Health Policy

The most recently concluded term of the US Supreme Court, which began on October 1, 2018, yielded a number of decisions of interest to health care professionals and to ObGyns in particular. Although the term was viewed by some observers as less consequential than other recent terms, a review of the cases decided paints a picture of a more important term than some commentators expected.

When the term began, the Court had only 8 justices—1 short of a full bench: Judge Brett Kavanaugh had not yet been confirmed by the Senate. He was confirmed on October 6, by a 50-48 vote, and Justice Kavanaugh immediately joined the Court and began to hear and decide cases.

Increasingly, important decisions affect medical practice

From the nature of practice (abortion), to payment for service (Medicare reimbursement), resolution of disputes (arbitration), and fraud and abuse (the federal False Claims Act), the decisions of the Court will have an impact on many areas of medical practice. Organized medicine increasingly has recognized the significance of the work of the Court; nowhere has this been more clearly demonstrated than with amicus curiae (friend of the court) briefs filed by medical organizations.

Amicus curiae briefs. These briefs are filed by persons or organizations not a party to a case the Court is hearing. Their legitimate purpose is to inform the Court of 1) special information within the expertise of the amicus (or amici, plural) or 2) consequences of the decision that might not be apparent from arguments made by the parties to the case. Sometimes, the Court cites amicus briefs for having provided important information about the case.

Filing amicus briefs is time-consuming and expensive; organizations do not file them for trivial reasons. Organizations frequently join together to file a joint brief, to share expenses and express to the Court a stronger position.

Three categories of health professionals file amicus briefs in ObGyn-related cases:

• Major national organizations, often representing broad interests of health care professions or institutions (the American Medical Association [AMA], the Association of American Medical Colleges, and the American Hospital Association [AHA]), have filed a number of amicus briefs over the years.
• Specialty boards increasingly file amicus briefs. For example, the American College of Obstetricians and Gynecologists (ACOG) and the American Society for Reproductive Medicine have filed briefs related to abortion issues.
• In reproductive issues, the American Association of Pro-Life Obstetricians and Gynecologists, the American College of Pediatricians, and the Christian Medical & Dental Associations have been active amicus filers—frequently taking positions different than, even inconsistent with, amicus briefs filed by major specialty boards.

Amicus briefs filed by medical associations provide strong clues to what is important to clinicians. We have looked at such briefs to help us identify topics and cases from the just-concluded term that can be of particular interest to you.

Continue to: Surveying the shadow docket...

Surveying the shadow docket. As part of our review of the past term, we also looked at the so-called shadow docket, which includes decisions regarding writs of certiorari (which cases it agrees to hear); stays (usually delaying implementation of a law); or denials of stays. (Persuading the Court to hear a case is not easy: It hears approximately 70 cases per year out of as many as 7,000 applications to be heard.)

Abortion ruling

At stake. A number of states recently enacted a variety of provisions that might make an abortion more difficult to obtain. Some of the cases challenging these restrictions are making their way through lower courts, and one day might be argued before the Supreme Court. However, the Court has not (yet) agreed to hear the substance of many new abortion-related provisions.

Box v Planned Parenthood of Indiana and Kentucky, Inc.

The Court decided only 1 abortion restriction case this term.¹ The Indiana law in question included 2 provisions that the Court considered:

Disposal of remains. The law regulated the manner in which abortion providers can dispose of fetal remains (ie, they cannot be treated as “infectious and pathologic waste”).

Motivation for seeking abortion. The Indiana law makes it illegal for an abortion provider to perform an abortion when the provider knows that the mother is seeking that abortion “solely” because of the fetus’s race, sex, diagnosis of Down syndrome, disability, or related characteristics.

Final rulings. The Court held that the disposal-of-remains provision is constitutional. The provision is “rationally related to the state’s interest in proper disposal of fetal remains.”² Planned Parenthood had not raised the issue of whether the law might impose an undue burden on a woman’s right to obtain an abortion, so the Court did not decide that issue.

The Court did not consider the constitutionality of the part of the law proscribing certain reasons for seeking an otherwise legal abortion; instead, it awaits lower courts’ review of the issue. Justice Clarence Thomas wrote an extensive concurring opinion suggesting that this law is intended to avoid abortion to achieve eugenic goals.³

Key developments from the shadow docket

The Court issued a stay preventing a Louisiana statute that requires physicians who perform abortions to have admitting privileges at a nearby hospital from going into effect, pending the outcome of litigation about that law.⁴ Four dissenters noted that all 4 physicians who perform abortions in Louisiana have such privileges. Chief Justice Roberts was the fifth vote to grant the stay. This case likely will make its way back to the Court, as will a number of other state laws being adopted. The issue may be back as soon as the term just starting.

The Court is also considering whether to take another Indiana case, Box v Planned Parenthood of Indiana and Kentucky, Inc. (Box II). This case involves an Indiana ultrasonography viewing option as part of the abortion consent process.⁵

The Court declined to hear cases from Louisiana and Kansas in which the states had cut off Medicaid funding to Planned Parenthood. Lower courts had stopped the implementation of those laws.⁶ The legal issue was whether private parties, as opposed to the federal government, had standing to bring the case. For now, the decision of the lower courts to stop implementation of the funding cutoff is in effect. There is a split in the Circuit Courts on the issue, however, making it likely that the Supreme Court will have to resolve it sooner or later.

Health care organizations have filed a number of amicus briefs in these and other cases involving new abortion regulations. ACOG and others filed a brief opposing a Louisiana law that requires abortion providers to have admitting privileges at a nearby facility,⁷ and a brief opposing a similar Oklahoma law.⁸ The Association of Pro-Life Obstetricians and Gynecologists and others filed amicus curiae briefs in Box II⁹ and in an Alabama case involving so-called dismemberment abortion.¹⁰

Continue to: Medicare payments...

Medicare payments

Azar, Secretary of Health and Human Services v Allina Health Services, et al¹¹

This case drew interest—and many amicus briefs—from health care providers, including the AMA and the AHA.^12,13 There was good reason for their interest: First, the case involved more than $3 billion in reimbursements; second, it represented a potentially important precedent about the rights of providers and patients to comment on Medicare reimbursement changes. The question involved the technical calculation of additional payments made to institutions that serve a disproportionate number of low-income patients (known as Medicare Fractions).

At stake. The issue was a statutory requirement for a 60-day public notice and comment period for rules that “change a substantive legal standard” governing the scope of benefits, eligibility, or payment for services.¹⁴ In 2014, the Secretary of the Department of Health and Human Services (HHS) in the Obama administration posted a spreadsheet announcing Medicare fractions rates for hospitals (for 2012)—without formal notice or comment regarding the formula used. (The spreadsheet listed what each qualifying institution would receive, but it was based on a formula that, as noted, had not been subject to public notice and comment.) The AMA and AHA briefs emphasized the importance of a notice and comment period, especially when Medicare reimbursement is involved.

Final ruling. The Court held that the HHS process violated the notice and comment provision, thereby invalidating the policy underlying the so-called spreadsheet reimbursement. The decision was significant: This was a careful statutory interpretation of the 60-day notice and comment period, not the reimbursement policy itself. Presumably, had the HHS Secretary provided for sufficient notice and comment, the formula used would have met the requirements for issuing reimbursement formulas.

Key points. Hospitals will collectively receive $3 or $4 billion as a consequence of the ruling. Perhaps more importantly, the decision signals that HHS is going to have to take seriously the requirement that it publish Medicare-related reimbursement policies for the 60-day period.

Liability under the False Claims Act

The False Claims Act (FCA) protects the federal government from fraudulent claims for payment and for shoddy goods and services. It incentivizes (by a percentage of recovery) private parties to bring cases to enforce the law.¹⁵ (Of course, the federal government also enforces the Act.)

At stake. The FCA has been of considerable concern to the AHA, the Association of American Medical Colleges, and other health care organizations—understandably so.¹⁶ As the AHA informed the Court in an amicus brief, “The prevalence of [FCA] cases has ballooned over the past three decades.... These suits disproportionately target healthcare entities.... Of the 767 new FCA cases filed in 2018, for example, 506 involved healthcare defendants.”¹⁷

Final ruling. The Court considered an ambiguity in the statute of limitations for these actions and the Court unanimously ruled to permit an extended time in which qui tam actions (private actions under the law) can be filed.¹⁸

Key points. As long a period as 10 years can pass between the time an FCA violation occurs and an action is brought. This decision is likely to increase the number of FCA actions against health care providers because the case can be filed many years after the conduct that gave rise to the complaint.

Continue to: Registering sex offenders...

Registering sex offenders

The Court upheld the constitutionality of the federal Sex Offender Registration and Notification Act (SORNA).¹⁹ Sex offenders must register and periodically report, in person, to law enforcement in every state in which the offender works, studies, or resides.

At stake. The case involved the applicability of SORNA registration obligations to those convicted of sex offenses before SORNA was adopted (pre-Act offenders).²⁰ The court upheld registration requirements for pre-Act offenders.

Arbitration

The Court continued its practice of deciding at least one case each term that emphasizes that federal law requires that courts rather strictly enforce agreements to arbitrate (instead of to litigate) future disputes.²¹ In another case, the Court ruled that there can be “class” or “joint” arbitration only if the agreement to arbitrate a dispute clearly permits such class arbitration.²²

Pharma’s liability regarding product risk

The Court somewhat limited the liability of pharmaceutical companies for failing to provide adequate warning about the risk that their products pose. The case against Merck involved 500 patients who took denosumab (Fosamax) and suffered atypical femoral fractures.²³

At stake. Because prescribing information (in which warnings are provided) must be approved by the US Food and Drug Administration (FDA), the legal test is: Would the FDA have refused to approve a change in the warning if Merck had “fully informed the FDA of the justifications for the warning” required by state law to avoid liability?^24,25 Lower-court judges (not juries) will be expected to apply this test in the future.

The doctor and the death penalty

The Court has established a rule that, when a prisoner facing capital punishment objects to a form of execution because it is too painful, he has to propose an alternative that is reasonably available. In one case,²⁶ a physician, an expert witness for the prisoner, did not answer some essential relative-pain questions (ie, would one procedure be more painful than another?).

At stake. The AMA filed an amicus brief in this case, indicating that it is unethical for physicians to participate in an execution. The brief noted that “testimony used to determine which method of execution would reduce physical suffering would constitute physician participation in capital punishment and would be unethical.”²⁷

The expert witness’s failure to answer the question on relative pain had the unfortunate result of reducing the likelihood that the prisoner would prevail in his request for an alternative method of execution.

Analysis

Despite obvious disagreements about big issues (notably, abortion and the death penalty) the Court maintained a courteous and civil demeanor—something not always seen nowadays in other branches of government. Here are facts about the Court’s term just concluded:

• The Court issued 72 merits opinions (about average).
• Only 39% of decisions were unanimous (compared with the average of 49% in recent terms).
• On the other hand, 26% of decisions were split 5 to 4 (compared with a 10% recent average).
• In those 5 to 4 decisions, Justices were in the majority as follows²⁸: Justice Gorsuch, 65%; Justice Kavanaugh, 61%; Justice Thomas, 60%; Chief Justice Roberts and Justices Ginsburg and Alito, each 55%; Justice Breyer, 50%; and Justices Sotomayor and Kagan each at 45%.
• There were 57 dissenting opinions—up from 48 in the previous term.
• What is referred to as “the liberal-conservative split” might seem more profound than it really is: “Every conservative member of the court at some point voted to form a majority with the liberal justices. And every liberal at least once left behind all of his or her usual voting partners to join the conservatives.”²⁹

Continue to: Last, it was a year of personal health issues for...

Last, it was a year of personal health issues for the Court: Justice Ginsburg had a diagnosis of lung cancer and was absent, following surgery, in January. Of retired Justices, Sandra Day O’Connor suffers from dementia and former Justice John Paul Stevens died.

In closing

The Court has accepted approximately 50 cases for the current term, which began on October 7. The first 2 days of the term were spent on arguments about, first, whether a state can abolish the insanity defense and, second, whether nondiscrimination laws (“based on sex”) prohibit discrimination based on sexual orientation or transgender status. Cases also will deal with Patient Protection and Affordable Care Act payments to providers; the Deferred Action for Childhood Arrivals, or DACA; the death penalty; and international child custody disputes. The Court will be accepting more cases for several months. It promises to be a very interesting term.

The most recently concluded term of the US Supreme Court, which began on October 1, 2018, yielded a number of decisions of interest to health care professionals and to ObGyns in particular. Although the term was viewed by some observers as less consequential than other recent terms, a review of the cases decided paints a picture of a more important term than some commentators expected.

When the term began, the Court had only 8 justices—1 short of a full bench: Judge Brett Kavanaugh had not yet been confirmed by the Senate. He was confirmed on October 6, by a 50-48 vote, and Justice Kavanaugh immediately joined the Court and began to hear and decide cases.

Increasingly, important decisions affect medical practice

From the nature of practice (abortion), to payment for service (Medicare reimbursement), resolution of disputes (arbitration), and fraud and abuse (the federal False Claims Act), the decisions of the Court will have an impact on many areas of medical practice. Organized medicine increasingly has recognized the significance of the work of the Court; nowhere has this been more clearly demonstrated than with amicus curiae (friend of the court) briefs filed by medical organizations.

Amicus curiae briefs. These briefs are filed by persons or organizations not a party to a case the Court is hearing. Their legitimate purpose is to inform the Court of 1) special information within the expertise of the amicus (or amici, plural) or 2) consequences of the decision that might not be apparent from arguments made by the parties to the case. Sometimes, the Court cites amicus briefs for having provided important information about the case.

Filing amicus briefs is time-consuming and expensive; organizations do not file them for trivial reasons. Organizations frequently join together to file a joint brief, to share expenses and express to the Court a stronger position.

Three categories of health professionals file amicus briefs in ObGyn-related cases:

• Major national organizations, often representing broad interests of health care professions or institutions (the American Medical Association [AMA], the Association of American Medical Colleges, and the American Hospital Association [AHA]), have filed a number of amicus briefs over the years.
• Specialty boards increasingly file amicus briefs. For example, the American College of Obstetricians and Gynecologists (ACOG) and the American Society for Reproductive Medicine have filed briefs related to abortion issues.
• In reproductive issues, the American Association of Pro-Life Obstetricians and Gynecologists, the American College of Pediatricians, and the Christian Medical & Dental Associations have been active amicus filers—frequently taking positions different than, even inconsistent with, amicus briefs filed by major specialty boards.

Amicus briefs filed by medical associations provide strong clues to what is important to clinicians. We have looked at such briefs to help us identify topics and cases from the just-concluded term that can be of particular interest to you.

Continue to: Surveying the shadow docket...

Surveying the shadow docket. As part of our review of the past term, we also looked at the so-called shadow docket, which includes decisions regarding writs of certiorari (which cases it agrees to hear); stays (usually delaying implementation of a law); or denials of stays. (Persuading the Court to hear a case is not easy: It hears approximately 70 cases per year out of as many as 7,000 applications to be heard.)

Abortion ruling

At stake. A number of states recently enacted a variety of provisions that might make an abortion more difficult to obtain. Some of the cases challenging these restrictions are making their way through lower courts, and one day might be argued before the Supreme Court. However, the Court has not (yet) agreed to hear the substance of many new abortion-related provisions.

Box v Planned Parenthood of Indiana and Kentucky, Inc.

The Court decided only 1 abortion restriction case this term.¹ The Indiana law in question included 2 provisions that the Court considered:

Disposal of remains. The law regulated the manner in which abortion providers can dispose of fetal remains (ie, they cannot be treated as “infectious and pathologic waste”).

Motivation for seeking abortion. The Indiana law makes it illegal for an abortion provider to perform an abortion when the provider knows that the mother is seeking that abortion “solely” because of the fetus’s race, sex, diagnosis of Down syndrome, disability, or related characteristics.

Final rulings. The Court held that the disposal-of-remains provision is constitutional. The provision is “rationally related to the state’s interest in proper disposal of fetal remains.”² Planned Parenthood had not raised the issue of whether the law might impose an undue burden on a woman’s right to obtain an abortion, so the Court did not decide that issue.

The Court did not consider the constitutionality of the part of the law proscribing certain reasons for seeking an otherwise legal abortion; instead, it awaits lower courts’ review of the issue. Justice Clarence Thomas wrote an extensive concurring opinion suggesting that this law is intended to avoid abortion to achieve eugenic goals.³

Key developments from the shadow docket

The Court issued a stay preventing a Louisiana statute that requires physicians who perform abortions to have admitting privileges at a nearby hospital from going into effect, pending the outcome of litigation about that law.⁴ Four dissenters noted that all 4 physicians who perform abortions in Louisiana have such privileges. Chief Justice Roberts was the fifth vote to grant the stay. This case likely will make its way back to the Court, as will a number of other state laws being adopted. The issue may be back as soon as the term just starting.

The Court is also considering whether to take another Indiana case, Box v Planned Parenthood of Indiana and Kentucky, Inc. (Box II). This case involves an Indiana ultrasonography viewing option as part of the abortion consent process.⁵

The Court declined to hear cases from Louisiana and Kansas in which the states had cut off Medicaid funding to Planned Parenthood. Lower courts had stopped the implementation of those laws.⁶ The legal issue was whether private parties, as opposed to the federal government, had standing to bring the case. For now, the decision of the lower courts to stop implementation of the funding cutoff is in effect. There is a split in the Circuit Courts on the issue, however, making it likely that the Supreme Court will have to resolve it sooner or later.

Health care organizations have filed a number of amicus briefs in these and other cases involving new abortion regulations. ACOG and others filed a brief opposing a Louisiana law that requires abortion providers to have admitting privileges at a nearby facility,⁷ and a brief opposing a similar Oklahoma law.⁸ The Association of Pro-Life Obstetricians and Gynecologists and others filed amicus curiae briefs in Box II⁹ and in an Alabama case involving so-called dismemberment abortion.¹⁰

Continue to: Medicare payments...

Medicare payments

Azar, Secretary of Health and Human Services v Allina Health Services, et al¹¹

This case drew interest—and many amicus briefs—from health care providers, including the AMA and the AHA.^12,13 There was good reason for their interest: First, the case involved more than $3 billion in reimbursements; second, it represented a potentially important precedent about the rights of providers and patients to comment on Medicare reimbursement changes. The question involved the technical calculation of additional payments made to institutions that serve a disproportionate number of low-income patients (known as Medicare Fractions).

At stake. The issue was a statutory requirement for a 60-day public notice and comment period for rules that “change a substantive legal standard” governing the scope of benefits, eligibility, or payment for services.¹⁴ In 2014, the Secretary of the Department of Health and Human Services (HHS) in the Obama administration posted a spreadsheet announcing Medicare fractions rates for hospitals (for 2012)—without formal notice or comment regarding the formula used. (The spreadsheet listed what each qualifying institution would receive, but it was based on a formula that, as noted, had not been subject to public notice and comment.) The AMA and AHA briefs emphasized the importance of a notice and comment period, especially when Medicare reimbursement is involved.

Final ruling. The Court held that the HHS process violated the notice and comment provision, thereby invalidating the policy underlying the so-called spreadsheet reimbursement. The decision was significant: This was a careful statutory interpretation of the 60-day notice and comment period, not the reimbursement policy itself. Presumably, had the HHS Secretary provided for sufficient notice and comment, the formula used would have met the requirements for issuing reimbursement formulas.

Key points. Hospitals will collectively receive $3 or $4 billion as a consequence of the ruling. Perhaps more importantly, the decision signals that HHS is going to have to take seriously the requirement that it publish Medicare-related reimbursement policies for the 60-day period.

Liability under the False Claims Act

The False Claims Act (FCA) protects the federal government from fraudulent claims for payment and for shoddy goods and services. It incentivizes (by a percentage of recovery) private parties to bring cases to enforce the law.¹⁵ (Of course, the federal government also enforces the Act.)

At stake. The FCA has been of considerable concern to the AHA, the Association of American Medical Colleges, and other health care organizations—understandably so.¹⁶ As the AHA informed the Court in an amicus brief, “The prevalence of [FCA] cases has ballooned over the past three decades.... These suits disproportionately target healthcare entities.... Of the 767 new FCA cases filed in 2018, for example, 506 involved healthcare defendants.”¹⁷

Final ruling. The Court considered an ambiguity in the statute of limitations for these actions and the Court unanimously ruled to permit an extended time in which qui tam actions (private actions under the law) can be filed.¹⁸

Key points. As long a period as 10 years can pass between the time an FCA violation occurs and an action is brought. This decision is likely to increase the number of FCA actions against health care providers because the case can be filed many years after the conduct that gave rise to the complaint.

Continue to: Registering sex offenders...

Registering sex offenders

The Court upheld the constitutionality of the federal Sex Offender Registration and Notification Act (SORNA).¹⁹ Sex offenders must register and periodically report, in person, to law enforcement in every state in which the offender works, studies, or resides.

At stake. The case involved the applicability of SORNA registration obligations to those convicted of sex offenses before SORNA was adopted (pre-Act offenders).²⁰ The court upheld registration requirements for pre-Act offenders.

Arbitration

The Court continued its practice of deciding at least one case each term that emphasizes that federal law requires that courts rather strictly enforce agreements to arbitrate (instead of to litigate) future disputes.²¹ In another case, the Court ruled that there can be “class” or “joint” arbitration only if the agreement to arbitrate a dispute clearly permits such class arbitration.²²

Pharma’s liability regarding product risk

The Court somewhat limited the liability of pharmaceutical companies for failing to provide adequate warning about the risk that their products pose. The case against Merck involved 500 patients who took denosumab (Fosamax) and suffered atypical femoral fractures.²³

At stake. Because prescribing information (in which warnings are provided) must be approved by the US Food and Drug Administration (FDA), the legal test is: Would the FDA have refused to approve a change in the warning if Merck had “fully informed the FDA of the justifications for the warning” required by state law to avoid liability?^24,25 Lower-court judges (not juries) will be expected to apply this test in the future.

The doctor and the death penalty

The Court has established a rule that, when a prisoner facing capital punishment objects to a form of execution because it is too painful, he has to propose an alternative that is reasonably available. In one case,²⁶ a physician, an expert witness for the prisoner, did not answer some essential relative-pain questions (ie, would one procedure be more painful than another?).

At stake. The AMA filed an amicus brief in this case, indicating that it is unethical for physicians to participate in an execution. The brief noted that “testimony used to determine which method of execution would reduce physical suffering would constitute physician participation in capital punishment and would be unethical.”²⁷

The expert witness’s failure to answer the question on relative pain had the unfortunate result of reducing the likelihood that the prisoner would prevail in his request for an alternative method of execution.

Analysis

Despite obvious disagreements about big issues (notably, abortion and the death penalty) the Court maintained a courteous and civil demeanor—something not always seen nowadays in other branches of government. Here are facts about the Court’s term just concluded:

• The Court issued 72 merits opinions (about average).
• Only 39% of decisions were unanimous (compared with the average of 49% in recent terms).
• On the other hand, 26% of decisions were split 5 to 4 (compared with a 10% recent average).
• In those 5 to 4 decisions, Justices were in the majority as follows²⁸: Justice Gorsuch, 65%; Justice Kavanaugh, 61%; Justice Thomas, 60%; Chief Justice Roberts and Justices Ginsburg and Alito, each 55%; Justice Breyer, 50%; and Justices Sotomayor and Kagan each at 45%.
• There were 57 dissenting opinions—up from 48 in the previous term.
• What is referred to as “the liberal-conservative split” might seem more profound than it really is: “Every conservative member of the court at some point voted to form a majority with the liberal justices. And every liberal at least once left behind all of his or her usual voting partners to join the conservatives.”²⁹

Continue to: Last, it was a year of personal health issues for...

Last, it was a year of personal health issues for the Court: Justice Ginsburg had a diagnosis of lung cancer and was absent, following surgery, in January. Of retired Justices, Sandra Day O’Connor suffers from dementia and former Justice John Paul Stevens died.

In closing

The Court has accepted approximately 50 cases for the current term, which began on October 7. The first 2 days of the term were spent on arguments about, first, whether a state can abolish the insanity defense and, second, whether nondiscrimination laws (“based on sex”) prohibit discrimination based on sexual orientation or transgender status. Cases also will deal with Patient Protection and Affordable Care Act payments to providers; the Deferred Action for Childhood Arrivals, or DACA; the death penalty; and international child custody disputes. The Court will be accepting more cases for several months. It promises to be a very interesting term.

The most recently concluded term of the US Supreme Court, which began on October 1, 2018, yielded a number of decisions of interest to health care professionals and to ObGyns in particular. Although the term was viewed by some observers as less consequential than other recent terms, a review of the cases decided paints a picture of a more important term than some commentators expected.

When the term began, the Court had only 8 justices—1 short of a full bench: Judge Brett Kavanaugh had not yet been confirmed by the Senate. He was confirmed on October 6, by a 50-48 vote, and Justice Kavanaugh immediately joined the Court and began to hear and decide cases.

Increasingly, important decisions affect medical practice

From the nature of practice (abortion), to payment for service (Medicare reimbursement), resolution of disputes (arbitration), and fraud and abuse (the federal False Claims Act), the decisions of the Court will have an impact on many areas of medical practice. Organized medicine increasingly has recognized the significance of the work of the Court; nowhere has this been more clearly demonstrated than with amicus curiae (friend of the court) briefs filed by medical organizations.

Amicus curiae briefs. These briefs are filed by persons or organizations not a party to a case the Court is hearing. Their legitimate purpose is to inform the Court of 1) special information within the expertise of the amicus (or amici, plural) or 2) consequences of the decision that might not be apparent from arguments made by the parties to the case. Sometimes, the Court cites amicus briefs for having provided important information about the case.

Filing amicus briefs is time-consuming and expensive; organizations do not file them for trivial reasons. Organizations frequently join together to file a joint brief, to share expenses and express to the Court a stronger position.

Three categories of health professionals file amicus briefs in ObGyn-related cases:

• Major national organizations, often representing broad interests of health care professions or institutions (the American Medical Association [AMA], the Association of American Medical Colleges, and the American Hospital Association [AHA]), have filed a number of amicus briefs over the years.
• Specialty boards increasingly file amicus briefs. For example, the American College of Obstetricians and Gynecologists (ACOG) and the American Society for Reproductive Medicine have filed briefs related to abortion issues.
• In reproductive issues, the American Association of Pro-Life Obstetricians and Gynecologists, the American College of Pediatricians, and the Christian Medical & Dental Associations have been active amicus filers—frequently taking positions different than, even inconsistent with, amicus briefs filed by major specialty boards.

Amicus briefs filed by medical associations provide strong clues to what is important to clinicians. We have looked at such briefs to help us identify topics and cases from the just-concluded term that can be of particular interest to you.

Continue to: Surveying the shadow docket...

Surveying the shadow docket. As part of our review of the past term, we also looked at the so-called shadow docket, which includes decisions regarding writs of certiorari (which cases it agrees to hear); stays (usually delaying implementation of a law); or denials of stays. (Persuading the Court to hear a case is not easy: It hears approximately 70 cases per year out of as many as 7,000 applications to be heard.)

Abortion ruling

At stake. A number of states recently enacted a variety of provisions that might make an abortion more difficult to obtain. Some of the cases challenging these restrictions are making their way through lower courts, and one day might be argued before the Supreme Court. However, the Court has not (yet) agreed to hear the substance of many new abortion-related provisions.

Box v Planned Parenthood of Indiana and Kentucky, Inc.

The Court decided only 1 abortion restriction case this term.¹ The Indiana law in question included 2 provisions that the Court considered:

Disposal of remains. The law regulated the manner in which abortion providers can dispose of fetal remains (ie, they cannot be treated as “infectious and pathologic waste”).

Motivation for seeking abortion. The Indiana law makes it illegal for an abortion provider to perform an abortion when the provider knows that the mother is seeking that abortion “solely” because of the fetus’s race, sex, diagnosis of Down syndrome, disability, or related characteristics.

Final rulings. The Court held that the disposal-of-remains provision is constitutional. The provision is “rationally related to the state’s interest in proper disposal of fetal remains.”² Planned Parenthood had not raised the issue of whether the law might impose an undue burden on a woman’s right to obtain an abortion, so the Court did not decide that issue.

The Court did not consider the constitutionality of the part of the law proscribing certain reasons for seeking an otherwise legal abortion; instead, it awaits lower courts’ review of the issue. Justice Clarence Thomas wrote an extensive concurring opinion suggesting that this law is intended to avoid abortion to achieve eugenic goals.³

Key developments from the shadow docket

The Court issued a stay preventing a Louisiana statute that requires physicians who perform abortions to have admitting privileges at a nearby hospital from going into effect, pending the outcome of litigation about that law.⁴ Four dissenters noted that all 4 physicians who perform abortions in Louisiana have such privileges. Chief Justice Roberts was the fifth vote to grant the stay. This case likely will make its way back to the Court, as will a number of other state laws being adopted. The issue may be back as soon as the term just starting.

The Court is also considering whether to take another Indiana case, Box v Planned Parenthood of Indiana and Kentucky, Inc. (Box II). This case involves an Indiana ultrasonography viewing option as part of the abortion consent process.⁵

The Court declined to hear cases from Louisiana and Kansas in which the states had cut off Medicaid funding to Planned Parenthood. Lower courts had stopped the implementation of those laws.⁶ The legal issue was whether private parties, as opposed to the federal government, had standing to bring the case. For now, the decision of the lower courts to stop implementation of the funding cutoff is in effect. There is a split in the Circuit Courts on the issue, however, making it likely that the Supreme Court will have to resolve it sooner or later.

Health care organizations have filed a number of amicus briefs in these and other cases involving new abortion regulations. ACOG and others filed a brief opposing a Louisiana law that requires abortion providers to have admitting privileges at a nearby facility,⁷ and a brief opposing a similar Oklahoma law.⁸ The Association of Pro-Life Obstetricians and Gynecologists and others filed amicus curiae briefs in Box II⁹ and in an Alabama case involving so-called dismemberment abortion.¹⁰

Continue to: Medicare payments...

Medicare payments

Azar, Secretary of Health and Human Services v Allina Health Services, et al¹¹

This case drew interest—and many amicus briefs—from health care providers, including the AMA and the AHA.^12,13 There was good reason for their interest: First, the case involved more than $3 billion in reimbursements; second, it represented a potentially important precedent about the rights of providers and patients to comment on Medicare reimbursement changes. The question involved the technical calculation of additional payments made to institutions that serve a disproportionate number of low-income patients (known as Medicare Fractions).

At stake. The issue was a statutory requirement for a 60-day public notice and comment period for rules that “change a substantive legal standard” governing the scope of benefits, eligibility, or payment for services.¹⁴ In 2014, the Secretary of the Department of Health and Human Services (HHS) in the Obama administration posted a spreadsheet announcing Medicare fractions rates for hospitals (for 2012)—without formal notice or comment regarding the formula used. (The spreadsheet listed what each qualifying institution would receive, but it was based on a formula that, as noted, had not been subject to public notice and comment.) The AMA and AHA briefs emphasized the importance of a notice and comment period, especially when Medicare reimbursement is involved.

Final ruling. The Court held that the HHS process violated the notice and comment provision, thereby invalidating the policy underlying the so-called spreadsheet reimbursement. The decision was significant: This was a careful statutory interpretation of the 60-day notice and comment period, not the reimbursement policy itself. Presumably, had the HHS Secretary provided for sufficient notice and comment, the formula used would have met the requirements for issuing reimbursement formulas.

Key points. Hospitals will collectively receive $3 or $4 billion as a consequence of the ruling. Perhaps more importantly, the decision signals that HHS is going to have to take seriously the requirement that it publish Medicare-related reimbursement policies for the 60-day period.

Liability under the False Claims Act

The False Claims Act (FCA) protects the federal government from fraudulent claims for payment and for shoddy goods and services. It incentivizes (by a percentage of recovery) private parties to bring cases to enforce the law.¹⁵ (Of course, the federal government also enforces the Act.)

At stake. The FCA has been of considerable concern to the AHA, the Association of American Medical Colleges, and other health care organizations—understandably so.¹⁶ As the AHA informed the Court in an amicus brief, “The prevalence of [FCA] cases has ballooned over the past three decades.... These suits disproportionately target healthcare entities.... Of the 767 new FCA cases filed in 2018, for example, 506 involved healthcare defendants.”¹⁷

Final ruling. The Court considered an ambiguity in the statute of limitations for these actions and the Court unanimously ruled to permit an extended time in which qui tam actions (private actions under the law) can be filed.¹⁸

Key points. As long a period as 10 years can pass between the time an FCA violation occurs and an action is brought. This decision is likely to increase the number of FCA actions against health care providers because the case can be filed many years after the conduct that gave rise to the complaint.

Continue to: Registering sex offenders...

Registering sex offenders

The Court upheld the constitutionality of the federal Sex Offender Registration and Notification Act (SORNA).¹⁹ Sex offenders must register and periodically report, in person, to law enforcement in every state in which the offender works, studies, or resides.

At stake. The case involved the applicability of SORNA registration obligations to those convicted of sex offenses before SORNA was adopted (pre-Act offenders).²⁰ The court upheld registration requirements for pre-Act offenders.

Arbitration

The Court continued its practice of deciding at least one case each term that emphasizes that federal law requires that courts rather strictly enforce agreements to arbitrate (instead of to litigate) future disputes.²¹ In another case, the Court ruled that there can be “class” or “joint” arbitration only if the agreement to arbitrate a dispute clearly permits such class arbitration.²²

Pharma’s liability regarding product risk

The Court somewhat limited the liability of pharmaceutical companies for failing to provide adequate warning about the risk that their products pose. The case against Merck involved 500 patients who took denosumab (Fosamax) and suffered atypical femoral fractures.²³

At stake. Because prescribing information (in which warnings are provided) must be approved by the US Food and Drug Administration (FDA), the legal test is: Would the FDA have refused to approve a change in the warning if Merck had “fully informed the FDA of the justifications for the warning” required by state law to avoid liability?^24,25 Lower-court judges (not juries) will be expected to apply this test in the future.

The doctor and the death penalty

The Court has established a rule that, when a prisoner facing capital punishment objects to a form of execution because it is too painful, he has to propose an alternative that is reasonably available. In one case,²⁶ a physician, an expert witness for the prisoner, did not answer some essential relative-pain questions (ie, would one procedure be more painful than another?).

At stake. The AMA filed an amicus brief in this case, indicating that it is unethical for physicians to participate in an execution. The brief noted that “testimony used to determine which method of execution would reduce physical suffering would constitute physician participation in capital punishment and would be unethical.”²⁷

The expert witness’s failure to answer the question on relative pain had the unfortunate result of reducing the likelihood that the prisoner would prevail in his request for an alternative method of execution.

Analysis

Despite obvious disagreements about big issues (notably, abortion and the death penalty) the Court maintained a courteous and civil demeanor—something not always seen nowadays in other branches of government. Here are facts about the Court’s term just concluded:

• The Court issued 72 merits opinions (about average).
• Only 39% of decisions were unanimous (compared with the average of 49% in recent terms).
• On the other hand, 26% of decisions were split 5 to 4 (compared with a 10% recent average).
• In those 5 to 4 decisions, Justices were in the majority as follows²⁸: Justice Gorsuch, 65%; Justice Kavanaugh, 61%; Justice Thomas, 60%; Chief Justice Roberts and Justices Ginsburg and Alito, each 55%; Justice Breyer, 50%; and Justices Sotomayor and Kagan each at 45%.
• There were 57 dissenting opinions—up from 48 in the previous term.
• What is referred to as “the liberal-conservative split” might seem more profound than it really is: “Every conservative member of the court at some point voted to form a majority with the liberal justices. And every liberal at least once left behind all of his or her usual voting partners to join the conservatives.”²⁹

Continue to: Last, it was a year of personal health issues for...

Last, it was a year of personal health issues for the Court: Justice Ginsburg had a diagnosis of lung cancer and was absent, following surgery, in January. Of retired Justices, Sandra Day O’Connor suffers from dementia and former Justice John Paul Stevens died.

In closing

The Court has accepted approximately 50 cases for the current term, which began on October 7. The first 2 days of the term were spent on arguments about, first, whether a state can abolish the insanity defense and, second, whether nondiscrimination laws (“based on sex”) prohibit discrimination based on sexual orientation or transgender status. Cases also will deal with Patient Protection and Affordable Care Act payments to providers; the Deferred Action for Childhood Arrivals, or DACA; the death penalty; and international child custody disputes. The Court will be accepting more cases for several months. It promises to be a very interesting term.

In the current health care environment, hospitals are constantly challenged to improve quality metrics and deliver better health care outcomes. One means to achieving quality improvement is through the use of order sets, groups of related orders that a health care provider (HCP) can place with either a few keystrokes or mouse clicks.¹

Historically, design of order sets has largely focused on clicking checkboxes containing evidence-based practices. According to Bates and colleagues and the Institute for Safe Medication Practices, incorporating evidence-based medicine (EBM) into order sets is not by itself sufficient.^2,3Execution of proper design coupled with simplicity and provider efficiency is paramount to HCP buy-in, increased likelihood of order set adherence, and to potentially better outcomes.

In this article, we outline advancements in order set design. These improvements increase provider efficiency and ease of use; incorporate human factors engineering (HFE); apply failure mode and effects analysis; and include EBM.

Methods

An inpatient nicotine replacement therapy (NRT) order was developed as part of a multifaceted solution to improve tobacco cessation care at the James A. Haley Veterans’ Hospital (JAHVH) in Tampa, Florida, a complexity level 1a facility. This NRT order set used the 4-step order set design framework the authors’ developed (for additional information about the NRT order set, contact the authors). We distinguish order set design technique between 2 different inpatient NRT order sets. The first order set in the comparison (Figure 1) is an inpatient NRT order set of unknown origin—it is common for US Department of Veterans Affairs (VA) medical facilities to share order sets and other resources. The second order set (Figure 2) is an inpatient NRT order set we designed using our 4-step process for comparison in this article. No institutional review board approval was required as this work met criteria for operational improvement activities exempt from ethics review.

Justin Iannello, DO, MBA, was the team leader and developer of the 4-step order set design technique. The intervention team consisted of 4 internal medicine physicians with expertise in quality improvement and patient safety: 1 certified professional in patient safety and certified as a Lean Six Sigma Black Belt; 2 physicians certified as Lean Six Sigma Black Belts; and 1 physician certified as a Lean Six Sigma Green Belt. Two inpatient clinical pharmacists and 1 quality management specialist also were involved in its development.

Development of a new NRT order set was felt to be an integral part of the tobacco cessation care delivery process. An NRT order set perceived by users as value-added required a solution that merged EBM with standardization and applied quality improvement principles. The result was an approach to order set design that focused on 4 key questions: Is the order set efficient and easy to use/navigate? Is human factors engineering incorporated? Is failure mode and effects analysis applied? Are evidence-based practices included?

Ease of Use and Navigation

Implementing an order set that is efficient and easy to use or navigate seems straightforward but can be difficult to execute. Figure 1 shows many detailed options consisting of different combinations of nicotine patches, lozenges, and gum. Also included are oral tobacco cessation options (bupropion and varenicline). Although more options may seem better, confusion about appropriate medication selection can occur.

According to Heath and Heath, too many options can result in lack of action.⁴ For example, Heath and Heath discuss a food store that offered 6 free samples of different jams on one day and 24 jams the following day. The customers who sampled 6 different types of jam were 10 times more likely to buy jam. The authors concluded that the more options available, the more difficulty a potential buyer has in deciding on a course of action.⁴

In clinical situations where a HCP is using an order set, the number of options can mean the difference between use vs avoidance if the choices are overwhelming. HCPs process layers of detail every day when creating differential diagnoses and treatment plans. While that level of detail is necessary clinically, that same level of detail included in orders sets can create challenges for HCPs.

Figure 2 advances the order set in Figure 1 by providing a simpler and cleaner design, so HCPs can more easily review and process the information. This order set design minimizes the number of options available to help users make the right decision, focusing on value for the appropriate setting and audience. In other words, order sets should not be a “one size fits all” approach.

Order sets should be tailored to the appropriate clinical setting (eg, inpatient acute care, outpatient clinic setting, etc) and HCP (eg, hospitalist, tobacco cessation specialist, etc). We are comparing NRT order sets designed for HCPs who do not routinely prescribe oral tobacco cessation products in the inpatient setting. When possible, autogenerated bundle orders should also be used according to evidence-based recommendations (such as nicotine patch tapers) for ease of use and further simplification of order sets.

Finally, usability testing known as “evaluating a product or service by testing it with representative users” helps further refine an order set.⁵Usability testing should be applied during all phases of order set development with end user(s) as it helps identify problems with order set design prior to implementation. By applying usability testing, the order set becomes more meaningful and valued by the user.

Human Factors Engineering

HFE is “the study of all the factors that make it easier to do the work in the right way.”⁶ HFE seeks to identify, align, and apply processes for people and the world within which they live and work to promote safe and efficient practices, especially in relation to the technology and physical design features in their work environment.⁶

The average American adult makes about 35,000 decisions per day.⁷ Thus, there is potential for error at any moment. Design that does not take HFE into account can be dangerous. For example, when tube feed and IV line connectors look similar and are compatible, patients may inadvertently receive food administered directly into their bloodstream.⁸

HFE can and should be applied to order sets. Everything from the look, feel, and verbiage of an order set affects potential outcomes. For example, consider the impact even seemingly minor modifications can have on outcomes simply by guiding users in a different way: Figure 1 provides NRT options based on cigarette use per day, whereas Figure 2 conveys pack use per day in relation to the equivalent number of cigarettes used daily. These differences may seem small; however, it helps guide users to the right choice when considering that health care providers have been historically trained on social history gathering that emphasizes packs per day and pack-years.

Failure Mode and Effects Analysis

Failure mode and effects analysis (FMEA) is “a structured way to identify and address potential problems, or failures and their resulting effects on the system or process before an adverse event occurs.”⁹ The benefit of an order set must be weighed against the risk during development. FMEA should be applied during order set design to assess and limit risk just as with any other clinical care process.

FMEA examines both level of risk and frequency of risk occurrence associated with a new proposed process. For example, let’s evaluate an order set designed for pain control after surgery that consists of multiple high-risk opioids along with antihistamine medications for as-needed itch relief (a non-life-threatening adverse event (AE) of opioids well known by the medical community). An interdisciplinary FMEA team consisting of subject matter experts may examine how the process should flow in step-by-step detail and then discuss the benefit of a process and risk for potential error. A FMEA team would then analyze what could go wrong with each part of the process and assign a level of risk and risk frequency for various steps in the process, and then decide that certain steps should be modified or eliminated. Perhaps after FMEA, a facility might conclude that the risk of serious complications is high when you combine opioid use with antihistamine medications. The facility could decide to remove antihistamine medications from an order set if it is determined that risks outweigh benefits. While a root cause analysis might identify the cause of an AE after order set use, these situations can be prevented with FMEA.

When applying FMEA to Figure 1, while bupropion is known as an evidence-based oral tobacco cessation option, there is the possibility that bupropion could be inadvertently prescribed from the order set in a hospitalized patient with alcohol withdrawal and withdrawal seizure history. These potentially dangerous situations can be avoided with FMEA. Thus, although bupropion may be evidence-based for NRT, decisions regarding order set design using EBM alone are insufficient.

The practitioner must consider possible unintended consequences within order sets and target treatment options to the appropriate setting and audience. Although Figure 1 may appear to be more inclusive, the interdisciplinary committee designing the inpatient NRT order set felt there was heightened risk with introducing bupropion in Figure 1 and decided the risk would be lowered by removing bupropion from the redesigned NRT order set (Figure 2). In addition to the goal of balancing availability of NRT options with acceptable risk, Figure 2 also focused on building an NRT order set most applicable to the inpatient setting.

Including Evidence-Based Practices

EBM has become a routine part of clinical decision making. Therefore, including EBM in order set design is vital. EBM for NRT has demonstrated that combination therapy is more effective than is monotherapy to help tobacco users quit. Incremental doses of NRT are recommended for patients who use tobacco more frequently.¹⁰

As shown in Figures 1 and 2, both order set designs incorporate EBM for NRT. Although the importance of implementing EBM is evident, critical factors, such as HFE and FMEA make a difference with well-designed order sets.

Results

The 4-step order set design technique was used during development of an inpatient NRT order set at the JAHVH. Results for the inpatient Joint Commission Tobacco Treatment Measures were obtained from the Veterans Health Administration quality metric reporting system known as Strategic Analytics for Improvement and Learning (SAIL). SAIL performance measure outcomes, which include the inpatient Joint Commission Tobacco Treatment Measures, are derived from chart reviews conducted by the External Peer Review Program. Outcomes demonstrated that TOB-2 and TOB-3 (2 inpatient Joint Commission Tobacco Treatment Measures) known as tob20 and tob40, respectively, within SAIL improved by more than 300% after development of an NRT order set using the 4-step order set design framework along with implementation of a multifaceted tobacco cessation care delivery system at JAHVH.

Discussion

While the overall tobacco cessation care delivery system contributed to improved outcomes with the inpatient Joint Commission Tobacco Treatment Measures at JAHVH, the NRT order set was a cornerstone of the design. Although using our order set design technique does not necessarily guarantee successful outcomes, we believe using the 4-step order set design process increases the value of order sets and has potential to improve quality outcomes.

Limitations

Although improved outcomes following implementation of our NRT order set suggest correlation, causation cannot be proven. Also while the NRT order set is believed to have helped tremendously with outcomes, the entire tobacco cessation care delivery system at JAHVH contributed to the results. In addition, the inpatient Joint Commission Tobacco Treatment Measures help improve processes for tobacco cessation care. However, we are uncertain whether the results of our improvement efforts helped patients stop tobacco use. Further studies are needed to determine impact on population health. Finally, our results were based on improvement work done at a single center. Further studies are necessary to see whether results are reproducible.

Conclusion

There was significant improvement with the inpatient Joint Commission Tobacco Treatment Measures outcomes following development of a tobacco cessation care delivery system that included design of an inpatient NRT order set using a 4-step process we developed. This 4-step structure includes emphasis on efficiency and ease of use; human factors engineering; failure mode and effects analysis; and incorporation of evidence-based medicine (Box.) Postimplementation results showed improvement of the inpatient Joint Commission Tobacco Treatment Measures by greater than 3-fold at a single hospital.

The next steps for this initiative include testing the 4-step order set design process in multiple clinical settings to determine the effectiveness of this approach in other areas of clinical care.

In the current health care environment, hospitals are constantly challenged to improve quality metrics and deliver better health care outcomes. One means to achieving quality improvement is through the use of order sets, groups of related orders that a health care provider (HCP) can place with either a few keystrokes or mouse clicks.¹

Historically, design of order sets has largely focused on clicking checkboxes containing evidence-based practices. According to Bates and colleagues and the Institute for Safe Medication Practices, incorporating evidence-based medicine (EBM) into order sets is not by itself sufficient.^2,3Execution of proper design coupled with simplicity and provider efficiency is paramount to HCP buy-in, increased likelihood of order set adherence, and to potentially better outcomes.

In this article, we outline advancements in order set design. These improvements increase provider efficiency and ease of use; incorporate human factors engineering (HFE); apply failure mode and effects analysis; and include EBM.

Methods

An inpatient nicotine replacement therapy (NRT) order was developed as part of a multifaceted solution to improve tobacco cessation care at the James A. Haley Veterans’ Hospital (JAHVH) in Tampa, Florida, a complexity level 1a facility. This NRT order set used the 4-step order set design framework the authors’ developed (for additional information about the NRT order set, contact the authors). We distinguish order set design technique between 2 different inpatient NRT order sets. The first order set in the comparison (Figure 1) is an inpatient NRT order set of unknown origin—it is common for US Department of Veterans Affairs (VA) medical facilities to share order sets and other resources. The second order set (Figure 2) is an inpatient NRT order set we designed using our 4-step process for comparison in this article. No institutional review board approval was required as this work met criteria for operational improvement activities exempt from ethics review.

Justin Iannello, DO, MBA, was the team leader and developer of the 4-step order set design technique. The intervention team consisted of 4 internal medicine physicians with expertise in quality improvement and patient safety: 1 certified professional in patient safety and certified as a Lean Six Sigma Black Belt; 2 physicians certified as Lean Six Sigma Black Belts; and 1 physician certified as a Lean Six Sigma Green Belt. Two inpatient clinical pharmacists and 1 quality management specialist also were involved in its development.

Development of a new NRT order set was felt to be an integral part of the tobacco cessation care delivery process. An NRT order set perceived by users as value-added required a solution that merged EBM with standardization and applied quality improvement principles. The result was an approach to order set design that focused on 4 key questions: Is the order set efficient and easy to use/navigate? Is human factors engineering incorporated? Is failure mode and effects analysis applied? Are evidence-based practices included?

Ease of Use and Navigation

Implementing an order set that is efficient and easy to use or navigate seems straightforward but can be difficult to execute. Figure 1 shows many detailed options consisting of different combinations of nicotine patches, lozenges, and gum. Also included are oral tobacco cessation options (bupropion and varenicline). Although more options may seem better, confusion about appropriate medication selection can occur.

According to Heath and Heath, too many options can result in lack of action.⁴ For example, Heath and Heath discuss a food store that offered 6 free samples of different jams on one day and 24 jams the following day. The customers who sampled 6 different types of jam were 10 times more likely to buy jam. The authors concluded that the more options available, the more difficulty a potential buyer has in deciding on a course of action.⁴

In clinical situations where a HCP is using an order set, the number of options can mean the difference between use vs avoidance if the choices are overwhelming. HCPs process layers of detail every day when creating differential diagnoses and treatment plans. While that level of detail is necessary clinically, that same level of detail included in orders sets can create challenges for HCPs.

Figure 2 advances the order set in Figure 1 by providing a simpler and cleaner design, so HCPs can more easily review and process the information. This order set design minimizes the number of options available to help users make the right decision, focusing on value for the appropriate setting and audience. In other words, order sets should not be a “one size fits all” approach.

Order sets should be tailored to the appropriate clinical setting (eg, inpatient acute care, outpatient clinic setting, etc) and HCP (eg, hospitalist, tobacco cessation specialist, etc). We are comparing NRT order sets designed for HCPs who do not routinely prescribe oral tobacco cessation products in the inpatient setting. When possible, autogenerated bundle orders should also be used according to evidence-based recommendations (such as nicotine patch tapers) for ease of use and further simplification of order sets.

Finally, usability testing known as “evaluating a product or service by testing it with representative users” helps further refine an order set.⁵Usability testing should be applied during all phases of order set development with end user(s) as it helps identify problems with order set design prior to implementation. By applying usability testing, the order set becomes more meaningful and valued by the user.

Human Factors Engineering

HFE is “the study of all the factors that make it easier to do the work in the right way.”⁶ HFE seeks to identify, align, and apply processes for people and the world within which they live and work to promote safe and efficient practices, especially in relation to the technology and physical design features in their work environment.⁶

The average American adult makes about 35,000 decisions per day.⁷ Thus, there is potential for error at any moment. Design that does not take HFE into account can be dangerous. For example, when tube feed and IV line connectors look similar and are compatible, patients may inadvertently receive food administered directly into their bloodstream.⁸

HFE can and should be applied to order sets. Everything from the look, feel, and verbiage of an order set affects potential outcomes. For example, consider the impact even seemingly minor modifications can have on outcomes simply by guiding users in a different way: Figure 1 provides NRT options based on cigarette use per day, whereas Figure 2 conveys pack use per day in relation to the equivalent number of cigarettes used daily. These differences may seem small; however, it helps guide users to the right choice when considering that health care providers have been historically trained on social history gathering that emphasizes packs per day and pack-years.

Failure Mode and Effects Analysis

Failure mode and effects analysis (FMEA) is “a structured way to identify and address potential problems, or failures and their resulting effects on the system or process before an adverse event occurs.”⁹ The benefit of an order set must be weighed against the risk during development. FMEA should be applied during order set design to assess and limit risk just as with any other clinical care process.

FMEA examines both level of risk and frequency of risk occurrence associated with a new proposed process. For example, let’s evaluate an order set designed for pain control after surgery that consists of multiple high-risk opioids along with antihistamine medications for as-needed itch relief (a non-life-threatening adverse event (AE) of opioids well known by the medical community). An interdisciplinary FMEA team consisting of subject matter experts may examine how the process should flow in step-by-step detail and then discuss the benefit of a process and risk for potential error. A FMEA team would then analyze what could go wrong with each part of the process and assign a level of risk and risk frequency for various steps in the process, and then decide that certain steps should be modified or eliminated. Perhaps after FMEA, a facility might conclude that the risk of serious complications is high when you combine opioid use with antihistamine medications. The facility could decide to remove antihistamine medications from an order set if it is determined that risks outweigh benefits. While a root cause analysis might identify the cause of an AE after order set use, these situations can be prevented with FMEA.

When applying FMEA to Figure 1, while bupropion is known as an evidence-based oral tobacco cessation option, there is the possibility that bupropion could be inadvertently prescribed from the order set in a hospitalized patient with alcohol withdrawal and withdrawal seizure history. These potentially dangerous situations can be avoided with FMEA. Thus, although bupropion may be evidence-based for NRT, decisions regarding order set design using EBM alone are insufficient.

The practitioner must consider possible unintended consequences within order sets and target treatment options to the appropriate setting and audience. Although Figure 1 may appear to be more inclusive, the interdisciplinary committee designing the inpatient NRT order set felt there was heightened risk with introducing bupropion in Figure 1 and decided the risk would be lowered by removing bupropion from the redesigned NRT order set (Figure 2). In addition to the goal of balancing availability of NRT options with acceptable risk, Figure 2 also focused on building an NRT order set most applicable to the inpatient setting.

Including Evidence-Based Practices

EBM has become a routine part of clinical decision making. Therefore, including EBM in order set design is vital. EBM for NRT has demonstrated that combination therapy is more effective than is monotherapy to help tobacco users quit. Incremental doses of NRT are recommended for patients who use tobacco more frequently.¹⁰

As shown in Figures 1 and 2, both order set designs incorporate EBM for NRT. Although the importance of implementing EBM is evident, critical factors, such as HFE and FMEA make a difference with well-designed order sets.

Results

The 4-step order set design technique was used during development of an inpatient NRT order set at the JAHVH. Results for the inpatient Joint Commission Tobacco Treatment Measures were obtained from the Veterans Health Administration quality metric reporting system known as Strategic Analytics for Improvement and Learning (SAIL). SAIL performance measure outcomes, which include the inpatient Joint Commission Tobacco Treatment Measures, are derived from chart reviews conducted by the External Peer Review Program. Outcomes demonstrated that TOB-2 and TOB-3 (2 inpatient Joint Commission Tobacco Treatment Measures) known as tob20 and tob40, respectively, within SAIL improved by more than 300% after development of an NRT order set using the 4-step order set design framework along with implementation of a multifaceted tobacco cessation care delivery system at JAHVH.

Discussion

While the overall tobacco cessation care delivery system contributed to improved outcomes with the inpatient Joint Commission Tobacco Treatment Measures at JAHVH, the NRT order set was a cornerstone of the design. Although using our order set design technique does not necessarily guarantee successful outcomes, we believe using the 4-step order set design process increases the value of order sets and has potential to improve quality outcomes.

Limitations

Although improved outcomes following implementation of our NRT order set suggest correlation, causation cannot be proven. Also while the NRT order set is believed to have helped tremendously with outcomes, the entire tobacco cessation care delivery system at JAHVH contributed to the results. In addition, the inpatient Joint Commission Tobacco Treatment Measures help improve processes for tobacco cessation care. However, we are uncertain whether the results of our improvement efforts helped patients stop tobacco use. Further studies are needed to determine impact on population health. Finally, our results were based on improvement work done at a single center. Further studies are necessary to see whether results are reproducible.

Conclusion

There was significant improvement with the inpatient Joint Commission Tobacco Treatment Measures outcomes following development of a tobacco cessation care delivery system that included design of an inpatient NRT order set using a 4-step process we developed. This 4-step structure includes emphasis on efficiency and ease of use; human factors engineering; failure mode and effects analysis; and incorporation of evidence-based medicine (Box.) Postimplementation results showed improvement of the inpatient Joint Commission Tobacco Treatment Measures by greater than 3-fold at a single hospital.

The next steps for this initiative include testing the 4-step order set design process in multiple clinical settings to determine the effectiveness of this approach in other areas of clinical care.

In the current health care environment, hospitals are constantly challenged to improve quality metrics and deliver better health care outcomes. One means to achieving quality improvement is through the use of order sets, groups of related orders that a health care provider (HCP) can place with either a few keystrokes or mouse clicks.¹

Historically, design of order sets has largely focused on clicking checkboxes containing evidence-based practices. According to Bates and colleagues and the Institute for Safe Medication Practices, incorporating evidence-based medicine (EBM) into order sets is not by itself sufficient.^2,3Execution of proper design coupled with simplicity and provider efficiency is paramount to HCP buy-in, increased likelihood of order set adherence, and to potentially better outcomes.

In this article, we outline advancements in order set design. These improvements increase provider efficiency and ease of use; incorporate human factors engineering (HFE); apply failure mode and effects analysis; and include EBM.

Methods

An inpatient nicotine replacement therapy (NRT) order was developed as part of a multifaceted solution to improve tobacco cessation care at the James A. Haley Veterans’ Hospital (JAHVH) in Tampa, Florida, a complexity level 1a facility. This NRT order set used the 4-step order set design framework the authors’ developed (for additional information about the NRT order set, contact the authors). We distinguish order set design technique between 2 different inpatient NRT order sets. The first order set in the comparison (Figure 1) is an inpatient NRT order set of unknown origin—it is common for US Department of Veterans Affairs (VA) medical facilities to share order sets and other resources. The second order set (Figure 2) is an inpatient NRT order set we designed using our 4-step process for comparison in this article. No institutional review board approval was required as this work met criteria for operational improvement activities exempt from ethics review.

Justin Iannello, DO, MBA, was the team leader and developer of the 4-step order set design technique. The intervention team consisted of 4 internal medicine physicians with expertise in quality improvement and patient safety: 1 certified professional in patient safety and certified as a Lean Six Sigma Black Belt; 2 physicians certified as Lean Six Sigma Black Belts; and 1 physician certified as a Lean Six Sigma Green Belt. Two inpatient clinical pharmacists and 1 quality management specialist also were involved in its development.

Development of a new NRT order set was felt to be an integral part of the tobacco cessation care delivery process. An NRT order set perceived by users as value-added required a solution that merged EBM with standardization and applied quality improvement principles. The result was an approach to order set design that focused on 4 key questions: Is the order set efficient and easy to use/navigate? Is human factors engineering incorporated? Is failure mode and effects analysis applied? Are evidence-based practices included?

Ease of Use and Navigation

Implementing an order set that is efficient and easy to use or navigate seems straightforward but can be difficult to execute. Figure 1 shows many detailed options consisting of different combinations of nicotine patches, lozenges, and gum. Also included are oral tobacco cessation options (bupropion and varenicline). Although more options may seem better, confusion about appropriate medication selection can occur.

According to Heath and Heath, too many options can result in lack of action.⁴ For example, Heath and Heath discuss a food store that offered 6 free samples of different jams on one day and 24 jams the following day. The customers who sampled 6 different types of jam were 10 times more likely to buy jam. The authors concluded that the more options available, the more difficulty a potential buyer has in deciding on a course of action.⁴

In clinical situations where a HCP is using an order set, the number of options can mean the difference between use vs avoidance if the choices are overwhelming. HCPs process layers of detail every day when creating differential diagnoses and treatment plans. While that level of detail is necessary clinically, that same level of detail included in orders sets can create challenges for HCPs.

Figure 2 advances the order set in Figure 1 by providing a simpler and cleaner design, so HCPs can more easily review and process the information. This order set design minimizes the number of options available to help users make the right decision, focusing on value for the appropriate setting and audience. In other words, order sets should not be a “one size fits all” approach.

Order sets should be tailored to the appropriate clinical setting (eg, inpatient acute care, outpatient clinic setting, etc) and HCP (eg, hospitalist, tobacco cessation specialist, etc). We are comparing NRT order sets designed for HCPs who do not routinely prescribe oral tobacco cessation products in the inpatient setting. When possible, autogenerated bundle orders should also be used according to evidence-based recommendations (such as nicotine patch tapers) for ease of use and further simplification of order sets.

Finally, usability testing known as “evaluating a product or service by testing it with representative users” helps further refine an order set.⁵Usability testing should be applied during all phases of order set development with end user(s) as it helps identify problems with order set design prior to implementation. By applying usability testing, the order set becomes more meaningful and valued by the user.

Human Factors Engineering

HFE is “the study of all the factors that make it easier to do the work in the right way.”⁶ HFE seeks to identify, align, and apply processes for people and the world within which they live and work to promote safe and efficient practices, especially in relation to the technology and physical design features in their work environment.⁶

The average American adult makes about 35,000 decisions per day.⁷ Thus, there is potential for error at any moment. Design that does not take HFE into account can be dangerous. For example, when tube feed and IV line connectors look similar and are compatible, patients may inadvertently receive food administered directly into their bloodstream.⁸

HFE can and should be applied to order sets. Everything from the look, feel, and verbiage of an order set affects potential outcomes. For example, consider the impact even seemingly minor modifications can have on outcomes simply by guiding users in a different way: Figure 1 provides NRT options based on cigarette use per day, whereas Figure 2 conveys pack use per day in relation to the equivalent number of cigarettes used daily. These differences may seem small; however, it helps guide users to the right choice when considering that health care providers have been historically trained on social history gathering that emphasizes packs per day and pack-years.

Failure Mode and Effects Analysis

Failure mode and effects analysis (FMEA) is “a structured way to identify and address potential problems, or failures and their resulting effects on the system or process before an adverse event occurs.”⁹ The benefit of an order set must be weighed against the risk during development. FMEA should be applied during order set design to assess and limit risk just as with any other clinical care process.

FMEA examines both level of risk and frequency of risk occurrence associated with a new proposed process. For example, let’s evaluate an order set designed for pain control after surgery that consists of multiple high-risk opioids along with antihistamine medications for as-needed itch relief (a non-life-threatening adverse event (AE) of opioids well known by the medical community). An interdisciplinary FMEA team consisting of subject matter experts may examine how the process should flow in step-by-step detail and then discuss the benefit of a process and risk for potential error. A FMEA team would then analyze what could go wrong with each part of the process and assign a level of risk and risk frequency for various steps in the process, and then decide that certain steps should be modified or eliminated. Perhaps after FMEA, a facility might conclude that the risk of serious complications is high when you combine opioid use with antihistamine medications. The facility could decide to remove antihistamine medications from an order set if it is determined that risks outweigh benefits. While a root cause analysis might identify the cause of an AE after order set use, these situations can be prevented with FMEA.

When applying FMEA to Figure 1, while bupropion is known as an evidence-based oral tobacco cessation option, there is the possibility that bupropion could be inadvertently prescribed from the order set in a hospitalized patient with alcohol withdrawal and withdrawal seizure history. These potentially dangerous situations can be avoided with FMEA. Thus, although bupropion may be evidence-based for NRT, decisions regarding order set design using EBM alone are insufficient.

The practitioner must consider possible unintended consequences within order sets and target treatment options to the appropriate setting and audience. Although Figure 1 may appear to be more inclusive, the interdisciplinary committee designing the inpatient NRT order set felt there was heightened risk with introducing bupropion in Figure 1 and decided the risk would be lowered by removing bupropion from the redesigned NRT order set (Figure 2). In addition to the goal of balancing availability of NRT options with acceptable risk, Figure 2 also focused on building an NRT order set most applicable to the inpatient setting.

Including Evidence-Based Practices

EBM has become a routine part of clinical decision making. Therefore, including EBM in order set design is vital. EBM for NRT has demonstrated that combination therapy is more effective than is monotherapy to help tobacco users quit. Incremental doses of NRT are recommended for patients who use tobacco more frequently.¹⁰

As shown in Figures 1 and 2, both order set designs incorporate EBM for NRT. Although the importance of implementing EBM is evident, critical factors, such as HFE and FMEA make a difference with well-designed order sets.

Results

The 4-step order set design technique was used during development of an inpatient NRT order set at the JAHVH. Results for the inpatient Joint Commission Tobacco Treatment Measures were obtained from the Veterans Health Administration quality metric reporting system known as Strategic Analytics for Improvement and Learning (SAIL). SAIL performance measure outcomes, which include the inpatient Joint Commission Tobacco Treatment Measures, are derived from chart reviews conducted by the External Peer Review Program. Outcomes demonstrated that TOB-2 and TOB-3 (2 inpatient Joint Commission Tobacco Treatment Measures) known as tob20 and tob40, respectively, within SAIL improved by more than 300% after development of an NRT order set using the 4-step order set design framework along with implementation of a multifaceted tobacco cessation care delivery system at JAHVH.

Discussion

While the overall tobacco cessation care delivery system contributed to improved outcomes with the inpatient Joint Commission Tobacco Treatment Measures at JAHVH, the NRT order set was a cornerstone of the design. Although using our order set design technique does not necessarily guarantee successful outcomes, we believe using the 4-step order set design process increases the value of order sets and has potential to improve quality outcomes.

Limitations

Although improved outcomes following implementation of our NRT order set suggest correlation, causation cannot be proven. Also while the NRT order set is believed to have helped tremendously with outcomes, the entire tobacco cessation care delivery system at JAHVH contributed to the results. In addition, the inpatient Joint Commission Tobacco Treatment Measures help improve processes for tobacco cessation care. However, we are uncertain whether the results of our improvement efforts helped patients stop tobacco use. Further studies are needed to determine impact on population health. Finally, our results were based on improvement work done at a single center. Further studies are necessary to see whether results are reproducible.

Conclusion

There was significant improvement with the inpatient Joint Commission Tobacco Treatment Measures outcomes following development of a tobacco cessation care delivery system that included design of an inpatient NRT order set using a 4-step process we developed. This 4-step structure includes emphasis on efficiency and ease of use; human factors engineering; failure mode and effects analysis; and incorporation of evidence-based medicine (Box.) Postimplementation results showed improvement of the inpatient Joint Commission Tobacco Treatment Measures by greater than 3-fold at a single hospital.

The next steps for this initiative include testing the 4-step order set design process in multiple clinical settings to determine the effectiveness of this approach in other areas of clinical care.

"Extreme remedies are very appropriate for extreme diseases"
- Hippocrates Aphorisms

On March 5, 2019, the US Food and Drug Administration (FDA) approved a nasal spray formulation of the drug ketamine, an old anesthetic that has been put to a new use over the past 10 years as therapy for treatment-resistant severe depression. Ketamine, known on the street as Special K, has long been known to cause dissociation, hallucinations, and other hallucinogenic effects. In many randomized controlled trials, subanesthetic doses administered intravenously have demonstrated rapid and often dramatic relief of depressive symptoms.

Neuroscientists have heralded ketamine as the paradigm of the glutamatergic modifying drug class, which represents the first real breakthrough in the pharmaceutical treatment of depression in decades.¹ There have been 2 major pharmacologic limitations attached to this promising new treatment: the IV form of the drug and the short duration of its antidepressant effect. Pharmaceutical companies and neuroscientists predictably have been engaging in a fast and furious race to successfully overcome these obstacles, hoping to win fame and fortune and bring hope and help to the millions of patients who have failed to fully respond to or been unable to tolerate existing therapies for mood disorders.²

When the FDA approved Spravato (esketamine), a nasal administration of ketamine, many people hoped that researchers had succeeded in overcoming these barriers. The risks of serious adverse events (AEs) as well as the potential for abuse and diversion led the FDA to limit prescriptions under a Risk Evaluation and Mitigation Strategy (REMS).³ Patients self-administer the nasal spray but only in a certified medical facility under the observation of a health care practitioner. Patients also must agree to remain on site for 2 hours after administration of the drug to ensure their safety. The FDA recommends the drug be given twice a week for 4 weeks along with a conventional monoamine-acting antidepressant.When the US Department of Veterans Affairs (VA) cleared the way for use of esketamine, less than 2 weeks after the FDA approval, it also launched a series of controversies over how to use the drug in its massive health care system, which is the subject of this editorial. On March 19, 2019, the VA announced that VA practitioners would be able to prescribe the nasal spray for patients who were determined to have treatment-resistant depression but only after appropriate clinical assessment and in accordance with their patients’ preferences.

A number of controversies have emerged surrounding the VA adoption of esketamine, including its cost/benefit/risk ratio and who should be able to access the medication. Each of these issues has onion layers of political, regulatory, and ethical concerns that can only be superficially noted here and warrant fuller unpeeling. In June The New York Times featured a story alleging that in response to the tragic tide of ever-increasing veteran suicides, the VA sanctioned esketamine prescribing despite its cost and the serious questions experts raised about the data the FDA cited to establish its safety and efficacy. Although the cost to the VA of Spravato is unclear, it is much higher than generic IV ketamine.⁴

The access controversy is almost the ethical inverse of the first. In June 2019, a Veterans Health Administration advisory panel voted against allowing general use of esketamine, limiting it to individual cases of patients who are preapproved and have failed 2 antidepressant trials. Esketamine will not be on the VA formulary for widespread use. Congressional and public advocacy groups have noted that the formulary decision came in the wake of ongoing attention to the role of the pharmaceutical industry in the VA’s rapid adoption of the drug.^5,6 For the thousands of veterans for whom the data show conventional antidepressants even in combination with other psychotropic medications and evidence-based psychotherapies resulted in AEs or only partial remission of depression symptoms, the VA’s restriction will likely seem unfair and even uncaring.⁷

As a practicing VA psychiatrist, I know firsthand how desperately we need new, more effective, and better-tolerated treatments for severe unipolar and bipolar depression. Although I have not prescribed ketamine or esketamine, several of my most respected colleagues do. I have seen patients with chronic, severe, depression respond and even recover in ways that seem just a little short of miraculous when compared with other therapies. Yet as a longtime student of the history of psychiatry, I have also seen that often the treatments that initially seem so auspicious, in time, turn out to have a dark side. Families, communities, the country, VA, and the US Department of Defense and its practitioners in and out of mental health cannot in any moral universe abide by the fact that 20 plus men and women who served take their lives every day.⁸

As the epigraph to this column notes, we must often try radical therapies for grave cases in drastic crises. Yet we must also in making serious public health decisions fraught with unseen consequences take all due and considered diligence that we do not violate the even more fundamental dictum of the Hippocratic School, “at least do not harm.” That means trying to balance safety and availability while VA conducts its own research in a precarious way that leaves almost no stakeholder completely happy.

"Extreme remedies are very appropriate for extreme diseases"
- Hippocrates Aphorisms

On March 5, 2019, the US Food and Drug Administration (FDA) approved a nasal spray formulation of the drug ketamine, an old anesthetic that has been put to a new use over the past 10 years as therapy for treatment-resistant severe depression. Ketamine, known on the street as Special K, has long been known to cause dissociation, hallucinations, and other hallucinogenic effects. In many randomized controlled trials, subanesthetic doses administered intravenously have demonstrated rapid and often dramatic relief of depressive symptoms.

Neuroscientists have heralded ketamine as the paradigm of the glutamatergic modifying drug class, which represents the first real breakthrough in the pharmaceutical treatment of depression in decades.¹ There have been 2 major pharmacologic limitations attached to this promising new treatment: the IV form of the drug and the short duration of its antidepressant effect. Pharmaceutical companies and neuroscientists predictably have been engaging in a fast and furious race to successfully overcome these obstacles, hoping to win fame and fortune and bring hope and help to the millions of patients who have failed to fully respond to or been unable to tolerate existing therapies for mood disorders.²

When the FDA approved Spravato (esketamine), a nasal administration of ketamine, many people hoped that researchers had succeeded in overcoming these barriers. The risks of serious adverse events (AEs) as well as the potential for abuse and diversion led the FDA to limit prescriptions under a Risk Evaluation and Mitigation Strategy (REMS).³ Patients self-administer the nasal spray but only in a certified medical facility under the observation of a health care practitioner. Patients also must agree to remain on site for 2 hours after administration of the drug to ensure their safety. The FDA recommends the drug be given twice a week for 4 weeks along with a conventional monoamine-acting antidepressant.When the US Department of Veterans Affairs (VA) cleared the way for use of esketamine, less than 2 weeks after the FDA approval, it also launched a series of controversies over how to use the drug in its massive health care system, which is the subject of this editorial. On March 19, 2019, the VA announced that VA practitioners would be able to prescribe the nasal spray for patients who were determined to have treatment-resistant depression but only after appropriate clinical assessment and in accordance with their patients’ preferences.

A number of controversies have emerged surrounding the VA adoption of esketamine, including its cost/benefit/risk ratio and who should be able to access the medication. Each of these issues has onion layers of political, regulatory, and ethical concerns that can only be superficially noted here and warrant fuller unpeeling. In June The New York Times featured a story alleging that in response to the tragic tide of ever-increasing veteran suicides, the VA sanctioned esketamine prescribing despite its cost and the serious questions experts raised about the data the FDA cited to establish its safety and efficacy. Although the cost to the VA of Spravato is unclear, it is much higher than generic IV ketamine.⁴

The access controversy is almost the ethical inverse of the first. In June 2019, a Veterans Health Administration advisory panel voted against allowing general use of esketamine, limiting it to individual cases of patients who are preapproved and have failed 2 antidepressant trials. Esketamine will not be on the VA formulary for widespread use. Congressional and public advocacy groups have noted that the formulary decision came in the wake of ongoing attention to the role of the pharmaceutical industry in the VA’s rapid adoption of the drug.^5,6 For the thousands of veterans for whom the data show conventional antidepressants even in combination with other psychotropic medications and evidence-based psychotherapies resulted in AEs or only partial remission of depression symptoms, the VA’s restriction will likely seem unfair and even uncaring.⁷

As a practicing VA psychiatrist, I know firsthand how desperately we need new, more effective, and better-tolerated treatments for severe unipolar and bipolar depression. Although I have not prescribed ketamine or esketamine, several of my most respected colleagues do. I have seen patients with chronic, severe, depression respond and even recover in ways that seem just a little short of miraculous when compared with other therapies. Yet as a longtime student of the history of psychiatry, I have also seen that often the treatments that initially seem so auspicious, in time, turn out to have a dark side. Families, communities, the country, VA, and the US Department of Defense and its practitioners in and out of mental health cannot in any moral universe abide by the fact that 20 plus men and women who served take their lives every day.⁸

As the epigraph to this column notes, we must often try radical therapies for grave cases in drastic crises. Yet we must also in making serious public health decisions fraught with unseen consequences take all due and considered diligence that we do not violate the even more fundamental dictum of the Hippocratic School, “at least do not harm.” That means trying to balance safety and availability while VA conducts its own research in a precarious way that leaves almost no stakeholder completely happy.

"Extreme remedies are very appropriate for extreme diseases"
- Hippocrates Aphorisms

On March 5, 2019, the US Food and Drug Administration (FDA) approved a nasal spray formulation of the drug ketamine, an old anesthetic that has been put to a new use over the past 10 years as therapy for treatment-resistant severe depression. Ketamine, known on the street as Special K, has long been known to cause dissociation, hallucinations, and other hallucinogenic effects. In many randomized controlled trials, subanesthetic doses administered intravenously have demonstrated rapid and often dramatic relief of depressive symptoms.

Neuroscientists have heralded ketamine as the paradigm of the glutamatergic modifying drug class, which represents the first real breakthrough in the pharmaceutical treatment of depression in decades.¹ There have been 2 major pharmacologic limitations attached to this promising new treatment: the IV form of the drug and the short duration of its antidepressant effect. Pharmaceutical companies and neuroscientists predictably have been engaging in a fast and furious race to successfully overcome these obstacles, hoping to win fame and fortune and bring hope and help to the millions of patients who have failed to fully respond to or been unable to tolerate existing therapies for mood disorders.²

When the FDA approved Spravato (esketamine), a nasal administration of ketamine, many people hoped that researchers had succeeded in overcoming these barriers. The risks of serious adverse events (AEs) as well as the potential for abuse and diversion led the FDA to limit prescriptions under a Risk Evaluation and Mitigation Strategy (REMS).³ Patients self-administer the nasal spray but only in a certified medical facility under the observation of a health care practitioner. Patients also must agree to remain on site for 2 hours after administration of the drug to ensure their safety. The FDA recommends the drug be given twice a week for 4 weeks along with a conventional monoamine-acting antidepressant.When the US Department of Veterans Affairs (VA) cleared the way for use of esketamine, less than 2 weeks after the FDA approval, it also launched a series of controversies over how to use the drug in its massive health care system, which is the subject of this editorial. On March 19, 2019, the VA announced that VA practitioners would be able to prescribe the nasal spray for patients who were determined to have treatment-resistant depression but only after appropriate clinical assessment and in accordance with their patients’ preferences.

A number of controversies have emerged surrounding the VA adoption of esketamine, including its cost/benefit/risk ratio and who should be able to access the medication. Each of these issues has onion layers of political, regulatory, and ethical concerns that can only be superficially noted here and warrant fuller unpeeling. In June The New York Times featured a story alleging that in response to the tragic tide of ever-increasing veteran suicides, the VA sanctioned esketamine prescribing despite its cost and the serious questions experts raised about the data the FDA cited to establish its safety and efficacy. Although the cost to the VA of Spravato is unclear, it is much higher than generic IV ketamine.⁴

The access controversy is almost the ethical inverse of the first. In June 2019, a Veterans Health Administration advisory panel voted against allowing general use of esketamine, limiting it to individual cases of patients who are preapproved and have failed 2 antidepressant trials. Esketamine will not be on the VA formulary for widespread use. Congressional and public advocacy groups have noted that the formulary decision came in the wake of ongoing attention to the role of the pharmaceutical industry in the VA’s rapid adoption of the drug.^5,6 For the thousands of veterans for whom the data show conventional antidepressants even in combination with other psychotropic medications and evidence-based psychotherapies resulted in AEs or only partial remission of depression symptoms, the VA’s restriction will likely seem unfair and even uncaring.⁷

As a practicing VA psychiatrist, I know firsthand how desperately we need new, more effective, and better-tolerated treatments for severe unipolar and bipolar depression. Although I have not prescribed ketamine or esketamine, several of my most respected colleagues do. I have seen patients with chronic, severe, depression respond and even recover in ways that seem just a little short of miraculous when compared with other therapies. Yet as a longtime student of the history of psychiatry, I have also seen that often the treatments that initially seem so auspicious, in time, turn out to have a dark side. Families, communities, the country, VA, and the US Department of Defense and its practitioners in and out of mental health cannot in any moral universe abide by the fact that 20 plus men and women who served take their lives every day.⁸

As the epigraph to this column notes, we must often try radical therapies for grave cases in drastic crises. Yet we must also in making serious public health decisions fraught with unseen consequences take all due and considered diligence that we do not violate the even more fundamental dictum of the Hippocratic School, “at least do not harm.” That means trying to balance safety and availability while VA conducts its own research in a precarious way that leaves almost no stakeholder completely happy.

Artificial intelligence (AI), first described in 1956, encompasses the field of computer science in which machines are trained to learn from experience. The term was popularized by the 1956 Dartmouth College Summer Research Project on Artificial Intelligence.¹ The field of AI is rapidly growing and has the potential to affect many aspects of our lives. The emerging importance of AI is demonstrated by a February 2019 executive order that launched the American AI Initiative, allocating resources and funding for AI development.² The executive order stresses the potential impact of AI in the health care field, including its potential utility to diagnose disease. Federal agencies were directed to invest in AI research and development to promote rapid breakthroughs in AI technology that may impact multiple areas of society.

Machine learning (ML), a subset of AI, was defined in 1959 by Arthur Samuel and is achieved by employing mathematic models to compute sample data sets.³ Originating from statistical linear models, neural networks were conceived to accomplish these tasks.⁴ These pioneering scientific achievements led to recent developments of deep neural networks. These models are developed to recognize patterns and achieve complex computational tasks within a matter of minutes, often far exceeding human ability.⁵ ML can increase efficiency with decreased computation time, high precision, and recall when compared with that of human decision making.⁶

ML has the potential for numerous applications in the health care field.^7-9 One promising application is in the field of anatomic pathology. ML allows representative images to be used to train a computer to recognize patterns from labeled photographs. Based on a set of images selected to represent a specific tissue or disease process, the computer can be trained to evaluate and recognize new and unique images from patients and render a diagnosis.¹⁰ Prior to modern ML models, users would have to import many thousands of training images to produce algorithms that could recognize patterns with high accuracy. Modern ML algorithms allow for a model known as transfer learning, such that far fewer images are required for training.^11-13

Two novel ML platforms available for public use are offered through Google (Mountain View, CA) and Apple (Cupertino, CA).^14,15 They each offer a user-friendly interface with minimal experience required in computer science. Google AutoML uses ML via cloud services to store and retrieve data with ease. No coding knowledge is required. The Apple Create ML Module provides computer-based ML, requiring only a few lines of code.

The Veterans Health Administration (VHA) is the largest single health care system in the US, and nearly 50 000 cancer cases are diagnosed at the VHA annually.¹⁶ Cancers of the lung and colon are among the most common sources of invasive cancer and are the 2 most common causes of cancer deaths in America.¹⁶ We have previously reported using Apple ML in detecting non-small cell lung cancers (NSCLCs), including adenocarcinomas and squamous cell carcinomas (SCCs); and colon cancers with accuracy.^17,18 In the present study, we expand on these findings by comparing Apple and Google ML platforms in a variety of common pathologic scenarios in veteran patients. Using limited training data, both programs are compared for precision and recall in differentiating conditions involving lung and colon pathology.

In the first 4 experiments, we evaluated the ability of the platforms to differentiate normal lung tissue from cancerous lung tissue, to distinguish lung adenocarcinoma from SCC, and to differentiate colon adenocarcinoma from normal colon tissue. Next, cases of colon adenocarcinoma were assessed to determine whether the presence or absence of the KRAS proto-oncogene could be determined histologically using the AI platforms. KRAS is found in a variety of cancers, including about 40% of colon adenocarcinomas.¹⁹ For colon cancers, the presence or absence of the mutation in KRAS has important implications for patients as it determines whether the tumor will respond to specific chemotherapy agents.²⁰ The presence of the KRAS gene is currently determined by complex molecular testing of tumor tissue.²¹ However, we assessed the potential of ML to determine whether the mutation is present by computerized morphologic analysis alone. Our last experiment examined the ability of the Apple and Google platforms to differentiate between adenocarcinomas of lung origin vs colon origin. This has potential utility in determining the site of origin of metastatic carcinoma.²²

Methods

Fifty cases of lung SCC, 50 cases of lung adenocarcinoma, and 50 cases of colon adenocarcinoma were randomly retrieved from our molecular database. Twenty-five colon adenocarcinoma cases were positive for mutation in KRAS, while 25 cases were negative for mutation in KRAS. Seven hundred fifty total images of lung tissue (250 benign lung tissue, 250 lung adenocarcinomas, and 250 lung SCCs) and 500 total images of colon tissue (250 benign colon tissue and 250 colon adenocarcinoma) were obtained using a Leica Microscope MC190 HD Camera (Wetzlar, Germany) connected to an Olympus BX41 microscope (Center Valley, PA) and the Leica Acquire 9072 software for Apple computers. All the images were captured at a resolution of 1024 x 768 pixels using a 60x dry objective. Lung tissue images were captured and saved on a 2012 Apple MacBook Pro computer, and colon images were captured and saved on a 2011 Apple iMac computer. Both computers were running macOS v10.13.

Creating Image Classifier Models Using Apple Create ML

Apple Create ML is a suite of products that use various tools to create and train custom ML models on Apple computers.¹⁵ The suite contains many features, including image classification to train a ML model to classify images, natural language processing to classify natural language text, and tabular data to train models that deal with labeling information or estimating new quantities. We used Create ML Image Classification to create image classifier models for our project (Appendix A).

Creating ML Modules Using Google Cloud AutoML Vision Beta

Google Cloud AutoML is a suite of machine learning products, including AutoML Vision, AutoML Natural Language and AutoML Translation.¹⁴ All Cloud AutoML machine learning products were in beta version at the time of experimentation. We used Cloud AutoML Vision beta to create ML modules for our project. Unlike Apple Create ML, which is run on a local Apple computer, the Google Cloud AutoML is run online using a Google Cloud account. There are no minimum specifications requirements for the local computer since it is using the cloud-based architecture (Appendix B).

Experiment 1

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect and subclassify NSCLC based on the histopathologic images. We created 3 classes of images (250 images each): benign lung tissue, lung adenocarcinoma, and lung SCC.

Experiment 2

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between normal lung tissue and NSCLC histopathologic images with 50/50 mixture of lung adenocarcinoma and lung SCC. We created 2 classes of images (250 images each): benign lung tissue and lung NSCLC.

Experiment 3

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and lung SCC histopathologic images. We created 2 classes of images (250 images each): adenocarcinoma and SCC.

Experiment 4

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect colon cancer histopathologic images regardless of mutation in KRAS status. We created 2 classes of images (250 images each): benign colon tissue and colon adenocarcinoma.

Experiment 5

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between colon adenocarcinoma with mutations in KRAS and colon adenocarcinoma without the mutation in KRAS histopathologic images. We created 2 classes of images (125 images each): colon adenocarcinoma cases with mutation in KRAS and colon adenocarcinoma cases without the mutation in KRAS.

Experiment 6

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and colon adenocarcinoma histopathologic images. We created 2 classes of images (250 images each): colon adenocarcinoma lung adenocarcinoma.

Results

Twelve machine learning models were created in 6 experiments using the Apple Create ML and the Google AutoML (Table). To investigate recall and precision differences between the Apple and the Google ML algorithms, we performed 2-tailed distribution, paired t tests. No statistically significant differences were found (P = .52 for recall and .60 for precision).

Overall, each model performed well in distinguishing between normal and neoplastic tissue for both lung and colon cancers. In subclassifying NSCLC into adenocarcinoma and SCC, the models were shown to have high levels of precision and recall. The models also were successful in distinguishing between lung and colonic origin of adenocarcinoma (Figures 1-4). However, both systems had trouble discerning colon adenocarcinoma with mutations in KRAS from adenocarcinoma without mutations in KRAS.

Discussion

Image classifier models using ML algorithms hold a promising future to revolutionize the health care field. ML products, such as those modules offered by Apple and Google, are easy to use and have a simple graphic user interface to allow individuals to train models to perform humanlike tasks in real time. In our experiments, we compared multiple algorithms to determine their ability to differentiate and subclassify histopathologic images with high precision and recall using common scenarios in treating veteran patients.

Analysis of the results revealed high precision and recall values illustrating the models’ ability to differentiate and detect benign lung tissue from lung SCC and lung adenocarcinoma in ML model 1, benign lung from NSCLC carcinoma in ML model 2, and benign colon from colonic adenocarcinoma in ML model 4. In ML model 3 and 6, both ML algorithms performed at a high level to differentiate lung SCC from lung adenocarcinoma and lung adenocarcinoma from colonic adenocarcinoma, respectively. Of note, ML model 5 had the lowest precision and recall values across both algorithms demonstrating the models’ limited utility in predicting molecular profiles, such as mutations in KRAS as tested here. This is not surprising as pathologists currently require complex molecular tests to detect mutations in KRAS reliably in colon cancer.

Both modules require minimal programming experience and are easy to use. In our comparison, we demonstrated critical distinguishing characteristics that differentiate the 2 products.

Apple Create ML image classifier is available for use on local Mac computers that use Xcode version 10 and macOS 10.14 or later, with just 3 lines of code required to perform computations. Although this product is limited to Apple computers, it is free to use, and images are stored on the computer hard drive. Of unique significance on the Apple system platform, images can be augmented to alter their appearance to enhance model training. For example, imported images can be cropped, rotated, blurred, and flipped, in order to optimize the model’s training abilities to recognize test images and perform pattern recognition. This feature is not as readily available on the Google platform. Apple Create ML Image classifier’s default training set consists of 75% of total imported images with 5% of the total images being randomly used as a validation set. The remaining 20% of images comprise the testing set. The module’s computational analysis to train the model is achieved in about 2 minutes on average. The score threshold is set at 50% and cannot be manipulated for each image class as in Google AutoML Vision.

Google AutoML Vision is open and can be accessed from many devices. It stores images on remote Google servers but requires computing fees after a $300 credit for 12 months. On AutoML Vision, random 80% of the total images are used in the training set, 10% are used in the validation set, and 10% are used in the testing set. It is important to highlight the different percentages used in the default settings on the respective modules. The time to train the Google AutoML Vision with default computational power is longer on average than Apple Create ML, with about 8 minutes required to train the machine learning module. However, it is possible to choose more computational power for an additional fee and decrease module training time. The user will receive e-mail alerts when the computer time begins and is completed. The computation time is calculated by subtracting the time of the initial e-mail from the final e-mail.

Based on our calculations, we determined there was no significant difference between the 2 machine learning algorithms tested at the default settings with recall and precision values obtained. These findings demonstrate the promise of using a ML algorithm to assist in the performance of human tasks and behaviors, specifically the diagnosis of histopathologic images. These results have numerous potential uses in clinical medicine. ML algorithms have been successfully applied to diagnostic and prognostic endeavors in pathology,^23-28 dermatology,^29-31 ophthalmology,³² cardiology,³³ and radiology.^34-36

Pathologists often use additional tests, such as special staining of tissues or molecular tests, to assist with accurate classification of tumors. ML platforms offer the potential of an additional tool for pathologists to use along with human microscopic interpretation.^37,38 In addition, the number of pathologists in the US is dramatically decreasing, and many other countries have marked physician shortages, especially in fields of specialized training such as pathology.^39-42 These models could readily assist physicians in underserved countries and impact shortages of pathologists elsewhere by providing more specific diagnoses in an expedited manner.⁴³

Finally, although we have explored the application of these platforms in common cancer scenarios, great potential exists to use similar techniques in the detection of other conditions. These include the potential for classification and risk assessment of precancerous lesions, infectious processes in tissue (eg, detection of tuberculosis or malaria),^24,44 inflammatory conditions (eg, arthritis subtypes, gout),⁴⁵ blood disorders (eg, abnormal blood cell morphology),⁴⁶ and many others. The potential of these technologies to improve health care delivery to veteran patients seems to be limited only by the imagination of the user.⁴⁷

Regarding the limited effectiveness in determining the presence or absence of mutations in KRAS for colon adenocarcinoma, it is mentioned that currently pathologists rely on complex molecular tests to detect the mutations at the DNA level.²¹ It is possible that the use of more extensive training data sets may improve recall and precision in cases such as these and warrants further study. Our experiments were limited to the stipulations placed by the free trial software agreements; no costs were expended to use the algorithms, though an Apple computer was required.

Conclusion

We have demonstrated the successful application of 2 readily available ML platforms in providing diagnostic guidance in differentiation between common cancer conditions in veteran patient populations. Although both platforms performed very well with no statistically significant differences in results, some distinctions are worth noting. Apple Create ML can be used on local computers but is limited to an Apple operating system. Google AutoML is not platform-specific but runs only via Google Cloud with associated computational fees. Using these readily available models, we demonstrated the vast potential of AI in diagnostic pathology. The application of AI to clinical medicine remains in the very early stages. The VA is uniquely poised to provide leadership as AI technologies will continue to dramatically change the future of health care, both in veteran and nonveteran patients nationwide.

Acknowledgments

The authors thank Paul Borkowski for his constructive criticism and proofreading of this manuscript. This material is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.

Artificial intelligence (AI), first described in 1956, encompasses the field of computer science in which machines are trained to learn from experience. The term was popularized by the 1956 Dartmouth College Summer Research Project on Artificial Intelligence.¹ The field of AI is rapidly growing and has the potential to affect many aspects of our lives. The emerging importance of AI is demonstrated by a February 2019 executive order that launched the American AI Initiative, allocating resources and funding for AI development.² The executive order stresses the potential impact of AI in the health care field, including its potential utility to diagnose disease. Federal agencies were directed to invest in AI research and development to promote rapid breakthroughs in AI technology that may impact multiple areas of society.

Machine learning (ML), a subset of AI, was defined in 1959 by Arthur Samuel and is achieved by employing mathematic models to compute sample data sets.³ Originating from statistical linear models, neural networks were conceived to accomplish these tasks.⁴ These pioneering scientific achievements led to recent developments of deep neural networks. These models are developed to recognize patterns and achieve complex computational tasks within a matter of minutes, often far exceeding human ability.⁵ ML can increase efficiency with decreased computation time, high precision, and recall when compared with that of human decision making.⁶

ML has the potential for numerous applications in the health care field.^7-9 One promising application is in the field of anatomic pathology. ML allows representative images to be used to train a computer to recognize patterns from labeled photographs. Based on a set of images selected to represent a specific tissue or disease process, the computer can be trained to evaluate and recognize new and unique images from patients and render a diagnosis.¹⁰ Prior to modern ML models, users would have to import many thousands of training images to produce algorithms that could recognize patterns with high accuracy. Modern ML algorithms allow for a model known as transfer learning, such that far fewer images are required for training.^11-13

Two novel ML platforms available for public use are offered through Google (Mountain View, CA) and Apple (Cupertino, CA).^14,15 They each offer a user-friendly interface with minimal experience required in computer science. Google AutoML uses ML via cloud services to store and retrieve data with ease. No coding knowledge is required. The Apple Create ML Module provides computer-based ML, requiring only a few lines of code.

The Veterans Health Administration (VHA) is the largest single health care system in the US, and nearly 50 000 cancer cases are diagnosed at the VHA annually.¹⁶ Cancers of the lung and colon are among the most common sources of invasive cancer and are the 2 most common causes of cancer deaths in America.¹⁶ We have previously reported using Apple ML in detecting non-small cell lung cancers (NSCLCs), including adenocarcinomas and squamous cell carcinomas (SCCs); and colon cancers with accuracy.^17,18 In the present study, we expand on these findings by comparing Apple and Google ML platforms in a variety of common pathologic scenarios in veteran patients. Using limited training data, both programs are compared for precision and recall in differentiating conditions involving lung and colon pathology.

In the first 4 experiments, we evaluated the ability of the platforms to differentiate normal lung tissue from cancerous lung tissue, to distinguish lung adenocarcinoma from SCC, and to differentiate colon adenocarcinoma from normal colon tissue. Next, cases of colon adenocarcinoma were assessed to determine whether the presence or absence of the KRAS proto-oncogene could be determined histologically using the AI platforms. KRAS is found in a variety of cancers, including about 40% of colon adenocarcinomas.¹⁹ For colon cancers, the presence or absence of the mutation in KRAS has important implications for patients as it determines whether the tumor will respond to specific chemotherapy agents.²⁰ The presence of the KRAS gene is currently determined by complex molecular testing of tumor tissue.²¹ However, we assessed the potential of ML to determine whether the mutation is present by computerized morphologic analysis alone. Our last experiment examined the ability of the Apple and Google platforms to differentiate between adenocarcinomas of lung origin vs colon origin. This has potential utility in determining the site of origin of metastatic carcinoma.²²

Methods

Fifty cases of lung SCC, 50 cases of lung adenocarcinoma, and 50 cases of colon adenocarcinoma were randomly retrieved from our molecular database. Twenty-five colon adenocarcinoma cases were positive for mutation in KRAS, while 25 cases were negative for mutation in KRAS. Seven hundred fifty total images of lung tissue (250 benign lung tissue, 250 lung adenocarcinomas, and 250 lung SCCs) and 500 total images of colon tissue (250 benign colon tissue and 250 colon adenocarcinoma) were obtained using a Leica Microscope MC190 HD Camera (Wetzlar, Germany) connected to an Olympus BX41 microscope (Center Valley, PA) and the Leica Acquire 9072 software for Apple computers. All the images were captured at a resolution of 1024 x 768 pixels using a 60x dry objective. Lung tissue images were captured and saved on a 2012 Apple MacBook Pro computer, and colon images were captured and saved on a 2011 Apple iMac computer. Both computers were running macOS v10.13.

Creating Image Classifier Models Using Apple Create ML

Apple Create ML is a suite of products that use various tools to create and train custom ML models on Apple computers.¹⁵ The suite contains many features, including image classification to train a ML model to classify images, natural language processing to classify natural language text, and tabular data to train models that deal with labeling information or estimating new quantities. We used Create ML Image Classification to create image classifier models for our project (Appendix A).

Creating ML Modules Using Google Cloud AutoML Vision Beta

Google Cloud AutoML is a suite of machine learning products, including AutoML Vision, AutoML Natural Language and AutoML Translation.¹⁴ All Cloud AutoML machine learning products were in beta version at the time of experimentation. We used Cloud AutoML Vision beta to create ML modules for our project. Unlike Apple Create ML, which is run on a local Apple computer, the Google Cloud AutoML is run online using a Google Cloud account. There are no minimum specifications requirements for the local computer since it is using the cloud-based architecture (Appendix B).

Experiment 1

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect and subclassify NSCLC based on the histopathologic images. We created 3 classes of images (250 images each): benign lung tissue, lung adenocarcinoma, and lung SCC.

Experiment 2

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between normal lung tissue and NSCLC histopathologic images with 50/50 mixture of lung adenocarcinoma and lung SCC. We created 2 classes of images (250 images each): benign lung tissue and lung NSCLC.

Experiment 3

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and lung SCC histopathologic images. We created 2 classes of images (250 images each): adenocarcinoma and SCC.

Experiment 4

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect colon cancer histopathologic images regardless of mutation in KRAS status. We created 2 classes of images (250 images each): benign colon tissue and colon adenocarcinoma.

Experiment 5

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between colon adenocarcinoma with mutations in KRAS and colon adenocarcinoma without the mutation in KRAS histopathologic images. We created 2 classes of images (125 images each): colon adenocarcinoma cases with mutation in KRAS and colon adenocarcinoma cases without the mutation in KRAS.

Experiment 6

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and colon adenocarcinoma histopathologic images. We created 2 classes of images (250 images each): colon adenocarcinoma lung adenocarcinoma.

Results

Twelve machine learning models were created in 6 experiments using the Apple Create ML and the Google AutoML (Table). To investigate recall and precision differences between the Apple and the Google ML algorithms, we performed 2-tailed distribution, paired t tests. No statistically significant differences were found (P = .52 for recall and .60 for precision).

Overall, each model performed well in distinguishing between normal and neoplastic tissue for both lung and colon cancers. In subclassifying NSCLC into adenocarcinoma and SCC, the models were shown to have high levels of precision and recall. The models also were successful in distinguishing between lung and colonic origin of adenocarcinoma (Figures 1-4). However, both systems had trouble discerning colon adenocarcinoma with mutations in KRAS from adenocarcinoma without mutations in KRAS.

Discussion

Image classifier models using ML algorithms hold a promising future to revolutionize the health care field. ML products, such as those modules offered by Apple and Google, are easy to use and have a simple graphic user interface to allow individuals to train models to perform humanlike tasks in real time. In our experiments, we compared multiple algorithms to determine their ability to differentiate and subclassify histopathologic images with high precision and recall using common scenarios in treating veteran patients.

Analysis of the results revealed high precision and recall values illustrating the models’ ability to differentiate and detect benign lung tissue from lung SCC and lung adenocarcinoma in ML model 1, benign lung from NSCLC carcinoma in ML model 2, and benign colon from colonic adenocarcinoma in ML model 4. In ML model 3 and 6, both ML algorithms performed at a high level to differentiate lung SCC from lung adenocarcinoma and lung adenocarcinoma from colonic adenocarcinoma, respectively. Of note, ML model 5 had the lowest precision and recall values across both algorithms demonstrating the models’ limited utility in predicting molecular profiles, such as mutations in KRAS as tested here. This is not surprising as pathologists currently require complex molecular tests to detect mutations in KRAS reliably in colon cancer.

Both modules require minimal programming experience and are easy to use. In our comparison, we demonstrated critical distinguishing characteristics that differentiate the 2 products.

Apple Create ML image classifier is available for use on local Mac computers that use Xcode version 10 and macOS 10.14 or later, with just 3 lines of code required to perform computations. Although this product is limited to Apple computers, it is free to use, and images are stored on the computer hard drive. Of unique significance on the Apple system platform, images can be augmented to alter their appearance to enhance model training. For example, imported images can be cropped, rotated, blurred, and flipped, in order to optimize the model’s training abilities to recognize test images and perform pattern recognition. This feature is not as readily available on the Google platform. Apple Create ML Image classifier’s default training set consists of 75% of total imported images with 5% of the total images being randomly used as a validation set. The remaining 20% of images comprise the testing set. The module’s computational analysis to train the model is achieved in about 2 minutes on average. The score threshold is set at 50% and cannot be manipulated for each image class as in Google AutoML Vision.

Google AutoML Vision is open and can be accessed from many devices. It stores images on remote Google servers but requires computing fees after a $300 credit for 12 months. On AutoML Vision, random 80% of the total images are used in the training set, 10% are used in the validation set, and 10% are used in the testing set. It is important to highlight the different percentages used in the default settings on the respective modules. The time to train the Google AutoML Vision with default computational power is longer on average than Apple Create ML, with about 8 minutes required to train the machine learning module. However, it is possible to choose more computational power for an additional fee and decrease module training time. The user will receive e-mail alerts when the computer time begins and is completed. The computation time is calculated by subtracting the time of the initial e-mail from the final e-mail.

Based on our calculations, we determined there was no significant difference between the 2 machine learning algorithms tested at the default settings with recall and precision values obtained. These findings demonstrate the promise of using a ML algorithm to assist in the performance of human tasks and behaviors, specifically the diagnosis of histopathologic images. These results have numerous potential uses in clinical medicine. ML algorithms have been successfully applied to diagnostic and prognostic endeavors in pathology,^23-28 dermatology,^29-31 ophthalmology,³² cardiology,³³ and radiology.^34-36

Pathologists often use additional tests, such as special staining of tissues or molecular tests, to assist with accurate classification of tumors. ML platforms offer the potential of an additional tool for pathologists to use along with human microscopic interpretation.^37,38 In addition, the number of pathologists in the US is dramatically decreasing, and many other countries have marked physician shortages, especially in fields of specialized training such as pathology.^39-42 These models could readily assist physicians in underserved countries and impact shortages of pathologists elsewhere by providing more specific diagnoses in an expedited manner.⁴³

Finally, although we have explored the application of these platforms in common cancer scenarios, great potential exists to use similar techniques in the detection of other conditions. These include the potential for classification and risk assessment of precancerous lesions, infectious processes in tissue (eg, detection of tuberculosis or malaria),^24,44 inflammatory conditions (eg, arthritis subtypes, gout),⁴⁵ blood disorders (eg, abnormal blood cell morphology),⁴⁶ and many others. The potential of these technologies to improve health care delivery to veteran patients seems to be limited only by the imagination of the user.⁴⁷

Regarding the limited effectiveness in determining the presence or absence of mutations in KRAS for colon adenocarcinoma, it is mentioned that currently pathologists rely on complex molecular tests to detect the mutations at the DNA level.²¹ It is possible that the use of more extensive training data sets may improve recall and precision in cases such as these and warrants further study. Our experiments were limited to the stipulations placed by the free trial software agreements; no costs were expended to use the algorithms, though an Apple computer was required.

Conclusion

We have demonstrated the successful application of 2 readily available ML platforms in providing diagnostic guidance in differentiation between common cancer conditions in veteran patient populations. Although both platforms performed very well with no statistically significant differences in results, some distinctions are worth noting. Apple Create ML can be used on local computers but is limited to an Apple operating system. Google AutoML is not platform-specific but runs only via Google Cloud with associated computational fees. Using these readily available models, we demonstrated the vast potential of AI in diagnostic pathology. The application of AI to clinical medicine remains in the very early stages. The VA is uniquely poised to provide leadership as AI technologies will continue to dramatically change the future of health care, both in veteran and nonveteran patients nationwide.

Acknowledgments

The authors thank Paul Borkowski for his constructive criticism and proofreading of this manuscript. This material is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.

Artificial intelligence (AI), first described in 1956, encompasses the field of computer science in which machines are trained to learn from experience. The term was popularized by the 1956 Dartmouth College Summer Research Project on Artificial Intelligence.¹ The field of AI is rapidly growing and has the potential to affect many aspects of our lives. The emerging importance of AI is demonstrated by a February 2019 executive order that launched the American AI Initiative, allocating resources and funding for AI development.² The executive order stresses the potential impact of AI in the health care field, including its potential utility to diagnose disease. Federal agencies were directed to invest in AI research and development to promote rapid breakthroughs in AI technology that may impact multiple areas of society.

Machine learning (ML), a subset of AI, was defined in 1959 by Arthur Samuel and is achieved by employing mathematic models to compute sample data sets.³ Originating from statistical linear models, neural networks were conceived to accomplish these tasks.⁴ These pioneering scientific achievements led to recent developments of deep neural networks. These models are developed to recognize patterns and achieve complex computational tasks within a matter of minutes, often far exceeding human ability.⁵ ML can increase efficiency with decreased computation time, high precision, and recall when compared with that of human decision making.⁶

ML has the potential for numerous applications in the health care field.^7-9 One promising application is in the field of anatomic pathology. ML allows representative images to be used to train a computer to recognize patterns from labeled photographs. Based on a set of images selected to represent a specific tissue or disease process, the computer can be trained to evaluate and recognize new and unique images from patients and render a diagnosis.¹⁰ Prior to modern ML models, users would have to import many thousands of training images to produce algorithms that could recognize patterns with high accuracy. Modern ML algorithms allow for a model known as transfer learning, such that far fewer images are required for training.^11-13

Two novel ML platforms available for public use are offered through Google (Mountain View, CA) and Apple (Cupertino, CA).^14,15 They each offer a user-friendly interface with minimal experience required in computer science. Google AutoML uses ML via cloud services to store and retrieve data with ease. No coding knowledge is required. The Apple Create ML Module provides computer-based ML, requiring only a few lines of code.

The Veterans Health Administration (VHA) is the largest single health care system in the US, and nearly 50 000 cancer cases are diagnosed at the VHA annually.¹⁶ Cancers of the lung and colon are among the most common sources of invasive cancer and are the 2 most common causes of cancer deaths in America.¹⁶ We have previously reported using Apple ML in detecting non-small cell lung cancers (NSCLCs), including adenocarcinomas and squamous cell carcinomas (SCCs); and colon cancers with accuracy.^17,18 In the present study, we expand on these findings by comparing Apple and Google ML platforms in a variety of common pathologic scenarios in veteran patients. Using limited training data, both programs are compared for precision and recall in differentiating conditions involving lung and colon pathology.

In the first 4 experiments, we evaluated the ability of the platforms to differentiate normal lung tissue from cancerous lung tissue, to distinguish lung adenocarcinoma from SCC, and to differentiate colon adenocarcinoma from normal colon tissue. Next, cases of colon adenocarcinoma were assessed to determine whether the presence or absence of the KRAS proto-oncogene could be determined histologically using the AI platforms. KRAS is found in a variety of cancers, including about 40% of colon adenocarcinomas.¹⁹ For colon cancers, the presence or absence of the mutation in KRAS has important implications for patients as it determines whether the tumor will respond to specific chemotherapy agents.²⁰ The presence of the KRAS gene is currently determined by complex molecular testing of tumor tissue.²¹ However, we assessed the potential of ML to determine whether the mutation is present by computerized morphologic analysis alone. Our last experiment examined the ability of the Apple and Google platforms to differentiate between adenocarcinomas of lung origin vs colon origin. This has potential utility in determining the site of origin of metastatic carcinoma.²²

Methods

Fifty cases of lung SCC, 50 cases of lung adenocarcinoma, and 50 cases of colon adenocarcinoma were randomly retrieved from our molecular database. Twenty-five colon adenocarcinoma cases were positive for mutation in KRAS, while 25 cases were negative for mutation in KRAS. Seven hundred fifty total images of lung tissue (250 benign lung tissue, 250 lung adenocarcinomas, and 250 lung SCCs) and 500 total images of colon tissue (250 benign colon tissue and 250 colon adenocarcinoma) were obtained using a Leica Microscope MC190 HD Camera (Wetzlar, Germany) connected to an Olympus BX41 microscope (Center Valley, PA) and the Leica Acquire 9072 software for Apple computers. All the images were captured at a resolution of 1024 x 768 pixels using a 60x dry objective. Lung tissue images were captured and saved on a 2012 Apple MacBook Pro computer, and colon images were captured and saved on a 2011 Apple iMac computer. Both computers were running macOS v10.13.

Creating Image Classifier Models Using Apple Create ML

Apple Create ML is a suite of products that use various tools to create and train custom ML models on Apple computers.¹⁵ The suite contains many features, including image classification to train a ML model to classify images, natural language processing to classify natural language text, and tabular data to train models that deal with labeling information or estimating new quantities. We used Create ML Image Classification to create image classifier models for our project (Appendix A).

Creating ML Modules Using Google Cloud AutoML Vision Beta

Google Cloud AutoML is a suite of machine learning products, including AutoML Vision, AutoML Natural Language and AutoML Translation.¹⁴ All Cloud AutoML machine learning products were in beta version at the time of experimentation. We used Cloud AutoML Vision beta to create ML modules for our project. Unlike Apple Create ML, which is run on a local Apple computer, the Google Cloud AutoML is run online using a Google Cloud account. There are no minimum specifications requirements for the local computer since it is using the cloud-based architecture (Appendix B).

Experiment 1

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect and subclassify NSCLC based on the histopathologic images. We created 3 classes of images (250 images each): benign lung tissue, lung adenocarcinoma, and lung SCC.

Experiment 2

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between normal lung tissue and NSCLC histopathologic images with 50/50 mixture of lung adenocarcinoma and lung SCC. We created 2 classes of images (250 images each): benign lung tissue and lung NSCLC.

Experiment 3

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and lung SCC histopathologic images. We created 2 classes of images (250 images each): adenocarcinoma and SCC.

Experiment 4

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to detect colon cancer histopathologic images regardless of mutation in KRAS status. We created 2 classes of images (250 images each): benign colon tissue and colon adenocarcinoma.

Experiment 5

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between colon adenocarcinoma with mutations in KRAS and colon adenocarcinoma without the mutation in KRAS histopathologic images. We created 2 classes of images (125 images each): colon adenocarcinoma cases with mutation in KRAS and colon adenocarcinoma cases without the mutation in KRAS.

Experiment 6

We compared Apple Create ML Image Classifier and Google AutoML Vision in their ability to differentiate between lung adenocarcinoma and colon adenocarcinoma histopathologic images. We created 2 classes of images (250 images each): colon adenocarcinoma lung adenocarcinoma.

Results

Twelve machine learning models were created in 6 experiments using the Apple Create ML and the Google AutoML (Table). To investigate recall and precision differences between the Apple and the Google ML algorithms, we performed 2-tailed distribution, paired t tests. No statistically significant differences were found (P = .52 for recall and .60 for precision).

Overall, each model performed well in distinguishing between normal and neoplastic tissue for both lung and colon cancers. In subclassifying NSCLC into adenocarcinoma and SCC, the models were shown to have high levels of precision and recall. The models also were successful in distinguishing between lung and colonic origin of adenocarcinoma (Figures 1-4). However, both systems had trouble discerning colon adenocarcinoma with mutations in KRAS from adenocarcinoma without mutations in KRAS.

Discussion

Image classifier models using ML algorithms hold a promising future to revolutionize the health care field. ML products, such as those modules offered by Apple and Google, are easy to use and have a simple graphic user interface to allow individuals to train models to perform humanlike tasks in real time. In our experiments, we compared multiple algorithms to determine their ability to differentiate and subclassify histopathologic images with high precision and recall using common scenarios in treating veteran patients.

Analysis of the results revealed high precision and recall values illustrating the models’ ability to differentiate and detect benign lung tissue from lung SCC and lung adenocarcinoma in ML model 1, benign lung from NSCLC carcinoma in ML model 2, and benign colon from colonic adenocarcinoma in ML model 4. In ML model 3 and 6, both ML algorithms performed at a high level to differentiate lung SCC from lung adenocarcinoma and lung adenocarcinoma from colonic adenocarcinoma, respectively. Of note, ML model 5 had the lowest precision and recall values across both algorithms demonstrating the models’ limited utility in predicting molecular profiles, such as mutations in KRAS as tested here. This is not surprising as pathologists currently require complex molecular tests to detect mutations in KRAS reliably in colon cancer.

Both modules require minimal programming experience and are easy to use. In our comparison, we demonstrated critical distinguishing characteristics that differentiate the 2 products.

Apple Create ML image classifier is available for use on local Mac computers that use Xcode version 10 and macOS 10.14 or later, with just 3 lines of code required to perform computations. Although this product is limited to Apple computers, it is free to use, and images are stored on the computer hard drive. Of unique significance on the Apple system platform, images can be augmented to alter their appearance to enhance model training. For example, imported images can be cropped, rotated, blurred, and flipped, in order to optimize the model’s training abilities to recognize test images and perform pattern recognition. This feature is not as readily available on the Google platform. Apple Create ML Image classifier’s default training set consists of 75% of total imported images with 5% of the total images being randomly used as a validation set. The remaining 20% of images comprise the testing set. The module’s computational analysis to train the model is achieved in about 2 minutes on average. The score threshold is set at 50% and cannot be manipulated for each image class as in Google AutoML Vision.

Google AutoML Vision is open and can be accessed from many devices. It stores images on remote Google servers but requires computing fees after a $300 credit for 12 months. On AutoML Vision, random 80% of the total images are used in the training set, 10% are used in the validation set, and 10% are used in the testing set. It is important to highlight the different percentages used in the default settings on the respective modules. The time to train the Google AutoML Vision with default computational power is longer on average than Apple Create ML, with about 8 minutes required to train the machine learning module. However, it is possible to choose more computational power for an additional fee and decrease module training time. The user will receive e-mail alerts when the computer time begins and is completed. The computation time is calculated by subtracting the time of the initial e-mail from the final e-mail.

Based on our calculations, we determined there was no significant difference between the 2 machine learning algorithms tested at the default settings with recall and precision values obtained. These findings demonstrate the promise of using a ML algorithm to assist in the performance of human tasks and behaviors, specifically the diagnosis of histopathologic images. These results have numerous potential uses in clinical medicine. ML algorithms have been successfully applied to diagnostic and prognostic endeavors in pathology,^23-28 dermatology,^29-31 ophthalmology,³² cardiology,³³ and radiology.^34-36

Pathologists often use additional tests, such as special staining of tissues or molecular tests, to assist with accurate classification of tumors. ML platforms offer the potential of an additional tool for pathologists to use along with human microscopic interpretation.^37,38 In addition, the number of pathologists in the US is dramatically decreasing, and many other countries have marked physician shortages, especially in fields of specialized training such as pathology.^39-42 These models could readily assist physicians in underserved countries and impact shortages of pathologists elsewhere by providing more specific diagnoses in an expedited manner.⁴³

Finally, although we have explored the application of these platforms in common cancer scenarios, great potential exists to use similar techniques in the detection of other conditions. These include the potential for classification and risk assessment of precancerous lesions, infectious processes in tissue (eg, detection of tuberculosis or malaria),^24,44 inflammatory conditions (eg, arthritis subtypes, gout),⁴⁵ blood disorders (eg, abnormal blood cell morphology),⁴⁶ and many others. The potential of these technologies to improve health care delivery to veteran patients seems to be limited only by the imagination of the user.⁴⁷

Regarding the limited effectiveness in determining the presence or absence of mutations in KRAS for colon adenocarcinoma, it is mentioned that currently pathologists rely on complex molecular tests to detect the mutations at the DNA level.²¹ It is possible that the use of more extensive training data sets may improve recall and precision in cases such as these and warrants further study. Our experiments were limited to the stipulations placed by the free trial software agreements; no costs were expended to use the algorithms, though an Apple computer was required.

Conclusion

We have demonstrated the successful application of 2 readily available ML platforms in providing diagnostic guidance in differentiation between common cancer conditions in veteran patient populations. Although both platforms performed very well with no statistically significant differences in results, some distinctions are worth noting. Apple Create ML can be used on local computers but is limited to an Apple operating system. Google AutoML is not platform-specific but runs only via Google Cloud with associated computational fees. Using these readily available models, we demonstrated the vast potential of AI in diagnostic pathology. The application of AI to clinical medicine remains in the very early stages. The VA is uniquely poised to provide leadership as AI technologies will continue to dramatically change the future of health care, both in veteran and nonveteran patients nationwide.

Acknowledgments

The authors thank Paul Borkowski for his constructive criticism and proofreading of this manuscript. This material is the result of work supported with resources and the use of facilities at the James A. Haley Veterans’ Hospital.

Physician burnout has been labeled a public health crisis by the Harvard School of Public Health and other institutions.¹ A 2018 Physician’s Foundation survey found that 78% of physicians had symptoms of burnout,² which result from chronic workplace stress and include feeling depleted of energy or exhausted, mentally distanced from or cynical about one’s job, and problems getting one’s job done successfully.³ Among ObGyns, almost half (46%) report burnout.⁴ One-third of ObGyns responded on a recent Medscape Burnout Report that the computerization of practice is contributing to their burnout, and 54% said too many bureaucratic tasks, including charting, were adding to their burnout.⁵

Inefficient electronic medical records (EMRs) have been implicated as one reason for burnout, with improvements in efficiency cited as one of several potential resolutions to the problem. About 96% of hospitals have adopted EMRs today, compared with only 9% in 2008,⁶ and many physicians report recognizing value in the technology. For instance, 60% of participants in Stanford Medicine’s 2018 National Physician Poll said EMRs had led to improved patient care. At the same time, however, about as many (59%) said EMRs needed a “complete overhaul” and that the systems had detracted from their professional satisfaction (54%) as well as from their clinical effectiveness (49%).⁷

With this roundtable, we explore the concerns with hours spent on the EMR with several experts, and whether it is a problem that has been contributing to burnout among staff at their institutions. In addition, are there solutions that their institutions have implemented that they can share to help to cope with the problem?
 

OBG Management: ObGyns report that the computerization of practice and too many bureaucratic tasks, including charting, are contributing to burnout. Do you see this problem at your institution?

John J. Dougherty, MD, MBA: Yes, absolutely. There is not a day that goes by that I don’t hear about or experience “Epic Fails.” (We use Epic’s EMR product at our institution.) Too many clicks are needed to navigate even the simplest tasks—finding notes or results, documenting visits, and billing for services are all unnecessarily complex. In addition, we are being held accountable for achieving a long and growing list of “metrics” measures, education projects (HealthStream), and productivity goals. When do we have time to treat patients? And it is not just practicing physicians and clinicians. Our resident physicians spend an inordinate amount of time in front of the computer documenting, placing orders, and transferring patients using a system with a very inefficient user interface, to say the least.

Megan L. Evans, MD, MPH: I absolutely agree. Over the years, my institution has created a conglomerate of EMRs, requiring physicians across the hospital to be fluent in a multitude of systems. For example, you finish your clinic notes in one system, sign off on discharge summaries in another, and complete your operative notes in an entirely different system. As busy attendings, it is hard to keep ahead of all of these tasks, especially when the systems do not talk to one another. Fortunately, my hospital is changing our EMR to a single system within the next year. Until then, however, we will work in this piecemeal system.

Mark Woodland, MS, MD: EMR and computerization of medicine is the number 1 issue relating to dissatisfaction by ObGyn providers in our institution. Providers are earnest in their attempt to be compliant with EMR requirements, but the reality is that they are dealing with an automated system that does not have realistic expectations for management of results, follow-up tasks, and patient communications for a human provider. The actual charting, ordering of tests and consults, and communication between providers has been enhanced. However, the “in-basket” of tasks to be accomplished are extraordinary and much of it relies on the provider, which requires an inordinate amount of time. Additionally, while other members of the medical staff are stationary at a desk, physicians and other providers are not. They are mobile between inpatient units, labor and delivery, operating rooms, and emergency rooms. Time management does not always allow for providers to access computers from all of these areas to facilitate their managing the expectations of the EMR. This requires providers to access the EMR at off hours, extending their workload. Finally, the EMR is neither personal nor friendly. It is not designed with the clinician in mind, and it is not fun or engaging for a provider.

Continue to: OBG Management: What solutions have been instituted...

OBG Management: What solutions have been instituted in order to help physicians with data entry into the EMR?

Dr. Dougherty: When our institution compared EMR offerings, EMR companies put their best collective marketing feet forward. The general notion, at least with the Epic EMR, was that “you can customize Epic to your liking.” It did not take long for a bunch of motivated Epic users to create “smart” stuff (lists, phrases, and texts) in an effort to customize workflows and create fancy-looking electronic notes. Shortly thereafter, it was obvious that, as an institution, our reporting efforts kept coming up short—our reports lacked accuracy and meaning. Everyone was documenting in different ways and in different areas. Considering that reports are currently generated using (mostly) discrete data entries (data placed in specific fields within the EMR), it became obvious that our data entry paradigm needed to change. Therefore, standardization became the leading buzzword. Our institution recently initiated a project aimed at standardizing our workflows and documentation habits. In addition, we have incorporated a third-party information exchange product into our health system data aggregation and analysis workflow. Much more needs to be done, but it is a start.

Dr. Evans: At my institution, as a group, we have created templates for routine procedures and visits that also auto populate billing codes. I know that some departments have used scribes. From the hospital side, there has been improved access to the EMR from home. Some of my colleagues like this feature; however, others, like myself, believe this contributes to some of our burnout. I like to leave work at work. Having the ability to continue working at home is not a solution in my mind.

Dr. Woodland: At our institution, we have engaged our chaperones and medical assistants to help facilitate completion of the medical records during the office visit. Providers work with their assistants to accommodate documentation of history and physical findings while also listening to the provider as they are speaking in order to document patient care plans and orders. This saves the clinicians time in reviewing and editing the record as well as making sure the appropriate care plan is instituted. Our EMR provider recently has begun experimenting with personalization of color themes as well as pictures as part of the interface. Having said this, I still ask, “Why have medical professionals allowed non–clinical agencies and information technology groups to run this show?” It is also inconceivable to me that this unfunded mandate—that has increased cost, decreased clinical efficiency, and decreased clinician satisfaction—has not been addressed by national and international medical communities.

OBG Management: What changes do you feel your EMR system needs to undergo?

Dr. Woodland: I feel that we need to appropriately manage expectations of the EMR and the institution with relation to EMR and providers. By this I mean that we need to make the EMR more user-friendly and appropriate for different clinicians as well as patients. We also need to manage expectations of our patients. In a digital age where immediate contact is the norm, we need to address the issue that the EMR is not social media but rather a communication tool for routine contact and information transmission. Emergencies are not typically addressed well through the EMR platform; they are better handled with a more appropriate communication interface.

Dr. Dougherty: I feel that the biggest change needed is a competent, simple, and standard user-interface. Our old charting methods were great on a number of levels. For instance, if I wanted to add an order, I flipped to the ”Orders” tab and entered an order. If I needed to document a note, I flipped to the “Notes” tab and started writing, etc. Obviously, manual charting had its downsides—like trying to decipher handwriting art! EMRs could easily adopt the stuff that worked from our old methods of documentation, while leveraging the advantages that computerized workflows can bring to practitioners, including efficient transfer of records, meaningful reporting, simple electronic ordering, and interprofessional communication portals.

Dr. Evans: Our systems need to better communicate with one another. I am in an academic practice, and I should be able to see labs, consultant notes, imaging, all in one spot to improve efficiency and ease with patient visits. Minimizing clicks would be helpful as well. I try to write as much as I can while in the room with a patient to avoid after-hours note writing, but it takes away from my interaction with each patient.

Continue to: OBG Management: With an aim toward alleviating burnout...

OBG Management: With an aim toward alleviating burnout, are there any tips you can offer your colleagues on interfacing with the EMR?

Dr. Evans: When I first started as a new attending, it would take me hours to finish my notes, partly because of the level of detail I would write in my history of present illness (HPI) and assessment and plan. One great piece of advice I received was to be satisfied with good notes, not perfect notes. I worked to consolidate my thoughts and use preconstructed phrases/paragraphs on common problems I saw. This saved time to focus on other aspects of my academic job.

Dr. Dougherty: We need to refocus on the patient first, and mold our systems to meet that priority. Much too often, we have our backs to the patients or spend too much time interfacing with our EMR systems, and our patients are not happy about it (as many surveys have demonstrated). More importantly, a renewed focus on patient care, not EMR care, would allow our practitioners to do what they signed up for—treating patients. In the meantime, I would suggest that practitioners stay away from EMR gimmicks and go back to old-style documentation practices (like those established by the Centers for Medicare and Medicaid Services in 1997 and 1998), and ask the IT folks to help with molding the EMR systems to meet your own standards, not the standards established by EMR companies. I am also very hopeful that the consumer will drive most of the health care-related data collection in the near future, thereby marginalizing the current generation of EMR systems.

Dr. Woodland: I would add that providers need to manage the EMR and not let the EMR manage them. Set up task reminders at point times to handle results and communications from the EMR and set up time in your schedule where you can facilitate meeting these tasks. When providers are out on vacation, make sure to have an out-of-office reminder built into their EMR so that patients and others know timing of potential responses. Try to make the EMR as enjoyable as possible and focus on the good points of the EMR, such as legibility, order verification, safety, and documentation.
 

OBG Management: Do you feel that the EMR has led to improved patient care?

Dr. Evans: Yes and no. Yes, in that it can be much easier to follow a patient’s health care history from other provider notes or prior surgeries. Information is searchable and legible. If an EMR is built correctly, it can save time for providers, through smart phrases and templates, and it can help providers with proper billing codes and documentation requirements. No, in that it can take away from important patient interaction. We are required to see more patients in less time all while using, at times, a cumbersome EMR system.

Dr. Woodland: This is a tricky question because the EMR has both positive and negative attributes. Certainly, the legibility and order verification has improved, but the ease of accessing information in the EMR has changed. Additionally, there has been a drastic increase in provider dissatisfaction that has not been addressed. Provider dissatisfaction can lead to problems in patient care. If there was a clear-cut increased value for the cost, I do not think the EMR would be such a huge focus of negative attention. Providers need to take back control of their EMR and their profession so that they can utilize the EMR as the tool it was supposed to be and not the dissatisfier that it has become.

Dr. Dougherty: I do not believe patient care has been improved by EMR systems, for all of the reasons we have discussed, and then some. But there is an enormous amount of potential, if we get the interface between humans and EMR systems right!

Physician burnout has been labeled a public health crisis by the Harvard School of Public Health and other institutions.¹ A 2018 Physician’s Foundation survey found that 78% of physicians had symptoms of burnout,² which result from chronic workplace stress and include feeling depleted of energy or exhausted, mentally distanced from or cynical about one’s job, and problems getting one’s job done successfully.³ Among ObGyns, almost half (46%) report burnout.⁴ One-third of ObGyns responded on a recent Medscape Burnout Report that the computerization of practice is contributing to their burnout, and 54% said too many bureaucratic tasks, including charting, were adding to their burnout.⁵

Inefficient electronic medical records (EMRs) have been implicated as one reason for burnout, with improvements in efficiency cited as one of several potential resolutions to the problem. About 96% of hospitals have adopted EMRs today, compared with only 9% in 2008,⁶ and many physicians report recognizing value in the technology. For instance, 60% of participants in Stanford Medicine’s 2018 National Physician Poll said EMRs had led to improved patient care. At the same time, however, about as many (59%) said EMRs needed a “complete overhaul” and that the systems had detracted from their professional satisfaction (54%) as well as from their clinical effectiveness (49%).⁷

With this roundtable, we explore the concerns with hours spent on the EMR with several experts, and whether it is a problem that has been contributing to burnout among staff at their institutions. In addition, are there solutions that their institutions have implemented that they can share to help to cope with the problem?
 

OBG Management: ObGyns report that the computerization of practice and too many bureaucratic tasks, including charting, are contributing to burnout. Do you see this problem at your institution?

John J. Dougherty, MD, MBA: Yes, absolutely. There is not a day that goes by that I don’t hear about or experience “Epic Fails.” (We use Epic’s EMR product at our institution.) Too many clicks are needed to navigate even the simplest tasks—finding notes or results, documenting visits, and billing for services are all unnecessarily complex. In addition, we are being held accountable for achieving a long and growing list of “metrics” measures, education projects (HealthStream), and productivity goals. When do we have time to treat patients? And it is not just practicing physicians and clinicians. Our resident physicians spend an inordinate amount of time in front of the computer documenting, placing orders, and transferring patients using a system with a very inefficient user interface, to say the least.

Megan L. Evans, MD, MPH: I absolutely agree. Over the years, my institution has created a conglomerate of EMRs, requiring physicians across the hospital to be fluent in a multitude of systems. For example, you finish your clinic notes in one system, sign off on discharge summaries in another, and complete your operative notes in an entirely different system. As busy attendings, it is hard to keep ahead of all of these tasks, especially when the systems do not talk to one another. Fortunately, my hospital is changing our EMR to a single system within the next year. Until then, however, we will work in this piecemeal system.

Mark Woodland, MS, MD: EMR and computerization of medicine is the number 1 issue relating to dissatisfaction by ObGyn providers in our institution. Providers are earnest in their attempt to be compliant with EMR requirements, but the reality is that they are dealing with an automated system that does not have realistic expectations for management of results, follow-up tasks, and patient communications for a human provider. The actual charting, ordering of tests and consults, and communication between providers has been enhanced. However, the “in-basket” of tasks to be accomplished are extraordinary and much of it relies on the provider, which requires an inordinate amount of time. Additionally, while other members of the medical staff are stationary at a desk, physicians and other providers are not. They are mobile between inpatient units, labor and delivery, operating rooms, and emergency rooms. Time management does not always allow for providers to access computers from all of these areas to facilitate their managing the expectations of the EMR. This requires providers to access the EMR at off hours, extending their workload. Finally, the EMR is neither personal nor friendly. It is not designed with the clinician in mind, and it is not fun or engaging for a provider.

Continue to: OBG Management: What solutions have been instituted...

OBG Management: What solutions have been instituted in order to help physicians with data entry into the EMR?

Dr. Dougherty: When our institution compared EMR offerings, EMR companies put their best collective marketing feet forward. The general notion, at least with the Epic EMR, was that “you can customize Epic to your liking.” It did not take long for a bunch of motivated Epic users to create “smart” stuff (lists, phrases, and texts) in an effort to customize workflows and create fancy-looking electronic notes. Shortly thereafter, it was obvious that, as an institution, our reporting efforts kept coming up short—our reports lacked accuracy and meaning. Everyone was documenting in different ways and in different areas. Considering that reports are currently generated using (mostly) discrete data entries (data placed in specific fields within the EMR), it became obvious that our data entry paradigm needed to change. Therefore, standardization became the leading buzzword. Our institution recently initiated a project aimed at standardizing our workflows and documentation habits. In addition, we have incorporated a third-party information exchange product into our health system data aggregation and analysis workflow. Much more needs to be done, but it is a start.

Dr. Evans: At my institution, as a group, we have created templates for routine procedures and visits that also auto populate billing codes. I know that some departments have used scribes. From the hospital side, there has been improved access to the EMR from home. Some of my colleagues like this feature; however, others, like myself, believe this contributes to some of our burnout. I like to leave work at work. Having the ability to continue working at home is not a solution in my mind.

Dr. Woodland: At our institution, we have engaged our chaperones and medical assistants to help facilitate completion of the medical records during the office visit. Providers work with their assistants to accommodate documentation of history and physical findings while also listening to the provider as they are speaking in order to document patient care plans and orders. This saves the clinicians time in reviewing and editing the record as well as making sure the appropriate care plan is instituted. Our EMR provider recently has begun experimenting with personalization of color themes as well as pictures as part of the interface. Having said this, I still ask, “Why have medical professionals allowed non–clinical agencies and information technology groups to run this show?” It is also inconceivable to me that this unfunded mandate—that has increased cost, decreased clinical efficiency, and decreased clinician satisfaction—has not been addressed by national and international medical communities.

OBG Management: What changes do you feel your EMR system needs to undergo?

Dr. Woodland: I feel that we need to appropriately manage expectations of the EMR and the institution with relation to EMR and providers. By this I mean that we need to make the EMR more user-friendly and appropriate for different clinicians as well as patients. We also need to manage expectations of our patients. In a digital age where immediate contact is the norm, we need to address the issue that the EMR is not social media but rather a communication tool for routine contact and information transmission. Emergencies are not typically addressed well through the EMR platform; they are better handled with a more appropriate communication interface.

Dr. Dougherty: I feel that the biggest change needed is a competent, simple, and standard user-interface. Our old charting methods were great on a number of levels. For instance, if I wanted to add an order, I flipped to the ”Orders” tab and entered an order. If I needed to document a note, I flipped to the “Notes” tab and started writing, etc. Obviously, manual charting had its downsides—like trying to decipher handwriting art! EMRs could easily adopt the stuff that worked from our old methods of documentation, while leveraging the advantages that computerized workflows can bring to practitioners, including efficient transfer of records, meaningful reporting, simple electronic ordering, and interprofessional communication portals.

Dr. Evans: Our systems need to better communicate with one another. I am in an academic practice, and I should be able to see labs, consultant notes, imaging, all in one spot to improve efficiency and ease with patient visits. Minimizing clicks would be helpful as well. I try to write as much as I can while in the room with a patient to avoid after-hours note writing, but it takes away from my interaction with each patient.

Continue to: OBG Management: With an aim toward alleviating burnout...

OBG Management: With an aim toward alleviating burnout, are there any tips you can offer your colleagues on interfacing with the EMR?

Dr. Evans: When I first started as a new attending, it would take me hours to finish my notes, partly because of the level of detail I would write in my history of present illness (HPI) and assessment and plan. One great piece of advice I received was to be satisfied with good notes, not perfect notes. I worked to consolidate my thoughts and use preconstructed phrases/paragraphs on common problems I saw. This saved time to focus on other aspects of my academic job.

Dr. Dougherty: We need to refocus on the patient first, and mold our systems to meet that priority. Much too often, we have our backs to the patients or spend too much time interfacing with our EMR systems, and our patients are not happy about it (as many surveys have demonstrated). More importantly, a renewed focus on patient care, not EMR care, would allow our practitioners to do what they signed up for—treating patients. In the meantime, I would suggest that practitioners stay away from EMR gimmicks and go back to old-style documentation practices (like those established by the Centers for Medicare and Medicaid Services in 1997 and 1998), and ask the IT folks to help with molding the EMR systems to meet your own standards, not the standards established by EMR companies. I am also very hopeful that the consumer will drive most of the health care-related data collection in the near future, thereby marginalizing the current generation of EMR systems.

Dr. Woodland: I would add that providers need to manage the EMR and not let the EMR manage them. Set up task reminders at point times to handle results and communications from the EMR and set up time in your schedule where you can facilitate meeting these tasks. When providers are out on vacation, make sure to have an out-of-office reminder built into their EMR so that patients and others know timing of potential responses. Try to make the EMR as enjoyable as possible and focus on the good points of the EMR, such as legibility, order verification, safety, and documentation.
 

OBG Management: Do you feel that the EMR has led to improved patient care?

Dr. Evans: Yes and no. Yes, in that it can be much easier to follow a patient’s health care history from other provider notes or prior surgeries. Information is searchable and legible. If an EMR is built correctly, it can save time for providers, through smart phrases and templates, and it can help providers with proper billing codes and documentation requirements. No, in that it can take away from important patient interaction. We are required to see more patients in less time all while using, at times, a cumbersome EMR system.

Dr. Woodland: This is a tricky question because the EMR has both positive and negative attributes. Certainly, the legibility and order verification has improved, but the ease of accessing information in the EMR has changed. Additionally, there has been a drastic increase in provider dissatisfaction that has not been addressed. Provider dissatisfaction can lead to problems in patient care. If there was a clear-cut increased value for the cost, I do not think the EMR would be such a huge focus of negative attention. Providers need to take back control of their EMR and their profession so that they can utilize the EMR as the tool it was supposed to be and not the dissatisfier that it has become.

Dr. Dougherty: I do not believe patient care has been improved by EMR systems, for all of the reasons we have discussed, and then some. But there is an enormous amount of potential, if we get the interface between humans and EMR systems right!

Physician burnout has been labeled a public health crisis by the Harvard School of Public Health and other institutions.¹ A 2018 Physician’s Foundation survey found that 78% of physicians had symptoms of burnout,² which result from chronic workplace stress and include feeling depleted of energy or exhausted, mentally distanced from or cynical about one’s job, and problems getting one’s job done successfully.³ Among ObGyns, almost half (46%) report burnout.⁴ One-third of ObGyns responded on a recent Medscape Burnout Report that the computerization of practice is contributing to their burnout, and 54% said too many bureaucratic tasks, including charting, were adding to their burnout.⁵

Inefficient electronic medical records (EMRs) have been implicated as one reason for burnout, with improvements in efficiency cited as one of several potential resolutions to the problem. About 96% of hospitals have adopted EMRs today, compared with only 9% in 2008,⁶ and many physicians report recognizing value in the technology. For instance, 60% of participants in Stanford Medicine’s 2018 National Physician Poll said EMRs had led to improved patient care. At the same time, however, about as many (59%) said EMRs needed a “complete overhaul” and that the systems had detracted from their professional satisfaction (54%) as well as from their clinical effectiveness (49%).⁷

With this roundtable, we explore the concerns with hours spent on the EMR with several experts, and whether it is a problem that has been contributing to burnout among staff at their institutions. In addition, are there solutions that their institutions have implemented that they can share to help to cope with the problem?
 

OBG Management: ObGyns report that the computerization of practice and too many bureaucratic tasks, including charting, are contributing to burnout. Do you see this problem at your institution?

John J. Dougherty, MD, MBA: Yes, absolutely. There is not a day that goes by that I don’t hear about or experience “Epic Fails.” (We use Epic’s EMR product at our institution.) Too many clicks are needed to navigate even the simplest tasks—finding notes or results, documenting visits, and billing for services are all unnecessarily complex. In addition, we are being held accountable for achieving a long and growing list of “metrics” measures, education projects (HealthStream), and productivity goals. When do we have time to treat patients? And it is not just practicing physicians and clinicians. Our resident physicians spend an inordinate amount of time in front of the computer documenting, placing orders, and transferring patients using a system with a very inefficient user interface, to say the least.

Megan L. Evans, MD, MPH: I absolutely agree. Over the years, my institution has created a conglomerate of EMRs, requiring physicians across the hospital to be fluent in a multitude of systems. For example, you finish your clinic notes in one system, sign off on discharge summaries in another, and complete your operative notes in an entirely different system. As busy attendings, it is hard to keep ahead of all of these tasks, especially when the systems do not talk to one another. Fortunately, my hospital is changing our EMR to a single system within the next year. Until then, however, we will work in this piecemeal system.

Mark Woodland, MS, MD: EMR and computerization of medicine is the number 1 issue relating to dissatisfaction by ObGyn providers in our institution. Providers are earnest in their attempt to be compliant with EMR requirements, but the reality is that they are dealing with an automated system that does not have realistic expectations for management of results, follow-up tasks, and patient communications for a human provider. The actual charting, ordering of tests and consults, and communication between providers has been enhanced. However, the “in-basket” of tasks to be accomplished are extraordinary and much of it relies on the provider, which requires an inordinate amount of time. Additionally, while other members of the medical staff are stationary at a desk, physicians and other providers are not. They are mobile between inpatient units, labor and delivery, operating rooms, and emergency rooms. Time management does not always allow for providers to access computers from all of these areas to facilitate their managing the expectations of the EMR. This requires providers to access the EMR at off hours, extending their workload. Finally, the EMR is neither personal nor friendly. It is not designed with the clinician in mind, and it is not fun or engaging for a provider.

Continue to: OBG Management: What solutions have been instituted...

OBG Management: What solutions have been instituted in order to help physicians with data entry into the EMR?

Dr. Dougherty: When our institution compared EMR offerings, EMR companies put their best collective marketing feet forward. The general notion, at least with the Epic EMR, was that “you can customize Epic to your liking.” It did not take long for a bunch of motivated Epic users to create “smart” stuff (lists, phrases, and texts) in an effort to customize workflows and create fancy-looking electronic notes. Shortly thereafter, it was obvious that, as an institution, our reporting efforts kept coming up short—our reports lacked accuracy and meaning. Everyone was documenting in different ways and in different areas. Considering that reports are currently generated using (mostly) discrete data entries (data placed in specific fields within the EMR), it became obvious that our data entry paradigm needed to change. Therefore, standardization became the leading buzzword. Our institution recently initiated a project aimed at standardizing our workflows and documentation habits. In addition, we have incorporated a third-party information exchange product into our health system data aggregation and analysis workflow. Much more needs to be done, but it is a start.

Dr. Evans: At my institution, as a group, we have created templates for routine procedures and visits that also auto populate billing codes. I know that some departments have used scribes. From the hospital side, there has been improved access to the EMR from home. Some of my colleagues like this feature; however, others, like myself, believe this contributes to some of our burnout. I like to leave work at work. Having the ability to continue working at home is not a solution in my mind.

Dr. Woodland: At our institution, we have engaged our chaperones and medical assistants to help facilitate completion of the medical records during the office visit. Providers work with their assistants to accommodate documentation of history and physical findings while also listening to the provider as they are speaking in order to document patient care plans and orders. This saves the clinicians time in reviewing and editing the record as well as making sure the appropriate care plan is instituted. Our EMR provider recently has begun experimenting with personalization of color themes as well as pictures as part of the interface. Having said this, I still ask, “Why have medical professionals allowed non–clinical agencies and information technology groups to run this show?” It is also inconceivable to me that this unfunded mandate—that has increased cost, decreased clinical efficiency, and decreased clinician satisfaction—has not been addressed by national and international medical communities.

OBG Management: What changes do you feel your EMR system needs to undergo?

Dr. Woodland: I feel that we need to appropriately manage expectations of the EMR and the institution with relation to EMR and providers. By this I mean that we need to make the EMR more user-friendly and appropriate for different clinicians as well as patients. We also need to manage expectations of our patients. In a digital age where immediate contact is the norm, we need to address the issue that the EMR is not social media but rather a communication tool for routine contact and information transmission. Emergencies are not typically addressed well through the EMR platform; they are better handled with a more appropriate communication interface.

Dr. Dougherty: I feel that the biggest change needed is a competent, simple, and standard user-interface. Our old charting methods were great on a number of levels. For instance, if I wanted to add an order, I flipped to the ”Orders” tab and entered an order. If I needed to document a note, I flipped to the “Notes” tab and started writing, etc. Obviously, manual charting had its downsides—like trying to decipher handwriting art! EMRs could easily adopt the stuff that worked from our old methods of documentation, while leveraging the advantages that computerized workflows can bring to practitioners, including efficient transfer of records, meaningful reporting, simple electronic ordering, and interprofessional communication portals.

Dr. Evans: Our systems need to better communicate with one another. I am in an academic practice, and I should be able to see labs, consultant notes, imaging, all in one spot to improve efficiency and ease with patient visits. Minimizing clicks would be helpful as well. I try to write as much as I can while in the room with a patient to avoid after-hours note writing, but it takes away from my interaction with each patient.

Continue to: OBG Management: With an aim toward alleviating burnout...

OBG Management: With an aim toward alleviating burnout, are there any tips you can offer your colleagues on interfacing with the EMR?

Dr. Evans: When I first started as a new attending, it would take me hours to finish my notes, partly because of the level of detail I would write in my history of present illness (HPI) and assessment and plan. One great piece of advice I received was to be satisfied with good notes, not perfect notes. I worked to consolidate my thoughts and use preconstructed phrases/paragraphs on common problems I saw. This saved time to focus on other aspects of my academic job.

Dr. Dougherty: We need to refocus on the patient first, and mold our systems to meet that priority. Much too often, we have our backs to the patients or spend too much time interfacing with our EMR systems, and our patients are not happy about it (as many surveys have demonstrated). More importantly, a renewed focus on patient care, not EMR care, would allow our practitioners to do what they signed up for—treating patients. In the meantime, I would suggest that practitioners stay away from EMR gimmicks and go back to old-style documentation practices (like those established by the Centers for Medicare and Medicaid Services in 1997 and 1998), and ask the IT folks to help with molding the EMR systems to meet your own standards, not the standards established by EMR companies. I am also very hopeful that the consumer will drive most of the health care-related data collection in the near future, thereby marginalizing the current generation of EMR systems.

Dr. Woodland: I would add that providers need to manage the EMR and not let the EMR manage them. Set up task reminders at point times to handle results and communications from the EMR and set up time in your schedule where you can facilitate meeting these tasks. When providers are out on vacation, make sure to have an out-of-office reminder built into their EMR so that patients and others know timing of potential responses. Try to make the EMR as enjoyable as possible and focus on the good points of the EMR, such as legibility, order verification, safety, and documentation.
 

OBG Management: Do you feel that the EMR has led to improved patient care?

Dr. Evans: Yes and no. Yes, in that it can be much easier to follow a patient’s health care history from other provider notes or prior surgeries. Information is searchable and legible. If an EMR is built correctly, it can save time for providers, through smart phrases and templates, and it can help providers with proper billing codes and documentation requirements. No, in that it can take away from important patient interaction. We are required to see more patients in less time all while using, at times, a cumbersome EMR system.

Dr. Woodland: This is a tricky question because the EMR has both positive and negative attributes. Certainly, the legibility and order verification has improved, but the ease of accessing information in the EMR has changed. Additionally, there has been a drastic increase in provider dissatisfaction that has not been addressed. Provider dissatisfaction can lead to problems in patient care. If there was a clear-cut increased value for the cost, I do not think the EMR would be such a huge focus of negative attention. Providers need to take back control of their EMR and their profession so that they can utilize the EMR as the tool it was supposed to be and not the dissatisfier that it has become.

Dr. Dougherty: I do not believe patient care has been improved by EMR systems, for all of the reasons we have discussed, and then some. But there is an enormous amount of potential, if we get the interface between humans and EMR systems right!

When first responders arrive at a scene where illicit drugs may be present, they could be at risk of dangerous exposure. They might inhale drugs; they can have contact through mucous membranes or through needlesticks.

A major concern is exposure to fentanyl or its analogues, which can lead to symptoms, including rapid onset of life-threatening respiratory depression. The exception is skin contact, which is not expected to have toxic effects if the visible contamination is removed promptly.

To help EMS providers and other responders protect themselves, the National Institute for Occupational Safety and Health (NIOSH) has released a new virtual toolkit with videos, infographics, and postcards based on NIOSH safety recommendations.

The resources highlight how best to assess the scene for hazards that may indicate the presence of illicit drugs and what to do—for example, use soap and water, not hand sanitizer (it doesn’t remove illicit drugs and may increase exposure), and don’t eat, drink, smoke, or use the bathroom in the affected area. The infographics also show how to decontaminate and prevent “take-home exposure” to protect responders’ families. The guidelines extend to procedures for protecting working dogs exposed to the drugs.

NIOSH notes that it has no occupational exposure data on fentanyl or its analogues for emergency responders. The recommendations are based on the reported toxicity and the chemical and physical properties of fentanyl and its analogues, NIOSH guidance for similar chemicals, recommendations from previous NIOSH health hazard evaluation reports, and “the basic principles of industrial hygiene.” As new research becomes available, NIOSH says, the recommendations will be updated.

The toolkit resources are shareable and available for disseminating via print, social media, text, and more. The kit is accessible at https://www.cdc.gov/niosh/topics/fentanyl/toolkit.html.

When first responders arrive at a scene where illicit drugs may be present, they could be at risk of dangerous exposure. They might inhale drugs; they can have contact through mucous membranes or through needlesticks.

A major concern is exposure to fentanyl or its analogues, which can lead to symptoms, including rapid onset of life-threatening respiratory depression. The exception is skin contact, which is not expected to have toxic effects if the visible contamination is removed promptly.

To help EMS providers and other responders protect themselves, the National Institute for Occupational Safety and Health (NIOSH) has released a new virtual toolkit with videos, infographics, and postcards based on NIOSH safety recommendations.

The resources highlight how best to assess the scene for hazards that may indicate the presence of illicit drugs and what to do—for example, use soap and water, not hand sanitizer (it doesn’t remove illicit drugs and may increase exposure), and don’t eat, drink, smoke, or use the bathroom in the affected area. The infographics also show how to decontaminate and prevent “take-home exposure” to protect responders’ families. The guidelines extend to procedures for protecting working dogs exposed to the drugs.

NIOSH notes that it has no occupational exposure data on fentanyl or its analogues for emergency responders. The recommendations are based on the reported toxicity and the chemical and physical properties of fentanyl and its analogues, NIOSH guidance for similar chemicals, recommendations from previous NIOSH health hazard evaluation reports, and “the basic principles of industrial hygiene.” As new research becomes available, NIOSH says, the recommendations will be updated.

The toolkit resources are shareable and available for disseminating via print, social media, text, and more. The kit is accessible at https://www.cdc.gov/niosh/topics/fentanyl/toolkit.html.

When first responders arrive at a scene where illicit drugs may be present, they could be at risk of dangerous exposure. They might inhale drugs; they can have contact through mucous membranes or through needlesticks.

A major concern is exposure to fentanyl or its analogues, which can lead to symptoms, including rapid onset of life-threatening respiratory depression. The exception is skin contact, which is not expected to have toxic effects if the visible contamination is removed promptly.

To help EMS providers and other responders protect themselves, the National Institute for Occupational Safety and Health (NIOSH) has released a new virtual toolkit with videos, infographics, and postcards based on NIOSH safety recommendations.

The resources highlight how best to assess the scene for hazards that may indicate the presence of illicit drugs and what to do—for example, use soap and water, not hand sanitizer (it doesn’t remove illicit drugs and may increase exposure), and don’t eat, drink, smoke, or use the bathroom in the affected area. The infographics also show how to decontaminate and prevent “take-home exposure” to protect responders’ families. The guidelines extend to procedures for protecting working dogs exposed to the drugs.

NIOSH notes that it has no occupational exposure data on fentanyl or its analogues for emergency responders. The recommendations are based on the reported toxicity and the chemical and physical properties of fentanyl and its analogues, NIOSH guidance for similar chemicals, recommendations from previous NIOSH health hazard evaluation reports, and “the basic principles of industrial hygiene.” As new research becomes available, NIOSH says, the recommendations will be updated.

The toolkit resources are shareable and available for disseminating via print, social media, text, and more. The kit is accessible at https://www.cdc.gov/niosh/topics/fentanyl/toolkit.html.

The updated smoking policy goes into effect for employees, patients, visitors, volunteers, contractors, and vendors, whether they smoke cigarettes, cigars, pipes, or even electronic and vaping devices, and whenever they are on the grounds of VA health care facilities, including parking areas.

The new policy comes after the VA reviewed research on second- and thirdhand smoke and best practices in the health care industry. “There is no risk-free level of exposure to tobacco smoke,” the VA’s Smokefree website says. Overwhelming evidence shows exposure to secondhand smoke has significant medical risks. Moreover, a growing body of evidence shows exposure to thirdhand smoke (residual nicotine and other chemicals left on indoor surfaces) also is a health hazard. The residue is thought to react with indoor pollutants to create a toxic mix that clings long after smoking has stopped and cannot be eliminated by opening windows, or using fans, or other means of clearing rooms.

“We are not alone in recognizing the importance of creating a smoke-free campus,” said VA Secretary Robert Wilkie. He notes that as of 2014, 4000 health care facilities and 4 national health care systems in the US have implemented smoke-free grounds.

National Association of Government employees will begin implementing the policy as of October 1, and have until January 1, 2020, to fully comply. Smoking shelters will be closed, although each facility will independently determine the disposition of smoking areas and shelters.

The new policy does not mean anyone has to quit smoking but to encourage quitting, the VA offers resources, including www.publichealth.va.gov/smoking/quit/index.asp. More tips and tools are available at the Smokefree Veteran website: https://veterans.smokefree.gov. SmokefreeVET is a text-messaging program (https://veterans.smokefree.gov/tools-tips-vet/smokefreevet) that provides 24/7 support to help veterans quit for good. Employees can contact their facility for resources.

The policies are available at https://www.va.gov/health/smokefree.

The updated smoking policy goes into effect for employees, patients, visitors, volunteers, contractors, and vendors, whether they smoke cigarettes, cigars, pipes, or even electronic and vaping devices, and whenever they are on the grounds of VA health care facilities, including parking areas.

The new policy comes after the VA reviewed research on second- and thirdhand smoke and best practices in the health care industry. “There is no risk-free level of exposure to tobacco smoke,” the VA’s Smokefree website says. Overwhelming evidence shows exposure to secondhand smoke has significant medical risks. Moreover, a growing body of evidence shows exposure to thirdhand smoke (residual nicotine and other chemicals left on indoor surfaces) also is a health hazard. The residue is thought to react with indoor pollutants to create a toxic mix that clings long after smoking has stopped and cannot be eliminated by opening windows, or using fans, or other means of clearing rooms.

“We are not alone in recognizing the importance of creating a smoke-free campus,” said VA Secretary Robert Wilkie. He notes that as of 2014, 4000 health care facilities and 4 national health care systems in the US have implemented smoke-free grounds.

National Association of Government employees will begin implementing the policy as of October 1, and have until January 1, 2020, to fully comply. Smoking shelters will be closed, although each facility will independently determine the disposition of smoking areas and shelters.

The new policy does not mean anyone has to quit smoking but to encourage quitting, the VA offers resources, including www.publichealth.va.gov/smoking/quit/index.asp. More tips and tools are available at the Smokefree Veteran website: https://veterans.smokefree.gov. SmokefreeVET is a text-messaging program (https://veterans.smokefree.gov/tools-tips-vet/smokefreevet) that provides 24/7 support to help veterans quit for good. Employees can contact their facility for resources.

The policies are available at https://www.va.gov/health/smokefree.

The updated smoking policy goes into effect for employees, patients, visitors, volunteers, contractors, and vendors, whether they smoke cigarettes, cigars, pipes, or even electronic and vaping devices, and whenever they are on the grounds of VA health care facilities, including parking areas.

The new policy comes after the VA reviewed research on second- and thirdhand smoke and best practices in the health care industry. “There is no risk-free level of exposure to tobacco smoke,” the VA’s Smokefree website says. Overwhelming evidence shows exposure to secondhand smoke has significant medical risks. Moreover, a growing body of evidence shows exposure to thirdhand smoke (residual nicotine and other chemicals left on indoor surfaces) also is a health hazard. The residue is thought to react with indoor pollutants to create a toxic mix that clings long after smoking has stopped and cannot be eliminated by opening windows, or using fans, or other means of clearing rooms.

“We are not alone in recognizing the importance of creating a smoke-free campus,” said VA Secretary Robert Wilkie. He notes that as of 2014, 4000 health care facilities and 4 national health care systems in the US have implemented smoke-free grounds.

National Association of Government employees will begin implementing the policy as of October 1, and have until January 1, 2020, to fully comply. Smoking shelters will be closed, although each facility will independently determine the disposition of smoking areas and shelters.

The new policy does not mean anyone has to quit smoking but to encourage quitting, the VA offers resources, including www.publichealth.va.gov/smoking/quit/index.asp. More tips and tools are available at the Smokefree Veteran website: https://veterans.smokefree.gov. SmokefreeVET is a text-messaging program (https://veterans.smokefree.gov/tools-tips-vet/smokefreevet) that provides 24/7 support to help veterans quit for good. Employees can contact their facility for resources.

The policies are available at https://www.va.gov/health/smokefree.

Heparin is the anticoagulant of choice when a rapid anticoagulant is indicated: Onset of action is immediate when administered IV as a bolus.¹ The major anticoagulant effect of heparin is mediated by heparin/antithrombin (AT) interaction. Heparin/AT inactivates factor IIa (thrombin) and factors Xa, IXa, XIa, and XIIa. Heparin is approved for multiple indications, such as venous thromboembolism (VTE) treatment and prophylaxis of medical and surgical patients; stroke prevention in atrial fibrillation (AF); acute coronary syndrome (ACS); vascular and cardiac surgeries; and various interventional procedures (eg, diagnostic angiography and percutaneous coronary intervention [PCI]). It also is used as an anticoagulant in blood transfusions, extracorporeal circulation, and for maintaining patency of central vascular access devices (CVADs).

About 60% of the crude heparin used to manufacture heparin in the US originates in China, derived from porcine mucosa. African swine fever, a contagious virus with no cure, has eliminated about 25% to 35% of China’s pig population, or about 150 million pigs. In July 2019, members of the US House of Representatives Committee on Energy and Commerce sent a letter to the US Food and Drug Administration asking for details on the potential impact of African swine fever on the supply of heparin.²

The US Department of Veterans Affairs (VA) heath care system is currently experiencing a shortage of heparin vials and syringes. It is unclear when resolution of this shortage will occur as it could resolve within several weeks or as late as January 2020.³ Although vials and syringes are the current products that are affected, it is possible the shortage may eventually include IV heparin bags as well.

Since the foremost objective of VA health care providers is to provide timely access to medications for veterans, strategies to conserve unfractionated heparin (UfH) must be used since it is a first-line therapy where few evidence-based alternatives exist. Conservation strategies may include drug rationing, therapeutic substitution, and compounding of needed products using the limited stock available in the pharmacy.⁴ It is important that all staff are educated on facility strategies in order to be familiar with alternatives and limit the potential for near misses, adverse events, and provider frustration.

In shortage situations, the VA-Pharmacy Benefits Management (PBM) defers decisions regarding drug preservation, processes to shift to viable alternatives, and the best practice for safe transitions to local facilities and their subject matter experts.⁵ At the VA Tennessee Valley Healthcare System, a 1A, tertiary, dual campus health care system, a pharmacy task force has formed to track drug shortages impacting the facility’s efficiencies and budgets. This group communicates with the Pharmacy and Therapeutics committee about potential risks to patient care and develops shortage briefs (following an SBAR [situation, background, assessment, recommendation] design) generally authored and championed by at least 1 clinical pharmacy specialist and supervising physicians who are field experts. Prior to dissemination, the SBAR undergoes a rapid peer-review process.

To date, VA PBM has not issued specific guidance on how pharmacists should proceed in case of a shortage. However, we recommend strategies that may be considered for implementation during a potential UfH shortage. For example, pharmacists can use therapeutic alternatives for which best available evidence suggests no disadvantage.⁴ The Table lists alternative agents according to indication and patient-specific considerations that may preclude use. Existing UfH products may also be used for drug compounding (eg, use current stock to provide an indicated aliquot) to meet the need of prioritized patients.⁴ In addition, we suggest prioritizing current UfH/heparinized saline for use for the following groups of patients⁴:

• Emergent/urgent cardiac surgery^1,6;
• Hemodialysis patients^1,7-9 for which the low-molecular-weight heparin (LMWH) dalteparin is deemed inappropriate or the patient is not monitored in the intensive care unit for regional citrate administration;
• VTE prophylaxis for patients with epidurals or chest tubes for which urgent invasive management may occur, recent cardiac or neurosurgery, or for patients with a creatine clearance < 15 mL/min or receiving hemodialysis^10-12;
• Vascular surgery (eg, limb ischemia) and interventions (eg, carotid stenting, endarterectomy)^13,14;
• Mesenteric ischemia (venous thrombosis) with a potential to proceed to laparotomy¹⁵;
• Critically ill patients with arterial lines for which normal saline is deemed inappropriate for line flushing¹⁶;
• Electrophysiology procedures (eg, AF ablation)¹⁷; and
• Contraindication to use of a long-acting alternative listed in the table or a medical necessity exists for using a rapidly reversible agent. Examples for this category include but are not limited to recent gastrointestinal bleeding, central nervous system lesion, and select neurologic diagnoses (eg, cerebral venous sinus thrombosis with hemorrhage, thrombus in vertebral basilar system or anterior circulation, intraparenchymal hemorrhage plus mechanical valve, medium to large cardioembolic stroke with intracardiac thrombus).

Conclusion

The UfH drug shortage represents a significant threat to public health and is a major challenge for US health care systems, including the Veterans Health Administration. Overreliance on a predominant source of crude heparin has affected multiple UfH manufacturers and products. Current alternatives to UfH include low-molecular-weight heparins, IV direct thrombin inhibitors, and SC fondaparinux, with selection supported by guidelines or evolving literature. However, the shortage has the potential to expand to other injectables, such as dalteparin and enoxaparin, and severely limit care for veterans. It is vital that clinicians rapidly address the current shortage by creating a plan to develop efficient and equitable access to UfH, continue to assess supply and update stakeholders, and select evidence-based alternatives while maintaining focus on efficacy and safety.

Acknowledgments

The authors thank Ashley Yost, PharmD, for her coordination of the multidisciplinary task force assigned to efficiently manage the heparin drug shortage. This material is the result of work supported with resources and the use of facilities at the VA Tennessee Valley Healthcare System in Nashville, Tennessee.

Heparin is the anticoagulant of choice when a rapid anticoagulant is indicated: Onset of action is immediate when administered IV as a bolus.¹ The major anticoagulant effect of heparin is mediated by heparin/antithrombin (AT) interaction. Heparin/AT inactivates factor IIa (thrombin) and factors Xa, IXa, XIa, and XIIa. Heparin is approved for multiple indications, such as venous thromboembolism (VTE) treatment and prophylaxis of medical and surgical patients; stroke prevention in atrial fibrillation (AF); acute coronary syndrome (ACS); vascular and cardiac surgeries; and various interventional procedures (eg, diagnostic angiography and percutaneous coronary intervention [PCI]). It also is used as an anticoagulant in blood transfusions, extracorporeal circulation, and for maintaining patency of central vascular access devices (CVADs).

About 60% of the crude heparin used to manufacture heparin in the US originates in China, derived from porcine mucosa. African swine fever, a contagious virus with no cure, has eliminated about 25% to 35% of China’s pig population, or about 150 million pigs. In July 2019, members of the US House of Representatives Committee on Energy and Commerce sent a letter to the US Food and Drug Administration asking for details on the potential impact of African swine fever on the supply of heparin.²

The US Department of Veterans Affairs (VA) heath care system is currently experiencing a shortage of heparin vials and syringes. It is unclear when resolution of this shortage will occur as it could resolve within several weeks or as late as January 2020.³ Although vials and syringes are the current products that are affected, it is possible the shortage may eventually include IV heparin bags as well.

Since the foremost objective of VA health care providers is to provide timely access to medications for veterans, strategies to conserve unfractionated heparin (UfH) must be used since it is a first-line therapy where few evidence-based alternatives exist. Conservation strategies may include drug rationing, therapeutic substitution, and compounding of needed products using the limited stock available in the pharmacy.⁴ It is important that all staff are educated on facility strategies in order to be familiar with alternatives and limit the potential for near misses, adverse events, and provider frustration.

In shortage situations, the VA-Pharmacy Benefits Management (PBM) defers decisions regarding drug preservation, processes to shift to viable alternatives, and the best practice for safe transitions to local facilities and their subject matter experts.⁵ At the VA Tennessee Valley Healthcare System, a 1A, tertiary, dual campus health care system, a pharmacy task force has formed to track drug shortages impacting the facility’s efficiencies and budgets. This group communicates with the Pharmacy and Therapeutics committee about potential risks to patient care and develops shortage briefs (following an SBAR [situation, background, assessment, recommendation] design) generally authored and championed by at least 1 clinical pharmacy specialist and supervising physicians who are field experts. Prior to dissemination, the SBAR undergoes a rapid peer-review process.

To date, VA PBM has not issued specific guidance on how pharmacists should proceed in case of a shortage. However, we recommend strategies that may be considered for implementation during a potential UfH shortage. For example, pharmacists can use therapeutic alternatives for which best available evidence suggests no disadvantage.⁴ The Table lists alternative agents according to indication and patient-specific considerations that may preclude use. Existing UfH products may also be used for drug compounding (eg, use current stock to provide an indicated aliquot) to meet the need of prioritized patients.⁴ In addition, we suggest prioritizing current UfH/heparinized saline for use for the following groups of patients⁴:

• Emergent/urgent cardiac surgery^1,6;
• Hemodialysis patients^1,7-9 for which the low-molecular-weight heparin (LMWH) dalteparin is deemed inappropriate or the patient is not monitored in the intensive care unit for regional citrate administration;
• VTE prophylaxis for patients with epidurals or chest tubes for which urgent invasive management may occur, recent cardiac or neurosurgery, or for patients with a creatine clearance < 15 mL/min or receiving hemodialysis^10-12;
• Vascular surgery (eg, limb ischemia) and interventions (eg, carotid stenting, endarterectomy)^13,14;
• Mesenteric ischemia (venous thrombosis) with a potential to proceed to laparotomy¹⁵;
• Critically ill patients with arterial lines for which normal saline is deemed inappropriate for line flushing¹⁶;
• Electrophysiology procedures (eg, AF ablation)¹⁷; and
• Contraindication to use of a long-acting alternative listed in the table or a medical necessity exists for using a rapidly reversible agent. Examples for this category include but are not limited to recent gastrointestinal bleeding, central nervous system lesion, and select neurologic diagnoses (eg, cerebral venous sinus thrombosis with hemorrhage, thrombus in vertebral basilar system or anterior circulation, intraparenchymal hemorrhage plus mechanical valve, medium to large cardioembolic stroke with intracardiac thrombus).

Conclusion

The UfH drug shortage represents a significant threat to public health and is a major challenge for US health care systems, including the Veterans Health Administration. Overreliance on a predominant source of crude heparin has affected multiple UfH manufacturers and products. Current alternatives to UfH include low-molecular-weight heparins, IV direct thrombin inhibitors, and SC fondaparinux, with selection supported by guidelines or evolving literature. However, the shortage has the potential to expand to other injectables, such as dalteparin and enoxaparin, and severely limit care for veterans. It is vital that clinicians rapidly address the current shortage by creating a plan to develop efficient and equitable access to UfH, continue to assess supply and update stakeholders, and select evidence-based alternatives while maintaining focus on efficacy and safety.

Acknowledgments

The authors thank Ashley Yost, PharmD, for her coordination of the multidisciplinary task force assigned to efficiently manage the heparin drug shortage. This material is the result of work supported with resources and the use of facilities at the VA Tennessee Valley Healthcare System in Nashville, Tennessee.

Heparin is the anticoagulant of choice when a rapid anticoagulant is indicated: Onset of action is immediate when administered IV as a bolus.¹ The major anticoagulant effect of heparin is mediated by heparin/antithrombin (AT) interaction. Heparin/AT inactivates factor IIa (thrombin) and factors Xa, IXa, XIa, and XIIa. Heparin is approved for multiple indications, such as venous thromboembolism (VTE) treatment and prophylaxis of medical and surgical patients; stroke prevention in atrial fibrillation (AF); acute coronary syndrome (ACS); vascular and cardiac surgeries; and various interventional procedures (eg, diagnostic angiography and percutaneous coronary intervention [PCI]). It also is used as an anticoagulant in blood transfusions, extracorporeal circulation, and for maintaining patency of central vascular access devices (CVADs).

About 60% of the crude heparin used to manufacture heparin in the US originates in China, derived from porcine mucosa. African swine fever, a contagious virus with no cure, has eliminated about 25% to 35% of China’s pig population, or about 150 million pigs. In July 2019, members of the US House of Representatives Committee on Energy and Commerce sent a letter to the US Food and Drug Administration asking for details on the potential impact of African swine fever on the supply of heparin.²

The US Department of Veterans Affairs (VA) heath care system is currently experiencing a shortage of heparin vials and syringes. It is unclear when resolution of this shortage will occur as it could resolve within several weeks or as late as January 2020.³ Although vials and syringes are the current products that are affected, it is possible the shortage may eventually include IV heparin bags as well.

Since the foremost objective of VA health care providers is to provide timely access to medications for veterans, strategies to conserve unfractionated heparin (UfH) must be used since it is a first-line therapy where few evidence-based alternatives exist. Conservation strategies may include drug rationing, therapeutic substitution, and compounding of needed products using the limited stock available in the pharmacy.⁴ It is important that all staff are educated on facility strategies in order to be familiar with alternatives and limit the potential for near misses, adverse events, and provider frustration.

In shortage situations, the VA-Pharmacy Benefits Management (PBM) defers decisions regarding drug preservation, processes to shift to viable alternatives, and the best practice for safe transitions to local facilities and their subject matter experts.⁵ At the VA Tennessee Valley Healthcare System, a 1A, tertiary, dual campus health care system, a pharmacy task force has formed to track drug shortages impacting the facility’s efficiencies and budgets. This group communicates with the Pharmacy and Therapeutics committee about potential risks to patient care and develops shortage briefs (following an SBAR [situation, background, assessment, recommendation] design) generally authored and championed by at least 1 clinical pharmacy specialist and supervising physicians who are field experts. Prior to dissemination, the SBAR undergoes a rapid peer-review process.

To date, VA PBM has not issued specific guidance on how pharmacists should proceed in case of a shortage. However, we recommend strategies that may be considered for implementation during a potential UfH shortage. For example, pharmacists can use therapeutic alternatives for which best available evidence suggests no disadvantage.⁴ The Table lists alternative agents according to indication and patient-specific considerations that may preclude use. Existing UfH products may also be used for drug compounding (eg, use current stock to provide an indicated aliquot) to meet the need of prioritized patients.⁴ In addition, we suggest prioritizing current UfH/heparinized saline for use for the following groups of patients⁴:

• Emergent/urgent cardiac surgery^1,6;
• Hemodialysis patients^1,7-9 for which the low-molecular-weight heparin (LMWH) dalteparin is deemed inappropriate or the patient is not monitored in the intensive care unit for regional citrate administration;
• VTE prophylaxis for patients with epidurals or chest tubes for which urgent invasive management may occur, recent cardiac or neurosurgery, or for patients with a creatine clearance < 15 mL/min or receiving hemodialysis^10-12;
• Vascular surgery (eg, limb ischemia) and interventions (eg, carotid stenting, endarterectomy)^13,14;
• Mesenteric ischemia (venous thrombosis) with a potential to proceed to laparotomy¹⁵;
• Critically ill patients with arterial lines for which normal saline is deemed inappropriate for line flushing¹⁶;
• Electrophysiology procedures (eg, AF ablation)¹⁷; and
• Contraindication to use of a long-acting alternative listed in the table or a medical necessity exists for using a rapidly reversible agent. Examples for this category include but are not limited to recent gastrointestinal bleeding, central nervous system lesion, and select neurologic diagnoses (eg, cerebral venous sinus thrombosis with hemorrhage, thrombus in vertebral basilar system or anterior circulation, intraparenchymal hemorrhage plus mechanical valve, medium to large cardioembolic stroke with intracardiac thrombus).

Conclusion

The UfH drug shortage represents a significant threat to public health and is a major challenge for US health care systems, including the Veterans Health Administration. Overreliance on a predominant source of crude heparin has affected multiple UfH manufacturers and products. Current alternatives to UfH include low-molecular-weight heparins, IV direct thrombin inhibitors, and SC fondaparinux, with selection supported by guidelines or evolving literature. However, the shortage has the potential to expand to other injectables, such as dalteparin and enoxaparin, and severely limit care for veterans. It is vital that clinicians rapidly address the current shortage by creating a plan to develop efficient and equitable access to UfH, continue to assess supply and update stakeholders, and select evidence-based alternatives while maintaining focus on efficacy and safety.

Acknowledgments

The authors thank Ashley Yost, PharmD, for her coordination of the multidisciplinary task force assigned to efficiently manage the heparin drug shortage. This material is the result of work supported with resources and the use of facilities at the VA Tennessee Valley Healthcare System in Nashville, Tennessee.

CASE Patient has adverse effects from halted estrogen pills

JR twists her hands nervously as you step into the room. “They stopped my hormones,” she sighs as you pull up her lab results.

JR recently had been admitted to an inpatient cardiology unit for several days for a heart failure exacerbation. Her ankles are still swollen beneath her floral print skirt, but she is breathing much easier now. She is back at your primary care office, hoping to get clearance to restart her estrogen pills.

JR reports having mood swings and terrible nightmares while not taking her hormones, which she has been taking for more than 3 years. She hesitates before sharing, “One of the doctors kept asking me questions about my sex life that had nothing to do with my heart condition. I don’t want to go back there.”

Providing compassionate and comprehensive care to gender-nonconforming individuals is challenging for a multitude of reasons, from clinician ignorance to systemic discrimination. About 33% of transgender patients reported being harassed, denied care, or even being assaulted when seeking health care, while 23% reported avoiding going to the doctor altogether when sick or injured out of fear of discrimination.¹

Unfortunately, now, further increases to barriers to care may be put in place. In late May of this year, the Department of Health and Human Services (HHS) proposed new regulations that would reverse previous regulations granted through section 1557 of the Affordable Care Act (ACA)—the Health Care Rights Law—which affirmed the rights of gender nonbinary persons to medical care. Among the proposed changes is the elimination of protections against discrimination in health care based on gender identity.² The proposed regulation changes come on the heels of a federal court case, which seeks to declare that hospital systems may turn away patients based on gender identity.³

Unraveling rights afforded under the ACA

The Health Care Rights Law was passed under the ACA; it prohibits discrimination based on race, color, national origin, sex, age, and disability in health programs and activities receiving federal financial assistance. Multiple lower courts have supported that the rights of transgender individuals is included within these protections against discrimination on the basis of sex.⁴ These court rulings not only have ensured the ability of gender-diverse individuals to access care but also have enforced insurance coverage of therapies for gender dysphoria. It was only in 2014 that Medicaid began providing coverage for gender-affirming surgeries and eliminating language that such procedures were “experimental” or “cosmetic.” The 2016 passage of the ACA mandated that private insurance companies follow suit. Unfortunately, the recent proposed regulation changes to the Health Care Rights Law may spark a reversal from insurance companies as well. Such a setback would affect gender-diverse individuals’ hormone treatments as well as their ability to access a full spectrum of care within the health care system.

Continue to: ACOG urges nondiscriminatory practices...

ACOG urges nondiscriminatory practices

The proposed regulation changes to the Health Care Rights Law are from the Conscience and Religious Freedom Division of the HHS Office for Civil Rights, which was established in 2018 and has been advocating for the rights of health care providers to refuse to treat patients based on their own religious beliefs.⁵ We argue, however, that providing care to persons of varying backgrounds is not an assault on our individual liberties but rather a privilege as providers. As obstetrician-gynecologists, it may be easy to only consider cis-gendered women our responsibility. But our field also emphasizes individual empowerment above all else—we fight every day for our patients’ rights to contraception, fertility, pregnancy, parenthood, and sexual freedoms. Let us continue speaking up for the rights of all those who need gynecologic care, regardless of the pronouns they use.

“The American College of Obstetricians and Gynecologists urges health care providers to foster nondiscriminatory practices and policies to increase identification and to facilitate quality health care for transgender individuals, both in assisting with the transition if desired as well as providing long-term preventive health care.”⁶

We urge you to take action

• Reach out to your local representatives about protecting transgender health access
• Educate yourself on the unique needs of transgender individuals
• Read personal accounts
• Share your personal story
• Find referring providers near your practice

CASE Patient has adverse effects from halted estrogen pills

JR twists her hands nervously as you step into the room. “They stopped my hormones,” she sighs as you pull up her lab results.

JR recently had been admitted to an inpatient cardiology unit for several days for a heart failure exacerbation. Her ankles are still swollen beneath her floral print skirt, but she is breathing much easier now. She is back at your primary care office, hoping to get clearance to restart her estrogen pills.

JR reports having mood swings and terrible nightmares while not taking her hormones, which she has been taking for more than 3 years. She hesitates before sharing, “One of the doctors kept asking me questions about my sex life that had nothing to do with my heart condition. I don’t want to go back there.”

Providing compassionate and comprehensive care to gender-nonconforming individuals is challenging for a multitude of reasons, from clinician ignorance to systemic discrimination. About 33% of transgender patients reported being harassed, denied care, or even being assaulted when seeking health care, while 23% reported avoiding going to the doctor altogether when sick or injured out of fear of discrimination.¹

Unfortunately, now, further increases to barriers to care may be put in place. In late May of this year, the Department of Health and Human Services (HHS) proposed new regulations that would reverse previous regulations granted through section 1557 of the Affordable Care Act (ACA)—the Health Care Rights Law—which affirmed the rights of gender nonbinary persons to medical care. Among the proposed changes is the elimination of protections against discrimination in health care based on gender identity.² The proposed regulation changes come on the heels of a federal court case, which seeks to declare that hospital systems may turn away patients based on gender identity.³

Unraveling rights afforded under the ACA

The Health Care Rights Law was passed under the ACA; it prohibits discrimination based on race, color, national origin, sex, age, and disability in health programs and activities receiving federal financial assistance. Multiple lower courts have supported that the rights of transgender individuals is included within these protections against discrimination on the basis of sex.⁴ These court rulings not only have ensured the ability of gender-diverse individuals to access care but also have enforced insurance coverage of therapies for gender dysphoria. It was only in 2014 that Medicaid began providing coverage for gender-affirming surgeries and eliminating language that such procedures were “experimental” or “cosmetic.” The 2016 passage of the ACA mandated that private insurance companies follow suit. Unfortunately, the recent proposed regulation changes to the Health Care Rights Law may spark a reversal from insurance companies as well. Such a setback would affect gender-diverse individuals’ hormone treatments as well as their ability to access a full spectrum of care within the health care system.

Continue to: ACOG urges nondiscriminatory practices...

ACOG urges nondiscriminatory practices

The proposed regulation changes to the Health Care Rights Law are from the Conscience and Religious Freedom Division of the HHS Office for Civil Rights, which was established in 2018 and has been advocating for the rights of health care providers to refuse to treat patients based on their own religious beliefs.⁵ We argue, however, that providing care to persons of varying backgrounds is not an assault on our individual liberties but rather a privilege as providers. As obstetrician-gynecologists, it may be easy to only consider cis-gendered women our responsibility. But our field also emphasizes individual empowerment above all else—we fight every day for our patients’ rights to contraception, fertility, pregnancy, parenthood, and sexual freedoms. Let us continue speaking up for the rights of all those who need gynecologic care, regardless of the pronouns they use.

“The American College of Obstetricians and Gynecologists urges health care providers to foster nondiscriminatory practices and policies to increase identification and to facilitate quality health care for transgender individuals, both in assisting with the transition if desired as well as providing long-term preventive health care.”⁶

We urge you to take action

• Reach out to your local representatives about protecting transgender health access
• Educate yourself on the unique needs of transgender individuals
• Read personal accounts
• Share your personal story
• Find referring providers near your practice

CASE Patient has adverse effects from halted estrogen pills

JR twists her hands nervously as you step into the room. “They stopped my hormones,” she sighs as you pull up her lab results.

JR recently had been admitted to an inpatient cardiology unit for several days for a heart failure exacerbation. Her ankles are still swollen beneath her floral print skirt, but she is breathing much easier now. She is back at your primary care office, hoping to get clearance to restart her estrogen pills.

JR reports having mood swings and terrible nightmares while not taking her hormones, which she has been taking for more than 3 years. She hesitates before sharing, “One of the doctors kept asking me questions about my sex life that had nothing to do with my heart condition. I don’t want to go back there.”

Providing compassionate and comprehensive care to gender-nonconforming individuals is challenging for a multitude of reasons, from clinician ignorance to systemic discrimination. About 33% of transgender patients reported being harassed, denied care, or even being assaulted when seeking health care, while 23% reported avoiding going to the doctor altogether when sick or injured out of fear of discrimination.¹

Unfortunately, now, further increases to barriers to care may be put in place. In late May of this year, the Department of Health and Human Services (HHS) proposed new regulations that would reverse previous regulations granted through section 1557 of the Affordable Care Act (ACA)—the Health Care Rights Law—which affirmed the rights of gender nonbinary persons to medical care. Among the proposed changes is the elimination of protections against discrimination in health care based on gender identity.² The proposed regulation changes come on the heels of a federal court case, which seeks to declare that hospital systems may turn away patients based on gender identity.³

Unraveling rights afforded under the ACA

The Health Care Rights Law was passed under the ACA; it prohibits discrimination based on race, color, national origin, sex, age, and disability in health programs and activities receiving federal financial assistance. Multiple lower courts have supported that the rights of transgender individuals is included within these protections against discrimination on the basis of sex.⁴ These court rulings not only have ensured the ability of gender-diverse individuals to access care but also have enforced insurance coverage of therapies for gender dysphoria. It was only in 2014 that Medicaid began providing coverage for gender-affirming surgeries and eliminating language that such procedures were “experimental” or “cosmetic.” The 2016 passage of the ACA mandated that private insurance companies follow suit. Unfortunately, the recent proposed regulation changes to the Health Care Rights Law may spark a reversal from insurance companies as well. Such a setback would affect gender-diverse individuals’ hormone treatments as well as their ability to access a full spectrum of care within the health care system.

Continue to: ACOG urges nondiscriminatory practices...

ACOG urges nondiscriminatory practices

The proposed regulation changes to the Health Care Rights Law are from the Conscience and Religious Freedom Division of the HHS Office for Civil Rights, which was established in 2018 and has been advocating for the rights of health care providers to refuse to treat patients based on their own religious beliefs.⁵ We argue, however, that providing care to persons of varying backgrounds is not an assault on our individual liberties but rather a privilege as providers. As obstetrician-gynecologists, it may be easy to only consider cis-gendered women our responsibility. But our field also emphasizes individual empowerment above all else—we fight every day for our patients’ rights to contraception, fertility, pregnancy, parenthood, and sexual freedoms. Let us continue speaking up for the rights of all those who need gynecologic care, regardless of the pronouns they use.

“The American College of Obstetricians and Gynecologists urges health care providers to foster nondiscriminatory practices and policies to increase identification and to facilitate quality health care for transgender individuals, both in assisting with the transition if desired as well as providing long-term preventive health care.”⁶

We urge you to take action

• Reach out to your local representatives about protecting transgender health access
• Educate yourself on the unique needs of transgender individuals
• Read personal accounts
• Share your personal story
• Find referring providers near your practice

Vitamin E acetate is one possible culprit in the mysterious vaping-associated lung disease that has killed three patients, sickened 450, and baffled clinicians and investigators all summer.

mauro grigollo/Thinkstock

Another death may be linked to the disorder, officials said during a joint press briefing held by the Centers for Disease Control and Prevention and the Food and Drug Administration. In all, 450 potential cases have been reported and e-cigarette use confirmed in 215. Cases have occurred in 33 states and one territory. A total of 84% of the patients reported having used tetrahydrocannabinol (THC) products in e-cigarette devices.

A preliminary report on the situation by Jennifer Layden, MD, of the department of public health in Illinois and colleagues – including a preliminary case definition – was simultaneously released in the New England Journal of Medicine (2019 Sep 6. doi: 10.1056/NEJMoa1911614).

No single device or substance was common to all the cases, leading officials to issue a blanket warning against e-cigarettes, especially those containing THC.

“We believe a chemical exposure is likely related, but more information is needed to determine what substances. Some labs have identified vitamin E acetate in some samples,” said Dana Meaney-Delman, MD, MPH, incident manager, CDC 2019 Lung Injury Response. “Continued investigation is needed to identify the risk associated with a specific product or substance.”

Besides vitamin E acetate, federal labs are looking at other cannabinoids, cutting agents, diluting agents, pesticides, opioids, and toxins.

Officials also issued a general warning about the products. Youths, young people, and pregnant women should never use e-cigarettes, they cautioned, and no one should buy them from a noncertified source, a street vendor, or a social contact. Even cartridges originally obtained from a certified source should never have been altered in any way.

Dr. Layden and colleagues reported that bilateral lung infiltrates was characterized in 98% of the 53 patients hospitalized with the recently reported e-cigarette–induced lung injury. Nonspecific constitutional symptoms, including fever, chills, weight loss, and fatigue, were present in all of the patients.

Patients may show some symptoms days or even weeks before acute respiratory failure develops, and many had sought medical help before that. All presented with bilateral lung infiltrates, part of an evolving case definition. Many complained of nonspecific constitutional symptoms, including fever, chills, gastrointestinal symptoms, and weight loss. Of the patients who underwent bronchoscopy, many were diagnosed as having lipoid pneumonia, a rare condition characterized by lipid-laden macrophages.

“We don’t know the significance of the lipid-containing macrophages, and we don’t know if the lipids are endogenous or exogenous,” Dr. Meaney-Delman said.

The incidence of such cases appears to be rising rapidly, Dr. Layden noted. An epidemiologic review of cases in Illinois found that the mean monthly rate of visits related to severe respiratory illness in June-August was twice that observed during the same months last year.
 

msullivan@mdedge.com

SOURCE: Layden JE et al. N Engl J Med. 2019 Sep 6. doi: 1 0.1056/NEJMoa1911614.

Vitamin E acetate is one possible culprit in the mysterious vaping-associated lung disease that has killed three patients, sickened 450, and baffled clinicians and investigators all summer.

mauro grigollo/Thinkstock

Another death may be linked to the disorder, officials said during a joint press briefing held by the Centers for Disease Control and Prevention and the Food and Drug Administration. In all, 450 potential cases have been reported and e-cigarette use confirmed in 215. Cases have occurred in 33 states and one territory. A total of 84% of the patients reported having used tetrahydrocannabinol (THC) products in e-cigarette devices.

A preliminary report on the situation by Jennifer Layden, MD, of the department of public health in Illinois and colleagues – including a preliminary case definition – was simultaneously released in the New England Journal of Medicine (2019 Sep 6. doi: 10.1056/NEJMoa1911614).

No single device or substance was common to all the cases, leading officials to issue a blanket warning against e-cigarettes, especially those containing THC.

“We believe a chemical exposure is likely related, but more information is needed to determine what substances. Some labs have identified vitamin E acetate in some samples,” said Dana Meaney-Delman, MD, MPH, incident manager, CDC 2019 Lung Injury Response. “Continued investigation is needed to identify the risk associated with a specific product or substance.”

Besides vitamin E acetate, federal labs are looking at other cannabinoids, cutting agents, diluting agents, pesticides, opioids, and toxins.

Officials also issued a general warning about the products. Youths, young people, and pregnant women should never use e-cigarettes, they cautioned, and no one should buy them from a noncertified source, a street vendor, or a social contact. Even cartridges originally obtained from a certified source should never have been altered in any way.

Dr. Layden and colleagues reported that bilateral lung infiltrates was characterized in 98% of the 53 patients hospitalized with the recently reported e-cigarette–induced lung injury. Nonspecific constitutional symptoms, including fever, chills, weight loss, and fatigue, were present in all of the patients.

Patients may show some symptoms days or even weeks before acute respiratory failure develops, and many had sought medical help before that. All presented with bilateral lung infiltrates, part of an evolving case definition. Many complained of nonspecific constitutional symptoms, including fever, chills, gastrointestinal symptoms, and weight loss. Of the patients who underwent bronchoscopy, many were diagnosed as having lipoid pneumonia, a rare condition characterized by lipid-laden macrophages.

“We don’t know the significance of the lipid-containing macrophages, and we don’t know if the lipids are endogenous or exogenous,” Dr. Meaney-Delman said.

The incidence of such cases appears to be rising rapidly, Dr. Layden noted. An epidemiologic review of cases in Illinois found that the mean monthly rate of visits related to severe respiratory illness in June-August was twice that observed during the same months last year.
 

msullivan@mdedge.com

SOURCE: Layden JE et al. N Engl J Med. 2019 Sep 6. doi: 1 0.1056/NEJMoa1911614.

Vitamin E acetate is one possible culprit in the mysterious vaping-associated lung disease that has killed three patients, sickened 450, and baffled clinicians and investigators all summer.

mauro grigollo/Thinkstock

Another death may be linked to the disorder, officials said during a joint press briefing held by the Centers for Disease Control and Prevention and the Food and Drug Administration. In all, 450 potential cases have been reported and e-cigarette use confirmed in 215. Cases have occurred in 33 states and one territory. A total of 84% of the patients reported having used tetrahydrocannabinol (THC) products in e-cigarette devices.

A preliminary report on the situation by Jennifer Layden, MD, of the department of public health in Illinois and colleagues – including a preliminary case definition – was simultaneously released in the New England Journal of Medicine (2019 Sep 6. doi: 10.1056/NEJMoa1911614).

No single device or substance was common to all the cases, leading officials to issue a blanket warning against e-cigarettes, especially those containing THC.

“We believe a chemical exposure is likely related, but more information is needed to determine what substances. Some labs have identified vitamin E acetate in some samples,” said Dana Meaney-Delman, MD, MPH, incident manager, CDC 2019 Lung Injury Response. “Continued investigation is needed to identify the risk associated with a specific product or substance.”

Besides vitamin E acetate, federal labs are looking at other cannabinoids, cutting agents, diluting agents, pesticides, opioids, and toxins.

Officials also issued a general warning about the products. Youths, young people, and pregnant women should never use e-cigarettes, they cautioned, and no one should buy them from a noncertified source, a street vendor, or a social contact. Even cartridges originally obtained from a certified source should never have been altered in any way.

Dr. Layden and colleagues reported that bilateral lung infiltrates was characterized in 98% of the 53 patients hospitalized with the recently reported e-cigarette–induced lung injury. Nonspecific constitutional symptoms, including fever, chills, weight loss, and fatigue, were present in all of the patients.

Patients may show some symptoms days or even weeks before acute respiratory failure develops, and many had sought medical help before that. All presented with bilateral lung infiltrates, part of an evolving case definition. Many complained of nonspecific constitutional symptoms, including fever, chills, gastrointestinal symptoms, and weight loss. Of the patients who underwent bronchoscopy, many were diagnosed as having lipoid pneumonia, a rare condition characterized by lipid-laden macrophages.

“We don’t know the significance of the lipid-containing macrophages, and we don’t know if the lipids are endogenous or exogenous,” Dr. Meaney-Delman said.

The incidence of such cases appears to be rising rapidly, Dr. Layden noted. An epidemiologic review of cases in Illinois found that the mean monthly rate of visits related to severe respiratory illness in June-August was twice that observed during the same months last year.
 

msullivan@mdedge.com

SOURCE: Layden JE et al. N Engl J Med. 2019 Sep 6. doi: 1 0.1056/NEJMoa1911614.