An important limitation of our process is that calls could occur several hours after discharge between an on‐call resident and an on‐call outpatient physician rather than between the PCP and the discharging resident, limiting appropriate information exchange. Though verbal discharge communication is a desirable goal for many reasons, the current project did not focus on the quality of the call or the information that was transmitted to the PCP. Additionally, direct attending‐to‐attending communication may be valuable with medically or socially complex discharges, but we did not have a process to facilitate this. We also did not measure what effect our new process had on outcomes such as quality of patient and family transition from hospital or physician satisfaction. The existence of programs similar to our PPL subspecialty referral line may be limited to large institutions. However, it should be noted that although some internal resource reallocation was necessary within PPL, no actual staffing increases were required despite a large increase in call volume. It may be that any hospital operator system could be adapted for this purpose with modest additional resources. Finally, although our EHR system is widely utilized, there are many competing systems in the market, and our intervention required utilization of EHR capabilities that may not be present in all systems. However, our EHR intervention utilized existing functionality and did not require modification of the system.

This project focused on discharge phone calls to primary care physicians for patients hospitalized on the hospital medicine service. Because communication with the PCP should ideally occur prior to discharge, future work will include identifying a more proximal trigger than the discharge order to which to link the EHR trigger for discharge communication. Other next steps to improve handoff effectiveness and optimize the efficiency of our process include identifying essential information that should be transmitted to the primary care physician at the time of the phone call, developing processes to ensure communication of this information, measuring PCP satisfaction with this communication, and measuring the impact on patient outcomes. Finally, though expert opinion indicates that verbal handoffs may have safety advantages over nonverbal handoffs, studies comparing the safety and efficacy of verbal versus nonverbal handoffs at patient discharge are lacking. Studies establishing the relative efficacy and safety of verbal versus nonverbal handoffs at hospital discharge are needed. Knowledge gained from these activities could inform future projects centered on the spread of the process to other hospital services and/or other hospitals.

CONCLUSION

We increased the percentage of calls initiated to PCPs at patient discharge from 52% to 97% and the percentage of calls completed between HM physicians and PCPs to 93% through the use of a standardized discharge communication process coupled with a basic EHR messaging functionality. The results of this study may be of interest for further testing and adaptation for any institution with an electronic healthcare system.

Disclosure: Nothing to report.

Timely and reliable communication of important data between hospital‐based physicians and primary care physicians is critical for prevention of medical adverse events.[1, 2] Extrapolation from high‐performance organizations outside of medicine suggests that verbal communication is an important component of patient handoffs.[3, 4] Though the Joint Commission does not mandate verbal communication during handoffs per se, stipulating instead that handoff participants have an opportunity to ask and respond to questions,[5] there is some evidence that primary care providers prefer verbal handoffs at least for certain patients such as those with medical complexity.[6] Verbal communication offers the receiver the opportunity to ask questions, but in practice, 2‐way verbal communication is often difficult to achieve at hospital discharge.

At our institution, hospital medicine (HM) physicians serve as the primary inpatient providers for nearly 90% of all general pediatric admissions. When the HM service was established, primary care physicians (PCPs) and HM physicians together agreed upon an expectation for verbal, physician‐to‐physician communication at the time of discharge. Discharge communication is provided by either residents or attendings depending on the facility. A telephone operator service called Physician Priority Link (PPL) was made available to facilitate this communication. The PPL service is staffed 24/7 by operators whose only responsibilities are to connect providers inside and outside the institution. By utilizing this service, PCPs could respond in a nonemergent fashion to discharge phone calls.

Over the last several years, PCPs have observed high variation in the reliability of discharge communication phone calls. A review of PPL phone records in 2009 showed that only 52% of HM discharges had a record of a call initiated to the PCP on the day of discharge. The overall goal of this improvement project was to improve the completion of verbal handoffs from HM physicians (residents or attendings) to PCPs. The specific aim of the project was to increase the proportion of completed verbal handoffs from on‐call residents or attendings to PCPs within 24 hours of discharge to more than 90% within 18 months.

METHODS

Planning the Intervention

The project was championed by physicians on the HM service and supported by a chief resident, PPL administrators, and 2 information technology analysts.

At the onset of the project, the team mapped the process for completing a discharge call to the PCPs, conducted a modified failure mode and effects analysis,[7, 8] and examined the key drivers used to prioritize interventions (Figure 1). Through the modified failure modes effect analysis, the team was able to identify system issues that led to unsuccessful communication: failure of call initiation, absence of an identified PCP, long wait times on hold, failure of PCP to call back, and failure of the call to be documented. These failure modes informed the key drivers to achieving the study aim. Figure 2 depicts the final key drivers, which were revised through testing and learning.

Figure 1

Figure 2

Interventions to Standardization the Communication Process

To facilitate initiation of calls to PCPs at hospital discharge, the improvement team created a standard process using the PPL service (Figure 3). All patients discharged from the HM service were included in the process. Discharging physicians (who were usually but not always residents, depending on the facility), were instructed to call the PPL operator at the time of discharge. The PPL operator would then page the patient's PCP. It was the responsibility of the discharging physician to identify a PCP prior to discharge. Instances where no PCP was identified were counted as process failures because no phone call could be made. The expectation for the PCPs was that they would return the page within 20 minutes. PPL operators would then page back to the discharging physician to connect the 2 parties with the expectation that the discharging physician respond within 2 to 4 minutes to the PPL operator's page. Standardization of all calls through PPL allowed efficient tracking of incomplete calls and operators to reattempt calls that were not completed. This process also shifted the burden of following up on incomplete calls to PPL. The use of PPL to make the connection also allowed the physician to complete other work while awaiting a call back from the PCP.

Figure 3

RESULTS

From July 2011 to March 2014, there were 6313 discharges from the HM service. The process measure (percentage of calls initiated) improved from 50% to 97% after 4 interventions (Figure 4). Data for the outcome measure (percentage of calls completed) were collected starting in August 2012, shortly after linking the EHR discharge order to the discharge call. Over the first 8 weeks, our median was 80%, which increased to a median of 93% (Figure 5). These results were sustained for 18 months.

Figure 4

Figure 5

Several key interventions were identified that were critical to achievement of our goal. Standardization of the communication process through PPL was temporally associated with a shift in the median rate of call initiation from 52% to 72%. Use of the discharge order to initiate discharge communication was associated with an increase from 72% to 97%. Finally, the percentage of completed verbal handoffs increased to more than 93% following batching of phone calls to PCPs at specific times during the day.

DISCUSSION

We used improvement and reliability science methods to implement a successful process for improving verbal handoffs from HM physicians to PCPs within 24 hours of discharge to 93%. This result has been sustained for 18 months.

Utilization of the PPL call center for flexible call facilitation along with support for data analysis and leveraging the EHR to automate the process increased reliability, leading to rapid improvement. Prior to mandating the use of PPL to connect discharging physicians with PCPs, the exact rate of successful handoffs in our institution was not known. We do know, however, that only 52% of calls were initiated, so clearly a large gap was present prior to our improvement work. Data collection from the PPL system was automated so that accurate, timely, and sustainable data could be provided, greatly aiding improvement efforts. Flexibility in call‐back timing was also crucial, because coordinating the availability of PCPs and discharging physicians is often challenging. The EHR‐initiated process for discharge communication was a key intervention, and improvement of our process measure to 97% performance was associated with this implementation. Two final interventions: (1) assignment of responsibility for communication to a team pager held by a designated resident and (2) batching of calls to specific times streamlined the EHR‐initiated process and were associated with achievement of our main outcome goal of >90% completed verbal communication.

There are several reports of successful interventions to improve receipt or content of discharge summaries by PCPs following hospital discharge available in the literature.[14, 15, 16, 17, 18, 19, 20] Recently, Shen et al. reported on the success of a multisite improvement collaborative involving pediatric hospitalist programs at community hospitals whose aim was to improve the timely documentation of communication directed at PCPs.[21] In their report, all 7 hospital sites that participated in the collaborative for more than 4 months were able to demonstrate substantial improvement in documentation of some form of communication directed at PCPs (whether by e‐mail, fax, or telephone call), from a baseline of approximately 50% to more than 90%. A limitation of their study was that they were unable to document whether PCPs had received any information or by what method. A recent survey of PCPs by Sheu et al. indicated that for many discharges, information in addition to that present in the EHR was desirable to ensure a safe transition of care.[6] Two‐way communication, such as with a phone call, allows for senders to verify information receipt and for receivers to ask questions to ensure complete information. To our knowledge, there have been no previous reports describing processes for improving verbal communication between hospitalist services and PCPs at discharge.

It may be that use of the call system allowed PCPs to return phone calls regarding discharges at convenient stopping points in their day while allowing discharging physicians to initiate a call without having to wait on hold. Interestingly, though we anticipated the need for additional PPL resources during the course of this improvement, the final process was efficient enough that PPL did not require additional staffing to accommodate the higher call volume.

A key insight during our implementation was that relying on the EHR to initiate every discharge communication created disruption of resident workflow due to disregard of patient, resident, and PCP factors. This was reflected by the improvement in call initiation (our process measure) following this intervention, whereas at the same time call completion (our outcome measure) remained below goal. To achieve our goal of completing verbal communication required a process that was highly reliable yet flexible enough to allow discharging physicians to complete the call in the unpredictable environment of inpatient care. Ultimately, this was achieved by allowing discharging physicians to initiate the process when convenient, and allowing for the EHR‐initiated process to function as a backup strategy to identify and mitigate failures of initiation.

An important limitation of our study was the lack of PCPs on the improvement team, likely making the success of the project more difficult than it might have been. For example, during the study we did not measure the time PCPs spent on hold or how many reattempts were needed to complete the communication loop. Immediately following the completion of our study, it became apparent that physicians returning calls for our own institution's primary care clinic were experiencing regular workflow interruptions and occasional hold times more than 20 minutes, necessitating ongoing further work to determine the root causes and solutions to these problems. Though this work is ongoing, average PCP hold times measured from a sample of call reviews in 2013 to 2014 was 3 minutes and 15 seconds.

This study has several other limitations. We were unable to account for phone calls to PCPs initiated outside of the new process. It may be that PCPs were called more than 52% of the time at baseline due to noncompliance with the new protocol. Also, we only have data for call completion starting after implementation of the link between the discharge order and the discharge phone call, making the baseline appear artificially high and precluding any analysis of how earlier interventions affected our outcome metric. Communication with PCPs should ideally occur prior to discharge. An important limitation of our process is that calls could occur several hours after discharge between an on‐call resident and an on‐call outpatient physician rather than between the PCP and the discharging resident, limiting appropriate information exchange. Though verbal discharge communication is a desirable goal for many reasons, the current project did not focus on the quality of the call or the information that was transmitted to the PCP. Additionally, direct attending‐to‐attending communication may be valuable with medically or socially complex discharges, but we did not have a process to facilitate this. We also did not measure what effect our new process had on outcomes such as quality of patient and family transition from hospital or physician satisfaction. The existence of programs similar to our PPL subspecialty referral line may be limited to large institutions. However, it should be noted that although some internal resource reallocation was necessary within PPL, no actual staffing increases were required despite a large increase in call volume. It may be that any hospital operator system could be adapted for this purpose with modest additional resources. Finally, although our EHR system is widely utilized, there are many competing systems in the market, and our intervention required utilization of EHR capabilities that may not be present in all systems. However, our EHR intervention utilized existing functionality and did not require modification of the system.

This project focused on discharge phone calls to primary care physicians for patients hospitalized on the hospital medicine service. Because communication with the PCP should ideally occur prior to discharge, future work will include identifying a more proximal trigger than the discharge order to which to link the EHR trigger for discharge communication. Other next steps to improve handoff effectiveness and optimize the efficiency of our process include identifying essential information that should be transmitted to the primary care physician at the time of the phone call, developing processes to ensure communication of this information, measuring PCP satisfaction with this communication, and measuring the impact on patient outcomes. Finally, though expert opinion indicates that verbal handoffs may have safety advantages over nonverbal handoffs, studies comparing the safety and efficacy of verbal versus nonverbal handoffs at patient discharge are lacking. Studies establishing the relative efficacy and safety of verbal versus nonverbal handoffs at hospital discharge are needed. Knowledge gained from these activities could inform future projects centered on the spread of the process to other hospital services and/or other hospitals.

CONCLUSION

We increased the percentage of calls initiated to PCPs at patient discharge from 52% to 97% and the percentage of calls completed between HM physicians and PCPs to 93% through the use of a standardized discharge communication process coupled with a basic EHR messaging functionality. The results of this study may be of interest for further testing and adaptation for any institution with an electronic healthcare system.

Disclosure: Nothing to report.

Community‐acquired pneumonia (CAP) is a common and serious infection in children. With more than 150,000 children requiring hospitalization annually, CAP is the fifth most prevalent and the second most costly diagnosis of all pediatric hospitalizations in the United States.[1, 2, 3]

In August 2011, the Pediatric Infectious Diseases Society (PIDS) and the Infectious Diseases Society of America (IDSA) published an evidence‐based guideline for the management of CAP in children. This guideline recommended that fully immunized children without underlying complications who require hospitalization receive an aminopenicillin as first‐line antibiotic therapy.[4] Additionally, the guideline recommends empirically adding a macrolide to an aminopenicillin when atypical pneumonia is a diagnostic consideration.

This recommendation was a substantial departure from practice for hospitals nationwide, as a multicenter study of children's hospitals (20052010) demonstrated that <10% of patients diagnosed with CAP received aminopenicillins as empiric therapy.[5] Since publication of the PIDS/IDSA guidelines, the use of aminopenicillins has increased significantly across institutions, but the majority of hospitalized patients still receive broad‐spectrum cephalosporin therapy for CAP.[6]

At baseline, 30% of patients hospitalized with CAP received guideline‐recommended antibiotic therapy at our institution. Through the use of quality‐improvement methods, the proportion of patients receiving guideline‐recommended therapy increased to 100%.[7] The objective of this study was to ensure that there were not unintended negative consequences to guideline implementation. Specifically, we sought to identify changes in length of stay (LOS), hospital costs, and treatment failures associated with use of guideline‐recommended antibiotic therapy for children hospitalized with uncomplicated CAP.

METHODS

RESULTS

Of the 220 unique patients included, 122 (55%) were male. The median age was 2.9 years (IQR: 1.36.3 years). Empiric guideline‐recommended therapy was prescribed to 168 (76%) patients (Table 1). Aminopenicillins were the most common guideline‐recommended therapy, accounting for 84% of guideline‐recommended antibiotics. An additional 10% of patients received the guideline‐recommended combination therapy with an aminopenicillin and a macrolide. Nonguideline‐recommended therapy included third‐generation cephalosporin antibiotics (54%) and macrolide monotherapy (33%).

Those who received empiric guideline‐recommended antibiotic therapy were similar to those who received nonguideline‐recommended therapy with respect to sex, Emergency Severity Index, physical exam findings on presentation, oxygen requirement in the first 24 hours, abnormal laboratory findings, presence of effusion on chest radiograph, and need for additional imaging (Table 1). However, patients in the guideline‐recommended therapy group were significantly younger (median 2.5 years vs 5.6 years, P0.01), more likely to have elevated respiratory rate on presentation (60.2% vs 44.4%, P=0.04), and more likely to have an infiltrate on chest radiograph (86.3% vs 73.6%, P=0.03) (Table 1). Patients who received nonguideline‐recommended macrolide monotherapy had a median age of 7.4 years (IQR: 5.89.8 years).

Median hospital LOS for the total cohort was 1.3 days (IQR: 0.91.9 days) (Table 2). There were no differences in LOS between patients who received and did not receive guideline‐recommended therapy in the unadjusted or the adjusted model (Table 3).

Median total costs of the index hospitalization for the total cohort were $4097 (IQR: $2657$6054), with median inpatient pharmacy costs of $92 (IQR: $40$183) (Table 2). There were no differences in total or inpatient pharmacy costs for patients who received guideline‐recommended therapy compared with those who did not in unadjusted or adjusted analyses. Fourteen patients (6.4%) had antibiotic therapy broadened during hospitalization, 10 were initially prescribed guideline‐recommended therapy, and 4 were initially prescribed nonguideline‐recommended therapy (Table 4).

Of the 9 pneumonia‐related ED revisits within 30 days of discharge, 7 occurred in patients prescribed empiric guideline‐recommended therapy (Table 5). No ED revisit resulted in hospital readmission or antibiotic change related to pneumonia. Two ED revisits resulted in new antibiotic prescriptions for diagnoses other than pneumonia.

Two patients were readmitted for a pneumonia‐related illness within 30 days of discharge; 1 had received guideline‐recommended therapy (Table 5). Both patients were directly admitted to the inpatient ward without an associated ED visit. Antibiotic class was not changed for either patient upon readmission, despite the decision to convert to intravenous form.

DISCUSSION

In this retrospective cohort study, patients who received empiric guideline‐recommended antibiotic therapy on admission for CAP had no difference in LOS, total cost of hospitalization, or inpatient pharmacy costs compared with those who received therapy that varied from guideline recommendations. Our study suggests that prescribing narrow‐spectrum therapy and, in some circumstances, combination therapy, as recommended by the 2011 PIDS/IDSA pneumonia guideline, did not result in negative unintended consequences.

In our study, children receiving guideline‐recommended therapy were younger, more likely to have elevated respiratory rate on presentation, and more likely to have an infiltrate on chest radiograph. We hypothesize the age difference is a reflection of common use of nonguideline macrolide monotherapy in the older, school‐age child, as macrolides are commonly used for coverage of Mycoplasma pneumonia in older children with CAP.[15] Children receiving macrolide monotherapy were older than those receiving guideline‐recommended therapy (median age of 7.4 years and 2.5 years, respectively). We also hypothesize that some providers may prescribe macrolide monotherapy to children deemed less ill than expected for CAP (eg, normal percutaneous oxygen saturation). This hypothesis is supported by the finding that 60% of patients who had a normal respiratory rate and received nonguideline therapy were prescribed macrolide monotherapy. We did control for the characteristics that varied between the two treatment groups in our models to eliminate potential confounding.

One prior study evaluated the effects of guideline implementation in CAP. In evaluation of a clinical practice guideline that recommended ampicillin as first‐line therapy for CAP, no significant difference was found following guideline introduction in the number of treatment failures (defined as the need to broaden therapy or development of complicated pneumonia within 48 hours or 30‐day inpatient readmission).[16] Our study builds on these findings by directly comparing outcomes between recipients of guideline and nonguideline therapy, which was not done in the prestudy or poststudy design of prior work.[16] We believe that classifying patients based on empiric therapy received rather than timing of guideline introduction thoroughly examines the effect of following guideline recommendations. Additionally, outcomes other than treatment failures were examined in our study including LOS, costs, and ED revisits.

Our results are similar to other observational studies that compared narrow‐ and broad‐spectrum antibiotics for children hospitalized with CAP. Using administrative data, Williams et al. found no significant difference in LOS or cost between narrow‐ and broad‐spectrum intravenous antibiotic recipients ages 6 months to 18 years at 43 children's hospitals.[17] Queen et al. compared narrow‐ and broad‐spectrum antibiotic therapy for children ages 2 months to 18 years at 4 children's hospitals.[18] Differences in average daily cost were not significant, but children who received narrow‐spectrum antibiotics had a significantly shorter LOS. Finally, an observational study of 319 Israeli children <2 years of age found no significant difference in duration of oxygen requirement, LOS, or need for change in antibiotic therapy between the 66 children prescribed aminopenicillins and the 253 children prescribed cefuroxime.[19] These studies suggest that prescribing narrow‐spectrum antimicrobial therapy, as per the guideline recommendations, results in similar outcomes to broad‐spectrum antimicrobial therapy.

Our study adds to prior studies that compared narrow‐ and broad‐spectrum therapy by comparing outcomes associated with guideline‐recommended therapy to nonguideline therapy. We considered antibiotic therapy as guideline recommended if appropriately chosen per the guideline, not just by simple classification of antibiotic. For example, the use of a cephalosporin in a patient with aminopenicillin allergy was considered guideline‐recommended therapy. We chose to classify the exposure of empiric antibiotic therapy in this manner to reflect true clinical application of the guideline, as not all children are able to receive aminopenicillins. Additionally, as our study stemmed from our prior improvement work aimed at increasing guideline adherent therapy,[7] we have a higher frequency of narrow‐spectrum antibiotic use than prior studies. In our study, almost two‐thirds of patients received narrow‐spectrum therapy, whereas narrow‐spectrum use in prior studies ranged from 10% to 33%.[17, 18, 19] Finally, we were able to confirm the diagnosis of pneumonia via medical record review and to adjust for severity of illness using clinical variables including vital signs, physical exam findings, and laboratory and radiologic study results.

This study must be interpreted in the context of several limitations. First, our study population was defined through discharge diagnosis codes, and therefore dependent on the accuracy of coding. However, we minimized potential for misclassification through use of a previously validated approach to identify patients with CAP and through medical record review to confirm the diagnosis. Second, we may be unable to detect very small differences in outcomes given limited power, specifically for outcomes of LOS, ED revisits, hospital readmissions, and need to broaden antibiotic therapy. Third, residual confounding may be present. Although we controlled for many clinical variables in our analyses, antibiotic prescribing practices may be influenced by unmeasured factors. The potential of system level confounding is mitigated by standardized care for patients with CAP at our institution. Prior system‐level changes using quality‐improvement science have resulted in a high level of adherence with guideline‐recommended antimicrobials as well as standardized medical discharge criteria.[7, 20] Additionally, nonmedical factors may influence LOS, limiting its use as an outcome measure. This limitation was minimized in our study by standardizing medical discharge criteria. Prior work at our institution demonstrated that the majority of patients, including those with CAP, were discharged within 2 hours of meeting medical discharge criteria.[20] Fourth, discharged patients who experienced adverse outcomes may have received care at a nearby adult emergency department or at their pediatrician's office. Although these events would not have been captured in our electronic health record, serious complications due to treatment failure (eg, empyema) would require hospitalization. As our hospital is the only children's hospital in the Greater Cincinnati metropolitan area, these patents would receive care at our institution. Therefore, any misclassification of revisits or readmissions is likely to be minimal. Finally, study patients were admitted to a large tertiary academic children's hospital, warranting further investigation to determine if these findings can be translated to smaller community settings.

In conclusion, receipt of guideline‐recommended antibiotic therapy for patients hospitalized with CAP was not associated with increases in LOS, total costs of hospitalization, or inpatient pharmacy costs. Our findings highlight the importance of changing antibiotic prescribing practices to reflect guideline recommendations, as there was no evidence of negative unintended consequences with our local practice change.

Community‐acquired pneumonia (CAP) is a common and serious infection in children. With more than 150,000 children requiring hospitalization annually, CAP is the fifth most prevalent and the second most costly diagnosis of all pediatric hospitalizations in the United States.[1, 2, 3]

In August 2011, the Pediatric Infectious Diseases Society (PIDS) and the Infectious Diseases Society of America (IDSA) published an evidence‐based guideline for the management of CAP in children. This guideline recommended that fully immunized children without underlying complications who require hospitalization receive an aminopenicillin as first‐line antibiotic therapy.[4] Additionally, the guideline recommends empirically adding a macrolide to an aminopenicillin when atypical pneumonia is a diagnostic consideration.

This recommendation was a substantial departure from practice for hospitals nationwide, as a multicenter study of children's hospitals (20052010) demonstrated that <10% of patients diagnosed with CAP received aminopenicillins as empiric therapy.[5] Since publication of the PIDS/IDSA guidelines, the use of aminopenicillins has increased significantly across institutions, but the majority of hospitalized patients still receive broad‐spectrum cephalosporin therapy for CAP.[6]

At baseline, 30% of patients hospitalized with CAP received guideline‐recommended antibiotic therapy at our institution. Through the use of quality‐improvement methods, the proportion of patients receiving guideline‐recommended therapy increased to 100%.[7] The objective of this study was to ensure that there were not unintended negative consequences to guideline implementation. Specifically, we sought to identify changes in length of stay (LOS), hospital costs, and treatment failures associated with use of guideline‐recommended antibiotic therapy for children hospitalized with uncomplicated CAP.

METHODS

RESULTS

Of the 220 unique patients included, 122 (55%) were male. The median age was 2.9 years (IQR: 1.36.3 years). Empiric guideline‐recommended therapy was prescribed to 168 (76%) patients (Table 1). Aminopenicillins were the most common guideline‐recommended therapy, accounting for 84% of guideline‐recommended antibiotics. An additional 10% of patients received the guideline‐recommended combination therapy with an aminopenicillin and a macrolide. Nonguideline‐recommended therapy included third‐generation cephalosporin antibiotics (54%) and macrolide monotherapy (33%).

Those who received empiric guideline‐recommended antibiotic therapy were similar to those who received nonguideline‐recommended therapy with respect to sex, Emergency Severity Index, physical exam findings on presentation, oxygen requirement in the first 24 hours, abnormal laboratory findings, presence of effusion on chest radiograph, and need for additional imaging (Table 1). However, patients in the guideline‐recommended therapy group were significantly younger (median 2.5 years vs 5.6 years, P0.01), more likely to have elevated respiratory rate on presentation (60.2% vs 44.4%, P=0.04), and more likely to have an infiltrate on chest radiograph (86.3% vs 73.6%, P=0.03) (Table 1). Patients who received nonguideline‐recommended macrolide monotherapy had a median age of 7.4 years (IQR: 5.89.8 years).

Median hospital LOS for the total cohort was 1.3 days (IQR: 0.91.9 days) (Table 2). There were no differences in LOS between patients who received and did not receive guideline‐recommended therapy in the unadjusted or the adjusted model (Table 3).

Median total costs of the index hospitalization for the total cohort were $4097 (IQR: $2657$6054), with median inpatient pharmacy costs of $92 (IQR: $40$183) (Table 2). There were no differences in total or inpatient pharmacy costs for patients who received guideline‐recommended therapy compared with those who did not in unadjusted or adjusted analyses. Fourteen patients (6.4%) had antibiotic therapy broadened during hospitalization, 10 were initially prescribed guideline‐recommended therapy, and 4 were initially prescribed nonguideline‐recommended therapy (Table 4).

Of the 9 pneumonia‐related ED revisits within 30 days of discharge, 7 occurred in patients prescribed empiric guideline‐recommended therapy (Table 5). No ED revisit resulted in hospital readmission or antibiotic change related to pneumonia. Two ED revisits resulted in new antibiotic prescriptions for diagnoses other than pneumonia.

Two patients were readmitted for a pneumonia‐related illness within 30 days of discharge; 1 had received guideline‐recommended therapy (Table 5). Both patients were directly admitted to the inpatient ward without an associated ED visit. Antibiotic class was not changed for either patient upon readmission, despite the decision to convert to intravenous form.

DISCUSSION

In this retrospective cohort study, patients who received empiric guideline‐recommended antibiotic therapy on admission for CAP had no difference in LOS, total cost of hospitalization, or inpatient pharmacy costs compared with those who received therapy that varied from guideline recommendations. Our study suggests that prescribing narrow‐spectrum therapy and, in some circumstances, combination therapy, as recommended by the 2011 PIDS/IDSA pneumonia guideline, did not result in negative unintended consequences.

In our study, children receiving guideline‐recommended therapy were younger, more likely to have elevated respiratory rate on presentation, and more likely to have an infiltrate on chest radiograph. We hypothesize the age difference is a reflection of common use of nonguideline macrolide monotherapy in the older, school‐age child, as macrolides are commonly used for coverage of Mycoplasma pneumonia in older children with CAP.[15] Children receiving macrolide monotherapy were older than those receiving guideline‐recommended therapy (median age of 7.4 years and 2.5 years, respectively). We also hypothesize that some providers may prescribe macrolide monotherapy to children deemed less ill than expected for CAP (eg, normal percutaneous oxygen saturation). This hypothesis is supported by the finding that 60% of patients who had a normal respiratory rate and received nonguideline therapy were prescribed macrolide monotherapy. We did control for the characteristics that varied between the two treatment groups in our models to eliminate potential confounding.

One prior study evaluated the effects of guideline implementation in CAP. In evaluation of a clinical practice guideline that recommended ampicillin as first‐line therapy for CAP, no significant difference was found following guideline introduction in the number of treatment failures (defined as the need to broaden therapy or development of complicated pneumonia within 48 hours or 30‐day inpatient readmission).[16] Our study builds on these findings by directly comparing outcomes between recipients of guideline and nonguideline therapy, which was not done in the prestudy or poststudy design of prior work.[16] We believe that classifying patients based on empiric therapy received rather than timing of guideline introduction thoroughly examines the effect of following guideline recommendations. Additionally, outcomes other than treatment failures were examined in our study including LOS, costs, and ED revisits.

Our results are similar to other observational studies that compared narrow‐ and broad‐spectrum antibiotics for children hospitalized with CAP. Using administrative data, Williams et al. found no significant difference in LOS or cost between narrow‐ and broad‐spectrum intravenous antibiotic recipients ages 6 months to 18 years at 43 children's hospitals.[17] Queen et al. compared narrow‐ and broad‐spectrum antibiotic therapy for children ages 2 months to 18 years at 4 children's hospitals.[18] Differences in average daily cost were not significant, but children who received narrow‐spectrum antibiotics had a significantly shorter LOS. Finally, an observational study of 319 Israeli children <2 years of age found no significant difference in duration of oxygen requirement, LOS, or need for change in antibiotic therapy between the 66 children prescribed aminopenicillins and the 253 children prescribed cefuroxime.[19] These studies suggest that prescribing narrow‐spectrum antimicrobial therapy, as per the guideline recommendations, results in similar outcomes to broad‐spectrum antimicrobial therapy.

Our study adds to prior studies that compared narrow‐ and broad‐spectrum therapy by comparing outcomes associated with guideline‐recommended therapy to nonguideline therapy. We considered antibiotic therapy as guideline recommended if appropriately chosen per the guideline, not just by simple classification of antibiotic. For example, the use of a cephalosporin in a patient with aminopenicillin allergy was considered guideline‐recommended therapy. We chose to classify the exposure of empiric antibiotic therapy in this manner to reflect true clinical application of the guideline, as not all children are able to receive aminopenicillins. Additionally, as our study stemmed from our prior improvement work aimed at increasing guideline adherent therapy,[7] we have a higher frequency of narrow‐spectrum antibiotic use than prior studies. In our study, almost two‐thirds of patients received narrow‐spectrum therapy, whereas narrow‐spectrum use in prior studies ranged from 10% to 33%.[17, 18, 19] Finally, we were able to confirm the diagnosis of pneumonia via medical record review and to adjust for severity of illness using clinical variables including vital signs, physical exam findings, and laboratory and radiologic study results.

This study must be interpreted in the context of several limitations. First, our study population was defined through discharge diagnosis codes, and therefore dependent on the accuracy of coding. However, we minimized potential for misclassification through use of a previously validated approach to identify patients with CAP and through medical record review to confirm the diagnosis. Second, we may be unable to detect very small differences in outcomes given limited power, specifically for outcomes of LOS, ED revisits, hospital readmissions, and need to broaden antibiotic therapy. Third, residual confounding may be present. Although we controlled for many clinical variables in our analyses, antibiotic prescribing practices may be influenced by unmeasured factors. The potential of system level confounding is mitigated by standardized care for patients with CAP at our institution. Prior system‐level changes using quality‐improvement science have resulted in a high level of adherence with guideline‐recommended antimicrobials as well as standardized medical discharge criteria.[7, 20] Additionally, nonmedical factors may influence LOS, limiting its use as an outcome measure. This limitation was minimized in our study by standardizing medical discharge criteria. Prior work at our institution demonstrated that the majority of patients, including those with CAP, were discharged within 2 hours of meeting medical discharge criteria.[20] Fourth, discharged patients who experienced adverse outcomes may have received care at a nearby adult emergency department or at their pediatrician's office. Although these events would not have been captured in our electronic health record, serious complications due to treatment failure (eg, empyema) would require hospitalization. As our hospital is the only children's hospital in the Greater Cincinnati metropolitan area, these patents would receive care at our institution. Therefore, any misclassification of revisits or readmissions is likely to be minimal. Finally, study patients were admitted to a large tertiary academic children's hospital, warranting further investigation to determine if these findings can be translated to smaller community settings.

In conclusion, receipt of guideline‐recommended antibiotic therapy for patients hospitalized with CAP was not associated with increases in LOS, total costs of hospitalization, or inpatient pharmacy costs. Our findings highlight the importance of changing antibiotic prescribing practices to reflect guideline recommendations, as there was no evidence of negative unintended consequences with our local practice change.

Community‐acquired pneumonia (CAP) is a common and serious infection in children. With more than 150,000 children requiring hospitalization annually, CAP is the fifth most prevalent and the second most costly diagnosis of all pediatric hospitalizations in the United States.[1, 2, 3]

In August 2011, the Pediatric Infectious Diseases Society (PIDS) and the Infectious Diseases Society of America (IDSA) published an evidence‐based guideline for the management of CAP in children. This guideline recommended that fully immunized children without underlying complications who require hospitalization receive an aminopenicillin as first‐line antibiotic therapy.[4] Additionally, the guideline recommends empirically adding a macrolide to an aminopenicillin when atypical pneumonia is a diagnostic consideration.

This recommendation was a substantial departure from practice for hospitals nationwide, as a multicenter study of children's hospitals (20052010) demonstrated that <10% of patients diagnosed with CAP received aminopenicillins as empiric therapy.[5] Since publication of the PIDS/IDSA guidelines, the use of aminopenicillins has increased significantly across institutions, but the majority of hospitalized patients still receive broad‐spectrum cephalosporin therapy for CAP.[6]

At baseline, 30% of patients hospitalized with CAP received guideline‐recommended antibiotic therapy at our institution. Through the use of quality‐improvement methods, the proportion of patients receiving guideline‐recommended therapy increased to 100%.[7] The objective of this study was to ensure that there were not unintended negative consequences to guideline implementation. Specifically, we sought to identify changes in length of stay (LOS), hospital costs, and treatment failures associated with use of guideline‐recommended antibiotic therapy for children hospitalized with uncomplicated CAP.

METHODS

RESULTS

Of the 220 unique patients included, 122 (55%) were male. The median age was 2.9 years (IQR: 1.36.3 years). Empiric guideline‐recommended therapy was prescribed to 168 (76%) patients (Table 1). Aminopenicillins were the most common guideline‐recommended therapy, accounting for 84% of guideline‐recommended antibiotics. An additional 10% of patients received the guideline‐recommended combination therapy with an aminopenicillin and a macrolide. Nonguideline‐recommended therapy included third‐generation cephalosporin antibiotics (54%) and macrolide monotherapy (33%).

Those who received empiric guideline‐recommended antibiotic therapy were similar to those who received nonguideline‐recommended therapy with respect to sex, Emergency Severity Index, physical exam findings on presentation, oxygen requirement in the first 24 hours, abnormal laboratory findings, presence of effusion on chest radiograph, and need for additional imaging (Table 1). However, patients in the guideline‐recommended therapy group were significantly younger (median 2.5 years vs 5.6 years, P0.01), more likely to have elevated respiratory rate on presentation (60.2% vs 44.4%, P=0.04), and more likely to have an infiltrate on chest radiograph (86.3% vs 73.6%, P=0.03) (Table 1). Patients who received nonguideline‐recommended macrolide monotherapy had a median age of 7.4 years (IQR: 5.89.8 years).

Median hospital LOS for the total cohort was 1.3 days (IQR: 0.91.9 days) (Table 2). There were no differences in LOS between patients who received and did not receive guideline‐recommended therapy in the unadjusted or the adjusted model (Table 3).

Median total costs of the index hospitalization for the total cohort were $4097 (IQR: $2657$6054), with median inpatient pharmacy costs of $92 (IQR: $40$183) (Table 2). There were no differences in total or inpatient pharmacy costs for patients who received guideline‐recommended therapy compared with those who did not in unadjusted or adjusted analyses. Fourteen patients (6.4%) had antibiotic therapy broadened during hospitalization, 10 were initially prescribed guideline‐recommended therapy, and 4 were initially prescribed nonguideline‐recommended therapy (Table 4).

Of the 9 pneumonia‐related ED revisits within 30 days of discharge, 7 occurred in patients prescribed empiric guideline‐recommended therapy (Table 5). No ED revisit resulted in hospital readmission or antibiotic change related to pneumonia. Two ED revisits resulted in new antibiotic prescriptions for diagnoses other than pneumonia.

Two patients were readmitted for a pneumonia‐related illness within 30 days of discharge; 1 had received guideline‐recommended therapy (Table 5). Both patients were directly admitted to the inpatient ward without an associated ED visit. Antibiotic class was not changed for either patient upon readmission, despite the decision to convert to intravenous form.

DISCUSSION

In this retrospective cohort study, patients who received empiric guideline‐recommended antibiotic therapy on admission for CAP had no difference in LOS, total cost of hospitalization, or inpatient pharmacy costs compared with those who received therapy that varied from guideline recommendations. Our study suggests that prescribing narrow‐spectrum therapy and, in some circumstances, combination therapy, as recommended by the 2011 PIDS/IDSA pneumonia guideline, did not result in negative unintended consequences.

In our study, children receiving guideline‐recommended therapy were younger, more likely to have elevated respiratory rate on presentation, and more likely to have an infiltrate on chest radiograph. We hypothesize the age difference is a reflection of common use of nonguideline macrolide monotherapy in the older, school‐age child, as macrolides are commonly used for coverage of Mycoplasma pneumonia in older children with CAP.[15] Children receiving macrolide monotherapy were older than those receiving guideline‐recommended therapy (median age of 7.4 years and 2.5 years, respectively). We also hypothesize that some providers may prescribe macrolide monotherapy to children deemed less ill than expected for CAP (eg, normal percutaneous oxygen saturation). This hypothesis is supported by the finding that 60% of patients who had a normal respiratory rate and received nonguideline therapy were prescribed macrolide monotherapy. We did control for the characteristics that varied between the two treatment groups in our models to eliminate potential confounding.

One prior study evaluated the effects of guideline implementation in CAP. In evaluation of a clinical practice guideline that recommended ampicillin as first‐line therapy for CAP, no significant difference was found following guideline introduction in the number of treatment failures (defined as the need to broaden therapy or development of complicated pneumonia within 48 hours or 30‐day inpatient readmission).[16] Our study builds on these findings by directly comparing outcomes between recipients of guideline and nonguideline therapy, which was not done in the prestudy or poststudy design of prior work.[16] We believe that classifying patients based on empiric therapy received rather than timing of guideline introduction thoroughly examines the effect of following guideline recommendations. Additionally, outcomes other than treatment failures were examined in our study including LOS, costs, and ED revisits.

Our results are similar to other observational studies that compared narrow‐ and broad‐spectrum antibiotics for children hospitalized with CAP. Using administrative data, Williams et al. found no significant difference in LOS or cost between narrow‐ and broad‐spectrum intravenous antibiotic recipients ages 6 months to 18 years at 43 children's hospitals.[17] Queen et al. compared narrow‐ and broad‐spectrum antibiotic therapy for children ages 2 months to 18 years at 4 children's hospitals.[18] Differences in average daily cost were not significant, but children who received narrow‐spectrum antibiotics had a significantly shorter LOS. Finally, an observational study of 319 Israeli children <2 years of age found no significant difference in duration of oxygen requirement, LOS, or need for change in antibiotic therapy between the 66 children prescribed aminopenicillins and the 253 children prescribed cefuroxime.[19] These studies suggest that prescribing narrow‐spectrum antimicrobial therapy, as per the guideline recommendations, results in similar outcomes to broad‐spectrum antimicrobial therapy.

Our study adds to prior studies that compared narrow‐ and broad‐spectrum therapy by comparing outcomes associated with guideline‐recommended therapy to nonguideline therapy. We considered antibiotic therapy as guideline recommended if appropriately chosen per the guideline, not just by simple classification of antibiotic. For example, the use of a cephalosporin in a patient with aminopenicillin allergy was considered guideline‐recommended therapy. We chose to classify the exposure of empiric antibiotic therapy in this manner to reflect true clinical application of the guideline, as not all children are able to receive aminopenicillins. Additionally, as our study stemmed from our prior improvement work aimed at increasing guideline adherent therapy,[7] we have a higher frequency of narrow‐spectrum antibiotic use than prior studies. In our study, almost two‐thirds of patients received narrow‐spectrum therapy, whereas narrow‐spectrum use in prior studies ranged from 10% to 33%.[17, 18, 19] Finally, we were able to confirm the diagnosis of pneumonia via medical record review and to adjust for severity of illness using clinical variables including vital signs, physical exam findings, and laboratory and radiologic study results.

This study must be interpreted in the context of several limitations. First, our study population was defined through discharge diagnosis codes, and therefore dependent on the accuracy of coding. However, we minimized potential for misclassification through use of a previously validated approach to identify patients with CAP and through medical record review to confirm the diagnosis. Second, we may be unable to detect very small differences in outcomes given limited power, specifically for outcomes of LOS, ED revisits, hospital readmissions, and need to broaden antibiotic therapy. Third, residual confounding may be present. Although we controlled for many clinical variables in our analyses, antibiotic prescribing practices may be influenced by unmeasured factors. The potential of system level confounding is mitigated by standardized care for patients with CAP at our institution. Prior system‐level changes using quality‐improvement science have resulted in a high level of adherence with guideline‐recommended antimicrobials as well as standardized medical discharge criteria.[7, 20] Additionally, nonmedical factors may influence LOS, limiting its use as an outcome measure. This limitation was minimized in our study by standardizing medical discharge criteria. Prior work at our institution demonstrated that the majority of patients, including those with CAP, were discharged within 2 hours of meeting medical discharge criteria.[20] Fourth, discharged patients who experienced adverse outcomes may have received care at a nearby adult emergency department or at their pediatrician's office. Although these events would not have been captured in our electronic health record, serious complications due to treatment failure (eg, empyema) would require hospitalization. As our hospital is the only children's hospital in the Greater Cincinnati metropolitan area, these patents would receive care at our institution. Therefore, any misclassification of revisits or readmissions is likely to be minimal. Finally, study patients were admitted to a large tertiary academic children's hospital, warranting further investigation to determine if these findings can be translated to smaller community settings.

In conclusion, receipt of guideline‐recommended antibiotic therapy for patients hospitalized with CAP was not associated with increases in LOS, total costs of hospitalization, or inpatient pharmacy costs. Our findings highlight the importance of changing antibiotic prescribing practices to reflect guideline recommendations, as there was no evidence of negative unintended consequences with our local practice change.

Acute illness requiring hospitalization can be overwhelming for children and their families who are coping with illness and the synthesis of information from a variety of healthcare providers.[1] Patient and family centeredness is endorsed by the Institute of Medicine and the American Academy of Pediatrics[2, 3] as central to quality healthcare. In academic institutions, the presence of medical students and residents adds to the number of providers families encounter. In July 2011, the Accreditation Council for Graduate Medical Education implemented new duty hour restrictions, limiting first year residents to a maximum of 16 hour shifts.[4] Consequently, caregivers and patients may be in contact with more healthcare providers; this fractured care may confuse patients and caregivers, and increase dissatisfaction with care.[5]

The primary objective of our study was to determine the effect of a face sheet tool on the percentage of medical team members correctly identified by caregivers. The secondary objective was to determine the effect of a face sheet tool on the evaluation and satisfaction rating of the medical team by caregivers. We hypothesized that caregivers who receive the face sheet tool will correctly identify a greater percentage of team members by name and role and have higher overall satisfaction with their hospital stay.

METHODS

We performed a prospective controlled study on 2 general pediatric units at Cincinnati Children's Hospital Medical Center (CCHMC). Patients on the intervention unit received the face sheet tool, whereas the concurrent control unit maintained usual procedures. Both units have 24 beds and care for general pediatric patients primarily covered by 4 resident teams and the hospital medicine faculty. Two paired resident teams composed of 2 senior residents, 3 to 4 interns, and 4 medical students primarily admit to each general pediatric unit. Team members rotate through day and night shifts. All employees and rotating students are required to wear the hospital issued identification badge that includes their names, photos, credentials, and role. The study was conducted from November 1, 2011 to November 30, 2011.

Included patients were admitted to the study units by the usual protocol at our hospital, in which nurse patient‐flow coordinators determine bed assignments. We excluded families whose children had an inpatient hospital stay of <12 hours and families who did not speak English. All patient families scheduled to be discharged later in the day on weekday mornings from the 2 study units were approached for study participation. Families were not compensated for their participation.

A face sheet tool, which is a sheet of paper with pictures and names of the intervention team attendings, senior residents, interns, and medical students as well as a description of team member roles, was distributed to patients and their caregivers. The face sheet tools were created using Microsoft Publisher (Microsoft Corp., Redmond, WA). Neither families nor providers were blinded to the intervention, and the residents assumed responsibility for introducing the face sheet tool to families.

For our primary outcome measure, the research coordinator asked participating caregivers to match provider photographs with names and roles by placing laminated pictures backed with Velcro tape in the appropriate position on a laminated poster sheet. Initially, we collected overall accuracy of identification by name and role. In the second week, we began collecting specific data on the attending physician.

The satisfaction survey consisted of the American Board of Internal Medicine (ABIM) patient satisfaction questionnaire, composed of 10, 5‐point Likert scale questions,[6, 7] and an overall rating of hospital question, On a scale from 1 to 10, with 1 being the worst possible hospital and 10 being the best possible hospital, what number would you rate this hospital? from the Hospital Consumer Assessment of Health Plans Survey.[8] Questions were asked aloud and families responded to the questions orally. A written list was also provided to families. We collected data on length of stay (LOS) at the time of outcome assessment as well as previous hospitalizations.

RESULTS

A total of 96 families were approached for enrollment (50 in the intervention and 46 in the control). Of these, 86 families agreed to participate. Three families in the intervention group did not receive the face sheet tool and were excluded from analysis, leaving an analytic cohort of 83 (41 in intervention and 42 in control). Attending recognition by role was collected from 54 families (28 in intervention group and 26 in control group) and by name from 34 families (15 in intervention group and 19 in control group). Table 1 displays characteristics of each group. Among the 83 study participants, LOS at time of outcome assessment ranged from 0.4 to 12.0 days, and the number of medical team members that cared for these patients ranged from 3 to 14.

Families in the intervention group had a higher percentage of correctly identified members of the medical team by name and role as compared to the control group (Table 2). These findings remained significant after adjusting for LOS and prior hospitalization. In addition, in a subset of families with attending data available, more families accurately identified attending name and attending role in the intervention as compared to control group.

No significant differences were noted between the groups when comparing all individual ABIM survey question scores or the overall hospital satisfaction rating (Table 2). Scores in both intervention and control groups were high in all categories.

DISCUSSION

Caregivers given the face sheet tool were better able to identify medical team members by name and role than caregivers in the control group. Previous studies have shown similar results.[9, 10] Families encountered a large number of providers (median of 8) during stays that were on average quite brief (median LOS of 23.6 hours). Despite the significant increase in caregivers' ability to identify providers, the effect was modest.

Our findings add to prior work on face sheet tools in pediatrics and internal medicine.[9, 10, 11] Our study occurred after the residency duty hour restrictions. We described the high number of providers that families encounter in this context. It is the first study to our knowledge to quantify the number of providers that families encounter after these changes and to report on how well families can identify these clinicians by name and role. Unlike other studies, satisfaction scores were not improved.[9] Potential reasons for this include: (1) caregiver knowledge of 2 to 4 key members of the team and not the whole team may be the primary driver of satisfaction, (2) caregiver activation or empowerment may be a more responsive measure than overall satisfaction, and (3) our satisfaction measures may have ceiling effects and/or be elevated in both groups by social desirability bias.

Our study highlights the need for further investigation of quality outcomes associated with residency work hour changes.[12, 13, 14] Specifically, exposure to large numbers of providers may hinder families from accurately identifying those entrusted with the care of their loved one. Of note, our research coordinator needed to present as many as 14 provider pictures to 1 family with a hospital stay of <24 hours. Large numbers of providers may create challenges in building rapport, ensuring effective communication and developing trust with families. We chose to evaluate identification of each team member by caregivers; our findings are suggestive of the need for alternative strategies. A more valuable intervention might target identification of key team members (eg, attending, primary intern, primary senior resident). A policy statement regarding transitions of care recommended the establishment of mechanisms to ensure patients and their families know who is responsible for their care.[15] Efforts toward achieving this goal are essential.

This study has several limitations. The study was completed at a single institution, and thus generalizability may be limited. Although the intervention and control units have similar characteristics, randomization did not occur at the patient level. The control group had significantly more patients who had greater than 1 admission compared to the intervention group. Patients enrolled in the study were from a weekday convenience sample; therefore, potential differences in results based on weekend admissions were unable to be assessed. The exclusion of nonEnglish‐speaking families could limit generalizability to this population. Social desirability bias may have elevated the scores in both groups. Providers tasked with the responsibility of introducing the face sheet tool to families did so in a nonstandardized way and may have interacted differently with families compared to the control team. Finally, our project's aim was focused on the effect of a face sheet tool on the identification and satisfaction rating of the medical team by caregivers. Truly family‐centered care would include efforts to improve families' knowledge of and satisfaction with all members of the healthcare team.

A photo‐based face sheet tool helped caregivers better identify their child's care providers by name and role in the hospital. Satisfaction scores were similar in both groups.

Acute illness requiring hospitalization can be overwhelming for children and their families who are coping with illness and the synthesis of information from a variety of healthcare providers.[1] Patient and family centeredness is endorsed by the Institute of Medicine and the American Academy of Pediatrics[2, 3] as central to quality healthcare. In academic institutions, the presence of medical students and residents adds to the number of providers families encounter. In July 2011, the Accreditation Council for Graduate Medical Education implemented new duty hour restrictions, limiting first year residents to a maximum of 16 hour shifts.[4] Consequently, caregivers and patients may be in contact with more healthcare providers; this fractured care may confuse patients and caregivers, and increase dissatisfaction with care.[5]

The primary objective of our study was to determine the effect of a face sheet tool on the percentage of medical team members correctly identified by caregivers. The secondary objective was to determine the effect of a face sheet tool on the evaluation and satisfaction rating of the medical team by caregivers. We hypothesized that caregivers who receive the face sheet tool will correctly identify a greater percentage of team members by name and role and have higher overall satisfaction with their hospital stay.

METHODS

We performed a prospective controlled study on 2 general pediatric units at Cincinnati Children's Hospital Medical Center (CCHMC). Patients on the intervention unit received the face sheet tool, whereas the concurrent control unit maintained usual procedures. Both units have 24 beds and care for general pediatric patients primarily covered by 4 resident teams and the hospital medicine faculty. Two paired resident teams composed of 2 senior residents, 3 to 4 interns, and 4 medical students primarily admit to each general pediatric unit. Team members rotate through day and night shifts. All employees and rotating students are required to wear the hospital issued identification badge that includes their names, photos, credentials, and role. The study was conducted from November 1, 2011 to November 30, 2011.

Included patients were admitted to the study units by the usual protocol at our hospital, in which nurse patient‐flow coordinators determine bed assignments. We excluded families whose children had an inpatient hospital stay of <12 hours and families who did not speak English. All patient families scheduled to be discharged later in the day on weekday mornings from the 2 study units were approached for study participation. Families were not compensated for their participation.

A face sheet tool, which is a sheet of paper with pictures and names of the intervention team attendings, senior residents, interns, and medical students as well as a description of team member roles, was distributed to patients and their caregivers. The face sheet tools were created using Microsoft Publisher (Microsoft Corp., Redmond, WA). Neither families nor providers were blinded to the intervention, and the residents assumed responsibility for introducing the face sheet tool to families.

For our primary outcome measure, the research coordinator asked participating caregivers to match provider photographs with names and roles by placing laminated pictures backed with Velcro tape in the appropriate position on a laminated poster sheet. Initially, we collected overall accuracy of identification by name and role. In the second week, we began collecting specific data on the attending physician.

The satisfaction survey consisted of the American Board of Internal Medicine (ABIM) patient satisfaction questionnaire, composed of 10, 5‐point Likert scale questions,[6, 7] and an overall rating of hospital question, On a scale from 1 to 10, with 1 being the worst possible hospital and 10 being the best possible hospital, what number would you rate this hospital? from the Hospital Consumer Assessment of Health Plans Survey.[8] Questions were asked aloud and families responded to the questions orally. A written list was also provided to families. We collected data on length of stay (LOS) at the time of outcome assessment as well as previous hospitalizations.

RESULTS

A total of 96 families were approached for enrollment (50 in the intervention and 46 in the control). Of these, 86 families agreed to participate. Three families in the intervention group did not receive the face sheet tool and were excluded from analysis, leaving an analytic cohort of 83 (41 in intervention and 42 in control). Attending recognition by role was collected from 54 families (28 in intervention group and 26 in control group) and by name from 34 families (15 in intervention group and 19 in control group). Table 1 displays characteristics of each group. Among the 83 study participants, LOS at time of outcome assessment ranged from 0.4 to 12.0 days, and the number of medical team members that cared for these patients ranged from 3 to 14.

Families in the intervention group had a higher percentage of correctly identified members of the medical team by name and role as compared to the control group (Table 2). These findings remained significant after adjusting for LOS and prior hospitalization. In addition, in a subset of families with attending data available, more families accurately identified attending name and attending role in the intervention as compared to control group.

No significant differences were noted between the groups when comparing all individual ABIM survey question scores or the overall hospital satisfaction rating (Table 2). Scores in both intervention and control groups were high in all categories.

DISCUSSION

Caregivers given the face sheet tool were better able to identify medical team members by name and role than caregivers in the control group. Previous studies have shown similar results.[9, 10] Families encountered a large number of providers (median of 8) during stays that were on average quite brief (median LOS of 23.6 hours). Despite the significant increase in caregivers' ability to identify providers, the effect was modest.

Our findings add to prior work on face sheet tools in pediatrics and internal medicine.[9, 10, 11] Our study occurred after the residency duty hour restrictions. We described the high number of providers that families encounter in this context. It is the first study to our knowledge to quantify the number of providers that families encounter after these changes and to report on how well families can identify these clinicians by name and role. Unlike other studies, satisfaction scores were not improved.[9] Potential reasons for this include: (1) caregiver knowledge of 2 to 4 key members of the team and not the whole team may be the primary driver of satisfaction, (2) caregiver activation or empowerment may be a more responsive measure than overall satisfaction, and (3) our satisfaction measures may have ceiling effects and/or be elevated in both groups by social desirability bias.

Our study highlights the need for further investigation of quality outcomes associated with residency work hour changes.[12, 13, 14] Specifically, exposure to large numbers of providers may hinder families from accurately identifying those entrusted with the care of their loved one. Of note, our research coordinator needed to present as many as 14 provider pictures to 1 family with a hospital stay of <24 hours. Large numbers of providers may create challenges in building rapport, ensuring effective communication and developing trust with families. We chose to evaluate identification of each team member by caregivers; our findings are suggestive of the need for alternative strategies. A more valuable intervention might target identification of key team members (eg, attending, primary intern, primary senior resident). A policy statement regarding transitions of care recommended the establishment of mechanisms to ensure patients and their families know who is responsible for their care.[15] Efforts toward achieving this goal are essential.

This study has several limitations. The study was completed at a single institution, and thus generalizability may be limited. Although the intervention and control units have similar characteristics, randomization did not occur at the patient level. The control group had significantly more patients who had greater than 1 admission compared to the intervention group. Patients enrolled in the study were from a weekday convenience sample; therefore, potential differences in results based on weekend admissions were unable to be assessed. The exclusion of nonEnglish‐speaking families could limit generalizability to this population. Social desirability bias may have elevated the scores in both groups. Providers tasked with the responsibility of introducing the face sheet tool to families did so in a nonstandardized way and may have interacted differently with families compared to the control team. Finally, our project's aim was focused on the effect of a face sheet tool on the identification and satisfaction rating of the medical team by caregivers. Truly family‐centered care would include efforts to improve families' knowledge of and satisfaction with all members of the healthcare team.

A photo‐based face sheet tool helped caregivers better identify their child's care providers by name and role in the hospital. Satisfaction scores were similar in both groups.

Acute illness requiring hospitalization can be overwhelming for children and their families who are coping with illness and the synthesis of information from a variety of healthcare providers.[1] Patient and family centeredness is endorsed by the Institute of Medicine and the American Academy of Pediatrics[2, 3] as central to quality healthcare. In academic institutions, the presence of medical students and residents adds to the number of providers families encounter. In July 2011, the Accreditation Council for Graduate Medical Education implemented new duty hour restrictions, limiting first year residents to a maximum of 16 hour shifts.[4] Consequently, caregivers and patients may be in contact with more healthcare providers; this fractured care may confuse patients and caregivers, and increase dissatisfaction with care.[5]

The primary objective of our study was to determine the effect of a face sheet tool on the percentage of medical team members correctly identified by caregivers. The secondary objective was to determine the effect of a face sheet tool on the evaluation and satisfaction rating of the medical team by caregivers. We hypothesized that caregivers who receive the face sheet tool will correctly identify a greater percentage of team members by name and role and have higher overall satisfaction with their hospital stay.

METHODS

We performed a prospective controlled study on 2 general pediatric units at Cincinnati Children's Hospital Medical Center (CCHMC). Patients on the intervention unit received the face sheet tool, whereas the concurrent control unit maintained usual procedures. Both units have 24 beds and care for general pediatric patients primarily covered by 4 resident teams and the hospital medicine faculty. Two paired resident teams composed of 2 senior residents, 3 to 4 interns, and 4 medical students primarily admit to each general pediatric unit. Team members rotate through day and night shifts. All employees and rotating students are required to wear the hospital issued identification badge that includes their names, photos, credentials, and role. The study was conducted from November 1, 2011 to November 30, 2011.

Included patients were admitted to the study units by the usual protocol at our hospital, in which nurse patient‐flow coordinators determine bed assignments. We excluded families whose children had an inpatient hospital stay of <12 hours and families who did not speak English. All patient families scheduled to be discharged later in the day on weekday mornings from the 2 study units were approached for study participation. Families were not compensated for their participation.

A face sheet tool, which is a sheet of paper with pictures and names of the intervention team attendings, senior residents, interns, and medical students as well as a description of team member roles, was distributed to patients and their caregivers. The face sheet tools were created using Microsoft Publisher (Microsoft Corp., Redmond, WA). Neither families nor providers were blinded to the intervention, and the residents assumed responsibility for introducing the face sheet tool to families.

For our primary outcome measure, the research coordinator asked participating caregivers to match provider photographs with names and roles by placing laminated pictures backed with Velcro tape in the appropriate position on a laminated poster sheet. Initially, we collected overall accuracy of identification by name and role. In the second week, we began collecting specific data on the attending physician.

The satisfaction survey consisted of the American Board of Internal Medicine (ABIM) patient satisfaction questionnaire, composed of 10, 5‐point Likert scale questions,[6, 7] and an overall rating of hospital question, On a scale from 1 to 10, with 1 being the worst possible hospital and 10 being the best possible hospital, what number would you rate this hospital? from the Hospital Consumer Assessment of Health Plans Survey.[8] Questions were asked aloud and families responded to the questions orally. A written list was also provided to families. We collected data on length of stay (LOS) at the time of outcome assessment as well as previous hospitalizations.

RESULTS

A total of 96 families were approached for enrollment (50 in the intervention and 46 in the control). Of these, 86 families agreed to participate. Three families in the intervention group did not receive the face sheet tool and were excluded from analysis, leaving an analytic cohort of 83 (41 in intervention and 42 in control). Attending recognition by role was collected from 54 families (28 in intervention group and 26 in control group) and by name from 34 families (15 in intervention group and 19 in control group). Table 1 displays characteristics of each group. Among the 83 study participants, LOS at time of outcome assessment ranged from 0.4 to 12.0 days, and the number of medical team members that cared for these patients ranged from 3 to 14.

Families in the intervention group had a higher percentage of correctly identified members of the medical team by name and role as compared to the control group (Table 2). These findings remained significant after adjusting for LOS and prior hospitalization. In addition, in a subset of families with attending data available, more families accurately identified attending name and attending role in the intervention as compared to control group.

No significant differences were noted between the groups when comparing all individual ABIM survey question scores or the overall hospital satisfaction rating (Table 2). Scores in both intervention and control groups were high in all categories.

DISCUSSION

Caregivers given the face sheet tool were better able to identify medical team members by name and role than caregivers in the control group. Previous studies have shown similar results.[9, 10] Families encountered a large number of providers (median of 8) during stays that were on average quite brief (median LOS of 23.6 hours). Despite the significant increase in caregivers' ability to identify providers, the effect was modest.

Our findings add to prior work on face sheet tools in pediatrics and internal medicine.[9, 10, 11] Our study occurred after the residency duty hour restrictions. We described the high number of providers that families encounter in this context. It is the first study to our knowledge to quantify the number of providers that families encounter after these changes and to report on how well families can identify these clinicians by name and role. Unlike other studies, satisfaction scores were not improved.[9] Potential reasons for this include: (1) caregiver knowledge of 2 to 4 key members of the team and not the whole team may be the primary driver of satisfaction, (2) caregiver activation or empowerment may be a more responsive measure than overall satisfaction, and (3) our satisfaction measures may have ceiling effects and/or be elevated in both groups by social desirability bias.

Our study highlights the need for further investigation of quality outcomes associated with residency work hour changes.[12, 13, 14] Specifically, exposure to large numbers of providers may hinder families from accurately identifying those entrusted with the care of their loved one. Of note, our research coordinator needed to present as many as 14 provider pictures to 1 family with a hospital stay of <24 hours. Large numbers of providers may create challenges in building rapport, ensuring effective communication and developing trust with families. We chose to evaluate identification of each team member by caregivers; our findings are suggestive of the need for alternative strategies. A more valuable intervention might target identification of key team members (eg, attending, primary intern, primary senior resident). A policy statement regarding transitions of care recommended the establishment of mechanisms to ensure patients and their families know who is responsible for their care.[15] Efforts toward achieving this goal are essential.

This study has several limitations. The study was completed at a single institution, and thus generalizability may be limited. Although the intervention and control units have similar characteristics, randomization did not occur at the patient level. The control group had significantly more patients who had greater than 1 admission compared to the intervention group. Patients enrolled in the study were from a weekday convenience sample; therefore, potential differences in results based on weekend admissions were unable to be assessed. The exclusion of nonEnglish‐speaking families could limit generalizability to this population. Social desirability bias may have elevated the scores in both groups. Providers tasked with the responsibility of introducing the face sheet tool to families did so in a nonstandardized way and may have interacted differently with families compared to the control team. Finally, our project's aim was focused on the effect of a face sheet tool on the identification and satisfaction rating of the medical team by caregivers. Truly family‐centered care would include efforts to improve families' knowledge of and satisfaction with all members of the healthcare team.

A photo‐based face sheet tool helped caregivers better identify their child's care providers by name and role in the hospital. Satisfaction scores were similar in both groups.