Aspirin (acetylsalicylic acid) and its progenitors are valuable medications with a history spanning at least 4 millennia. An enormous number of patients take aspirin for a variety of reasons, and managing their therapy around the time of surgery can be challenging, as Drs. Prabhakaran and Whinney discuss in this issue.¹ Even after 4,000 years, we are still learning about these remarkable drugs.

See related article

LEARNING WHAT SALICYLATES ARE

LEARNING (AND IGNORING) WHAT ASPIRIN CAN DO

In the 1940s, a general practitioner in California named Lawrence Craven recognized that many of his post-tonsillectomy patients had to be hospitalized for bleeding after he started recommending they use aspirin-containing chewing gum for pain relief.⁴ Under the then-debated hypothesis that myocardial infarction (MI) involves thrombosis, he recommended that adult men should take aspirin daily. He believed that women had lower rates of MI because they were more likely to take aspirin, something that men did not view as a “masculine” thing to do.

In a series of letters in journals such as the Mississippi Valley Medical Journal,⁵ Craven reported his observations of very low rates of MI and no strokes in aspirin users. Given the nonrigorous nature of his research and the obscure journals in which he published, his findings languished for many years. Ironically, he died of an MI in 1957.

LEARNING HOW ASPIRIN WORKS (AND A FEW OTHER THINGS)

The history of aspirin research illustrates how the fields of hemostasis and inflammation are now linked.

In the late 1960s, Weiss et al⁶ reported that aspirin rapidly and irreversibly inhibits platelet aggregation. In parallel, using biological assays in work that eventually led to the Nobel Prize, Vane⁷ discovered that inflammation involves the de novo synthesis of prostaglandins and that aspirin directly inhibits this synthesis. Further work connecting these lines of investigation led us to understand that platelet aggregation is enhanced by the prostaglandin derivative thromboxane A2, produced by cyclooxygenase-1, and that aspirin irreversibly inhibits this enzyme by acetylation.

LEARNING WHEN TO USE ASPIRIN

After decades of research ranging from the Physicians’ Health Study to well-named trials such as ARRIVE, ASCEND, and ASPREE, we now know that taking daily low doses of aspirin for primary prevention can reduce the risk of cardiovascular events and may reduce the risk of colorectal cancer—but at the cost of an increased risk of bleeding.⁸

Which patients will gain the most benefit and incur the least risk is still debated. What is certain, however, is that aspirin has an important role in acute coronary syndromes, secondary prevention of MI and stroke, and prevention of thrombosis after coronary stent placement. In the perioperative setting, we are learning that aspirin may benefit patients with myocardial injury after noncardiac surgery, a recently described clinical entity associated with surprisingly high mortality rates.^9,10

The perioperative period is a dangerous time—surgical stress, hypercoagulability, inflammation, pain, and hemodynamic changes predispose to plaque rupture and supply-demand imbalance. It is therefore logical to hope aspirin would provide protection for at-risk patients in this context.

Unfortunately, results from the second Perioperative Ischemic Evaluation trial have dampened enthusiasm.¹¹ Aspirin has now joined clonidine and beta-blockers on the list of interventions that probably do not reduce perioperative cardiovascular mortality rates. Other than protecting against stent thrombosis, aspirin’s main perioperative effect is to increase bleeding. Consequently, some surgical procedures mandate withdrawal of aspirin.

WHAT WE STILL NEED TO LEARN

Over the years, we have learned the broad outlines of using aspirin to prevent and treat cardiovascular disease, to relieve pain and inflammation (its original purpose), and to prevent stent thrombosis. 

However, many details remain to be filled in. We need to better define groups who should and should not take aspirin for primary prevention. We also need to understand aspirin’s role in cancer chemoprevention, to find better ways to mitigate its undesirable effects, and to study its role in treating myocardial injury after noncardiac surgery.

Finally, we need to determine which (if any) patients without coronary stents will benefit from continuing their aspirin perioperatively or even initiating aspirin therapy preoperatively.

Will humanity still be using salicylates 4,000 years from now? Probably not. But what we have learned and will continue to learn from this remarkable group of medications will certainly inform new and better therapies in the years to come.

Aspirin (acetylsalicylic acid) and its progenitors are valuable medications with a history spanning at least 4 millennia. An enormous number of patients take aspirin for a variety of reasons, and managing their therapy around the time of surgery can be challenging, as Drs. Prabhakaran and Whinney discuss in this issue.¹ Even after 4,000 years, we are still learning about these remarkable drugs.

See related article

LEARNING WHAT SALICYLATES ARE

LEARNING (AND IGNORING) WHAT ASPIRIN CAN DO

In the 1940s, a general practitioner in California named Lawrence Craven recognized that many of his post-tonsillectomy patients had to be hospitalized for bleeding after he started recommending they use aspirin-containing chewing gum for pain relief.⁴ Under the then-debated hypothesis that myocardial infarction (MI) involves thrombosis, he recommended that adult men should take aspirin daily. He believed that women had lower rates of MI because they were more likely to take aspirin, something that men did not view as a “masculine” thing to do.

In a series of letters in journals such as the Mississippi Valley Medical Journal,⁵ Craven reported his observations of very low rates of MI and no strokes in aspirin users. Given the nonrigorous nature of his research and the obscure journals in which he published, his findings languished for many years. Ironically, he died of an MI in 1957.

LEARNING HOW ASPIRIN WORKS (AND A FEW OTHER THINGS)

The history of aspirin research illustrates how the fields of hemostasis and inflammation are now linked.

In the late 1960s, Weiss et al⁶ reported that aspirin rapidly and irreversibly inhibits platelet aggregation. In parallel, using biological assays in work that eventually led to the Nobel Prize, Vane⁷ discovered that inflammation involves the de novo synthesis of prostaglandins and that aspirin directly inhibits this synthesis. Further work connecting these lines of investigation led us to understand that platelet aggregation is enhanced by the prostaglandin derivative thromboxane A2, produced by cyclooxygenase-1, and that aspirin irreversibly inhibits this enzyme by acetylation.

LEARNING WHEN TO USE ASPIRIN

After decades of research ranging from the Physicians’ Health Study to well-named trials such as ARRIVE, ASCEND, and ASPREE, we now know that taking daily low doses of aspirin for primary prevention can reduce the risk of cardiovascular events and may reduce the risk of colorectal cancer—but at the cost of an increased risk of bleeding.⁸

Which patients will gain the most benefit and incur the least risk is still debated. What is certain, however, is that aspirin has an important role in acute coronary syndromes, secondary prevention of MI and stroke, and prevention of thrombosis after coronary stent placement. In the perioperative setting, we are learning that aspirin may benefit patients with myocardial injury after noncardiac surgery, a recently described clinical entity associated with surprisingly high mortality rates.^9,10

The perioperative period is a dangerous time—surgical stress, hypercoagulability, inflammation, pain, and hemodynamic changes predispose to plaque rupture and supply-demand imbalance. It is therefore logical to hope aspirin would provide protection for at-risk patients in this context.

Unfortunately, results from the second Perioperative Ischemic Evaluation trial have dampened enthusiasm.¹¹ Aspirin has now joined clonidine and beta-blockers on the list of interventions that probably do not reduce perioperative cardiovascular mortality rates. Other than protecting against stent thrombosis, aspirin’s main perioperative effect is to increase bleeding. Consequently, some surgical procedures mandate withdrawal of aspirin.

WHAT WE STILL NEED TO LEARN

Over the years, we have learned the broad outlines of using aspirin to prevent and treat cardiovascular disease, to relieve pain and inflammation (its original purpose), and to prevent stent thrombosis. 

However, many details remain to be filled in. We need to better define groups who should and should not take aspirin for primary prevention. We also need to understand aspirin’s role in cancer chemoprevention, to find better ways to mitigate its undesirable effects, and to study its role in treating myocardial injury after noncardiac surgery.

Finally, we need to determine which (if any) patients without coronary stents will benefit from continuing their aspirin perioperatively or even initiating aspirin therapy preoperatively.

Will humanity still be using salicylates 4,000 years from now? Probably not. But what we have learned and will continue to learn from this remarkable group of medications will certainly inform new and better therapies in the years to come.

Aspirin (acetylsalicylic acid) and its progenitors are valuable medications with a history spanning at least 4 millennia. An enormous number of patients take aspirin for a variety of reasons, and managing their therapy around the time of surgery can be challenging, as Drs. Prabhakaran and Whinney discuss in this issue.¹ Even after 4,000 years, we are still learning about these remarkable drugs.

See related article

LEARNING WHAT SALICYLATES ARE

LEARNING (AND IGNORING) WHAT ASPIRIN CAN DO

In the 1940s, a general practitioner in California named Lawrence Craven recognized that many of his post-tonsillectomy patients had to be hospitalized for bleeding after he started recommending they use aspirin-containing chewing gum for pain relief.⁴ Under the then-debated hypothesis that myocardial infarction (MI) involves thrombosis, he recommended that adult men should take aspirin daily. He believed that women had lower rates of MI because they were more likely to take aspirin, something that men did not view as a “masculine” thing to do.

In a series of letters in journals such as the Mississippi Valley Medical Journal,⁵ Craven reported his observations of very low rates of MI and no strokes in aspirin users. Given the nonrigorous nature of his research and the obscure journals in which he published, his findings languished for many years. Ironically, he died of an MI in 1957.

LEARNING HOW ASPIRIN WORKS (AND A FEW OTHER THINGS)

The history of aspirin research illustrates how the fields of hemostasis and inflammation are now linked.

In the late 1960s, Weiss et al⁶ reported that aspirin rapidly and irreversibly inhibits platelet aggregation. In parallel, using biological assays in work that eventually led to the Nobel Prize, Vane⁷ discovered that inflammation involves the de novo synthesis of prostaglandins and that aspirin directly inhibits this synthesis. Further work connecting these lines of investigation led us to understand that platelet aggregation is enhanced by the prostaglandin derivative thromboxane A2, produced by cyclooxygenase-1, and that aspirin irreversibly inhibits this enzyme by acetylation.

LEARNING WHEN TO USE ASPIRIN

After decades of research ranging from the Physicians’ Health Study to well-named trials such as ARRIVE, ASCEND, and ASPREE, we now know that taking daily low doses of aspirin for primary prevention can reduce the risk of cardiovascular events and may reduce the risk of colorectal cancer—but at the cost of an increased risk of bleeding.⁸

Which patients will gain the most benefit and incur the least risk is still debated. What is certain, however, is that aspirin has an important role in acute coronary syndromes, secondary prevention of MI and stroke, and prevention of thrombosis after coronary stent placement. In the perioperative setting, we are learning that aspirin may benefit patients with myocardial injury after noncardiac surgery, a recently described clinical entity associated with surprisingly high mortality rates.^9,10

The perioperative period is a dangerous time—surgical stress, hypercoagulability, inflammation, pain, and hemodynamic changes predispose to plaque rupture and supply-demand imbalance. It is therefore logical to hope aspirin would provide protection for at-risk patients in this context.

Unfortunately, results from the second Perioperative Ischemic Evaluation trial have dampened enthusiasm.¹¹ Aspirin has now joined clonidine and beta-blockers on the list of interventions that probably do not reduce perioperative cardiovascular mortality rates. Other than protecting against stent thrombosis, aspirin’s main perioperative effect is to increase bleeding. Consequently, some surgical procedures mandate withdrawal of aspirin.

WHAT WE STILL NEED TO LEARN

Over the years, we have learned the broad outlines of using aspirin to prevent and treat cardiovascular disease, to relieve pain and inflammation (its original purpose), and to prevent stent thrombosis. 

However, many details remain to be filled in. We need to better define groups who should and should not take aspirin for primary prevention. We also need to understand aspirin’s role in cancer chemoprevention, to find better ways to mitigate its undesirable effects, and to study its role in treating myocardial injury after noncardiac surgery.

Finally, we need to determine which (if any) patients without coronary stents will benefit from continuing their aspirin perioperatively or even initiating aspirin therapy preoperatively.

Will humanity still be using salicylates 4,000 years from now? Probably not. But what we have learned and will continue to learn from this remarkable group of medications will certainly inform new and better therapies in the years to come.

“My dear, here we must run as fast as we can, just to stay in place. And if you wish to go anywhere you must run twice as fast as that.”
—Lewis Carroll
Alice’s Adventures in Wonderland

The future of primary care internal medicine physicians is uncertain. According to a 2018 survey of internal medicine residents conducted by the American College of Physicians, only 11% were considering primary care as a career path.¹ In 1998, that number was 54%.²

See related commentary

Possible reasons are many:

• Lower pay compared with subspecialists in a pay system that rewards procedural competency over mental effort
• Work schedules less flexible than in other specialties (eg, hospital medicine practitioners may have 1 week on and 1 week off)
• Perceived lack of respect
• Increasing regulatory and record-keeping burdens
• Tyranny of 15- to 20-minute appointments (irrespective of patient complexity)
• Scope-of-practice concerns as other providers seek primary care equivalency status (eg, pharmacists, walk-in clinics, advanced practice providers, telemedicine providers).

The result is a projected shortage of primary care physicians of 21,100 to 55,200 by 2030, according to a 2019 report by the Association of American Medical Colleges,³ despite an expected growth in advanced practice providers in primary care such as nurse practitioners and physician assistants.

A practical result of this shortage will be even less patient access to primary care physicians. A 2017 national survey found that the average wait time for a new patient-physician appointment has already increased by 30% since 2014.⁴ The wait time to see a primary care physician varied between 29 days in major metropolitan areas (up 50% from 2014) and 56 days in mid-sized markets. The longest waits by market size were 109 days for new patients in Boston, MA, and 122 days for those living in Albany, NY.

What are the implications?

In this issue, Pravia and Diaz⁵ make the case that primary care providers must adapt their practices to meet the needs of younger generations by increasing their use of technology. We agree that telemedicine, wearable medical devices, and enhanced patient communication through the electronic medical record (EMR) are here to stay and should be embraced.

However, we have seen the challenges of adopting technologic advances without first making an adjustment to the volume-driven patient schedule. For such advances to be successfully integrated into a clinical practice, it is vital to be cognizant of the current challenges encountered in primary care internal medicine.

UNIQUE BURDENS ON PRIMARY CARE

In addition to the stress of addressing multiple complex medical problems within a short time, evaluating multiple medical problems often leads to increases in results to review, forms to complete, and calls to patients. Even treatment plans initiated by specialists are often deferred to primary care providers for dosing adjustments, follow-up laboratory testing, and monitoring.

Moreover, patients often seek a second opinion from their primary care provider regarding care provided by subspecialists, as they consider their primary care provider to be the doctor who knows them best. And though it can be personally gratifying to be considered a trusted partner in the patient’s care, these requests often result in additional phone calls to the office or another thing to address within a complex visit.

A large in-box can be daunting in the setting of increased EMR demands. Whether you have difficulty putting in basic orders or are an advanced user, each upgrade can make you feel like you’re using the EMR for the first time. This is a problem for all specialties, but in primary care, one is addressing a large spectrum of concerns, so there is less opportunity to use standardized templates that can help buffer the problem.

A study of primary care providers found that nearly 75% of each patient visit was spent on activities other than face-to-face patient care, including working with the EMR.⁶ Similarly, a study using in-office observations and after-hours diaries found that physicians from various specialties spend 2 hours on administrative duties for each hour that they see patients in the office, followed by an additional 1 to 2 hours of work after clinic, mostly devoted to the EMR.⁷

Clinicians using scribes to help with record-keeping duties often need to see more patients to compensate for the cost. Adding 2 to 3 patients to a daily schedule usually means adding more medical conditions to manage, with an exponential increase in testing and in-box burden.

The additional burden this coverage creates in primary care is often not well understood by those in other specialties.

Another burden unique to primary care providers is the nearly continuous publication of guidelines that are often confusing and discrepant. Because many high-impact guidelines represent expert consensus or evidence from specialist perspectives, they may not fit the primary care model or values: eg, primary care guidelines tend to place more emphasis on harms associated with screening.

Screening for breast and prostate cancers is a prime example. Both require shared decision-making based on patient preferences and values.^8,9 Detailed discussions about preventive screening can be difficult to achieve within the context of a medical visit owing to time limitations, especially if other medical conditions being addressed are equally controversial, such as blood pressure target goals. A decade ago, one could easily declare, “It’s time for your annual PSA test,” and move on to other concerns. Given the changing evidence, an informed patient is now likely to question whether this test should be done, how often it should be done, and whether a prostate examination should also be included.

The push toward population health has raised questions about the value of a preventive wellness visit, especially in healthy patients.^10,11 Arguments against the annual physical do not account for the value of these visits, which provide the opportunity to have time-intensive shared decision-making conversations and build a trusting patient-physician relationship. The value of the annual physical is not simply to do examinations for which there is limited evidence; it is a time for us to get to know our patients, to update their preventive needs (and the medical record), and to discuss which screening tests they may safely forgo to avoid unnecessary false-positives, leading to excess cost and harm.

This trusting relationship, developed over years, is likely to save both the patient and the healthcare system significant money. For example, it enables us to reassure patients that an antibiotic is not needed for their upper respiratory infection, to encourage them to try a dietary change before proceeding with computed tomography for their abdominal pain, or to discourage them from inappropriately aggressive screening tests that may result in overtesting or overdiagnosis.

Unfortunately, it is nearly impossible to accurately quantify these substantial benefits to the healthcare system and patients. And there is a real potential that recommendations against the annual physical may eventually affect future reimbursement, which would add to the time pressures of an already overburdened primary care workforce.

DO PRIMARY CARE PHYSICIANS MAKE A DIFFERENCE?

As medicine and technology evolve, patients have more ways to access care. However, the Internet also provides patients with access to more conflicting information than ever before, making it even more important for clinicians, as trusted partners in their patients’ health, to help patients navigate the waters of information and misinformation.

Studies have shown that having a primary care physician is associated with a longer life span, higher likelihood of reporting good health, and similar clinical outcomes for common conditions such as diabetes and hypertension when compared with subspecialty care, but at a lower cost and with less resource utilization.^12,13 In a study published in 2019, Basu et al¹² found that for every 10 additional primary care physicians per 100,000 population, there was an associated 51.5-day increase in life expectancy, compared with a 19.2-day increase for specialists. Cost savings also occur. Similarly, a review by the American College of Physicians¹³ found that each additional primary care physician per 10,000 population in a US state increased the state’s health quality ranking by more than 10 spots and reduced their overall spending per Medicare beneficiary. In contrast, an increase of 1 specialist per 10,000 population was linked to a 9-spot decrease in health-quality ranking and an increase in spending.

WHY CHOOSE PRIMARY CARE?

As medical students, we fell in love with internal medicine because of the complexity and intellectual challenges of working through a diagnostic dilemma. There is a certain excitement in not knowing what type of patients will show up that day.

Primary care’s focus on continuity and developing long-standing relationships with patients and their families is largely unmatched in the subspecialty field. It is satisfying to have a general knowledge of the human body, and the central vantage point with which to weigh different subspecialty recommendations. We feel such sentiments are common to those interested in primary care, but sadly, we believe these are not enough to sustain the future of primary care internal medicine.

IS THE FUTURE BRIGHT OR BLEAK?

Primary care internists must resist the call to “run twice as fast.” Instead, we need to look for ways where our unique skill sets can benefit the health of our nation while attracting students to internal medicine primary care. The following are potential areas for moving forward.

The aging of America

The US Census Bureau projects that by the year 2035, older adults will outnumber children for the first time in US history, and by the year 2060, nearly 25% of the US population will be 65 or older.¹⁴ The rise of the geriatric patient and the need for comprehensive care will create a continued demand for primary care internists. There certainly aren’t enough geriatricians to meet this need, and primary care internists are well trained to fill this gap.

The rise of the team approach

As we are learning, complex disease management benefits from a team approach. The rise of new models of care delivery such as accountable care organizations and patient-centered medical homes echo this reality. The day of a single provider “doing it all” is fading.

The focus on population health in these models has given rise to multidisciplinary teams—including physicians, nurses, advanced practice providers, social workers, and pharmacists—whose function is to help manage and improve the physical, mental, and social care of patients, often in a capitated payment system. The primary care internist can play a key role in leading these teams, and such partnerships may help lessen reliance on the current primary care hustle of 15- to 20-minute visits. In such models, it is possible that the internist will need to see each patient only once or twice a year, in a longer appointment slot, instead of 4 to 6 times per year in rushed visits. The hope is that this will encourage the relationship-building that is so important in primary care and reduce the time and volume scheduling burdens seen in the current fee-for-service system.

The joy of digging into a diagnostic dilemma has been a hallmark of internal medicine. The rise of technology should enable primary care internists to increase their diagnostic capabilities in the office without an overreliance on subspecialists.

Examples of technology that may benefit primary care are artificial intelligence with real-time diagnostic support, precision medicine, and office-based point-of-care ultrasonography.^15–17 By increasing the diagnostic power of an office-based visit, we hope that the prestige factor of primary care medicine will increase as internists incorporate such advances into their clinics—not to mention the joy of making an appropriate diagnosis in real time.

Reimbursement and the value of time

Time is a valuable commodity for primary care internists. Unfortunately, there seems to be less of it in today’s practice. Gone are the days when we could go to the doctors’ dining room to decompress, chat, and break bread with colleagues. Today, we are more likely to be found in front of our computers over lunch answering patients’ messages. Time is also a key reason that physicians express frustration with issues such as prior authorizations for medications. These tasks routinely take time away from what is valuable—the care of our patients.

The rise of innovative practice models such as direct primary care and concierge medicine can be seen as a market response to the frustrations of increasing regulatory complexity, billing hassles, and lack of time. However, some have cautioned that such models have the potential to worsen healthcare disparities because patients pay out of pocket for some or all of their care in these practices.¹⁸

Interestingly, the Centers for Medicare and Medicaid Services recently unveiled new voluntary payment models for primary care that go into effect in 2020. These models may allow for increased practice innovation. The 2 proposed options are Primary Care First (designed for small primary care practices) and Direct Contracting (aimed at larger practices). These models are designed to provide a predictable up-front payment stream (a set payment per beneficiary) to the primary care practice. Hopefully, these options will move primary care away from the current fee-for-service, multiple-patient-visit model.

The primary care model allows practices to “assume financial risk in exchange for reduced administrative burden and performance-based payments” and “introduces new, higher payments for practices that care for complex, chronically ill patients.”¹⁹ It is too soon to know the effectiveness of such models, but any reimbursement innovation should be met with cautious optimism.

In addition, the Centers for Medicare and Medicaid Services has recently moved to reduce requirements for documentation. For example, one can fully bill with a medical student note without needing to repeat visit notes.^20,21 Such changes should decrease the time needed to document the EMR and free up more time to care for patients.

A CALL TO ACTION

The national shortage of primary care providers points to the fact that this is a difficult career, and one that remains undervalued. One step we need to take is to protect the time we have with patients. It is doubtful that seeing a greater number of sicker patients each day, in addition to the responsibilities of proactive population-based care (“panel management”), will attract younger generations of physicians to fill this void, no matter what technology we adopt.

Keys to facilitating this change include understanding the value of primary care physicians, decreasing the burden of documentation, facilitating team-care options to support them, and expanding diagnostic tools available to use within primary care. If we don’t demand change, who will be there to take care of us when we grow old?

“My dear, here we must run as fast as we can, just to stay in place. And if you wish to go anywhere you must run twice as fast as that.”
—Lewis Carroll
Alice’s Adventures in Wonderland

The future of primary care internal medicine physicians is uncertain. According to a 2018 survey of internal medicine residents conducted by the American College of Physicians, only 11% were considering primary care as a career path.¹ In 1998, that number was 54%.²

See related commentary

Possible reasons are many:

• Lower pay compared with subspecialists in a pay system that rewards procedural competency over mental effort
• Work schedules less flexible than in other specialties (eg, hospital medicine practitioners may have 1 week on and 1 week off)
• Perceived lack of respect
• Increasing regulatory and record-keeping burdens
• Tyranny of 15- to 20-minute appointments (irrespective of patient complexity)
• Scope-of-practice concerns as other providers seek primary care equivalency status (eg, pharmacists, walk-in clinics, advanced practice providers, telemedicine providers).

The result is a projected shortage of primary care physicians of 21,100 to 55,200 by 2030, according to a 2019 report by the Association of American Medical Colleges,³ despite an expected growth in advanced practice providers in primary care such as nurse practitioners and physician assistants.

A practical result of this shortage will be even less patient access to primary care physicians. A 2017 national survey found that the average wait time for a new patient-physician appointment has already increased by 30% since 2014.⁴ The wait time to see a primary care physician varied between 29 days in major metropolitan areas (up 50% from 2014) and 56 days in mid-sized markets. The longest waits by market size were 109 days for new patients in Boston, MA, and 122 days for those living in Albany, NY.

What are the implications?

In this issue, Pravia and Diaz⁵ make the case that primary care providers must adapt their practices to meet the needs of younger generations by increasing their use of technology. We agree that telemedicine, wearable medical devices, and enhanced patient communication through the electronic medical record (EMR) are here to stay and should be embraced.

However, we have seen the challenges of adopting technologic advances without first making an adjustment to the volume-driven patient schedule. For such advances to be successfully integrated into a clinical practice, it is vital to be cognizant of the current challenges encountered in primary care internal medicine.

UNIQUE BURDENS ON PRIMARY CARE

In addition to the stress of addressing multiple complex medical problems within a short time, evaluating multiple medical problems often leads to increases in results to review, forms to complete, and calls to patients. Even treatment plans initiated by specialists are often deferred to primary care providers for dosing adjustments, follow-up laboratory testing, and monitoring.

Moreover, patients often seek a second opinion from their primary care provider regarding care provided by subspecialists, as they consider their primary care provider to be the doctor who knows them best. And though it can be personally gratifying to be considered a trusted partner in the patient’s care, these requests often result in additional phone calls to the office or another thing to address within a complex visit.

A large in-box can be daunting in the setting of increased EMR demands. Whether you have difficulty putting in basic orders or are an advanced user, each upgrade can make you feel like you’re using the EMR for the first time. This is a problem for all specialties, but in primary care, one is addressing a large spectrum of concerns, so there is less opportunity to use standardized templates that can help buffer the problem.

A study of primary care providers found that nearly 75% of each patient visit was spent on activities other than face-to-face patient care, including working with the EMR.⁶ Similarly, a study using in-office observations and after-hours diaries found that physicians from various specialties spend 2 hours on administrative duties for each hour that they see patients in the office, followed by an additional 1 to 2 hours of work after clinic, mostly devoted to the EMR.⁷

Clinicians using scribes to help with record-keeping duties often need to see more patients to compensate for the cost. Adding 2 to 3 patients to a daily schedule usually means adding more medical conditions to manage, with an exponential increase in testing and in-box burden.

The additional burden this coverage creates in primary care is often not well understood by those in other specialties.

Another burden unique to primary care providers is the nearly continuous publication of guidelines that are often confusing and discrepant. Because many high-impact guidelines represent expert consensus or evidence from specialist perspectives, they may not fit the primary care model or values: eg, primary care guidelines tend to place more emphasis on harms associated with screening.

Screening for breast and prostate cancers is a prime example. Both require shared decision-making based on patient preferences and values.^8,9 Detailed discussions about preventive screening can be difficult to achieve within the context of a medical visit owing to time limitations, especially if other medical conditions being addressed are equally controversial, such as blood pressure target goals. A decade ago, one could easily declare, “It’s time for your annual PSA test,” and move on to other concerns. Given the changing evidence, an informed patient is now likely to question whether this test should be done, how often it should be done, and whether a prostate examination should also be included.

The push toward population health has raised questions about the value of a preventive wellness visit, especially in healthy patients.^10,11 Arguments against the annual physical do not account for the value of these visits, which provide the opportunity to have time-intensive shared decision-making conversations and build a trusting patient-physician relationship. The value of the annual physical is not simply to do examinations for which there is limited evidence; it is a time for us to get to know our patients, to update their preventive needs (and the medical record), and to discuss which screening tests they may safely forgo to avoid unnecessary false-positives, leading to excess cost and harm.

This trusting relationship, developed over years, is likely to save both the patient and the healthcare system significant money. For example, it enables us to reassure patients that an antibiotic is not needed for their upper respiratory infection, to encourage them to try a dietary change before proceeding with computed tomography for their abdominal pain, or to discourage them from inappropriately aggressive screening tests that may result in overtesting or overdiagnosis.

Unfortunately, it is nearly impossible to accurately quantify these substantial benefits to the healthcare system and patients. And there is a real potential that recommendations against the annual physical may eventually affect future reimbursement, which would add to the time pressures of an already overburdened primary care workforce.

DO PRIMARY CARE PHYSICIANS MAKE A DIFFERENCE?

As medicine and technology evolve, patients have more ways to access care. However, the Internet also provides patients with access to more conflicting information than ever before, making it even more important for clinicians, as trusted partners in their patients’ health, to help patients navigate the waters of information and misinformation.

Studies have shown that having a primary care physician is associated with a longer life span, higher likelihood of reporting good health, and similar clinical outcomes for common conditions such as diabetes and hypertension when compared with subspecialty care, but at a lower cost and with less resource utilization.^12,13 In a study published in 2019, Basu et al¹² found that for every 10 additional primary care physicians per 100,000 population, there was an associated 51.5-day increase in life expectancy, compared with a 19.2-day increase for specialists. Cost savings also occur. Similarly, a review by the American College of Physicians¹³ found that each additional primary care physician per 10,000 population in a US state increased the state’s health quality ranking by more than 10 spots and reduced their overall spending per Medicare beneficiary. In contrast, an increase of 1 specialist per 10,000 population was linked to a 9-spot decrease in health-quality ranking and an increase in spending.

WHY CHOOSE PRIMARY CARE?

As medical students, we fell in love with internal medicine because of the complexity and intellectual challenges of working through a diagnostic dilemma. There is a certain excitement in not knowing what type of patients will show up that day.

Primary care’s focus on continuity and developing long-standing relationships with patients and their families is largely unmatched in the subspecialty field. It is satisfying to have a general knowledge of the human body, and the central vantage point with which to weigh different subspecialty recommendations. We feel such sentiments are common to those interested in primary care, but sadly, we believe these are not enough to sustain the future of primary care internal medicine.

IS THE FUTURE BRIGHT OR BLEAK?

Primary care internists must resist the call to “run twice as fast.” Instead, we need to look for ways where our unique skill sets can benefit the health of our nation while attracting students to internal medicine primary care. The following are potential areas for moving forward.

The aging of America

The US Census Bureau projects that by the year 2035, older adults will outnumber children for the first time in US history, and by the year 2060, nearly 25% of the US population will be 65 or older.¹⁴ The rise of the geriatric patient and the need for comprehensive care will create a continued demand for primary care internists. There certainly aren’t enough geriatricians to meet this need, and primary care internists are well trained to fill this gap.

The rise of the team approach

As we are learning, complex disease management benefits from a team approach. The rise of new models of care delivery such as accountable care organizations and patient-centered medical homes echo this reality. The day of a single provider “doing it all” is fading.

The focus on population health in these models has given rise to multidisciplinary teams—including physicians, nurses, advanced practice providers, social workers, and pharmacists—whose function is to help manage and improve the physical, mental, and social care of patients, often in a capitated payment system. The primary care internist can play a key role in leading these teams, and such partnerships may help lessen reliance on the current primary care hustle of 15- to 20-minute visits. In such models, it is possible that the internist will need to see each patient only once or twice a year, in a longer appointment slot, instead of 4 to 6 times per year in rushed visits. The hope is that this will encourage the relationship-building that is so important in primary care and reduce the time and volume scheduling burdens seen in the current fee-for-service system.

The joy of digging into a diagnostic dilemma has been a hallmark of internal medicine. The rise of technology should enable primary care internists to increase their diagnostic capabilities in the office without an overreliance on subspecialists.

Examples of technology that may benefit primary care are artificial intelligence with real-time diagnostic support, precision medicine, and office-based point-of-care ultrasonography.^15–17 By increasing the diagnostic power of an office-based visit, we hope that the prestige factor of primary care medicine will increase as internists incorporate such advances into their clinics—not to mention the joy of making an appropriate diagnosis in real time.

Reimbursement and the value of time

Time is a valuable commodity for primary care internists. Unfortunately, there seems to be less of it in today’s practice. Gone are the days when we could go to the doctors’ dining room to decompress, chat, and break bread with colleagues. Today, we are more likely to be found in front of our computers over lunch answering patients’ messages. Time is also a key reason that physicians express frustration with issues such as prior authorizations for medications. These tasks routinely take time away from what is valuable—the care of our patients.

The rise of innovative practice models such as direct primary care and concierge medicine can be seen as a market response to the frustrations of increasing regulatory complexity, billing hassles, and lack of time. However, some have cautioned that such models have the potential to worsen healthcare disparities because patients pay out of pocket for some or all of their care in these practices.¹⁸

Interestingly, the Centers for Medicare and Medicaid Services recently unveiled new voluntary payment models for primary care that go into effect in 2020. These models may allow for increased practice innovation. The 2 proposed options are Primary Care First (designed for small primary care practices) and Direct Contracting (aimed at larger practices). These models are designed to provide a predictable up-front payment stream (a set payment per beneficiary) to the primary care practice. Hopefully, these options will move primary care away from the current fee-for-service, multiple-patient-visit model.

The primary care model allows practices to “assume financial risk in exchange for reduced administrative burden and performance-based payments” and “introduces new, higher payments for practices that care for complex, chronically ill patients.”¹⁹ It is too soon to know the effectiveness of such models, but any reimbursement innovation should be met with cautious optimism.

In addition, the Centers for Medicare and Medicaid Services has recently moved to reduce requirements for documentation. For example, one can fully bill with a medical student note without needing to repeat visit notes.^20,21 Such changes should decrease the time needed to document the EMR and free up more time to care for patients.

A CALL TO ACTION

The national shortage of primary care providers points to the fact that this is a difficult career, and one that remains undervalued. One step we need to take is to protect the time we have with patients. It is doubtful that seeing a greater number of sicker patients each day, in addition to the responsibilities of proactive population-based care (“panel management”), will attract younger generations of physicians to fill this void, no matter what technology we adopt.

Keys to facilitating this change include understanding the value of primary care physicians, decreasing the burden of documentation, facilitating team-care options to support them, and expanding diagnostic tools available to use within primary care. If we don’t demand change, who will be there to take care of us when we grow old?

“My dear, here we must run as fast as we can, just to stay in place. And if you wish to go anywhere you must run twice as fast as that.”
—Lewis Carroll
Alice’s Adventures in Wonderland

The future of primary care internal medicine physicians is uncertain. According to a 2018 survey of internal medicine residents conducted by the American College of Physicians, only 11% were considering primary care as a career path.¹ In 1998, that number was 54%.²

See related commentary

Possible reasons are many:

• Lower pay compared with subspecialists in a pay system that rewards procedural competency over mental effort
• Work schedules less flexible than in other specialties (eg, hospital medicine practitioners may have 1 week on and 1 week off)
• Perceived lack of respect
• Increasing regulatory and record-keeping burdens
• Tyranny of 15- to 20-minute appointments (irrespective of patient complexity)
• Scope-of-practice concerns as other providers seek primary care equivalency status (eg, pharmacists, walk-in clinics, advanced practice providers, telemedicine providers).

The result is a projected shortage of primary care physicians of 21,100 to 55,200 by 2030, according to a 2019 report by the Association of American Medical Colleges,³ despite an expected growth in advanced practice providers in primary care such as nurse practitioners and physician assistants.

A practical result of this shortage will be even less patient access to primary care physicians. A 2017 national survey found that the average wait time for a new patient-physician appointment has already increased by 30% since 2014.⁴ The wait time to see a primary care physician varied between 29 days in major metropolitan areas (up 50% from 2014) and 56 days in mid-sized markets. The longest waits by market size were 109 days for new patients in Boston, MA, and 122 days for those living in Albany, NY.

What are the implications?

In this issue, Pravia and Diaz⁵ make the case that primary care providers must adapt their practices to meet the needs of younger generations by increasing their use of technology. We agree that telemedicine, wearable medical devices, and enhanced patient communication through the electronic medical record (EMR) are here to stay and should be embraced.

However, we have seen the challenges of adopting technologic advances without first making an adjustment to the volume-driven patient schedule. For such advances to be successfully integrated into a clinical practice, it is vital to be cognizant of the current challenges encountered in primary care internal medicine.

UNIQUE BURDENS ON PRIMARY CARE

In addition to the stress of addressing multiple complex medical problems within a short time, evaluating multiple medical problems often leads to increases in results to review, forms to complete, and calls to patients. Even treatment plans initiated by specialists are often deferred to primary care providers for dosing adjustments, follow-up laboratory testing, and monitoring.

Moreover, patients often seek a second opinion from their primary care provider regarding care provided by subspecialists, as they consider their primary care provider to be the doctor who knows them best. And though it can be personally gratifying to be considered a trusted partner in the patient’s care, these requests often result in additional phone calls to the office or another thing to address within a complex visit.

A large in-box can be daunting in the setting of increased EMR demands. Whether you have difficulty putting in basic orders or are an advanced user, each upgrade can make you feel like you’re using the EMR for the first time. This is a problem for all specialties, but in primary care, one is addressing a large spectrum of concerns, so there is less opportunity to use standardized templates that can help buffer the problem.

A study of primary care providers found that nearly 75% of each patient visit was spent on activities other than face-to-face patient care, including working with the EMR.⁶ Similarly, a study using in-office observations and after-hours diaries found that physicians from various specialties spend 2 hours on administrative duties for each hour that they see patients in the office, followed by an additional 1 to 2 hours of work after clinic, mostly devoted to the EMR.⁷

Clinicians using scribes to help with record-keeping duties often need to see more patients to compensate for the cost. Adding 2 to 3 patients to a daily schedule usually means adding more medical conditions to manage, with an exponential increase in testing and in-box burden.

The additional burden this coverage creates in primary care is often not well understood by those in other specialties.

Another burden unique to primary care providers is the nearly continuous publication of guidelines that are often confusing and discrepant. Because many high-impact guidelines represent expert consensus or evidence from specialist perspectives, they may not fit the primary care model or values: eg, primary care guidelines tend to place more emphasis on harms associated with screening.

Screening for breast and prostate cancers is a prime example. Both require shared decision-making based on patient preferences and values.^8,9 Detailed discussions about preventive screening can be difficult to achieve within the context of a medical visit owing to time limitations, especially if other medical conditions being addressed are equally controversial, such as blood pressure target goals. A decade ago, one could easily declare, “It’s time for your annual PSA test,” and move on to other concerns. Given the changing evidence, an informed patient is now likely to question whether this test should be done, how often it should be done, and whether a prostate examination should also be included.

The push toward population health has raised questions about the value of a preventive wellness visit, especially in healthy patients.^10,11 Arguments against the annual physical do not account for the value of these visits, which provide the opportunity to have time-intensive shared decision-making conversations and build a trusting patient-physician relationship. The value of the annual physical is not simply to do examinations for which there is limited evidence; it is a time for us to get to know our patients, to update their preventive needs (and the medical record), and to discuss which screening tests they may safely forgo to avoid unnecessary false-positives, leading to excess cost and harm.

This trusting relationship, developed over years, is likely to save both the patient and the healthcare system significant money. For example, it enables us to reassure patients that an antibiotic is not needed for their upper respiratory infection, to encourage them to try a dietary change before proceeding with computed tomography for their abdominal pain, or to discourage them from inappropriately aggressive screening tests that may result in overtesting or overdiagnosis.

Unfortunately, it is nearly impossible to accurately quantify these substantial benefits to the healthcare system and patients. And there is a real potential that recommendations against the annual physical may eventually affect future reimbursement, which would add to the time pressures of an already overburdened primary care workforce.

DO PRIMARY CARE PHYSICIANS MAKE A DIFFERENCE?

As medicine and technology evolve, patients have more ways to access care. However, the Internet also provides patients with access to more conflicting information than ever before, making it even more important for clinicians, as trusted partners in their patients’ health, to help patients navigate the waters of information and misinformation.

Studies have shown that having a primary care physician is associated with a longer life span, higher likelihood of reporting good health, and similar clinical outcomes for common conditions such as diabetes and hypertension when compared with subspecialty care, but at a lower cost and with less resource utilization.^12,13 In a study published in 2019, Basu et al¹² found that for every 10 additional primary care physicians per 100,000 population, there was an associated 51.5-day increase in life expectancy, compared with a 19.2-day increase for specialists. Cost savings also occur. Similarly, a review by the American College of Physicians¹³ found that each additional primary care physician per 10,000 population in a US state increased the state’s health quality ranking by more than 10 spots and reduced their overall spending per Medicare beneficiary. In contrast, an increase of 1 specialist per 10,000 population was linked to a 9-spot decrease in health-quality ranking and an increase in spending.

WHY CHOOSE PRIMARY CARE?

As medical students, we fell in love with internal medicine because of the complexity and intellectual challenges of working through a diagnostic dilemma. There is a certain excitement in not knowing what type of patients will show up that day.

Primary care’s focus on continuity and developing long-standing relationships with patients and their families is largely unmatched in the subspecialty field. It is satisfying to have a general knowledge of the human body, and the central vantage point with which to weigh different subspecialty recommendations. We feel such sentiments are common to those interested in primary care, but sadly, we believe these are not enough to sustain the future of primary care internal medicine.

IS THE FUTURE BRIGHT OR BLEAK?

Primary care internists must resist the call to “run twice as fast.” Instead, we need to look for ways where our unique skill sets can benefit the health of our nation while attracting students to internal medicine primary care. The following are potential areas for moving forward.

The aging of America

The US Census Bureau projects that by the year 2035, older adults will outnumber children for the first time in US history, and by the year 2060, nearly 25% of the US population will be 65 or older.¹⁴ The rise of the geriatric patient and the need for comprehensive care will create a continued demand for primary care internists. There certainly aren’t enough geriatricians to meet this need, and primary care internists are well trained to fill this gap.

The rise of the team approach

As we are learning, complex disease management benefits from a team approach. The rise of new models of care delivery such as accountable care organizations and patient-centered medical homes echo this reality. The day of a single provider “doing it all” is fading.

The focus on population health in these models has given rise to multidisciplinary teams—including physicians, nurses, advanced practice providers, social workers, and pharmacists—whose function is to help manage and improve the physical, mental, and social care of patients, often in a capitated payment system. The primary care internist can play a key role in leading these teams, and such partnerships may help lessen reliance on the current primary care hustle of 15- to 20-minute visits. In such models, it is possible that the internist will need to see each patient only once or twice a year, in a longer appointment slot, instead of 4 to 6 times per year in rushed visits. The hope is that this will encourage the relationship-building that is so important in primary care and reduce the time and volume scheduling burdens seen in the current fee-for-service system.

The joy of digging into a diagnostic dilemma has been a hallmark of internal medicine. The rise of technology should enable primary care internists to increase their diagnostic capabilities in the office without an overreliance on subspecialists.

Examples of technology that may benefit primary care are artificial intelligence with real-time diagnostic support, precision medicine, and office-based point-of-care ultrasonography.^15–17 By increasing the diagnostic power of an office-based visit, we hope that the prestige factor of primary care medicine will increase as internists incorporate such advances into their clinics—not to mention the joy of making an appropriate diagnosis in real time.

Reimbursement and the value of time

Time is a valuable commodity for primary care internists. Unfortunately, there seems to be less of it in today’s practice. Gone are the days when we could go to the doctors’ dining room to decompress, chat, and break bread with colleagues. Today, we are more likely to be found in front of our computers over lunch answering patients’ messages. Time is also a key reason that physicians express frustration with issues such as prior authorizations for medications. These tasks routinely take time away from what is valuable—the care of our patients.

The rise of innovative practice models such as direct primary care and concierge medicine can be seen as a market response to the frustrations of increasing regulatory complexity, billing hassles, and lack of time. However, some have cautioned that such models have the potential to worsen healthcare disparities because patients pay out of pocket for some or all of their care in these practices.¹⁸

Interestingly, the Centers for Medicare and Medicaid Services recently unveiled new voluntary payment models for primary care that go into effect in 2020. These models may allow for increased practice innovation. The 2 proposed options are Primary Care First (designed for small primary care practices) and Direct Contracting (aimed at larger practices). These models are designed to provide a predictable up-front payment stream (a set payment per beneficiary) to the primary care practice. Hopefully, these options will move primary care away from the current fee-for-service, multiple-patient-visit model.

The primary care model allows practices to “assume financial risk in exchange for reduced administrative burden and performance-based payments” and “introduces new, higher payments for practices that care for complex, chronically ill patients.”¹⁹ It is too soon to know the effectiveness of such models, but any reimbursement innovation should be met with cautious optimism.

In addition, the Centers for Medicare and Medicaid Services has recently moved to reduce requirements for documentation. For example, one can fully bill with a medical student note without needing to repeat visit notes.^20,21 Such changes should decrease the time needed to document the EMR and free up more time to care for patients.

A CALL TO ACTION

The national shortage of primary care providers points to the fact that this is a difficult career, and one that remains undervalued. One step we need to take is to protect the time we have with patients. It is doubtful that seeing a greater number of sicker patients each day, in addition to the responsibilities of proactive population-based care (“panel management”), will attract younger generations of physicians to fill this void, no matter what technology we adopt.

Keys to facilitating this change include understanding the value of primary care physicians, decreasing the burden of documentation, facilitating team-care options to support them, and expanding diagnostic tools available to use within primary care. If we don’t demand change, who will be there to take care of us when we grow old?

Autism spectrum disorder (ASD) is a neurodevelopmental condition typically diagnosed early in life: the median age at diagnosis is 52 months.¹ Because research demonstrates the benefits of early intervention,² when we think about people with ASD, we generally think about children and adolescents. 

See related article

However, autism spans the entirety of one’s life. This means that children with ASD will grow to be adults with ASD. The US Centers for Disease Control and Prevention estimated that 1 in 59 children were diagnosed with ASD during the surveillance year 2014,¹ which was nearly double the prevalence from just 8 years earlier,³ and a 15% increase since 2012.⁴ As these children grow up, this translates to an ever-growing number of adults with autism.

UNMET NEEDS

Healthcare, housing, and intellectual and developmental disability services for adults with ASD currently fall well short of meeting the needs of this exploding population. If solutions are to be realized, innovative approaches must be employed.

Swetlik et al,⁵ in this issue of the Journal, offer valuable insights into the challenges that practitioners and their adult patients with ASD encounter as a result of seismic shifts in diagnostic criteria, increasing prevalence, and changes to healthcare financial coverage. They also review behavioral and pharmacologic treatments, reproductive health, and caregiver fatigue and discuss the role of the physician and other healthcare practitioners who are likely to have only limited exposure to adult patients with ASD. These wide-ranging considerations speak to the complexity of the healthcare needs of this population.

Swetlik et al also underscore that transition planning is essential for primary care, psychiatry, behavioral health services, continuing education, skill development, and appropriate prevocational training for adolescents with ASD, and yet it is often underutilized or unavailable. There is a dearth of experienced practitioners across these disciplines to serve adults with ASD. The complexity of navigating bureaucratic processes to secure funding (typically Medicaid) supports the necessity of planning early to achieve desired outcomes for each young adult. Additionally, the number of Medicaid waivers that fund many supportive services are limited.

GROWING UP IS HARD; START PLANNING EARLY

Swetlik et al describe the stress these circumstances create for people with ASD and their families. Entering adulthood is a complicated process, fraught with emotional overtones that must include medical care, work considerations, legal and financial arrangements, and, for many, the search for an appropriate residential environment. Planning for these transitions should begin years before adulthood if the process is to work smoothly and effectively.

A transition involving a shift away from a team of familiar pediatric healthcare providers to unfamiliar adult practitioners can be distressing for any adolescent with a chronic condition. For those with ASD, who may have diminished socialization and communication skills, the transition can be especially challenging and must be handled with care.

This transition pales in comparison with the disruptive force of a permanent move out of the family home. Over the next 10 years, 500,000 youths in the United States will age out of school-based ASD services,⁶ and a great many of them will be put on long waiting lists for residential placement.⁷

For young adults with ASD, particularly those with complex needs, establishing an advantageous long-term living arrangement may mean the difference between a healthy, self-directed launch into a new phase of life, or a consequential misstep that exacerbates or worsens symptoms and creates new stressors for the young adult and his or her family. It is especially important that arrangements be made before an aging guardian starts to experience declining health.

Thoughtful and deliberate preplanning helps to reduce stress and prevent emergency placements, and promotes long-term quality of life for people with ASD.

For many years, the prevailing model for the provision of long-term care services for individuals with intellectual and developmental disabilities was institutional care. Large facilities, often located in expansive, self-contained campuses, provided around-the-clock care. Residents slept, ate, worked, and were expected to receive social and emotional fulfillment at the facility.

For some, this was an acceptable model. For many, it was not, but there were few available alternatives. At its best, this model provided a safe environment for its residents, but it did not facilitate achieving an integrated, self-directed life experience. At its worst, neglect and abuse were rampant.

Numerous legislative acts, court decisions, and advocacy efforts drove the deinstitutionalization movement for individuals with intellectual and developmental disabilities between the early 1960s and today. The 1999 case of Olmstead v LC⁸ was among the most significant. In this landmark case, in accordance with the 1990 Americans With Disabilities Act, the US Supreme Court ruled that people with disabilities have the right to receive state-funded services and support in the community rather than in institutions, as long as several criteria are met:

• Community supports are appropriate
• The individual desires to live in the community
• The accommodations to facilitate that arrangement are considered to be reasonable.

In the 20 years since the Olmstead decision, residential services for adults have shifted at an accelerated rate away from institutions toward smaller, community-based settings.^9,10 Community models include but are not limited to:

• Group homes that serve individuals with intellectual and developmental disabilities and provide 24-hour support
• Apartments or homes where individuals live and receive intermittent, less-intensive support
• Adult foster care.

DSM-5: AUTISM IS HETEROGENEOUS

In the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5),¹¹ ASD is characterized by persistent deficits in social interaction and social communication, which begin in early development and are observed in conjunction with restricted, repetitive behaviors, interests, or activities.

DSM-5 provides more than 20 examples of how these criteria might be met. Further, DSM-5 encourages clinicians to select diagnostic specifiers to address overall symptom severity, cognitive abilities, and associated medical conditions.

ONE RESIDENTIAL MODEL DOES NOT FIT ALL

The complex matrix of potential symptom manifestations in people with ASD clearly indicates the need for numerous distinctive residential models for adults with ASD.

One person with severe symptoms of ASD may require one-to-one staffing and proactive preparatory support in order to safely leave the house for a desired social experience. The person may be unable to read, to independently access public transportation, to cope with deviations in expected routine, to initiate conversation, or to remain calm if distressed. This person would benefit from a residential model that allows for a high staffing ratio, access to appropriate transportation, sophisticated autism-informed supports, and the availability of social experiences that are easily accessed—in other words, a very controlled environment.

Another person, with less severe symptoms and fewer behavioral challenges, who possesses a driver’s license and holds a job, may struggle with isolation and loneliness resulting from social inhibitions and skill deficits. This person’s support needs would differ, with emphasis placed on maintaining the appropriate social context rather than on providing a high level of individualized behavioral support.

The shift away from a one-size-fits-all institutional model for long-term care has benefited many individuals with intellectual and developmental disabilities who have experienced opportunities for community integration.

Still, for many adults with ASD, particularly those with complex needs and complex behavior profiles, the widespread conceptual shift to new and different models that assume that all people with intellectual and developmental disabilities will benefit from smaller, scattered-site settings is ill-fitting. It is erroneous to believe that for all adults with ASD, regardless of the complexity of their symptoms, living within a broader community of neurotypically developing neighbors breeds a richer sense of inclusion and connectivity.

FINDING CARE CAN BE DAUNTING

Families of adults with severe symptoms of ASD who seek placement in more traditional community residential models often find it difficult to find capable healthcare providers to serve them. Such settings are ill-equipped to deal with significantly challenging behaviors such as physical aggression, self-injury, property destruction, and elopement (wandering). These supported housing models lack the necessary staffing resources.

Further, publicly available funding options for stand-alone group homes do not typically allow for intensive supervision and management from professionals with expertise in autism. Without specialists who can  implement autism-specific best-practice methods for assessment, service planning, staff training, data collection, and the provision of visual and technological supports for residents, it is difficult to achieve desired outcomes. For example, patients can find it challenging to visit physicians’ offices for preventive and urgent care. Lacking a caregiver who is familiar with the adult patient with ASD and who can help express his or her concerns to healthcare providers, efficient evaluation of any potentially serious medical issue is a daunting task.

A residential model that is gaining popularity across the United States among families and individuals affected by ASD is the intentional community.

Although forms and functions may vary, intentional communities are planned residential developments that promote social cohesion and strive to meet the shared needs of its members. Intentional communities for adults with ASD are designed to meet their social, communication, sensory, and behavioral needs. Every detail from the selection of land, to the construction of housing, the selected staffing model, the daily structure, and the considerations for transportation and amenities are all informed by the specific needs of individuals with autism. Safety, integration, self-direction, independence, and social connectivity are common goals.   

Successful intentional communities designed for people with intellectual and developmental disabilities often have facilities devoted to recreation, continuing education, socialization, and supportive services. Staff members who work within these communities are highly trained in the unique needs of people with these disorders. Intentional communities aspire to embody the individualized, integrated community-living approach that the Olmstead decision called for, while simultaneously offering the resource-rich, safe, and supportive experience that a campus atmosphere can offer.

Almost all recently developed models allow for residents to live among neurotypical peers and have easy access to the broader community. Communities range in size from several condominiums on a cul-de-sac to expansive developments with more than a hundred homes.

The allure of an ASD-informed intentional community that provides for the social, vocational, health, and safety needs of its residents is similar to that which leads large numbers of aging, neurotypically developing individuals to seek out retirement communities. Nationally recognized models of intentional communities include First Place (Phoenix, AZ), Sweetwater Spectrum (Sonoma, CA), Cape Cod Village (Orleans, MA), and Bittersweet Farms in Ohio.

First Place is a 55-unit apartment complex near downtown Phoenix that identifies as “community-connected” and “transit-oriented.” Although there are some individuals in the complex who do not have ASD, the development was created for those who do. The goal is to enhance the quality of life for residents through the provision of housing, jobs, social opportunities, and a supportive community.

Sweetwater Spectrum is located blocks from the Sonoma downtown plaza, on just under 3 acres of land. It includes several 4-bedroom homes, a community center with a kitchen, exercise studio, media room, and library, an expansive organic garden, and an outdoor pool.

The Autism Housing Network lists more than 75 intentional communities on its resource page. There are many exciting models in development. For example, Monarch Center for Autism in Cleveland, OH, is planning to develop an innovative intentional community. It will include mixed supported living options for adults across the autism spectrum, separate housing options for parents and family members, on-site social and recreational opportunities, green space, and retail stores intended to serve members of the surrounding community and provide employment and socialization opportunities for its residents.

Casa Familia in South Florida will soon begin constructing a large intentional community that will include innovative housing options, classrooms, social areas, an auditorium, walkways, bike paths, pools, and social enterprises.

It is critical that these ASD intentional communities continue to emerge to meet the long-term needs of the rapidly growing and aging ASD population. 

THE TIME TO ACT IS NOW

Swetlik et al synthesize important, contemporary research on adult ASD healthcare considerations, pursuant to informing the many decisions that physicians and other healthcare professionals must make to address the diverse needs of this population. Their article advocates for further research and highlights the crisis surrounding the scarcity of practitioners specializing in adult ASD.

As for current healthcare providers, parents, care coordinators, and other stakeholders who are tasked with transition planning for individuals with ASD, particularly those with severe symptoms, the time to act is now, especially in creating new intentional community models.

Most adult healthcare providers have not been routinely charged with the responsibility, nor do they have the available time and resources to meet the social and communication needs of these patients. But when faced with an ever-expanding group of patients who demonstrate inadequate social and communication skills, the healthcare system must not turn a blind eye.

The symptoms of autism do not magically resolve when a child reaches adulthood. The medical community must partner with society at large to offer transitional solutions, including intentional communities, to the rapidly growing number of adults with ASD. Current demand outweighs supply, but if we work together, we can create innovative and highly effective solutions. After all, children with autism do not disappear. They grow into adults with autism.

Autism spectrum disorder (ASD) is a neurodevelopmental condition typically diagnosed early in life: the median age at diagnosis is 52 months.¹ Because research demonstrates the benefits of early intervention,² when we think about people with ASD, we generally think about children and adolescents. 

See related article

However, autism spans the entirety of one’s life. This means that children with ASD will grow to be adults with ASD. The US Centers for Disease Control and Prevention estimated that 1 in 59 children were diagnosed with ASD during the surveillance year 2014,¹ which was nearly double the prevalence from just 8 years earlier,³ and a 15% increase since 2012.⁴ As these children grow up, this translates to an ever-growing number of adults with autism.

UNMET NEEDS

Healthcare, housing, and intellectual and developmental disability services for adults with ASD currently fall well short of meeting the needs of this exploding population. If solutions are to be realized, innovative approaches must be employed.

Swetlik et al,⁵ in this issue of the Journal, offer valuable insights into the challenges that practitioners and their adult patients with ASD encounter as a result of seismic shifts in diagnostic criteria, increasing prevalence, and changes to healthcare financial coverage. They also review behavioral and pharmacologic treatments, reproductive health, and caregiver fatigue and discuss the role of the physician and other healthcare practitioners who are likely to have only limited exposure to adult patients with ASD. These wide-ranging considerations speak to the complexity of the healthcare needs of this population.

Swetlik et al also underscore that transition planning is essential for primary care, psychiatry, behavioral health services, continuing education, skill development, and appropriate prevocational training for adolescents with ASD, and yet it is often underutilized or unavailable. There is a dearth of experienced practitioners across these disciplines to serve adults with ASD. The complexity of navigating bureaucratic processes to secure funding (typically Medicaid) supports the necessity of planning early to achieve desired outcomes for each young adult. Additionally, the number of Medicaid waivers that fund many supportive services are limited.

GROWING UP IS HARD; START PLANNING EARLY

Swetlik et al describe the stress these circumstances create for people with ASD and their families. Entering adulthood is a complicated process, fraught with emotional overtones that must include medical care, work considerations, legal and financial arrangements, and, for many, the search for an appropriate residential environment. Planning for these transitions should begin years before adulthood if the process is to work smoothly and effectively.

A transition involving a shift away from a team of familiar pediatric healthcare providers to unfamiliar adult practitioners can be distressing for any adolescent with a chronic condition. For those with ASD, who may have diminished socialization and communication skills, the transition can be especially challenging and must be handled with care.

This transition pales in comparison with the disruptive force of a permanent move out of the family home. Over the next 10 years, 500,000 youths in the United States will age out of school-based ASD services,⁶ and a great many of them will be put on long waiting lists for residential placement.⁷

For young adults with ASD, particularly those with complex needs, establishing an advantageous long-term living arrangement may mean the difference between a healthy, self-directed launch into a new phase of life, or a consequential misstep that exacerbates or worsens symptoms and creates new stressors for the young adult and his or her family. It is especially important that arrangements be made before an aging guardian starts to experience declining health.

Thoughtful and deliberate preplanning helps to reduce stress and prevent emergency placements, and promotes long-term quality of life for people with ASD.

For many years, the prevailing model for the provision of long-term care services for individuals with intellectual and developmental disabilities was institutional care. Large facilities, often located in expansive, self-contained campuses, provided around-the-clock care. Residents slept, ate, worked, and were expected to receive social and emotional fulfillment at the facility.

For some, this was an acceptable model. For many, it was not, but there were few available alternatives. At its best, this model provided a safe environment for its residents, but it did not facilitate achieving an integrated, self-directed life experience. At its worst, neglect and abuse were rampant.

Numerous legislative acts, court decisions, and advocacy efforts drove the deinstitutionalization movement for individuals with intellectual and developmental disabilities between the early 1960s and today. The 1999 case of Olmstead v LC⁸ was among the most significant. In this landmark case, in accordance with the 1990 Americans With Disabilities Act, the US Supreme Court ruled that people with disabilities have the right to receive state-funded services and support in the community rather than in institutions, as long as several criteria are met:

• Community supports are appropriate
• The individual desires to live in the community
• The accommodations to facilitate that arrangement are considered to be reasonable.

In the 20 years since the Olmstead decision, residential services for adults have shifted at an accelerated rate away from institutions toward smaller, community-based settings.^9,10 Community models include but are not limited to:

• Group homes that serve individuals with intellectual and developmental disabilities and provide 24-hour support
• Apartments or homes where individuals live and receive intermittent, less-intensive support
• Adult foster care.

DSM-5: AUTISM IS HETEROGENEOUS

In the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5),¹¹ ASD is characterized by persistent deficits in social interaction and social communication, which begin in early development and are observed in conjunction with restricted, repetitive behaviors, interests, or activities.

DSM-5 provides more than 20 examples of how these criteria might be met. Further, DSM-5 encourages clinicians to select diagnostic specifiers to address overall symptom severity, cognitive abilities, and associated medical conditions.

ONE RESIDENTIAL MODEL DOES NOT FIT ALL

The complex matrix of potential symptom manifestations in people with ASD clearly indicates the need for numerous distinctive residential models for adults with ASD.

One person with severe symptoms of ASD may require one-to-one staffing and proactive preparatory support in order to safely leave the house for a desired social experience. The person may be unable to read, to independently access public transportation, to cope with deviations in expected routine, to initiate conversation, or to remain calm if distressed. This person would benefit from a residential model that allows for a high staffing ratio, access to appropriate transportation, sophisticated autism-informed supports, and the availability of social experiences that are easily accessed—in other words, a very controlled environment.

Another person, with less severe symptoms and fewer behavioral challenges, who possesses a driver’s license and holds a job, may struggle with isolation and loneliness resulting from social inhibitions and skill deficits. This person’s support needs would differ, with emphasis placed on maintaining the appropriate social context rather than on providing a high level of individualized behavioral support.

The shift away from a one-size-fits-all institutional model for long-term care has benefited many individuals with intellectual and developmental disabilities who have experienced opportunities for community integration.

Still, for many adults with ASD, particularly those with complex needs and complex behavior profiles, the widespread conceptual shift to new and different models that assume that all people with intellectual and developmental disabilities will benefit from smaller, scattered-site settings is ill-fitting. It is erroneous to believe that for all adults with ASD, regardless of the complexity of their symptoms, living within a broader community of neurotypically developing neighbors breeds a richer sense of inclusion and connectivity.

FINDING CARE CAN BE DAUNTING

Families of adults with severe symptoms of ASD who seek placement in more traditional community residential models often find it difficult to find capable healthcare providers to serve them. Such settings are ill-equipped to deal with significantly challenging behaviors such as physical aggression, self-injury, property destruction, and elopement (wandering). These supported housing models lack the necessary staffing resources.

Further, publicly available funding options for stand-alone group homes do not typically allow for intensive supervision and management from professionals with expertise in autism. Without specialists who can  implement autism-specific best-practice methods for assessment, service planning, staff training, data collection, and the provision of visual and technological supports for residents, it is difficult to achieve desired outcomes. For example, patients can find it challenging to visit physicians’ offices for preventive and urgent care. Lacking a caregiver who is familiar with the adult patient with ASD and who can help express his or her concerns to healthcare providers, efficient evaluation of any potentially serious medical issue is a daunting task.

A residential model that is gaining popularity across the United States among families and individuals affected by ASD is the intentional community.

Although forms and functions may vary, intentional communities are planned residential developments that promote social cohesion and strive to meet the shared needs of its members. Intentional communities for adults with ASD are designed to meet their social, communication, sensory, and behavioral needs. Every detail from the selection of land, to the construction of housing, the selected staffing model, the daily structure, and the considerations for transportation and amenities are all informed by the specific needs of individuals with autism. Safety, integration, self-direction, independence, and social connectivity are common goals.   

Successful intentional communities designed for people with intellectual and developmental disabilities often have facilities devoted to recreation, continuing education, socialization, and supportive services. Staff members who work within these communities are highly trained in the unique needs of people with these disorders. Intentional communities aspire to embody the individualized, integrated community-living approach that the Olmstead decision called for, while simultaneously offering the resource-rich, safe, and supportive experience that a campus atmosphere can offer.

Almost all recently developed models allow for residents to live among neurotypical peers and have easy access to the broader community. Communities range in size from several condominiums on a cul-de-sac to expansive developments with more than a hundred homes.

The allure of an ASD-informed intentional community that provides for the social, vocational, health, and safety needs of its residents is similar to that which leads large numbers of aging, neurotypically developing individuals to seek out retirement communities. Nationally recognized models of intentional communities include First Place (Phoenix, AZ), Sweetwater Spectrum (Sonoma, CA), Cape Cod Village (Orleans, MA), and Bittersweet Farms in Ohio.

First Place is a 55-unit apartment complex near downtown Phoenix that identifies as “community-connected” and “transit-oriented.” Although there are some individuals in the complex who do not have ASD, the development was created for those who do. The goal is to enhance the quality of life for residents through the provision of housing, jobs, social opportunities, and a supportive community.

Sweetwater Spectrum is located blocks from the Sonoma downtown plaza, on just under 3 acres of land. It includes several 4-bedroom homes, a community center with a kitchen, exercise studio, media room, and library, an expansive organic garden, and an outdoor pool.

The Autism Housing Network lists more than 75 intentional communities on its resource page. There are many exciting models in development. For example, Monarch Center for Autism in Cleveland, OH, is planning to develop an innovative intentional community. It will include mixed supported living options for adults across the autism spectrum, separate housing options for parents and family members, on-site social and recreational opportunities, green space, and retail stores intended to serve members of the surrounding community and provide employment and socialization opportunities for its residents.

Casa Familia in South Florida will soon begin constructing a large intentional community that will include innovative housing options, classrooms, social areas, an auditorium, walkways, bike paths, pools, and social enterprises.

It is critical that these ASD intentional communities continue to emerge to meet the long-term needs of the rapidly growing and aging ASD population. 

THE TIME TO ACT IS NOW

Swetlik et al synthesize important, contemporary research on adult ASD healthcare considerations, pursuant to informing the many decisions that physicians and other healthcare professionals must make to address the diverse needs of this population. Their article advocates for further research and highlights the crisis surrounding the scarcity of practitioners specializing in adult ASD.

As for current healthcare providers, parents, care coordinators, and other stakeholders who are tasked with transition planning for individuals with ASD, particularly those with severe symptoms, the time to act is now, especially in creating new intentional community models.

Most adult healthcare providers have not been routinely charged with the responsibility, nor do they have the available time and resources to meet the social and communication needs of these patients. But when faced with an ever-expanding group of patients who demonstrate inadequate social and communication skills, the healthcare system must not turn a blind eye.

The symptoms of autism do not magically resolve when a child reaches adulthood. The medical community must partner with society at large to offer transitional solutions, including intentional communities, to the rapidly growing number of adults with ASD. Current demand outweighs supply, but if we work together, we can create innovative and highly effective solutions. After all, children with autism do not disappear. They grow into adults with autism.

Autism spectrum disorder (ASD) is a neurodevelopmental condition typically diagnosed early in life: the median age at diagnosis is 52 months.¹ Because research demonstrates the benefits of early intervention,² when we think about people with ASD, we generally think about children and adolescents. 

See related article

However, autism spans the entirety of one’s life. This means that children with ASD will grow to be adults with ASD. The US Centers for Disease Control and Prevention estimated that 1 in 59 children were diagnosed with ASD during the surveillance year 2014,¹ which was nearly double the prevalence from just 8 years earlier,³ and a 15% increase since 2012.⁴ As these children grow up, this translates to an ever-growing number of adults with autism.

UNMET NEEDS

Healthcare, housing, and intellectual and developmental disability services for adults with ASD currently fall well short of meeting the needs of this exploding population. If solutions are to be realized, innovative approaches must be employed.

Swetlik et al,⁵ in this issue of the Journal, offer valuable insights into the challenges that practitioners and their adult patients with ASD encounter as a result of seismic shifts in diagnostic criteria, increasing prevalence, and changes to healthcare financial coverage. They also review behavioral and pharmacologic treatments, reproductive health, and caregiver fatigue and discuss the role of the physician and other healthcare practitioners who are likely to have only limited exposure to adult patients with ASD. These wide-ranging considerations speak to the complexity of the healthcare needs of this population.

Swetlik et al also underscore that transition planning is essential for primary care, psychiatry, behavioral health services, continuing education, skill development, and appropriate prevocational training for adolescents with ASD, and yet it is often underutilized or unavailable. There is a dearth of experienced practitioners across these disciplines to serve adults with ASD. The complexity of navigating bureaucratic processes to secure funding (typically Medicaid) supports the necessity of planning early to achieve desired outcomes for each young adult. Additionally, the number of Medicaid waivers that fund many supportive services are limited.

GROWING UP IS HARD; START PLANNING EARLY

Swetlik et al describe the stress these circumstances create for people with ASD and their families. Entering adulthood is a complicated process, fraught with emotional overtones that must include medical care, work considerations, legal and financial arrangements, and, for many, the search for an appropriate residential environment. Planning for these transitions should begin years before adulthood if the process is to work smoothly and effectively.

A transition involving a shift away from a team of familiar pediatric healthcare providers to unfamiliar adult practitioners can be distressing for any adolescent with a chronic condition. For those with ASD, who may have diminished socialization and communication skills, the transition can be especially challenging and must be handled with care.

This transition pales in comparison with the disruptive force of a permanent move out of the family home. Over the next 10 years, 500,000 youths in the United States will age out of school-based ASD services,⁶ and a great many of them will be put on long waiting lists for residential placement.⁷

For young adults with ASD, particularly those with complex needs, establishing an advantageous long-term living arrangement may mean the difference between a healthy, self-directed launch into a new phase of life, or a consequential misstep that exacerbates or worsens symptoms and creates new stressors for the young adult and his or her family. It is especially important that arrangements be made before an aging guardian starts to experience declining health.

Thoughtful and deliberate preplanning helps to reduce stress and prevent emergency placements, and promotes long-term quality of life for people with ASD.

For many years, the prevailing model for the provision of long-term care services for individuals with intellectual and developmental disabilities was institutional care. Large facilities, often located in expansive, self-contained campuses, provided around-the-clock care. Residents slept, ate, worked, and were expected to receive social and emotional fulfillment at the facility.

For some, this was an acceptable model. For many, it was not, but there were few available alternatives. At its best, this model provided a safe environment for its residents, but it did not facilitate achieving an integrated, self-directed life experience. At its worst, neglect and abuse were rampant.

Numerous legislative acts, court decisions, and advocacy efforts drove the deinstitutionalization movement for individuals with intellectual and developmental disabilities between the early 1960s and today. The 1999 case of Olmstead v LC⁸ was among the most significant. In this landmark case, in accordance with the 1990 Americans With Disabilities Act, the US Supreme Court ruled that people with disabilities have the right to receive state-funded services and support in the community rather than in institutions, as long as several criteria are met:

• Community supports are appropriate
• The individual desires to live in the community
• The accommodations to facilitate that arrangement are considered to be reasonable.

In the 20 years since the Olmstead decision, residential services for adults have shifted at an accelerated rate away from institutions toward smaller, community-based settings.^9,10 Community models include but are not limited to:

• Group homes that serve individuals with intellectual and developmental disabilities and provide 24-hour support
• Apartments or homes where individuals live and receive intermittent, less-intensive support
• Adult foster care.

DSM-5: AUTISM IS HETEROGENEOUS

In the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5),¹¹ ASD is characterized by persistent deficits in social interaction and social communication, which begin in early development and are observed in conjunction with restricted, repetitive behaviors, interests, or activities.

DSM-5 provides more than 20 examples of how these criteria might be met. Further, DSM-5 encourages clinicians to select diagnostic specifiers to address overall symptom severity, cognitive abilities, and associated medical conditions.

ONE RESIDENTIAL MODEL DOES NOT FIT ALL

The complex matrix of potential symptom manifestations in people with ASD clearly indicates the need for numerous distinctive residential models for adults with ASD.

One person with severe symptoms of ASD may require one-to-one staffing and proactive preparatory support in order to safely leave the house for a desired social experience. The person may be unable to read, to independently access public transportation, to cope with deviations in expected routine, to initiate conversation, or to remain calm if distressed. This person would benefit from a residential model that allows for a high staffing ratio, access to appropriate transportation, sophisticated autism-informed supports, and the availability of social experiences that are easily accessed—in other words, a very controlled environment.

Another person, with less severe symptoms and fewer behavioral challenges, who possesses a driver’s license and holds a job, may struggle with isolation and loneliness resulting from social inhibitions and skill deficits. This person’s support needs would differ, with emphasis placed on maintaining the appropriate social context rather than on providing a high level of individualized behavioral support.

The shift away from a one-size-fits-all institutional model for long-term care has benefited many individuals with intellectual and developmental disabilities who have experienced opportunities for community integration.

Still, for many adults with ASD, particularly those with complex needs and complex behavior profiles, the widespread conceptual shift to new and different models that assume that all people with intellectual and developmental disabilities will benefit from smaller, scattered-site settings is ill-fitting. It is erroneous to believe that for all adults with ASD, regardless of the complexity of their symptoms, living within a broader community of neurotypically developing neighbors breeds a richer sense of inclusion and connectivity.

FINDING CARE CAN BE DAUNTING

Families of adults with severe symptoms of ASD who seek placement in more traditional community residential models often find it difficult to find capable healthcare providers to serve them. Such settings are ill-equipped to deal with significantly challenging behaviors such as physical aggression, self-injury, property destruction, and elopement (wandering). These supported housing models lack the necessary staffing resources.

Further, publicly available funding options for stand-alone group homes do not typically allow for intensive supervision and management from professionals with expertise in autism. Without specialists who can  implement autism-specific best-practice methods for assessment, service planning, staff training, data collection, and the provision of visual and technological supports for residents, it is difficult to achieve desired outcomes. For example, patients can find it challenging to visit physicians’ offices for preventive and urgent care. Lacking a caregiver who is familiar with the adult patient with ASD and who can help express his or her concerns to healthcare providers, efficient evaluation of any potentially serious medical issue is a daunting task.

A residential model that is gaining popularity across the United States among families and individuals affected by ASD is the intentional community.

Although forms and functions may vary, intentional communities are planned residential developments that promote social cohesion and strive to meet the shared needs of its members. Intentional communities for adults with ASD are designed to meet their social, communication, sensory, and behavioral needs. Every detail from the selection of land, to the construction of housing, the selected staffing model, the daily structure, and the considerations for transportation and amenities are all informed by the specific needs of individuals with autism. Safety, integration, self-direction, independence, and social connectivity are common goals.   

Successful intentional communities designed for people with intellectual and developmental disabilities often have facilities devoted to recreation, continuing education, socialization, and supportive services. Staff members who work within these communities are highly trained in the unique needs of people with these disorders. Intentional communities aspire to embody the individualized, integrated community-living approach that the Olmstead decision called for, while simultaneously offering the resource-rich, safe, and supportive experience that a campus atmosphere can offer.

Almost all recently developed models allow for residents to live among neurotypical peers and have easy access to the broader community. Communities range in size from several condominiums on a cul-de-sac to expansive developments with more than a hundred homes.

The allure of an ASD-informed intentional community that provides for the social, vocational, health, and safety needs of its residents is similar to that which leads large numbers of aging, neurotypically developing individuals to seek out retirement communities. Nationally recognized models of intentional communities include First Place (Phoenix, AZ), Sweetwater Spectrum (Sonoma, CA), Cape Cod Village (Orleans, MA), and Bittersweet Farms in Ohio.

First Place is a 55-unit apartment complex near downtown Phoenix that identifies as “community-connected” and “transit-oriented.” Although there are some individuals in the complex who do not have ASD, the development was created for those who do. The goal is to enhance the quality of life for residents through the provision of housing, jobs, social opportunities, and a supportive community.

Sweetwater Spectrum is located blocks from the Sonoma downtown plaza, on just under 3 acres of land. It includes several 4-bedroom homes, a community center with a kitchen, exercise studio, media room, and library, an expansive organic garden, and an outdoor pool.

The Autism Housing Network lists more than 75 intentional communities on its resource page. There are many exciting models in development. For example, Monarch Center for Autism in Cleveland, OH, is planning to develop an innovative intentional community. It will include mixed supported living options for adults across the autism spectrum, separate housing options for parents and family members, on-site social and recreational opportunities, green space, and retail stores intended to serve members of the surrounding community and provide employment and socialization opportunities for its residents.

Casa Familia in South Florida will soon begin constructing a large intentional community that will include innovative housing options, classrooms, social areas, an auditorium, walkways, bike paths, pools, and social enterprises.

It is critical that these ASD intentional communities continue to emerge to meet the long-term needs of the rapidly growing and aging ASD population. 

THE TIME TO ACT IS NOW

Swetlik et al synthesize important, contemporary research on adult ASD healthcare considerations, pursuant to informing the many decisions that physicians and other healthcare professionals must make to address the diverse needs of this population. Their article advocates for further research and highlights the crisis surrounding the scarcity of practitioners specializing in adult ASD.

As for current healthcare providers, parents, care coordinators, and other stakeholders who are tasked with transition planning for individuals with ASD, particularly those with severe symptoms, the time to act is now, especially in creating new intentional community models.

Most adult healthcare providers have not been routinely charged with the responsibility, nor do they have the available time and resources to meet the social and communication needs of these patients. But when faced with an ever-expanding group of patients who demonstrate inadequate social and communication skills, the healthcare system must not turn a blind eye.

The symptoms of autism do not magically resolve when a child reaches adulthood. The medical community must partner with society at large to offer transitional solutions, including intentional communities, to the rapidly growing number of adults with ASD. Current demand outweighs supply, but if we work together, we can create innovative and highly effective solutions. After all, children with autism do not disappear. They grow into adults with autism.

To the Editor: We read with great pleasure the article by Sharayah et al about acute gastro­paresis in a patient with diabetic ketoacidosis.¹ However, in the case description, the authors reached a diagnosis of gastroparesis secondary to diabetic ketoacidosis without aptly ruling out some of its most common causes such as hypokalemia and other electrolyte imbalances seen in diabetic patients (in the setting of recurrent vomiting).

The authors also did not include the patient’s duration of diabetes or hemoglobin A_1c level, both of which are linked with gastroparesis in diabetic patients.² Pertinent biochemical information that can help readers formulate a rational approach and journey to making a diagnosis appears elusive in their article.

To the Editor: We read with great pleasure the article by Sharayah et al about acute gastro­paresis in a patient with diabetic ketoacidosis.¹ However, in the case description, the authors reached a diagnosis of gastroparesis secondary to diabetic ketoacidosis without aptly ruling out some of its most common causes such as hypokalemia and other electrolyte imbalances seen in diabetic patients (in the setting of recurrent vomiting).

The authors also did not include the patient’s duration of diabetes or hemoglobin A_1c level, both of which are linked with gastroparesis in diabetic patients.² Pertinent biochemical information that can help readers formulate a rational approach and journey to making a diagnosis appears elusive in their article.

To the Editor: We read with great pleasure the article by Sharayah et al about acute gastro­paresis in a patient with diabetic ketoacidosis.¹ However, in the case description, the authors reached a diagnosis of gastroparesis secondary to diabetic ketoacidosis without aptly ruling out some of its most common causes such as hypokalemia and other electrolyte imbalances seen in diabetic patients (in the setting of recurrent vomiting).

The authors also did not include the patient’s duration of diabetes or hemoglobin A_1c level, both of which are linked with gastroparesis in diabetic patients.² Pertinent biochemical information that can help readers formulate a rational approach and journey to making a diagnosis appears elusive in their article.

In Reply: We thank the readers for their letter. Our patient’s laboratory values at the time of presentation were as follows:

• Corrected sodium 142 mmol/L
• Potassium 5.5 mmol/L
• Phosphorus 6.6 mmol/L.

The rest of the electrolyte levels were within normal limits.

These reported electrolyte levels were unlikely to cause such gastroparesis. The patient’s hemoglobin A_1c was 8.7% at the time of presentation, with no previous values available. However, since abdominal computed tomography done 1 year before this presentation did not show stomach dilation and the patient was asymptomatic, his gastroparesis was presumed to be acute.

In Reply: We thank the readers for their letter. Our patient’s laboratory values at the time of presentation were as follows:

• Corrected sodium 142 mmol/L
• Potassium 5.5 mmol/L
• Phosphorus 6.6 mmol/L.

The rest of the electrolyte levels were within normal limits.

These reported electrolyte levels were unlikely to cause such gastroparesis. The patient’s hemoglobin A_1c was 8.7% at the time of presentation, with no previous values available. However, since abdominal computed tomography done 1 year before this presentation did not show stomach dilation and the patient was asymptomatic, his gastroparesis was presumed to be acute.

In Reply: We thank the readers for their letter. Our patient’s laboratory values at the time of presentation were as follows:

• Corrected sodium 142 mmol/L
• Potassium 5.5 mmol/L
• Phosphorus 6.6 mmol/L.

The rest of the electrolyte levels were within normal limits.

These reported electrolyte levels were unlikely to cause such gastroparesis. The patient’s hemoglobin A_1c was 8.7% at the time of presentation, with no previous values available. However, since abdominal computed tomography done 1 year before this presentation did not show stomach dilation and the patient was asymptomatic, his gastroparesis was presumed to be acute.

Infant mortality dropped slightly but not significantly in 2017, according to data released Aug. 1 by the National Center for Health Statistics, based on data from the National Vital Statistics System.

The rate for 2017 was 5.79 deaths per 1,000 live births, which was not statistically different from the rate of 5.87 in 2016, the National Center for Health Statistics said in a new report. Neonatal and postneonatal mortality – 3.85 and 1.94 per 1,000, respectively – both showed the same nonsignificant drop from 2016 to 2017.

About two-thirds of the infants who died in 2017 were children born preterm (less than 37 weeks’ gestation), the NCHS said, and “the mortality rate for infants born before 28 weeks of gestation [389.4 per 1,000] was 183 times the rate for term infants” born at 37-41 weeks.

Rates at the state level in 2017 ranged from a low of 3.66 deaths/1,000 live births in Massachusetts to a high of 8.73/1,000 in Mississippi. Washington (3.88) was the only other state with a rate below 4.0, while Arkansas (8.10) was the only other state above 8.0 (The District of Columbia had a rate of 8.16.). Infant mortality was significantly lower than the national rate in 11 states and significantly higher in 15 states and D.C., according to the report.

Overall, in 2017, 3,855,500 live births occurred, with 22,341 infants having died before the age of 1 year, data from the National Vital Statistics System’s linked birth/infant death file show. In 1995, the first year that the linked file was available, the corresponding numbers were 3,899,589 births and 29,505 deaths, for a rate of 7.57 deaths/1,000 live births.

rfranki@mdedge.com

Infant mortality dropped slightly but not significantly in 2017, according to data released Aug. 1 by the National Center for Health Statistics, based on data from the National Vital Statistics System.

The rate for 2017 was 5.79 deaths per 1,000 live births, which was not statistically different from the rate of 5.87 in 2016, the National Center for Health Statistics said in a new report. Neonatal and postneonatal mortality – 3.85 and 1.94 per 1,000, respectively – both showed the same nonsignificant drop from 2016 to 2017.

About two-thirds of the infants who died in 2017 were children born preterm (less than 37 weeks’ gestation), the NCHS said, and “the mortality rate for infants born before 28 weeks of gestation [389.4 per 1,000] was 183 times the rate for term infants” born at 37-41 weeks.

Rates at the state level in 2017 ranged from a low of 3.66 deaths/1,000 live births in Massachusetts to a high of 8.73/1,000 in Mississippi. Washington (3.88) was the only other state with a rate below 4.0, while Arkansas (8.10) was the only other state above 8.0 (The District of Columbia had a rate of 8.16.). Infant mortality was significantly lower than the national rate in 11 states and significantly higher in 15 states and D.C., according to the report.

Overall, in 2017, 3,855,500 live births occurred, with 22,341 infants having died before the age of 1 year, data from the National Vital Statistics System’s linked birth/infant death file show. In 1995, the first year that the linked file was available, the corresponding numbers were 3,899,589 births and 29,505 deaths, for a rate of 7.57 deaths/1,000 live births.

rfranki@mdedge.com

Infant mortality dropped slightly but not significantly in 2017, according to data released Aug. 1 by the National Center for Health Statistics, based on data from the National Vital Statistics System.

The rate for 2017 was 5.79 deaths per 1,000 live births, which was not statistically different from the rate of 5.87 in 2016, the National Center for Health Statistics said in a new report. Neonatal and postneonatal mortality – 3.85 and 1.94 per 1,000, respectively – both showed the same nonsignificant drop from 2016 to 2017.

About two-thirds of the infants who died in 2017 were children born preterm (less than 37 weeks’ gestation), the NCHS said, and “the mortality rate for infants born before 28 weeks of gestation [389.4 per 1,000] was 183 times the rate for term infants” born at 37-41 weeks.

Rates at the state level in 2017 ranged from a low of 3.66 deaths/1,000 live births in Massachusetts to a high of 8.73/1,000 in Mississippi. Washington (3.88) was the only other state with a rate below 4.0, while Arkansas (8.10) was the only other state above 8.0 (The District of Columbia had a rate of 8.16.). Infant mortality was significantly lower than the national rate in 11 states and significantly higher in 15 states and D.C., according to the report.

Overall, in 2017, 3,855,500 live births occurred, with 22,341 infants having died before the age of 1 year, data from the National Vital Statistics System’s linked birth/infant death file show. In 1995, the first year that the linked file was available, the corresponding numbers were 3,899,589 births and 29,505 deaths, for a rate of 7.57 deaths/1,000 live births.

rfranki@mdedge.com

Urine drug tests (UDTs) are useful clinical tools for assessing and monitoring the risk of misuse, abuse, and diversion when prescribing controlled substances, or for monitoring abstinence in patients with substance use disorders (SUDs). However, UDTs have been underutilized, and have been used without systematic documentation of reasons and results.^1,2 In addition, many clinicians may lack the knowledge needed to effectively interpret test results.^3,4 Although the reported use of UDTs is much higher among clinicians who are members of American Society of Addiction Medicine (ASAM), there is still a need for improved education.⁵

The appropriate use of UDTs strengthens the therapeutic relationship and promotes healthy behaviors and patients’ recovery. On the other hand, incorrect interpretation of test results may lead to missing potential aberrant behaviors, or inappropriate consequences for patients, such as discontinuing necessary medications or discharging them from care secondary to a perceived violation of a treatment contract due to unexpected positive or negative drug screening results.⁶ In this article, we review the basic concepts of UDTs and provide an algorithm to determine when to order these tests, how to interpret the results, and how to modify treatment accordingly.

Urine drug tests 101

Urine drug tests include rapid urine drug screening (UDS) and confirmatory tests. Urine drug screenings are usually based on various types of immunoassays. They are fast, sensitive, and cost-effective. Because immunoassays are antibody-mediated, they have significant false-positive and false-negative rates due to cross-reactivity and sensitivity of antibodies.⁷ For example, antibodies used in immunoassays to detect opioids are essentially morphine antibodies, and are not able to detect semisynthetic opioids or synthetic opioids (except hydrocodone).⁷ However, immunoassays specifically developed to detect oxycodone, buprenorphine, fentanyl, and methadone are available. On the other hand, antibodies can cross-react with molecules unrelated to proto-medicines or drug metabolites, but with similar antigenic determinants. For example, amphetamine immunoassays have high false-positive rates with many different classes of medications or substances.⁷

Urine drug tests based on mass spectrometry, gas chromatography/mass spectrometry (GC/MS), and liquid chromatography/mass spectrometry (LC/MS) are gold standards to confirm toxicology results. They are highly sensitive and specific, with accurate quantitative measurement. However, they are more expensive than UDS and usually need to be sent to a laboratory with capacity to perform GC/MS or LC/MS, with a turnaround time of up to 1 week.⁸ In clinical practice, we usually start with UDS tests and order confirmatory tests when needed.

When to order UDTs in outpatient psychiatry

On December 12, 2013, the ASAM released a white paper that suggests the use of drug testing as a primary prevention, diagnostic, and monitoring tool in the management of addiction or drug misuse and its application in a wide variety of medical settings.⁹ Many clinicians use treatment contracts when prescribing controlled substances as a part of a risk-mitigation strategy, and these contracts often include the use of UDTs. Urine drug tests provide objective evidence to support or negate self-report, because many people may underreport their use.¹⁰ The literature has shown significant “abnormal” urine test results, ranging from 9% to 53%, in patients receiving chronic opioid therapy.^2,11

The CDC and the American Academy of Pain Medicine recommend UDS before initiating any controlled substance for pain therapy.^12,13 They also suggest random drug testing at least once or twice a year for low-risk patients, and more frequent screening for high-risk patients, such as those with a history of addiction.^12,13 For example, for patients with opioid use disorder who participate in a methadone program, weekly UDTs are mandated for the first 90 days, and at least 8 UDTs a year are required after that.

However, UDTs carry significant stigma due to their association with SUDs. Talking with patients from the start of treatment helps to reduce this stigma, and makes it easier to have further discussions when patients have unexpected results during treatment. For example, clinicians can explain to patients that monitoring UDTs when prescribing controlled substances is similar to monitoring thyroid function with lithium use because treatment with a controlled substance carries an inherent risk of misuse, abuse, and diversion. For patients with SUDs, clinicians can explain that using UDTs to monitor their abstinence is similar to monitoring HbA_1c for glucose control in patients with diabetes.

Continue to: Factors that can affect UDT results

In addition to knowing when to order UDT, it is critical to know how to interpret the results of UDS and follow up with confirmatory tests when needed. Other than the limitations of the tests, the following factors could contribute to unexpected UDT results:

• the drug itself, including its half-life, metabolic pathways, and potential interactions with other medications
• how patients take their medications, including dose, frequency, and pattern of drug use
• all the medications that patients are taking, including prescription, over-the-counter, and herbal and supplemental preparations
• when the last dose of a prescribed controlled substance was taken. Always ask when the patient’s last dose was taken before you consider ordering a UDT.

To help better understand UDT results, Figure 1¹⁴ and Figure 2¹⁵ demonstrate metabolic pathways of commonly used benzodiazepines and opioids, respectively. There are several comprehensive reviews on commonly seen false positives and negatives for each drug or each class of drugs in immunoassays.^16-21 Confirmatory tests are usually very accurate. However, chiral analysis is needed to differentiate enantiomers, such as methamphetamine (active R-enantiomer) and selegiline, which is metabolized into L-methamphetamine (inactive S-enantiomer).²² In addition, detection of tetrahydrocannabivarin (THCV), an ingredient of the cannabis plant, via GC/MS can be used to distinguish between consumption of dronabinol and natural cannabis products.²³ The Table^16-21 summarizes the proto­type agents, other detectable agents in the same class, and false positives and negatives in immunoassays.

Interpreting UDT results and management strategies

Our Algorithm outlines how to interpret UDT results, and management strategies to consider based on whether the results are as expected or unexpected, with a few key caveats as described below.

Expected results

If there are no concerns based on the patient’s clinical presentation or collateral information, simply continue the current treatment. However, for patients taking medications that are undetectable by UDS (for example, regular use of clonazepam or oxycodone), consider ordering confirmatory tests at least once to ensure compliance, even when UDS results are negative.

Unexpected positive results, including the presence of illicit drugs and/or unprescribed licit drugs

Drug misuse, abuse, or dependence. The first step is to talk with the patient, who may acknowledge drug misuse, abuse, or dependence. Next, consider modifying the treatment plan; this may include more frequent monitoring and visits, limiting or discontinuing prescribed controlled substances, or referring the patient to inpatient or outpatient SUD treatment, as appropriate.

Continue to: Interference from medications or diet

Interference from medications or diets. One example of a positive opioid screening result due to interference from diet is the consumption of foods that contain poppy seeds. Because of this potential interference, the cutoff value for a positive opioid immunoassay in workplace drug testing was increased from 300 to 2,000 ug/L.²⁴ Educating patients regarding medication and lifestyle choices can help them avoid any interference with drug monitoring. Confirmatory tests can be ordered at the clinician’s discretion. The same principle applies to medication choice when prescribing. For example, a patient taking bupropion may experience a false positive result on a UDS for amphetamines, and a different antidepressant might be a better choice (Box 1).

Box 1

Urine sample tampering. Consider the possibility that urine samples could be substituted, especially when there are signs or indications of tampering, such as a positive pregnancy test for a male patient, or the presence of multiple prescription medications not prescribed to the patient. If there is high suspicion of urine sample tampering, consider observed urine sample collection.

When to order confirmatory tests for unexpected positive results.

Order a confirmatory test if a patient adamantly denies taking the substance(s) for which he/she has screened positive, and there’s no other explanation for the positive result. Continue the patient’s current treatment if the confirmatory test is negative. However, if the confirmatory test is positive, then modify the treatment plan (Algorithm).

Special circumstances.

A positive opioid screen in a patient who has been prescribed a synthetic or semisynthetic opioid indicates the patient is likely using opioids other than the one he/she has been prescribed. Similarly, clonazepam is expected to be negative in a benzodiazepine immunoassay. If such testing is positive, consider the possibility that the patient is taking other benzodiazepines, such as diazepam. The results of UDTs can also be complicated by common metabolites in the same class of drugs. For example, the presence of hydromorphone for patients taking hydrocodone does not necessarily indicate the use of hydromorphone, because hydromorphone is a metabolite of hydrocodone (Figure 2¹⁵).

Unexpected negative results

Prescribed medications exist in low concentration that are below the UDS detection threshold. This unexpected UDS result could occur if patients:

• take their medications less often than prescribed (because of financial difficulties or the patient feels better and does not think he/she needs it, etc.)
• hydrate too much (intentionally or unintentionally), are pregnant, or are fast metabolizers (Box 2)
• take other medications that increase the metabolism of the prescribed medication.

Box 2

Continue to: Further inquiry will...

Further inquiry will clarify these concerns. Clinicians should educate patients and manage accordingly. Confirmatory tests may be ordered upon clinicians’ discretion.

Urine sample tampering. Dilution or substitution of urine samples may lead to unexpected negative results. Usually, the urine sample will have abnormal parameters, including temperature, pH, specific gravity, urine creatinine level, or detection of adulterants. If needed, consider observed urine sample collection. Jaffee et al²⁵ reviewed tampering methods in urine drug testing.

Diversion or binge use of medications. If patients adamantly deny diverting or binge using their medication, order confirmatory tests. If the confirmatory test also is negative, modify the treatment plan accordingly, and consider the following options:

• adjust the medication dosage or frequency
• discontinue the medication
• conduct pill counts for more definitive evidence of diversion or misuse, especially if discontinuation may lead to potential harm (for example, for patients prescribed buprenorphine for opioid use disorder).

When to order confirmatory tests for unexpected negative results.

Because confirmatory tests also measure drug concentrations, clinicians sometimes order serial confirmatory testing to monitor lipophilic drugs after a patient reports discontinuation, such as in the case of a patient using marijuana, ketamine, or alprazolam. The level of a lipophilic drug, such as these 3, should continue to decline if the patient has discontinued using it. However, because the drug level is affected by how concentrated the urine samples are, it is necessary to compare the ratios of drug levels over urine creatinine levels.²⁶ Another use for confirmatory-quantitative testing is to detect “urine spiking,”^27,28 when a patient adds an unconsumed drug to his/her urine sample to produce a positive result without actually taking the drug (Box 3).

Box 3

When to consult lab specialists

Because many clinicians may find it challenging to stay abreast of all of the factors necessary to properly interpret UDT results, consulting with qualified laboratory professionals is appropriate when needed. For example, a patient was prescribed codeine, and his UDTs showed morphine as anticipated; however, the prescribing clinician suspected that the patient was also using heroin. In this case, consultation with a specialist may be warranted to look for 6-mono-acetylemorphine (6-MAM, a unique heroin metabolite) and/or the ratio of morphine to codeine.

Continue to: In summary...

In summary, UDTs are important tools to use in general psychiatry practice, especially when prescribing controlled substances. To use UDTs effectively, it is essential to possess knowledge of drug metabolism and the limitations of these tests. All immunoassay results should be considered as presumptive, and confirmatory tests are often needed for making treatment decisions. Many clinicians are unlikely to possess all the knowledge needed to correctly interpret UDTs, and in some cases, communication with qualified laboratory professionals may be necessary. In addition, the patient’s history and clinical presentation, collateral information, and data from prescription drug monitoring programs are all important factors to consider.

The cost of UDTs, variable insurance coverage, and a lack of on-site laboratory services can be deterrents to implementing UDTs as recommended. These factors vary significantly across regions, facilities, and insurance providers (see Related Resources). If faced with these issues and you expect to often need UDTs in your practice, consider using point-of-care UDTs as an alternative to improve access, convenience, and possibly cost.

Bottom Line

Urine drug tests (UDTs) should be standard clinical practice when prescribing controlled substances and treating patients with substance use disorders in the outpatient setting. Clinicians need to be knowledgeable about the limitations of UDTs, drug metabolism, and relevant patient history to interpret UDTs proficiently for optimal patient care. Consult laboratory specialists when needed to help interpret the results.

Related Resources

• Islam FA, Choudhry Z. Urine drug screens: Not just for job applicants. Current Psychiatry. 2018;17(12):43-44.
• HealthCare.gov. Health benefits & coverage: Mental health & substance abuse coverage. www.healthcare.gov/coverage/mental-health-substance-abuse-coverage/.

Drug Brand Names

Alprazolam • Xanax
Amphetamine • Adderall
Atomoxetine • Strattera
Buprenorphine • Subutex
Buprenorphine/naloxone • Suboxone, Zubsolv
Bupropion • Wellbutrin, Zyban
Chlordiazepoxide • Librium
Chlorpromazine • Thorazine
Clonazepam • Klonopin
Desipramine • Norpramin
Dextroamphetamine • Dexedrine, ProCentra
Diazepam • Valium
Doxepin • Silenor
Dronabinol • Marinol
Efavirenz • Sustiva
Ephedrine • Akovaz
Fentanyl • Actiq, Duragesic
Flurazepam • Dalmane
Hydrocodone • Hysingla, Zohydro ER
Hydromorphone • Dilaudid, Exalgo
Labetalol • Normodyne, Trandate
Lamotrigine • Lamictal
Lisdexamfetamine • Vyvanse
Lithium • Eskalith, Lithobid
Lorazepam • Ativan
Meperidine • Demerol
Metformin • Fortamet, Glucophage
Methadone • Dolophine, Methadose
Methylphenidate • Ritalin
Midazolam • Versed
Morphine • Kadian, MorphaBond
Nabilone • Cesamet
Naltrexone • Vivitrol
Oxaprozin • Daypro
Oxazepam • Serax
Oxycodone • Oxycontin
Oxymorphone • Opana
Phentermine • Adipex-P, Ionamin
Promethazine • Phenergan
Quetiapine • Seroquel
Ranitidine • Zantac
Rifampicin • Rifadin
Selegiline • Eldepryl, Zelapar
Sertraline • Zoloft
Temazepam • Restoril
Thioridazine • Mellaril
Tramadol • Conzip, Ultram
Trazodone • Desyrel
Triazolam • Halcion
Venlafaxine • Effexor
Verapamil • Calan, Verelan
Zolpidem • Ambien

Urine drug tests (UDTs) are useful clinical tools for assessing and monitoring the risk of misuse, abuse, and diversion when prescribing controlled substances, or for monitoring abstinence in patients with substance use disorders (SUDs). However, UDTs have been underutilized, and have been used without systematic documentation of reasons and results.^1,2 In addition, many clinicians may lack the knowledge needed to effectively interpret test results.^3,4 Although the reported use of UDTs is much higher among clinicians who are members of American Society of Addiction Medicine (ASAM), there is still a need for improved education.⁵

The appropriate use of UDTs strengthens the therapeutic relationship and promotes healthy behaviors and patients’ recovery. On the other hand, incorrect interpretation of test results may lead to missing potential aberrant behaviors, or inappropriate consequences for patients, such as discontinuing necessary medications or discharging them from care secondary to a perceived violation of a treatment contract due to unexpected positive or negative drug screening results.⁶ In this article, we review the basic concepts of UDTs and provide an algorithm to determine when to order these tests, how to interpret the results, and how to modify treatment accordingly.

Urine drug tests 101

Urine drug tests include rapid urine drug screening (UDS) and confirmatory tests. Urine drug screenings are usually based on various types of immunoassays. They are fast, sensitive, and cost-effective. Because immunoassays are antibody-mediated, they have significant false-positive and false-negative rates due to cross-reactivity and sensitivity of antibodies.⁷ For example, antibodies used in immunoassays to detect opioids are essentially morphine antibodies, and are not able to detect semisynthetic opioids or synthetic opioids (except hydrocodone).⁷ However, immunoassays specifically developed to detect oxycodone, buprenorphine, fentanyl, and methadone are available. On the other hand, antibodies can cross-react with molecules unrelated to proto-medicines or drug metabolites, but with similar antigenic determinants. For example, amphetamine immunoassays have high false-positive rates with many different classes of medications or substances.⁷

Urine drug tests based on mass spectrometry, gas chromatography/mass spectrometry (GC/MS), and liquid chromatography/mass spectrometry (LC/MS) are gold standards to confirm toxicology results. They are highly sensitive and specific, with accurate quantitative measurement. However, they are more expensive than UDS and usually need to be sent to a laboratory with capacity to perform GC/MS or LC/MS, with a turnaround time of up to 1 week.⁸ In clinical practice, we usually start with UDS tests and order confirmatory tests when needed.

When to order UDTs in outpatient psychiatry

On December 12, 2013, the ASAM released a white paper that suggests the use of drug testing as a primary prevention, diagnostic, and monitoring tool in the management of addiction or drug misuse and its application in a wide variety of medical settings.⁹ Many clinicians use treatment contracts when prescribing controlled substances as a part of a risk-mitigation strategy, and these contracts often include the use of UDTs. Urine drug tests provide objective evidence to support or negate self-report, because many people may underreport their use.¹⁰ The literature has shown significant “abnormal” urine test results, ranging from 9% to 53%, in patients receiving chronic opioid therapy.^2,11

The CDC and the American Academy of Pain Medicine recommend UDS before initiating any controlled substance for pain therapy.^12,13 They also suggest random drug testing at least once or twice a year for low-risk patients, and more frequent screening for high-risk patients, such as those with a history of addiction.^12,13 For example, for patients with opioid use disorder who participate in a methadone program, weekly UDTs are mandated for the first 90 days, and at least 8 UDTs a year are required after that.

However, UDTs carry significant stigma due to their association with SUDs. Talking with patients from the start of treatment helps to reduce this stigma, and makes it easier to have further discussions when patients have unexpected results during treatment. For example, clinicians can explain to patients that monitoring UDTs when prescribing controlled substances is similar to monitoring thyroid function with lithium use because treatment with a controlled substance carries an inherent risk of misuse, abuse, and diversion. For patients with SUDs, clinicians can explain that using UDTs to monitor their abstinence is similar to monitoring HbA_1c for glucose control in patients with diabetes.

Continue to: Factors that can affect UDT results

In addition to knowing when to order UDT, it is critical to know how to interpret the results of UDS and follow up with confirmatory tests when needed. Other than the limitations of the tests, the following factors could contribute to unexpected UDT results:

• the drug itself, including its half-life, metabolic pathways, and potential interactions with other medications
• how patients take their medications, including dose, frequency, and pattern of drug use
• all the medications that patients are taking, including prescription, over-the-counter, and herbal and supplemental preparations
• when the last dose of a prescribed controlled substance was taken. Always ask when the patient’s last dose was taken before you consider ordering a UDT.

To help better understand UDT results, Figure 1¹⁴ and Figure 2¹⁵ demonstrate metabolic pathways of commonly used benzodiazepines and opioids, respectively. There are several comprehensive reviews on commonly seen false positives and negatives for each drug or each class of drugs in immunoassays.^16-21 Confirmatory tests are usually very accurate. However, chiral analysis is needed to differentiate enantiomers, such as methamphetamine (active R-enantiomer) and selegiline, which is metabolized into L-methamphetamine (inactive S-enantiomer).²² In addition, detection of tetrahydrocannabivarin (THCV), an ingredient of the cannabis plant, via GC/MS can be used to distinguish between consumption of dronabinol and natural cannabis products.²³ The Table^16-21 summarizes the proto­type agents, other detectable agents in the same class, and false positives and negatives in immunoassays.

Interpreting UDT results and management strategies

Our Algorithm outlines how to interpret UDT results, and management strategies to consider based on whether the results are as expected or unexpected, with a few key caveats as described below.

Expected results

If there are no concerns based on the patient’s clinical presentation or collateral information, simply continue the current treatment. However, for patients taking medications that are undetectable by UDS (for example, regular use of clonazepam or oxycodone), consider ordering confirmatory tests at least once to ensure compliance, even when UDS results are negative.

Unexpected positive results, including the presence of illicit drugs and/or unprescribed licit drugs

Drug misuse, abuse, or dependence. The first step is to talk with the patient, who may acknowledge drug misuse, abuse, or dependence. Next, consider modifying the treatment plan; this may include more frequent monitoring and visits, limiting or discontinuing prescribed controlled substances, or referring the patient to inpatient or outpatient SUD treatment, as appropriate.

Continue to: Interference from medications or diet

Interference from medications or diets. One example of a positive opioid screening result due to interference from diet is the consumption of foods that contain poppy seeds. Because of this potential interference, the cutoff value for a positive opioid immunoassay in workplace drug testing was increased from 300 to 2,000 ug/L.²⁴ Educating patients regarding medication and lifestyle choices can help them avoid any interference with drug monitoring. Confirmatory tests can be ordered at the clinician’s discretion. The same principle applies to medication choice when prescribing. For example, a patient taking bupropion may experience a false positive result on a UDS for amphetamines, and a different antidepressant might be a better choice (Box 1).

Box 1

Urine sample tampering. Consider the possibility that urine samples could be substituted, especially when there are signs or indications of tampering, such as a positive pregnancy test for a male patient, or the presence of multiple prescription medications not prescribed to the patient. If there is high suspicion of urine sample tampering, consider observed urine sample collection.

When to order confirmatory tests for unexpected positive results.

Order a confirmatory test if a patient adamantly denies taking the substance(s) for which he/she has screened positive, and there’s no other explanation for the positive result. Continue the patient’s current treatment if the confirmatory test is negative. However, if the confirmatory test is positive, then modify the treatment plan (Algorithm).

Special circumstances.

A positive opioid screen in a patient who has been prescribed a synthetic or semisynthetic opioid indicates the patient is likely using opioids other than the one he/she has been prescribed. Similarly, clonazepam is expected to be negative in a benzodiazepine immunoassay. If such testing is positive, consider the possibility that the patient is taking other benzodiazepines, such as diazepam. The results of UDTs can also be complicated by common metabolites in the same class of drugs. For example, the presence of hydromorphone for patients taking hydrocodone does not necessarily indicate the use of hydromorphone, because hydromorphone is a metabolite of hydrocodone (Figure 2¹⁵).

Unexpected negative results

Prescribed medications exist in low concentration that are below the UDS detection threshold. This unexpected UDS result could occur if patients:

• take their medications less often than prescribed (because of financial difficulties or the patient feels better and does not think he/she needs it, etc.)
• hydrate too much (intentionally or unintentionally), are pregnant, or are fast metabolizers (Box 2)
• take other medications that increase the metabolism of the prescribed medication.

Box 2

Continue to: Further inquiry will...

Further inquiry will clarify these concerns. Clinicians should educate patients and manage accordingly. Confirmatory tests may be ordered upon clinicians’ discretion.

Urine sample tampering. Dilution or substitution of urine samples may lead to unexpected negative results. Usually, the urine sample will have abnormal parameters, including temperature, pH, specific gravity, urine creatinine level, or detection of adulterants. If needed, consider observed urine sample collection. Jaffee et al²⁵ reviewed tampering methods in urine drug testing.

Diversion or binge use of medications. If patients adamantly deny diverting or binge using their medication, order confirmatory tests. If the confirmatory test also is negative, modify the treatment plan accordingly, and consider the following options:

• adjust the medication dosage or frequency
• discontinue the medication
• conduct pill counts for more definitive evidence of diversion or misuse, especially if discontinuation may lead to potential harm (for example, for patients prescribed buprenorphine for opioid use disorder).

When to order confirmatory tests for unexpected negative results.

Because confirmatory tests also measure drug concentrations, clinicians sometimes order serial confirmatory testing to monitor lipophilic drugs after a patient reports discontinuation, such as in the case of a patient using marijuana, ketamine, or alprazolam. The level of a lipophilic drug, such as these 3, should continue to decline if the patient has discontinued using it. However, because the drug level is affected by how concentrated the urine samples are, it is necessary to compare the ratios of drug levels over urine creatinine levels.²⁶ Another use for confirmatory-quantitative testing is to detect “urine spiking,”^27,28 when a patient adds an unconsumed drug to his/her urine sample to produce a positive result without actually taking the drug (Box 3).

Box 3

When to consult lab specialists

Because many clinicians may find it challenging to stay abreast of all of the factors necessary to properly interpret UDT results, consulting with qualified laboratory professionals is appropriate when needed. For example, a patient was prescribed codeine, and his UDTs showed morphine as anticipated; however, the prescribing clinician suspected that the patient was also using heroin. In this case, consultation with a specialist may be warranted to look for 6-mono-acetylemorphine (6-MAM, a unique heroin metabolite) and/or the ratio of morphine to codeine.

Continue to: In summary...

In summary, UDTs are important tools to use in general psychiatry practice, especially when prescribing controlled substances. To use UDTs effectively, it is essential to possess knowledge of drug metabolism and the limitations of these tests. All immunoassay results should be considered as presumptive, and confirmatory tests are often needed for making treatment decisions. Many clinicians are unlikely to possess all the knowledge needed to correctly interpret UDTs, and in some cases, communication with qualified laboratory professionals may be necessary. In addition, the patient’s history and clinical presentation, collateral information, and data from prescription drug monitoring programs are all important factors to consider.

The cost of UDTs, variable insurance coverage, and a lack of on-site laboratory services can be deterrents to implementing UDTs as recommended. These factors vary significantly across regions, facilities, and insurance providers (see Related Resources). If faced with these issues and you expect to often need UDTs in your practice, consider using point-of-care UDTs as an alternative to improve access, convenience, and possibly cost.

Bottom Line

Urine drug tests (UDTs) should be standard clinical practice when prescribing controlled substances and treating patients with substance use disorders in the outpatient setting. Clinicians need to be knowledgeable about the limitations of UDTs, drug metabolism, and relevant patient history to interpret UDTs proficiently for optimal patient care. Consult laboratory specialists when needed to help interpret the results.

Related Resources

• Islam FA, Choudhry Z. Urine drug screens: Not just for job applicants. Current Psychiatry. 2018;17(12):43-44.
• HealthCare.gov. Health benefits & coverage: Mental health & substance abuse coverage. www.healthcare.gov/coverage/mental-health-substance-abuse-coverage/.

Drug Brand Names

Alprazolam • Xanax
Amphetamine • Adderall
Atomoxetine • Strattera
Buprenorphine • Subutex
Buprenorphine/naloxone • Suboxone, Zubsolv
Bupropion • Wellbutrin, Zyban
Chlordiazepoxide • Librium
Chlorpromazine • Thorazine
Clonazepam • Klonopin
Desipramine • Norpramin
Dextroamphetamine • Dexedrine, ProCentra
Diazepam • Valium
Doxepin • Silenor
Dronabinol • Marinol
Efavirenz • Sustiva
Ephedrine • Akovaz
Fentanyl • Actiq, Duragesic
Flurazepam • Dalmane
Hydrocodone • Hysingla, Zohydro ER
Hydromorphone • Dilaudid, Exalgo
Labetalol • Normodyne, Trandate
Lamotrigine • Lamictal
Lisdexamfetamine • Vyvanse
Lithium • Eskalith, Lithobid
Lorazepam • Ativan
Meperidine • Demerol
Metformin • Fortamet, Glucophage
Methadone • Dolophine, Methadose
Methylphenidate • Ritalin
Midazolam • Versed
Morphine • Kadian, MorphaBond
Nabilone • Cesamet
Naltrexone • Vivitrol
Oxaprozin • Daypro
Oxazepam • Serax
Oxycodone • Oxycontin
Oxymorphone • Opana
Phentermine • Adipex-P, Ionamin
Promethazine • Phenergan
Quetiapine • Seroquel
Ranitidine • Zantac
Rifampicin • Rifadin
Selegiline • Eldepryl, Zelapar
Sertraline • Zoloft
Temazepam • Restoril
Thioridazine • Mellaril
Tramadol • Conzip, Ultram
Trazodone • Desyrel
Triazolam • Halcion
Venlafaxine • Effexor
Verapamil • Calan, Verelan
Zolpidem • Ambien

Urine drug tests (UDTs) are useful clinical tools for assessing and monitoring the risk of misuse, abuse, and diversion when prescribing controlled substances, or for monitoring abstinence in patients with substance use disorders (SUDs). However, UDTs have been underutilized, and have been used without systematic documentation of reasons and results.^1,2 In addition, many clinicians may lack the knowledge needed to effectively interpret test results.^3,4 Although the reported use of UDTs is much higher among clinicians who are members of American Society of Addiction Medicine (ASAM), there is still a need for improved education.⁵

The appropriate use of UDTs strengthens the therapeutic relationship and promotes healthy behaviors and patients’ recovery. On the other hand, incorrect interpretation of test results may lead to missing potential aberrant behaviors, or inappropriate consequences for patients, such as discontinuing necessary medications or discharging them from care secondary to a perceived violation of a treatment contract due to unexpected positive or negative drug screening results.⁶ In this article, we review the basic concepts of UDTs and provide an algorithm to determine when to order these tests, how to interpret the results, and how to modify treatment accordingly.

Urine drug tests 101

Urine drug tests include rapid urine drug screening (UDS) and confirmatory tests. Urine drug screenings are usually based on various types of immunoassays. They are fast, sensitive, and cost-effective. Because immunoassays are antibody-mediated, they have significant false-positive and false-negative rates due to cross-reactivity and sensitivity of antibodies.⁷ For example, antibodies used in immunoassays to detect opioids are essentially morphine antibodies, and are not able to detect semisynthetic opioids or synthetic opioids (except hydrocodone).⁷ However, immunoassays specifically developed to detect oxycodone, buprenorphine, fentanyl, and methadone are available. On the other hand, antibodies can cross-react with molecules unrelated to proto-medicines or drug metabolites, but with similar antigenic determinants. For example, amphetamine immunoassays have high false-positive rates with many different classes of medications or substances.⁷

Urine drug tests based on mass spectrometry, gas chromatography/mass spectrometry (GC/MS), and liquid chromatography/mass spectrometry (LC/MS) are gold standards to confirm toxicology results. They are highly sensitive and specific, with accurate quantitative measurement. However, they are more expensive than UDS and usually need to be sent to a laboratory with capacity to perform GC/MS or LC/MS, with a turnaround time of up to 1 week.⁸ In clinical practice, we usually start with UDS tests and order confirmatory tests when needed.

When to order UDTs in outpatient psychiatry

On December 12, 2013, the ASAM released a white paper that suggests the use of drug testing as a primary prevention, diagnostic, and monitoring tool in the management of addiction or drug misuse and its application in a wide variety of medical settings.⁹ Many clinicians use treatment contracts when prescribing controlled substances as a part of a risk-mitigation strategy, and these contracts often include the use of UDTs. Urine drug tests provide objective evidence to support or negate self-report, because many people may underreport their use.¹⁰ The literature has shown significant “abnormal” urine test results, ranging from 9% to 53%, in patients receiving chronic opioid therapy.^2,11

The CDC and the American Academy of Pain Medicine recommend UDS before initiating any controlled substance for pain therapy.^12,13 They also suggest random drug testing at least once or twice a year for low-risk patients, and more frequent screening for high-risk patients, such as those with a history of addiction.^12,13 For example, for patients with opioid use disorder who participate in a methadone program, weekly UDTs are mandated for the first 90 days, and at least 8 UDTs a year are required after that.

However, UDTs carry significant stigma due to their association with SUDs. Talking with patients from the start of treatment helps to reduce this stigma, and makes it easier to have further discussions when patients have unexpected results during treatment. For example, clinicians can explain to patients that monitoring UDTs when prescribing controlled substances is similar to monitoring thyroid function with lithium use because treatment with a controlled substance carries an inherent risk of misuse, abuse, and diversion. For patients with SUDs, clinicians can explain that using UDTs to monitor their abstinence is similar to monitoring HbA_1c for glucose control in patients with diabetes.

Continue to: Factors that can affect UDT results

In addition to knowing when to order UDT, it is critical to know how to interpret the results of UDS and follow up with confirmatory tests when needed. Other than the limitations of the tests, the following factors could contribute to unexpected UDT results:

• the drug itself, including its half-life, metabolic pathways, and potential interactions with other medications
• how patients take their medications, including dose, frequency, and pattern of drug use
• all the medications that patients are taking, including prescription, over-the-counter, and herbal and supplemental preparations
• when the last dose of a prescribed controlled substance was taken. Always ask when the patient’s last dose was taken before you consider ordering a UDT.

To help better understand UDT results, Figure 1¹⁴ and Figure 2¹⁵ demonstrate metabolic pathways of commonly used benzodiazepines and opioids, respectively. There are several comprehensive reviews on commonly seen false positives and negatives for each drug or each class of drugs in immunoassays.^16-21 Confirmatory tests are usually very accurate. However, chiral analysis is needed to differentiate enantiomers, such as methamphetamine (active R-enantiomer) and selegiline, which is metabolized into L-methamphetamine (inactive S-enantiomer).²² In addition, detection of tetrahydrocannabivarin (THCV), an ingredient of the cannabis plant, via GC/MS can be used to distinguish between consumption of dronabinol and natural cannabis products.²³ The Table^16-21 summarizes the proto­type agents, other detectable agents in the same class, and false positives and negatives in immunoassays.

Interpreting UDT results and management strategies

Our Algorithm outlines how to interpret UDT results, and management strategies to consider based on whether the results are as expected or unexpected, with a few key caveats as described below.

Expected results

If there are no concerns based on the patient’s clinical presentation or collateral information, simply continue the current treatment. However, for patients taking medications that are undetectable by UDS (for example, regular use of clonazepam or oxycodone), consider ordering confirmatory tests at least once to ensure compliance, even when UDS results are negative.

Unexpected positive results, including the presence of illicit drugs and/or unprescribed licit drugs

Drug misuse, abuse, or dependence. The first step is to talk with the patient, who may acknowledge drug misuse, abuse, or dependence. Next, consider modifying the treatment plan; this may include more frequent monitoring and visits, limiting or discontinuing prescribed controlled substances, or referring the patient to inpatient or outpatient SUD treatment, as appropriate.

Continue to: Interference from medications or diet

Interference from medications or diets. One example of a positive opioid screening result due to interference from diet is the consumption of foods that contain poppy seeds. Because of this potential interference, the cutoff value for a positive opioid immunoassay in workplace drug testing was increased from 300 to 2,000 ug/L.²⁴ Educating patients regarding medication and lifestyle choices can help them avoid any interference with drug monitoring. Confirmatory tests can be ordered at the clinician’s discretion. The same principle applies to medication choice when prescribing. For example, a patient taking bupropion may experience a false positive result on a UDS for amphetamines, and a different antidepressant might be a better choice (Box 1).

Box 1

Urine sample tampering. Consider the possibility that urine samples could be substituted, especially when there are signs or indications of tampering, such as a positive pregnancy test for a male patient, or the presence of multiple prescription medications not prescribed to the patient. If there is high suspicion of urine sample tampering, consider observed urine sample collection.

When to order confirmatory tests for unexpected positive results.

Order a confirmatory test if a patient adamantly denies taking the substance(s) for which he/she has screened positive, and there’s no other explanation for the positive result. Continue the patient’s current treatment if the confirmatory test is negative. However, if the confirmatory test is positive, then modify the treatment plan (Algorithm).

Special circumstances.

A positive opioid screen in a patient who has been prescribed a synthetic or semisynthetic opioid indicates the patient is likely using opioids other than the one he/she has been prescribed. Similarly, clonazepam is expected to be negative in a benzodiazepine immunoassay. If such testing is positive, consider the possibility that the patient is taking other benzodiazepines, such as diazepam. The results of UDTs can also be complicated by common metabolites in the same class of drugs. For example, the presence of hydromorphone for patients taking hydrocodone does not necessarily indicate the use of hydromorphone, because hydromorphone is a metabolite of hydrocodone (Figure 2¹⁵).

Unexpected negative results

Prescribed medications exist in low concentration that are below the UDS detection threshold. This unexpected UDS result could occur if patients:

• take their medications less often than prescribed (because of financial difficulties or the patient feels better and does not think he/she needs it, etc.)
• hydrate too much (intentionally or unintentionally), are pregnant, or are fast metabolizers (Box 2)
• take other medications that increase the metabolism of the prescribed medication.

Box 2

Continue to: Further inquiry will...

Further inquiry will clarify these concerns. Clinicians should educate patients and manage accordingly. Confirmatory tests may be ordered upon clinicians’ discretion.

Urine sample tampering. Dilution or substitution of urine samples may lead to unexpected negative results. Usually, the urine sample will have abnormal parameters, including temperature, pH, specific gravity, urine creatinine level, or detection of adulterants. If needed, consider observed urine sample collection. Jaffee et al²⁵ reviewed tampering methods in urine drug testing.

Diversion or binge use of medications. If patients adamantly deny diverting or binge using their medication, order confirmatory tests. If the confirmatory test also is negative, modify the treatment plan accordingly, and consider the following options:

• adjust the medication dosage or frequency
• discontinue the medication
• conduct pill counts for more definitive evidence of diversion or misuse, especially if discontinuation may lead to potential harm (for example, for patients prescribed buprenorphine for opioid use disorder).

When to order confirmatory tests for unexpected negative results.

Because confirmatory tests also measure drug concentrations, clinicians sometimes order serial confirmatory testing to monitor lipophilic drugs after a patient reports discontinuation, such as in the case of a patient using marijuana, ketamine, or alprazolam. The level of a lipophilic drug, such as these 3, should continue to decline if the patient has discontinued using it. However, because the drug level is affected by how concentrated the urine samples are, it is necessary to compare the ratios of drug levels over urine creatinine levels.²⁶ Another use for confirmatory-quantitative testing is to detect “urine spiking,”^27,28 when a patient adds an unconsumed drug to his/her urine sample to produce a positive result without actually taking the drug (Box 3).

Box 3

When to consult lab specialists

Because many clinicians may find it challenging to stay abreast of all of the factors necessary to properly interpret UDT results, consulting with qualified laboratory professionals is appropriate when needed. For example, a patient was prescribed codeine, and his UDTs showed morphine as anticipated; however, the prescribing clinician suspected that the patient was also using heroin. In this case, consultation with a specialist may be warranted to look for 6-mono-acetylemorphine (6-MAM, a unique heroin metabolite) and/or the ratio of morphine to codeine.

Continue to: In summary...

In summary, UDTs are important tools to use in general psychiatry practice, especially when prescribing controlled substances. To use UDTs effectively, it is essential to possess knowledge of drug metabolism and the limitations of these tests. All immunoassay results should be considered as presumptive, and confirmatory tests are often needed for making treatment decisions. Many clinicians are unlikely to possess all the knowledge needed to correctly interpret UDTs, and in some cases, communication with qualified laboratory professionals may be necessary. In addition, the patient’s history and clinical presentation, collateral information, and data from prescription drug monitoring programs are all important factors to consider.

The cost of UDTs, variable insurance coverage, and a lack of on-site laboratory services can be deterrents to implementing UDTs as recommended. These factors vary significantly across regions, facilities, and insurance providers (see Related Resources). If faced with these issues and you expect to often need UDTs in your practice, consider using point-of-care UDTs as an alternative to improve access, convenience, and possibly cost.

Bottom Line

Urine drug tests (UDTs) should be standard clinical practice when prescribing controlled substances and treating patients with substance use disorders in the outpatient setting. Clinicians need to be knowledgeable about the limitations of UDTs, drug metabolism, and relevant patient history to interpret UDTs proficiently for optimal patient care. Consult laboratory specialists when needed to help interpret the results.

Related Resources

• Islam FA, Choudhry Z. Urine drug screens: Not just for job applicants. Current Psychiatry. 2018;17(12):43-44.
• HealthCare.gov. Health benefits & coverage: Mental health & substance abuse coverage. www.healthcare.gov/coverage/mental-health-substance-abuse-coverage/.

Drug Brand Names

Alprazolam • Xanax
Amphetamine • Adderall
Atomoxetine • Strattera
Buprenorphine • Subutex
Buprenorphine/naloxone • Suboxone, Zubsolv
Bupropion • Wellbutrin, Zyban
Chlordiazepoxide • Librium
Chlorpromazine • Thorazine
Clonazepam • Klonopin
Desipramine • Norpramin
Dextroamphetamine • Dexedrine, ProCentra
Diazepam • Valium
Doxepin • Silenor
Dronabinol • Marinol
Efavirenz • Sustiva
Ephedrine • Akovaz
Fentanyl • Actiq, Duragesic
Flurazepam • Dalmane
Hydrocodone • Hysingla, Zohydro ER
Hydromorphone • Dilaudid, Exalgo
Labetalol • Normodyne, Trandate
Lamotrigine • Lamictal
Lisdexamfetamine • Vyvanse
Lithium • Eskalith, Lithobid
Lorazepam • Ativan
Meperidine • Demerol
Metformin • Fortamet, Glucophage
Methadone • Dolophine, Methadose
Methylphenidate • Ritalin
Midazolam • Versed
Morphine • Kadian, MorphaBond
Nabilone • Cesamet
Naltrexone • Vivitrol
Oxaprozin • Daypro
Oxazepam • Serax
Oxycodone • Oxycontin
Oxymorphone • Opana
Phentermine • Adipex-P, Ionamin
Promethazine • Phenergan
Quetiapine • Seroquel
Ranitidine • Zantac
Rifampicin • Rifadin
Selegiline • Eldepryl, Zelapar
Sertraline • Zoloft
Temazepam • Restoril
Thioridazine • Mellaril
Tramadol • Conzip, Ultram
Trazodone • Desyrel
Triazolam • Halcion
Venlafaxine • Effexor
Verapamil • Calan, Verelan
Zolpidem • Ambien

Attention-deficit/hyperactivity disorder (ADHD) is the most common childhood neurodevelopmental disorder, affecting 8% to 12% of school-aged children in the United States^1-3 with significant impairments that often persist into adulthood.^4-8 Current guidelines recommend stimulant medication and/or behavioral therapies as first-line treatments for ADHD.^9,10 There is a wealth of evidence on the efficacy of stimulants in ADHD, with the most significant effects noted on core ADHD symptoms.^11,12 Additional evidence links stimulants to decreased long-term negative outcomes, including reduced school absences and grade retention,¹³ as well as modestly but significantly improved reading and math scores.¹⁴ Other studies have reported that individuals with ADHD who receive medication have decreased criminality,^15,16 motor vehicle accidents,^17,18 injuries,¹⁹ substance abuse,^20-22 and risk for subsequent and concurrent depression.²³ Therefore, the evidence suggests that consistent medication treatment helps improve outcomes for individuals with ADHD.

Adherence is defined as “the extent to which a person’s behavior (eg, taking medication) corresponds with agreed recommendations from a clinician.”²⁴ Unfortunately, pediatric ADHD medication adherence has been found to be poor (approximately 64%).^25-30 Nonadherence to ADHD medication has been linked to multiple factors, including caregiver/family and child/adolescent factors (Table 1), medication-related factors (Table 2), and health care/system factors (Table 3). Understanding and addressing these factors is essential to maximizing long-term outcomes. In this article, we review the factors associated with nonadherence to ADHD medication, and outline strategies to improve adherence.

Caregiver/family characteristics

Caregiver beliefs about ADHD and their attitudes toward treatment have been associated with the initiation of and adherence to ADHD medication. For example, caregivers who view a child’s difficulties as a medical disorder that requires a biologic intervention are more likely to accept and adhere to medication.³¹ Similarly, caregivers who perceive ADHD medication as safe, effective, and socially acceptable are more likely to be treatment-adherent.^32-35Other caregiver-related factors associated with improved ADHD medication adherence include:

• increased caregiver knowledge about ADHD³³
• receiving an ADHD diagnosis based on a thorough diagnostic process (ie, comprehensive psychological testing)³⁶
• satisfaction with information about medicine
• comfort with the treatment plan.³⁴

Socioeconomic status, family functioning, and caregiver mental health diagnoses (eg, ADHD, depression) have also been linked to ADHD medication adherence. Several studies, including the Multimodal Treatment Study of Children with ADHD,¹¹ a landmark study of stimulant medication for children with ADHD, have found an association between low income and decreased likelihood of receiving ADHD medication.^2,37-39 Further, Gau et al⁴⁰ found that negative caregiver-child relationships and family dysfunction were associated with poor medication adherence in children with ADHD.⁹ Prior studies have also shown that mothers of children with ADHD are more likely to have depression and/or anxiety,^41,42 and that caregivers with a history of mental health diagnoses are more accepting of initiating medication treatment for their children.⁴³ However, additional studies have found that caregiver mental health diagnoses decreased the likelihood of ADHD medication adherence.^40,44

Child characteristics

Child characteristics associated with decreased ADHD medication adherence include older age (eg, adolescents vs school-aged children),^{29,30,34,40,45-47} non-White race, Hispanic ethnicity,^29,33,48-51 female gender,^29,33,52 lower baseline ADHD symptom severity,^30,37 and child unwillingness to take medications.³⁴ However, prior studies have not been completely consistent about the relationship between child comorbid conditions (eg, oppositional defiant disorder [ODD], conduct disorder) and ADHD medication adherence. A few studies found that child comorbid conditions, especially ODD, mediate poor ADHD medication adherence, possibly secondary to an increased caregiver-child conflict.^30,53,54 However, other studies have reported that the presence of comorbid ODD, depression, and anxiety predicted increased adherence to ADHD medications.^37,46

Medication-related factors

Adverse effects of medications are the most commonly cited reason for ADHD medication nonadherence.^5,33,54-56 The adverse effects most often linked to nonadherence are reduced weight/appetite, increased aggressive behavior/irritability, and sleep difficulties.^54,57 Studies comparing methylphenidates and amphetamines, including 2 recent meta-analyses, suggest that amphetamines may be less well-tolerated on average, particularly with regard to emotional lability and irritability.^45,58,59 Therefore, clinicians might consider using methylphenidate-based preparations as first-line psychopharmacologic interventions in children with ADHD, as is consistent with the findings and conclusions drawn by a recent systematic review and meta-analysis of the comparative efficacy and tolerability of ADHD medications.⁶⁰

On the other hand, increased ADHD medication effectiveness has been associated with improved medication adherence.^5,34,54-56 Medication titration and dosing factors have also been shown to affect adherence. Specifically, adherence has been improved when ADHD medications are titrated in a systematic manner soon after starting treatment, and when families have an early first contact with a physician after starting medication (within 3 months).²⁸ In addition, use of a simplified dose regimen has been linked to better adherence: patients who are prescribed long-acting stimulants are more likely to adhere to treatment compared with patients who take short-acting formulations.^{26,40,49,61-63} It is possible that long-acting stimulants increase adherence because they produce more even and sustained effects on ADHD symptoms throughout the day, compared with short-acting formulations.⁶⁴ Furthermore, the inconvenience of taking multiple doses throughout the day, as well as the potential social stigma of mid-school day dosing, may negatively impact adherence to short-acting formulations.¹⁰

Continue to: Health care/system factors

Several studies have investigated the influence of health services factors on ADHD medication adherence. Specifically, limited transportation services and lack of mental health providers in the community have been linked to decreased ADHD medication adherence.^47,65,66 Furthermore, limited insurance coverage and higher costs of ADHD medications, which lead to substantial out-of-pocket payments for families, have been associated with decreased likelihood of ADHD medication adherence.^29,67

Clinician-related factors also can affect ADHD medication adherence. For example, a clinician’s lack knowledge of ADHD care can negatively impact ADHD medication adherence.⁶⁸ Two studies have documented improved ADHD medication adherence when treatment is provided by specialists (eg, child psychiatrists) rather than by community primary care providers, possibly because specialists are more likely to provide close stimulant titration and monitoring (ie, ≥ 3 visits in the first 90 days) and use higher maximum doses.^62,69 Furthermore, ADHD medication initiation and adherence are increased when patients have a strong working alliance with their clinician and trust the health care system,^31,34,35 as well as when there is a match between the caregiver’s and clinician’s perception of the cause, course, and best treatment practices for a child’s ADHD.⁶⁵

Strategies to improve medication adherence

A number of strategies to improve ADHD medication adherence can be derived from our knowledge of the factors that influence adherence.

Patient/family education. Unanswered questions about ADHD diagnosis, etiology, and medication adverse effects can negatively impact the ADHD treatment process. Therefore, patient/family education regarding ADHD and its management is necessary to improve medication adherence, because it helps families attain the knowledge, confidence, and motivation to manage their child’s condition.

Clinicians have an important role in educating patients about⁷⁰:

• the medications they are taking
• why they are taking them
• what the medications look like
• the time of medication administration
• the potential adverse effects
• what to do if adverse effects occur
• what regular testing/monitoring is necessary.

Clinicians can provide appropriate psychoeducation by sharing written materials and trusted websites with families (see Related Resources).

Behavioral strategies. Behavioral interventions have been among the most effective strategies for improving medication adherence in other chronic conditions.⁷¹ Behavioral strategies are likely to be particularly important for families of children with ADHD and comorbid conditions such as ODD because these families experience considerable caregiver-child conflict.⁷² Moreover, parents of children with ADHD are at higher risk for having ADHD and depression themselves,⁷³ both of which may interfere with a parent’s ability to obtain and administer medications consistently. Thus, for these families, using a combination of psychoeducation and behavioral strategies will be necessary to affect change in attitude and behavior. Behavioral strategies that can be used to improve medication adherence include:

• Technology-based interventions can reduce the impact of environmental barriers to adherence. For example, pharmacy automatic prescription renewal systems can reduce the likelihood of families failing to obtain ADHD medication refills. Pill reminder boxes, smartphone alerts, and setting various alarms can effectively prompt caregivers/patients to administer medication. In particular, these methods can be crucial in families for which multiple members have ADHD and its attendant difficulties with organization and task completion.
• Caregiver training may assist families in developing specific behavioral management skills that support adherence. This training can be as straightforward as instructing caregivers on the use of positive reinforcement when teaching their children to swallow pills. It may also encompass structured behavioral interventions aimed at training caregivers to utilize rewards and consequences in order to maximize medication adherence.⁷⁴

Continue to: Clinician interventions

Clinician interventions. Clinicians can use decision aids to help inform families about treatment options, promote shared decision making, and decrease uncertainty about the treatment plan⁷⁵ (see Related Resources). Early titration of ADHD medications and early first contact (within months of starting medication treatment) between caregivers and clinicians, whether via in-person visit, telephone, or email, have also been related to improved adherence.²⁸ Furthermore, clinicians can improve adherence by prescribing a simplified medication regimen (ie, long-acting formulations that provide full-day coverage). To address the negative impact of high out-of-pocket ADHD medication costs on adherence, clinicians can also prescribe generic preparations and/or “preferred” medications options on an individual patient’s formulary.

Because clinician knowledge and expertise in ADHD care has been linked to improved patient medication adherence,⁶⁸ clinicians are encouraged to use the American Academy of Pediatrics (AAP) guideline for diagnosis and treatment of ADHD, which includes a supplemental process of care algorithm (last published in 2011,¹⁰ with an updated guideline anticipated in 2019), as well as the AAP/National Institute for Children’s Health Quality (NICHQ) ADHD Toolkit,⁷⁶ which includes items helpful for ADHD diagnosis and treatment. The Society for Developmental and Behavioral Pediatrics is also developing a clinical practice guideline for the diagnosis and treatment of complex ADHD (ie, ADHD complicated by coexisting mental health, developmental, and/or psychosocial conditions or issues), with publication anticipated in 2019. Primary care providers can also improve their expertise in ADHD care by pursuing additional mental health–related trainings (such as those conducted by the REACH Institute).⁷⁷

Because receiving ADHD care from a specialist has been shown to improve medication initiation and adherence,^62,69 other strategies to address the short supply of child psychiatrists include offering incentives to medical students to pursue a career in child psychiatry (eg, loan forgiveness). Telepsychiatry and co-location of mental health specialists and primary care providers are additional innovative ways in which ADHD specialty care can be delivered to more patients.⁶⁴

Finally, providing culturally-sensitive care can strengthen the clinician-caregiver relationship and promote adherence to treatment. For example, clinicians can partner with local groups to increase their understanding of how different racial/ethnic groups perceive ADHD and its treatment.⁶⁴

Peer support models. Peers are credible role models who have a valued role in facilitating the use of mental health services by empowering families and enhancing service satisfaction.⁷⁸ In several communities in the United States, peer models using family advocates have been introduced.⁷⁹ Family advocates are typically caregivers of children who have special needs or have been involved in the mental health system. Their perspective—as peers and first-hand consumers of the health care and/or mental health system—can make them powerful and effective coaches to families of children with ADHD. By helping families to navigate ADHD care systems successfully, family advocates can play an important role in enhancing ADHD medication adherence, although further investigation is needed. In addition, the stigma around ADHD medication use, which adversely impacts adherence, can be mitigated if caregivers participate in organized ADHD-related support groups (eg, Children and Adults with ADHD [CHADD]).

Continue to: Health disparity-reducing interventions

Health disparity-reducing interventions. Successful health disparity-reducing interventions—such as those developed to enhance care of other chronic disorders including asthma and diabetes—can be applied to improve ADHD care. These interventions, which include medical-legal partnerships (eg, between clinicians, social workers, legal advocates, and community partners) in primary care centers, have been shown to improve health insurance coverage and therefore health care access.^80,81 Although some hardships linked to nonadherence (eg, low socioeconomic status) may not be amenable to health care–related interventions, screening for these hardships can identify children who are most at risk for poor adherence. This would alert clinicians to proactively identify barriers to adherence and implement mitigation strategies. This might include developing more streamlined, easier-to-follow management plans for these patients, such as those that can be delivered through pharmacist-physician collaborative programs⁸² and school-based therapy programs.^83-85

Bottom Line

Suboptimal adherence to medications for attention-deficit/hyperactivity disorder (ADHD) can be addressed through patient/family education, behavioral strategies, clinician interventions, peer support models, and health disparity-reducing interventions. By improving ADHD treatment adherence, these interventions have the potential to maximize long-term outcomes.

Related Resources

• Cohen Children’s Medical Center Northwell Health. The ADHD Medication Guide. www.ADHDMedicationGuide.com. Revised December 31, 2017.
• Cincinnati Children’s Hospital Medical Center. Decision aids to facilitate shared decision making in practice. www.cincinnatichildrens.org/service/j/anderson-center/ evidence-based-care/decision-aids.
• CHADD. Children and Adults with attention-deficit/ hyperactivity disorder. www.chadd.org.

Drug Brand Name

Methylphenidate • Concerta, Ritalin

Attention-deficit/hyperactivity disorder (ADHD) is the most common childhood neurodevelopmental disorder, affecting 8% to 12% of school-aged children in the United States^1-3 with significant impairments that often persist into adulthood.^4-8 Current guidelines recommend stimulant medication and/or behavioral therapies as first-line treatments for ADHD.^9,10 There is a wealth of evidence on the efficacy of stimulants in ADHD, with the most significant effects noted on core ADHD symptoms.^11,12 Additional evidence links stimulants to decreased long-term negative outcomes, including reduced school absences and grade retention,¹³ as well as modestly but significantly improved reading and math scores.¹⁴ Other studies have reported that individuals with ADHD who receive medication have decreased criminality,^15,16 motor vehicle accidents,^17,18 injuries,¹⁹ substance abuse,^20-22 and risk for subsequent and concurrent depression.²³ Therefore, the evidence suggests that consistent medication treatment helps improve outcomes for individuals with ADHD.

Adherence is defined as “the extent to which a person’s behavior (eg, taking medication) corresponds with agreed recommendations from a clinician.”²⁴ Unfortunately, pediatric ADHD medication adherence has been found to be poor (approximately 64%).^25-30 Nonadherence to ADHD medication has been linked to multiple factors, including caregiver/family and child/adolescent factors (Table 1), medication-related factors (Table 2), and health care/system factors (Table 3). Understanding and addressing these factors is essential to maximizing long-term outcomes. In this article, we review the factors associated with nonadherence to ADHD medication, and outline strategies to improve adherence.

Caregiver/family characteristics

Caregiver beliefs about ADHD and their attitudes toward treatment have been associated with the initiation of and adherence to ADHD medication. For example, caregivers who view a child’s difficulties as a medical disorder that requires a biologic intervention are more likely to accept and adhere to medication.³¹ Similarly, caregivers who perceive ADHD medication as safe, effective, and socially acceptable are more likely to be treatment-adherent.^32-35Other caregiver-related factors associated with improved ADHD medication adherence include:

• increased caregiver knowledge about ADHD³³
• receiving an ADHD diagnosis based on a thorough diagnostic process (ie, comprehensive psychological testing)³⁶
• satisfaction with information about medicine
• comfort with the treatment plan.³⁴

Socioeconomic status, family functioning, and caregiver mental health diagnoses (eg, ADHD, depression) have also been linked to ADHD medication adherence. Several studies, including the Multimodal Treatment Study of Children with ADHD,¹¹ a landmark study of stimulant medication for children with ADHD, have found an association between low income and decreased likelihood of receiving ADHD medication.^2,37-39 Further, Gau et al⁴⁰ found that negative caregiver-child relationships and family dysfunction were associated with poor medication adherence in children with ADHD.⁹ Prior studies have also shown that mothers of children with ADHD are more likely to have depression and/or anxiety,^41,42 and that caregivers with a history of mental health diagnoses are more accepting of initiating medication treatment for their children.⁴³ However, additional studies have found that caregiver mental health diagnoses decreased the likelihood of ADHD medication adherence.^40,44

Child characteristics

Child characteristics associated with decreased ADHD medication adherence include older age (eg, adolescents vs school-aged children),^{29,30,34,40,45-47} non-White race, Hispanic ethnicity,^29,33,48-51 female gender,^29,33,52 lower baseline ADHD symptom severity,^30,37 and child unwillingness to take medications.³⁴ However, prior studies have not been completely consistent about the relationship between child comorbid conditions (eg, oppositional defiant disorder [ODD], conduct disorder) and ADHD medication adherence. A few studies found that child comorbid conditions, especially ODD, mediate poor ADHD medication adherence, possibly secondary to an increased caregiver-child conflict.^30,53,54 However, other studies have reported that the presence of comorbid ODD, depression, and anxiety predicted increased adherence to ADHD medications.^37,46

Medication-related factors

Adverse effects of medications are the most commonly cited reason for ADHD medication nonadherence.^5,33,54-56 The adverse effects most often linked to nonadherence are reduced weight/appetite, increased aggressive behavior/irritability, and sleep difficulties.^54,57 Studies comparing methylphenidates and amphetamines, including 2 recent meta-analyses, suggest that amphetamines may be less well-tolerated on average, particularly with regard to emotional lability and irritability.^45,58,59 Therefore, clinicians might consider using methylphenidate-based preparations as first-line psychopharmacologic interventions in children with ADHD, as is consistent with the findings and conclusions drawn by a recent systematic review and meta-analysis of the comparative efficacy and tolerability of ADHD medications.⁶⁰

On the other hand, increased ADHD medication effectiveness has been associated with improved medication adherence.^5,34,54-56 Medication titration and dosing factors have also been shown to affect adherence. Specifically, adherence has been improved when ADHD medications are titrated in a systematic manner soon after starting treatment, and when families have an early first contact with a physician after starting medication (within 3 months).²⁸ In addition, use of a simplified dose regimen has been linked to better adherence: patients who are prescribed long-acting stimulants are more likely to adhere to treatment compared with patients who take short-acting formulations.^{26,40,49,61-63} It is possible that long-acting stimulants increase adherence because they produce more even and sustained effects on ADHD symptoms throughout the day, compared with short-acting formulations.⁶⁴ Furthermore, the inconvenience of taking multiple doses throughout the day, as well as the potential social stigma of mid-school day dosing, may negatively impact adherence to short-acting formulations.¹⁰

Continue to: Health care/system factors

Several studies have investigated the influence of health services factors on ADHD medication adherence. Specifically, limited transportation services and lack of mental health providers in the community have been linked to decreased ADHD medication adherence.^47,65,66 Furthermore, limited insurance coverage and higher costs of ADHD medications, which lead to substantial out-of-pocket payments for families, have been associated with decreased likelihood of ADHD medication adherence.^29,67

Clinician-related factors also can affect ADHD medication adherence. For example, a clinician’s lack knowledge of ADHD care can negatively impact ADHD medication adherence.⁶⁸ Two studies have documented improved ADHD medication adherence when treatment is provided by specialists (eg, child psychiatrists) rather than by community primary care providers, possibly because specialists are more likely to provide close stimulant titration and monitoring (ie, ≥ 3 visits in the first 90 days) and use higher maximum doses.^62,69 Furthermore, ADHD medication initiation and adherence are increased when patients have a strong working alliance with their clinician and trust the health care system,^31,34,35 as well as when there is a match between the caregiver’s and clinician’s perception of the cause, course, and best treatment practices for a child’s ADHD.⁶⁵

Strategies to improve medication adherence

A number of strategies to improve ADHD medication adherence can be derived from our knowledge of the factors that influence adherence.

Patient/family education. Unanswered questions about ADHD diagnosis, etiology, and medication adverse effects can negatively impact the ADHD treatment process. Therefore, patient/family education regarding ADHD and its management is necessary to improve medication adherence, because it helps families attain the knowledge, confidence, and motivation to manage their child’s condition.

Clinicians have an important role in educating patients about⁷⁰:

• the medications they are taking
• why they are taking them
• what the medications look like
• the time of medication administration
• the potential adverse effects
• what to do if adverse effects occur
• what regular testing/monitoring is necessary.

Clinicians can provide appropriate psychoeducation by sharing written materials and trusted websites with families (see Related Resources).

Behavioral strategies. Behavioral interventions have been among the most effective strategies for improving medication adherence in other chronic conditions.⁷¹ Behavioral strategies are likely to be particularly important for families of children with ADHD and comorbid conditions such as ODD because these families experience considerable caregiver-child conflict.⁷² Moreover, parents of children with ADHD are at higher risk for having ADHD and depression themselves,⁷³ both of which may interfere with a parent’s ability to obtain and administer medications consistently. Thus, for these families, using a combination of psychoeducation and behavioral strategies will be necessary to affect change in attitude and behavior. Behavioral strategies that can be used to improve medication adherence include:

• Technology-based interventions can reduce the impact of environmental barriers to adherence. For example, pharmacy automatic prescription renewal systems can reduce the likelihood of families failing to obtain ADHD medication refills. Pill reminder boxes, smartphone alerts, and setting various alarms can effectively prompt caregivers/patients to administer medication. In particular, these methods can be crucial in families for which multiple members have ADHD and its attendant difficulties with organization and task completion.
• Caregiver training may assist families in developing specific behavioral management skills that support adherence. This training can be as straightforward as instructing caregivers on the use of positive reinforcement when teaching their children to swallow pills. It may also encompass structured behavioral interventions aimed at training caregivers to utilize rewards and consequences in order to maximize medication adherence.⁷⁴

Continue to: Clinician interventions

Clinician interventions. Clinicians can use decision aids to help inform families about treatment options, promote shared decision making, and decrease uncertainty about the treatment plan⁷⁵ (see Related Resources). Early titration of ADHD medications and early first contact (within months of starting medication treatment) between caregivers and clinicians, whether via in-person visit, telephone, or email, have also been related to improved adherence.²⁸ Furthermore, clinicians can improve adherence by prescribing a simplified medication regimen (ie, long-acting formulations that provide full-day coverage). To address the negative impact of high out-of-pocket ADHD medication costs on adherence, clinicians can also prescribe generic preparations and/or “preferred” medications options on an individual patient’s formulary.

Because clinician knowledge and expertise in ADHD care has been linked to improved patient medication adherence,⁶⁸ clinicians are encouraged to use the American Academy of Pediatrics (AAP) guideline for diagnosis and treatment of ADHD, which includes a supplemental process of care algorithm (last published in 2011,¹⁰ with an updated guideline anticipated in 2019), as well as the AAP/National Institute for Children’s Health Quality (NICHQ) ADHD Toolkit,⁷⁶ which includes items helpful for ADHD diagnosis and treatment. The Society for Developmental and Behavioral Pediatrics is also developing a clinical practice guideline for the diagnosis and treatment of complex ADHD (ie, ADHD complicated by coexisting mental health, developmental, and/or psychosocial conditions or issues), with publication anticipated in 2019. Primary care providers can also improve their expertise in ADHD care by pursuing additional mental health–related trainings (such as those conducted by the REACH Institute).⁷⁷

Because receiving ADHD care from a specialist has been shown to improve medication initiation and adherence,^62,69 other strategies to address the short supply of child psychiatrists include offering incentives to medical students to pursue a career in child psychiatry (eg, loan forgiveness). Telepsychiatry and co-location of mental health specialists and primary care providers are additional innovative ways in which ADHD specialty care can be delivered to more patients.⁶⁴

Finally, providing culturally-sensitive care can strengthen the clinician-caregiver relationship and promote adherence to treatment. For example, clinicians can partner with local groups to increase their understanding of how different racial/ethnic groups perceive ADHD and its treatment.⁶⁴

Peer support models. Peers are credible role models who have a valued role in facilitating the use of mental health services by empowering families and enhancing service satisfaction.⁷⁸ In several communities in the United States, peer models using family advocates have been introduced.⁷⁹ Family advocates are typically caregivers of children who have special needs or have been involved in the mental health system. Their perspective—as peers and first-hand consumers of the health care and/or mental health system—can make them powerful and effective coaches to families of children with ADHD. By helping families to navigate ADHD care systems successfully, family advocates can play an important role in enhancing ADHD medication adherence, although further investigation is needed. In addition, the stigma around ADHD medication use, which adversely impacts adherence, can be mitigated if caregivers participate in organized ADHD-related support groups (eg, Children and Adults with ADHD [CHADD]).

Continue to: Health disparity-reducing interventions

Health disparity-reducing interventions. Successful health disparity-reducing interventions—such as those developed to enhance care of other chronic disorders including asthma and diabetes—can be applied to improve ADHD care. These interventions, which include medical-legal partnerships (eg, between clinicians, social workers, legal advocates, and community partners) in primary care centers, have been shown to improve health insurance coverage and therefore health care access.^80,81 Although some hardships linked to nonadherence (eg, low socioeconomic status) may not be amenable to health care–related interventions, screening for these hardships can identify children who are most at risk for poor adherence. This would alert clinicians to proactively identify barriers to adherence and implement mitigation strategies. This might include developing more streamlined, easier-to-follow management plans for these patients, such as those that can be delivered through pharmacist-physician collaborative programs⁸² and school-based therapy programs.^83-85

Bottom Line

Suboptimal adherence to medications for attention-deficit/hyperactivity disorder (ADHD) can be addressed through patient/family education, behavioral strategies, clinician interventions, peer support models, and health disparity-reducing interventions. By improving ADHD treatment adherence, these interventions have the potential to maximize long-term outcomes.

Related Resources

• Cohen Children’s Medical Center Northwell Health. The ADHD Medication Guide. www.ADHDMedicationGuide.com. Revised December 31, 2017.
• Cincinnati Children’s Hospital Medical Center. Decision aids to facilitate shared decision making in practice. www.cincinnatichildrens.org/service/j/anderson-center/ evidence-based-care/decision-aids.
• CHADD. Children and Adults with attention-deficit/ hyperactivity disorder. www.chadd.org.

Drug Brand Name

Methylphenidate • Concerta, Ritalin

Attention-deficit/hyperactivity disorder (ADHD) is the most common childhood neurodevelopmental disorder, affecting 8% to 12% of school-aged children in the United States^1-3 with significant impairments that often persist into adulthood.^4-8 Current guidelines recommend stimulant medication and/or behavioral therapies as first-line treatments for ADHD.^9,10 There is a wealth of evidence on the efficacy of stimulants in ADHD, with the most significant effects noted on core ADHD symptoms.^11,12 Additional evidence links stimulants to decreased long-term negative outcomes, including reduced school absences and grade retention,¹³ as well as modestly but significantly improved reading and math scores.¹⁴ Other studies have reported that individuals with ADHD who receive medication have decreased criminality,^15,16 motor vehicle accidents,^17,18 injuries,¹⁹ substance abuse,^20-22 and risk for subsequent and concurrent depression.²³ Therefore, the evidence suggests that consistent medication treatment helps improve outcomes for individuals with ADHD.

Adherence is defined as “the extent to which a person’s behavior (eg, taking medication) corresponds with agreed recommendations from a clinician.”²⁴ Unfortunately, pediatric ADHD medication adherence has been found to be poor (approximately 64%).^25-30 Nonadherence to ADHD medication has been linked to multiple factors, including caregiver/family and child/adolescent factors (Table 1), medication-related factors (Table 2), and health care/system factors (Table 3). Understanding and addressing these factors is essential to maximizing long-term outcomes. In this article, we review the factors associated with nonadherence to ADHD medication, and outline strategies to improve adherence.

Caregiver/family characteristics

Caregiver beliefs about ADHD and their attitudes toward treatment have been associated with the initiation of and adherence to ADHD medication. For example, caregivers who view a child’s difficulties as a medical disorder that requires a biologic intervention are more likely to accept and adhere to medication.³¹ Similarly, caregivers who perceive ADHD medication as safe, effective, and socially acceptable are more likely to be treatment-adherent.^32-35Other caregiver-related factors associated with improved ADHD medication adherence include:

• increased caregiver knowledge about ADHD³³
• receiving an ADHD diagnosis based on a thorough diagnostic process (ie, comprehensive psychological testing)³⁶
• satisfaction with information about medicine
• comfort with the treatment plan.³⁴

Socioeconomic status, family functioning, and caregiver mental health diagnoses (eg, ADHD, depression) have also been linked to ADHD medication adherence. Several studies, including the Multimodal Treatment Study of Children with ADHD,¹¹ a landmark study of stimulant medication for children with ADHD, have found an association between low income and decreased likelihood of receiving ADHD medication.^2,37-39 Further, Gau et al⁴⁰ found that negative caregiver-child relationships and family dysfunction were associated with poor medication adherence in children with ADHD.⁹ Prior studies have also shown that mothers of children with ADHD are more likely to have depression and/or anxiety,^41,42 and that caregivers with a history of mental health diagnoses are more accepting of initiating medication treatment for their children.⁴³ However, additional studies have found that caregiver mental health diagnoses decreased the likelihood of ADHD medication adherence.^40,44

Child characteristics

Child characteristics associated with decreased ADHD medication adherence include older age (eg, adolescents vs school-aged children),^{29,30,34,40,45-47} non-White race, Hispanic ethnicity,^29,33,48-51 female gender,^29,33,52 lower baseline ADHD symptom severity,^30,37 and child unwillingness to take medications.³⁴ However, prior studies have not been completely consistent about the relationship between child comorbid conditions (eg, oppositional defiant disorder [ODD], conduct disorder) and ADHD medication adherence. A few studies found that child comorbid conditions, especially ODD, mediate poor ADHD medication adherence, possibly secondary to an increased caregiver-child conflict.^30,53,54 However, other studies have reported that the presence of comorbid ODD, depression, and anxiety predicted increased adherence to ADHD medications.^37,46

Medication-related factors

Adverse effects of medications are the most commonly cited reason for ADHD medication nonadherence.^5,33,54-56 The adverse effects most often linked to nonadherence are reduced weight/appetite, increased aggressive behavior/irritability, and sleep difficulties.^54,57 Studies comparing methylphenidates and amphetamines, including 2 recent meta-analyses, suggest that amphetamines may be less well-tolerated on average, particularly with regard to emotional lability and irritability.^45,58,59 Therefore, clinicians might consider using methylphenidate-based preparations as first-line psychopharmacologic interventions in children with ADHD, as is consistent with the findings and conclusions drawn by a recent systematic review and meta-analysis of the comparative efficacy and tolerability of ADHD medications.⁶⁰

On the other hand, increased ADHD medication effectiveness has been associated with improved medication adherence.^5,34,54-56 Medication titration and dosing factors have also been shown to affect adherence. Specifically, adherence has been improved when ADHD medications are titrated in a systematic manner soon after starting treatment, and when families have an early first contact with a physician after starting medication (within 3 months).²⁸ In addition, use of a simplified dose regimen has been linked to better adherence: patients who are prescribed long-acting stimulants are more likely to adhere to treatment compared with patients who take short-acting formulations.^{26,40,49,61-63} It is possible that long-acting stimulants increase adherence because they produce more even and sustained effects on ADHD symptoms throughout the day, compared with short-acting formulations.⁶⁴ Furthermore, the inconvenience of taking multiple doses throughout the day, as well as the potential social stigma of mid-school day dosing, may negatively impact adherence to short-acting formulations.¹⁰

Continue to: Health care/system factors

Several studies have investigated the influence of health services factors on ADHD medication adherence. Specifically, limited transportation services and lack of mental health providers in the community have been linked to decreased ADHD medication adherence.^47,65,66 Furthermore, limited insurance coverage and higher costs of ADHD medications, which lead to substantial out-of-pocket payments for families, have been associated with decreased likelihood of ADHD medication adherence.^29,67

Clinician-related factors also can affect ADHD medication adherence. For example, a clinician’s lack knowledge of ADHD care can negatively impact ADHD medication adherence.⁶⁸ Two studies have documented improved ADHD medication adherence when treatment is provided by specialists (eg, child psychiatrists) rather than by community primary care providers, possibly because specialists are more likely to provide close stimulant titration and monitoring (ie, ≥ 3 visits in the first 90 days) and use higher maximum doses.^62,69 Furthermore, ADHD medication initiation and adherence are increased when patients have a strong working alliance with their clinician and trust the health care system,^31,34,35 as well as when there is a match between the caregiver’s and clinician’s perception of the cause, course, and best treatment practices for a child’s ADHD.⁶⁵

Strategies to improve medication adherence

A number of strategies to improve ADHD medication adherence can be derived from our knowledge of the factors that influence adherence.

Patient/family education. Unanswered questions about ADHD diagnosis, etiology, and medication adverse effects can negatively impact the ADHD treatment process. Therefore, patient/family education regarding ADHD and its management is necessary to improve medication adherence, because it helps families attain the knowledge, confidence, and motivation to manage their child’s condition.

Clinicians have an important role in educating patients about⁷⁰:

• the medications they are taking
• why they are taking them
• what the medications look like
• the time of medication administration
• the potential adverse effects
• what to do if adverse effects occur
• what regular testing/monitoring is necessary.

Clinicians can provide appropriate psychoeducation by sharing written materials and trusted websites with families (see Related Resources).

Behavioral strategies. Behavioral interventions have been among the most effective strategies for improving medication adherence in other chronic conditions.⁷¹ Behavioral strategies are likely to be particularly important for families of children with ADHD and comorbid conditions such as ODD because these families experience considerable caregiver-child conflict.⁷² Moreover, parents of children with ADHD are at higher risk for having ADHD and depression themselves,⁷³ both of which may interfere with a parent’s ability to obtain and administer medications consistently. Thus, for these families, using a combination of psychoeducation and behavioral strategies will be necessary to affect change in attitude and behavior. Behavioral strategies that can be used to improve medication adherence include:

• Technology-based interventions can reduce the impact of environmental barriers to adherence. For example, pharmacy automatic prescription renewal systems can reduce the likelihood of families failing to obtain ADHD medication refills. Pill reminder boxes, smartphone alerts, and setting various alarms can effectively prompt caregivers/patients to administer medication. In particular, these methods can be crucial in families for which multiple members have ADHD and its attendant difficulties with organization and task completion.
• Caregiver training may assist families in developing specific behavioral management skills that support adherence. This training can be as straightforward as instructing caregivers on the use of positive reinforcement when teaching their children to swallow pills. It may also encompass structured behavioral interventions aimed at training caregivers to utilize rewards and consequences in order to maximize medication adherence.⁷⁴

Continue to: Clinician interventions

Clinician interventions. Clinicians can use decision aids to help inform families about treatment options, promote shared decision making, and decrease uncertainty about the treatment plan⁷⁵ (see Related Resources). Early titration of ADHD medications and early first contact (within months of starting medication treatment) between caregivers and clinicians, whether via in-person visit, telephone, or email, have also been related to improved adherence.²⁸ Furthermore, clinicians can improve adherence by prescribing a simplified medication regimen (ie, long-acting formulations that provide full-day coverage). To address the negative impact of high out-of-pocket ADHD medication costs on adherence, clinicians can also prescribe generic preparations and/or “preferred” medications options on an individual patient’s formulary.

Because clinician knowledge and expertise in ADHD care has been linked to improved patient medication adherence,⁶⁸ clinicians are encouraged to use the American Academy of Pediatrics (AAP) guideline for diagnosis and treatment of ADHD, which includes a supplemental process of care algorithm (last published in 2011,¹⁰ with an updated guideline anticipated in 2019), as well as the AAP/National Institute for Children’s Health Quality (NICHQ) ADHD Toolkit,⁷⁶ which includes items helpful for ADHD diagnosis and treatment. The Society for Developmental and Behavioral Pediatrics is also developing a clinical practice guideline for the diagnosis and treatment of complex ADHD (ie, ADHD complicated by coexisting mental health, developmental, and/or psychosocial conditions or issues), with publication anticipated in 2019. Primary care providers can also improve their expertise in ADHD care by pursuing additional mental health–related trainings (such as those conducted by the REACH Institute).⁷⁷

Because receiving ADHD care from a specialist has been shown to improve medication initiation and adherence,^62,69 other strategies to address the short supply of child psychiatrists include offering incentives to medical students to pursue a career in child psychiatry (eg, loan forgiveness). Telepsychiatry and co-location of mental health specialists and primary care providers are additional innovative ways in which ADHD specialty care can be delivered to more patients.⁶⁴

Finally, providing culturally-sensitive care can strengthen the clinician-caregiver relationship and promote adherence to treatment. For example, clinicians can partner with local groups to increase their understanding of how different racial/ethnic groups perceive ADHD and its treatment.⁶⁴

Peer support models. Peers are credible role models who have a valued role in facilitating the use of mental health services by empowering families and enhancing service satisfaction.⁷⁸ In several communities in the United States, peer models using family advocates have been introduced.⁷⁹ Family advocates are typically caregivers of children who have special needs or have been involved in the mental health system. Their perspective—as peers and first-hand consumers of the health care and/or mental health system—can make them powerful and effective coaches to families of children with ADHD. By helping families to navigate ADHD care systems successfully, family advocates can play an important role in enhancing ADHD medication adherence, although further investigation is needed. In addition, the stigma around ADHD medication use, which adversely impacts adherence, can be mitigated if caregivers participate in organized ADHD-related support groups (eg, Children and Adults with ADHD [CHADD]).

Continue to: Health disparity-reducing interventions

Health disparity-reducing interventions. Successful health disparity-reducing interventions—such as those developed to enhance care of other chronic disorders including asthma and diabetes—can be applied to improve ADHD care. These interventions, which include medical-legal partnerships (eg, between clinicians, social workers, legal advocates, and community partners) in primary care centers, have been shown to improve health insurance coverage and therefore health care access.^80,81 Although some hardships linked to nonadherence (eg, low socioeconomic status) may not be amenable to health care–related interventions, screening for these hardships can identify children who are most at risk for poor adherence. This would alert clinicians to proactively identify barriers to adherence and implement mitigation strategies. This might include developing more streamlined, easier-to-follow management plans for these patients, such as those that can be delivered through pharmacist-physician collaborative programs⁸² and school-based therapy programs.^83-85

Bottom Line

Suboptimal adherence to medications for attention-deficit/hyperactivity disorder (ADHD) can be addressed through patient/family education, behavioral strategies, clinician interventions, peer support models, and health disparity-reducing interventions. By improving ADHD treatment adherence, these interventions have the potential to maximize long-term outcomes.

Related Resources

• Cohen Children’s Medical Center Northwell Health. The ADHD Medication Guide. www.ADHDMedicationGuide.com. Revised December 31, 2017.
• Cincinnati Children’s Hospital Medical Center. Decision aids to facilitate shared decision making in practice. www.cincinnatichildrens.org/service/j/anderson-center/ evidence-based-care/decision-aids.
• CHADD. Children and Adults with attention-deficit/ hyperactivity disorder. www.chadd.org.

Drug Brand Name

Methylphenidate • Concerta, Ritalin

For many people, artificial intelligence (AI) brings to mind some form of humanoid robot that speaks and acts like a human. However, AI is much more than merely robotics and machines. Professor John McCarthy of Stanford University, who first coined the term “artificial intelligence” in the early 1950s, defined it as “the science and engineering of making intelligent machines, especially intelligent computer programs”; he defined intelligence as “the computational part of the ability to achieve goals.^”1 Artificial intelligence also is commonly defined as the development of computer systems able to perform tasks that normally require human intelligence.² English Mathematician Alan Turing is considered one of the forefathers of AI research, and devised the first test to determine if a computer program was intelligent (Box 1³). Today, AI has established itself as an integral part of medicine and psychiatry.

Box 1

The semantics of AI

Two subsets of AI are machine learning and deep learning.^4,5 Machine learning is defined as a set of methods that can automatically detect patterns in data and then use the uncovered pattern to predict future data.⁴ Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts.⁵

Machine learning can be supervised, semi-supervised, or unsupervised. The majority of practical machine learning uses supervised learning, where all data are labeled and an algorithm is used to learn the mapping function from the input to the output. In unsupervised learning, all data are unlabeled and the algorithm models the underlying structure of the data by itself. Semi-supervised learning is a mixture of both.⁶

Many researchers also categorize AI into 2 types: general or “strong” AI, and narrow or “weak” AI. Strong AI is defined as computers that can think on a level at least equal to humans and are able to experience emotions and even consciousness.⁷ Weak AI includes adding “thinking-like” features to computers to make them more useful tools. Almost all AI technologies available today are considered to be weak AI.

AI in medicine

AI is being developed for a broad range of applications in medicine. This includes informatics approaches, including learning in health management systems such as electronic health records, and actively guiding physicians in their treatment decisions.⁸

AI has been applied to assist administrative workflows that reach beyond automated non-patient care activities such as chart documentation and placing orders. One example is the Judy Reitz Capacity Command Center, which was designed and built with GE Healthcare Partners.⁹ It combines AI technology in the form of systems engineering and predictive analytics to better manage multiple workflows in different administrative settings, including patient safety, volume, flow, and access to care.⁹

In April 2018, Intel Corporation surveyed 200 health-care decision makers in the United States regarding their use of AI in practice and their attitudes toward it.¹⁰ Overall, 37% of respondents reported using AI and 54% expected to increase their use of AI in the next 5 years. Clinical use of AI (77%) was more common than administrative use (41%) or financial use (26 %).¹⁰

Continue to: Box 2

Box 2^11-19 describes studies that evaluated the clinical use of AI in specialties other than psychiatry.

Box 2

How can AI be used in psychiatry?

Artificially intelligent technologies have been used in psychiatry for several decades. One of the earliest examples is ELIZA, a computer program published by Professor Joseph Weizenbaum of the Massachusetts Institute of Technology in 1966.²⁰ ELIZA consisted of a language analyzer and a script or a set of rules to improvise around a certain theme; the script DOCTOR was used to simulate a Rogerian psychotherapist.²⁰

The application of AI in psychiatry has come a long way since the pioneering work of Weizenbaum. A recent study examined AI’s ability to distinguish between an individual who had suicidal ideation vs a control group. Machine-learning algorithms were used to evaluate functional MRI scans of 34 participants (17 who had suicidal ideation and 17 controls) to identify certain neural signatures of concepts related to life and death.²¹ The machine-learning algorithms were able to distinguish between these 2 groups with 91% accuracy. They also were able to distinguish between individuals who attempted suicide and those who did not with 94% accuracy.²¹

A study from the University of Cincinnati looked at using machine learning and natural language processing to distinguish genuine suicide notes from “fake” suicide notes that had been written by a healthy control group.²² Sixty-six notes were evaluated and categorized by 11 mental health professionals (psychiatrists, social workers, and emergency medicine physicians) and 31 PGY-3 residents. The accuracy of their results was compared with that of 9 machine-learning algorithms.²² The best machine-learning algorithm accurately classified the notes 78% of the time, compared with 63% of the time for the mental health professionals and 49% of the time for the residents.²²

Researchers at Vanderbilt University examined using machine learning to predict suicide risk.²³ They developed algorithms to scan electronic health records of 5,167 adults, 3,250 of whom had attempted suicide. In a review of the patients’ data from 1 week to 2 years before the attempt, the algorithms looked for certain predictors of suicide attempts, including recurrent depression, psychotic disorder, and substance use. The algorithm was 80% accurate at predicting whether a patient would attempt suicide within the next 2 years, and 84% accurate at predicting an attempt within the next week.²³

Continue to: In a prospective study...

In a prospective study, researchers at Cincinnati Children’s Hospital used a machine-learning algorithm to evaluate 379 patients who were categorized into 3 groups: suicidal, mentally ill but not suicidal, or controls.²⁴ All participants completed a standardized behavioral rating scale and participated in a semi-structured interview. Based on the participants’ linguistic and acoustic characteristics, the algorithm was able to classify them into the 3 groups with 85% accuracy.²⁴

Many studies have looked at using language analysis to predicting the risk of psychosis in at-risk individuals. In one study, researchers evaluated individuals known to be at high risk for developing psychosis, some of whom eventually did develop psychosis.²⁵ Participants were asked to retell a story and to answer questions about that story. Researchers fed the transcripts of these interviews into a language analysis program that looked at semantic coherence, syntactic complexity, and other factors. The algorithm was able to predict the future occurrence of psychosis with 82% accuracy. Participants who converted to psychosis had decreased semantic coherence and reduced syntactic complexity.²⁵

A similar study looked at 34 at-risk youth in an attempt to predict who would develop psychosis based on speech pattern analysis.²⁶ The participants underwent baseline interviews and were assessed quarterly for 2.5 years. The algorithm was able to predict who would develop psychosis with 100% accuracy.²⁶

Challenges and limitations

The amount of research about applying machine learning to various fields of psychiatry continues to grow. With this increased interest, there have been reports of bias and human influence in the various stages of machine learning. Therefore, being aware of these challenges and engaging in practices to minimize their effects are necessary. Such practices include providing more details on data collection and processing, and constantly evaluating machine learning models for their relevance and utility to the research question proposed.²⁷

As is the case with most innovative, fast-growing technologies, AI has its fair share of criticisms and concerns. Critics have focused on the potential threat of privacy issues, medical errors, and ethical concerns. Researchers at the Stanford Center for Biomedical Ethics emphasize the importance of being aware of the different types of bias that humans and algorithm designs can introduce into health data.²⁸

Continue to: The Nuffield Council on Bioethics...

The Nuffield Council on Bioethics also emphasizes the importance of identifying the ethical issues raised by using AI in health care. Concerns include erroneous decisions made by AI and determining who is responsible for such errors, difficulty in validating the outputs of AI systems, and the potential for AI to be used for malicious purposes.²⁹

For clinicians who are considering implementing AI into their practice, it is vital to recognize where this technology belongs in a workflow and in the decision-making process. Jeffery Axt, a researcher on the clinical applications of AI, encourages clinicians to view using AI as a consulting tool to eliminate the element of fear associated with not having control over diagnostics and management.³⁰

What’s on the horizon

Research into using AI in psychiatry has drawn the attention of large companies. IBM is building an automated speech analysis application that uses machine learning to provide a real-time overview of a patient’s mental health.³¹ Social media platforms are also starting to incorporate AI technologies to scan posts for language and image patterns suggestive of suicidal thoughts or behavior.³²

“Chat bots”—AI that can conduct a conversation in natural language—are becoming popular as well. Woebot is a cognitive-behavioral therapy–based chat bot designed by a Stanford psychologist that can be accessed through Facebook Messenger. In a 2-week study, 70 young adults (age 18 to 28) with depression were randomly assigned to use Woebot or to read mental health e-books.³³ Participants who used Woebot experienced a significant reduction in depressive symptoms as measured by change in score on the Patient Health Questionnaire-9, while those assigned to the reading group did not.³³

Other researchers have focused on identifying patterns of inattention, hyperactivity, and impulsivity in children using AI technologies such as computer vision, machine learning, and data mining. For example, researchers at the University of Texas at Arlington and Yale University are analyzing data from watching children perform certain tasks involving attention, decision making, and emotion management.³⁴ There have been several advances in using AI to note abnormalities in a child’s gaze pattern that might suggest autism.³⁵

Continue to: A project at...

A project at the University of Southern California called SimSensei/Multisense uses software to track real-time behavior descriptors such as facial expressions, body postures, and acoustic features that can help identify psychological distress.³⁶ This software is combined with a virtual human platform that communicates with the patient as a therapist would.³⁶

The future of AI in health care appears to have great possibilities. Putting aside irrational fears of being replaced by computers one day, AI may someday be highly transformative, leading to vast improvements in patient care.

Bottom Line

Artificial intelligence (AI) —the development of computer systems able to perform tasks that normally require human intelligence—is being developed for use in a wide range of medical specialties. Potential uses in psychiatry include predicting a patient’s risk for suicide or psychosis. Privacy concerns, ethical issues, and the potential for medical errors are among the challenges of AI use in psychiatry.

Related Resources

• Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Mol Psychiatry. 2019. doi:10.1038/s41380-019-0365-9.
• Kretzschmar K, Tyroll H, Pavarini G, et al; NeurOx Young People’s Advisory Group. Can your phone be your therapist? Young people’s ethical perspectives on the use of fully automated conversational agents (chatbots) in mental health support. Biomed Inform Insights. 2019;11:1178222619829083. doi: 10.1177/1178222619829083.

For many people, artificial intelligence (AI) brings to mind some form of humanoid robot that speaks and acts like a human. However, AI is much more than merely robotics and machines. Professor John McCarthy of Stanford University, who first coined the term “artificial intelligence” in the early 1950s, defined it as “the science and engineering of making intelligent machines, especially intelligent computer programs”; he defined intelligence as “the computational part of the ability to achieve goals.^”1 Artificial intelligence also is commonly defined as the development of computer systems able to perform tasks that normally require human intelligence.² English Mathematician Alan Turing is considered one of the forefathers of AI research, and devised the first test to determine if a computer program was intelligent (Box 1³). Today, AI has established itself as an integral part of medicine and psychiatry.

Box 1

The semantics of AI

Two subsets of AI are machine learning and deep learning.^4,5 Machine learning is defined as a set of methods that can automatically detect patterns in data and then use the uncovered pattern to predict future data.⁴ Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts.⁵

Machine learning can be supervised, semi-supervised, or unsupervised. The majority of practical machine learning uses supervised learning, where all data are labeled and an algorithm is used to learn the mapping function from the input to the output. In unsupervised learning, all data are unlabeled and the algorithm models the underlying structure of the data by itself. Semi-supervised learning is a mixture of both.⁶

Many researchers also categorize AI into 2 types: general or “strong” AI, and narrow or “weak” AI. Strong AI is defined as computers that can think on a level at least equal to humans and are able to experience emotions and even consciousness.⁷ Weak AI includes adding “thinking-like” features to computers to make them more useful tools. Almost all AI technologies available today are considered to be weak AI.

AI in medicine

AI is being developed for a broad range of applications in medicine. This includes informatics approaches, including learning in health management systems such as electronic health records, and actively guiding physicians in their treatment decisions.⁸

AI has been applied to assist administrative workflows that reach beyond automated non-patient care activities such as chart documentation and placing orders. One example is the Judy Reitz Capacity Command Center, which was designed and built with GE Healthcare Partners.⁹ It combines AI technology in the form of systems engineering and predictive analytics to better manage multiple workflows in different administrative settings, including patient safety, volume, flow, and access to care.⁹

In April 2018, Intel Corporation surveyed 200 health-care decision makers in the United States regarding their use of AI in practice and their attitudes toward it.¹⁰ Overall, 37% of respondents reported using AI and 54% expected to increase their use of AI in the next 5 years. Clinical use of AI (77%) was more common than administrative use (41%) or financial use (26 %).¹⁰

Continue to: Box 2

Box 2^11-19 describes studies that evaluated the clinical use of AI in specialties other than psychiatry.

Box 2

How can AI be used in psychiatry?

Artificially intelligent technologies have been used in psychiatry for several decades. One of the earliest examples is ELIZA, a computer program published by Professor Joseph Weizenbaum of the Massachusetts Institute of Technology in 1966.²⁰ ELIZA consisted of a language analyzer and a script or a set of rules to improvise around a certain theme; the script DOCTOR was used to simulate a Rogerian psychotherapist.²⁰

The application of AI in psychiatry has come a long way since the pioneering work of Weizenbaum. A recent study examined AI’s ability to distinguish between an individual who had suicidal ideation vs a control group. Machine-learning algorithms were used to evaluate functional MRI scans of 34 participants (17 who had suicidal ideation and 17 controls) to identify certain neural signatures of concepts related to life and death.²¹ The machine-learning algorithms were able to distinguish between these 2 groups with 91% accuracy. They also were able to distinguish between individuals who attempted suicide and those who did not with 94% accuracy.²¹

A study from the University of Cincinnati looked at using machine learning and natural language processing to distinguish genuine suicide notes from “fake” suicide notes that had been written by a healthy control group.²² Sixty-six notes were evaluated and categorized by 11 mental health professionals (psychiatrists, social workers, and emergency medicine physicians) and 31 PGY-3 residents. The accuracy of their results was compared with that of 9 machine-learning algorithms.²² The best machine-learning algorithm accurately classified the notes 78% of the time, compared with 63% of the time for the mental health professionals and 49% of the time for the residents.²²

Researchers at Vanderbilt University examined using machine learning to predict suicide risk.²³ They developed algorithms to scan electronic health records of 5,167 adults, 3,250 of whom had attempted suicide. In a review of the patients’ data from 1 week to 2 years before the attempt, the algorithms looked for certain predictors of suicide attempts, including recurrent depression, psychotic disorder, and substance use. The algorithm was 80% accurate at predicting whether a patient would attempt suicide within the next 2 years, and 84% accurate at predicting an attempt within the next week.²³

Continue to: In a prospective study...

In a prospective study, researchers at Cincinnati Children’s Hospital used a machine-learning algorithm to evaluate 379 patients who were categorized into 3 groups: suicidal, mentally ill but not suicidal, or controls.²⁴ All participants completed a standardized behavioral rating scale and participated in a semi-structured interview. Based on the participants’ linguistic and acoustic characteristics, the algorithm was able to classify them into the 3 groups with 85% accuracy.²⁴

Many studies have looked at using language analysis to predicting the risk of psychosis in at-risk individuals. In one study, researchers evaluated individuals known to be at high risk for developing psychosis, some of whom eventually did develop psychosis.²⁵ Participants were asked to retell a story and to answer questions about that story. Researchers fed the transcripts of these interviews into a language analysis program that looked at semantic coherence, syntactic complexity, and other factors. The algorithm was able to predict the future occurrence of psychosis with 82% accuracy. Participants who converted to psychosis had decreased semantic coherence and reduced syntactic complexity.²⁵

A similar study looked at 34 at-risk youth in an attempt to predict who would develop psychosis based on speech pattern analysis.²⁶ The participants underwent baseline interviews and were assessed quarterly for 2.5 years. The algorithm was able to predict who would develop psychosis with 100% accuracy.²⁶

Challenges and limitations

The amount of research about applying machine learning to various fields of psychiatry continues to grow. With this increased interest, there have been reports of bias and human influence in the various stages of machine learning. Therefore, being aware of these challenges and engaging in practices to minimize their effects are necessary. Such practices include providing more details on data collection and processing, and constantly evaluating machine learning models for their relevance and utility to the research question proposed.²⁷

As is the case with most innovative, fast-growing technologies, AI has its fair share of criticisms and concerns. Critics have focused on the potential threat of privacy issues, medical errors, and ethical concerns. Researchers at the Stanford Center for Biomedical Ethics emphasize the importance of being aware of the different types of bias that humans and algorithm designs can introduce into health data.²⁸

Continue to: The Nuffield Council on Bioethics...

The Nuffield Council on Bioethics also emphasizes the importance of identifying the ethical issues raised by using AI in health care. Concerns include erroneous decisions made by AI and determining who is responsible for such errors, difficulty in validating the outputs of AI systems, and the potential for AI to be used for malicious purposes.²⁹

For clinicians who are considering implementing AI into their practice, it is vital to recognize where this technology belongs in a workflow and in the decision-making process. Jeffery Axt, a researcher on the clinical applications of AI, encourages clinicians to view using AI as a consulting tool to eliminate the element of fear associated with not having control over diagnostics and management.³⁰

What’s on the horizon

Research into using AI in psychiatry has drawn the attention of large companies. IBM is building an automated speech analysis application that uses machine learning to provide a real-time overview of a patient’s mental health.³¹ Social media platforms are also starting to incorporate AI technologies to scan posts for language and image patterns suggestive of suicidal thoughts or behavior.³²

“Chat bots”—AI that can conduct a conversation in natural language—are becoming popular as well. Woebot is a cognitive-behavioral therapy–based chat bot designed by a Stanford psychologist that can be accessed through Facebook Messenger. In a 2-week study, 70 young adults (age 18 to 28) with depression were randomly assigned to use Woebot or to read mental health e-books.³³ Participants who used Woebot experienced a significant reduction in depressive symptoms as measured by change in score on the Patient Health Questionnaire-9, while those assigned to the reading group did not.³³

Other researchers have focused on identifying patterns of inattention, hyperactivity, and impulsivity in children using AI technologies such as computer vision, machine learning, and data mining. For example, researchers at the University of Texas at Arlington and Yale University are analyzing data from watching children perform certain tasks involving attention, decision making, and emotion management.³⁴ There have been several advances in using AI to note abnormalities in a child’s gaze pattern that might suggest autism.³⁵

Continue to: A project at...

A project at the University of Southern California called SimSensei/Multisense uses software to track real-time behavior descriptors such as facial expressions, body postures, and acoustic features that can help identify psychological distress.³⁶ This software is combined with a virtual human platform that communicates with the patient as a therapist would.³⁶

The future of AI in health care appears to have great possibilities. Putting aside irrational fears of being replaced by computers one day, AI may someday be highly transformative, leading to vast improvements in patient care.

Bottom Line

Artificial intelligence (AI) —the development of computer systems able to perform tasks that normally require human intelligence—is being developed for use in a wide range of medical specialties. Potential uses in psychiatry include predicting a patient’s risk for suicide or psychosis. Privacy concerns, ethical issues, and the potential for medical errors are among the challenges of AI use in psychiatry.

Related Resources

• Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Mol Psychiatry. 2019. doi:10.1038/s41380-019-0365-9.
• Kretzschmar K, Tyroll H, Pavarini G, et al; NeurOx Young People’s Advisory Group. Can your phone be your therapist? Young people’s ethical perspectives on the use of fully automated conversational agents (chatbots) in mental health support. Biomed Inform Insights. 2019;11:1178222619829083. doi: 10.1177/1178222619829083.

For many people, artificial intelligence (AI) brings to mind some form of humanoid robot that speaks and acts like a human. However, AI is much more than merely robotics and machines. Professor John McCarthy of Stanford University, who first coined the term “artificial intelligence” in the early 1950s, defined it as “the science and engineering of making intelligent machines, especially intelligent computer programs”; he defined intelligence as “the computational part of the ability to achieve goals.^”1 Artificial intelligence also is commonly defined as the development of computer systems able to perform tasks that normally require human intelligence.² English Mathematician Alan Turing is considered one of the forefathers of AI research, and devised the first test to determine if a computer program was intelligent (Box 1³). Today, AI has established itself as an integral part of medicine and psychiatry.

Box 1

The semantics of AI

Two subsets of AI are machine learning and deep learning.^4,5 Machine learning is defined as a set of methods that can automatically detect patterns in data and then use the uncovered pattern to predict future data.⁴ Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts.⁵

Machine learning can be supervised, semi-supervised, or unsupervised. The majority of practical machine learning uses supervised learning, where all data are labeled and an algorithm is used to learn the mapping function from the input to the output. In unsupervised learning, all data are unlabeled and the algorithm models the underlying structure of the data by itself. Semi-supervised learning is a mixture of both.⁶

Many researchers also categorize AI into 2 types: general or “strong” AI, and narrow or “weak” AI. Strong AI is defined as computers that can think on a level at least equal to humans and are able to experience emotions and even consciousness.⁷ Weak AI includes adding “thinking-like” features to computers to make them more useful tools. Almost all AI technologies available today are considered to be weak AI.

AI in medicine

AI is being developed for a broad range of applications in medicine. This includes informatics approaches, including learning in health management systems such as electronic health records, and actively guiding physicians in their treatment decisions.⁸

AI has been applied to assist administrative workflows that reach beyond automated non-patient care activities such as chart documentation and placing orders. One example is the Judy Reitz Capacity Command Center, which was designed and built with GE Healthcare Partners.⁹ It combines AI technology in the form of systems engineering and predictive analytics to better manage multiple workflows in different administrative settings, including patient safety, volume, flow, and access to care.⁹

In April 2018, Intel Corporation surveyed 200 health-care decision makers in the United States regarding their use of AI in practice and their attitudes toward it.¹⁰ Overall, 37% of respondents reported using AI and 54% expected to increase their use of AI in the next 5 years. Clinical use of AI (77%) was more common than administrative use (41%) or financial use (26 %).¹⁰

Continue to: Box 2

Box 2^11-19 describes studies that evaluated the clinical use of AI in specialties other than psychiatry.

Box 2

How can AI be used in psychiatry?

Artificially intelligent technologies have been used in psychiatry for several decades. One of the earliest examples is ELIZA, a computer program published by Professor Joseph Weizenbaum of the Massachusetts Institute of Technology in 1966.²⁰ ELIZA consisted of a language analyzer and a script or a set of rules to improvise around a certain theme; the script DOCTOR was used to simulate a Rogerian psychotherapist.²⁰

The application of AI in psychiatry has come a long way since the pioneering work of Weizenbaum. A recent study examined AI’s ability to distinguish between an individual who had suicidal ideation vs a control group. Machine-learning algorithms were used to evaluate functional MRI scans of 34 participants (17 who had suicidal ideation and 17 controls) to identify certain neural signatures of concepts related to life and death.²¹ The machine-learning algorithms were able to distinguish between these 2 groups with 91% accuracy. They also were able to distinguish between individuals who attempted suicide and those who did not with 94% accuracy.²¹

A study from the University of Cincinnati looked at using machine learning and natural language processing to distinguish genuine suicide notes from “fake” suicide notes that had been written by a healthy control group.²² Sixty-six notes were evaluated and categorized by 11 mental health professionals (psychiatrists, social workers, and emergency medicine physicians) and 31 PGY-3 residents. The accuracy of their results was compared with that of 9 machine-learning algorithms.²² The best machine-learning algorithm accurately classified the notes 78% of the time, compared with 63% of the time for the mental health professionals and 49% of the time for the residents.²²

Researchers at Vanderbilt University examined using machine learning to predict suicide risk.²³ They developed algorithms to scan electronic health records of 5,167 adults, 3,250 of whom had attempted suicide. In a review of the patients’ data from 1 week to 2 years before the attempt, the algorithms looked for certain predictors of suicide attempts, including recurrent depression, psychotic disorder, and substance use. The algorithm was 80% accurate at predicting whether a patient would attempt suicide within the next 2 years, and 84% accurate at predicting an attempt within the next week.²³

Continue to: In a prospective study...

In a prospective study, researchers at Cincinnati Children’s Hospital used a machine-learning algorithm to evaluate 379 patients who were categorized into 3 groups: suicidal, mentally ill but not suicidal, or controls.²⁴ All participants completed a standardized behavioral rating scale and participated in a semi-structured interview. Based on the participants’ linguistic and acoustic characteristics, the algorithm was able to classify them into the 3 groups with 85% accuracy.²⁴

Many studies have looked at using language analysis to predicting the risk of psychosis in at-risk individuals. In one study, researchers evaluated individuals known to be at high risk for developing psychosis, some of whom eventually did develop psychosis.²⁵ Participants were asked to retell a story and to answer questions about that story. Researchers fed the transcripts of these interviews into a language analysis program that looked at semantic coherence, syntactic complexity, and other factors. The algorithm was able to predict the future occurrence of psychosis with 82% accuracy. Participants who converted to psychosis had decreased semantic coherence and reduced syntactic complexity.²⁵

A similar study looked at 34 at-risk youth in an attempt to predict who would develop psychosis based on speech pattern analysis.²⁶ The participants underwent baseline interviews and were assessed quarterly for 2.5 years. The algorithm was able to predict who would develop psychosis with 100% accuracy.²⁶

Challenges and limitations

The amount of research about applying machine learning to various fields of psychiatry continues to grow. With this increased interest, there have been reports of bias and human influence in the various stages of machine learning. Therefore, being aware of these challenges and engaging in practices to minimize their effects are necessary. Such practices include providing more details on data collection and processing, and constantly evaluating machine learning models for their relevance and utility to the research question proposed.²⁷

As is the case with most innovative, fast-growing technologies, AI has its fair share of criticisms and concerns. Critics have focused on the potential threat of privacy issues, medical errors, and ethical concerns. Researchers at the Stanford Center for Biomedical Ethics emphasize the importance of being aware of the different types of bias that humans and algorithm designs can introduce into health data.²⁸

Continue to: The Nuffield Council on Bioethics...

The Nuffield Council on Bioethics also emphasizes the importance of identifying the ethical issues raised by using AI in health care. Concerns include erroneous decisions made by AI and determining who is responsible for such errors, difficulty in validating the outputs of AI systems, and the potential for AI to be used for malicious purposes.²⁹

For clinicians who are considering implementing AI into their practice, it is vital to recognize where this technology belongs in a workflow and in the decision-making process. Jeffery Axt, a researcher on the clinical applications of AI, encourages clinicians to view using AI as a consulting tool to eliminate the element of fear associated with not having control over diagnostics and management.³⁰

What’s on the horizon

Research into using AI in psychiatry has drawn the attention of large companies. IBM is building an automated speech analysis application that uses machine learning to provide a real-time overview of a patient’s mental health.³¹ Social media platforms are also starting to incorporate AI technologies to scan posts for language and image patterns suggestive of suicidal thoughts or behavior.³²

“Chat bots”—AI that can conduct a conversation in natural language—are becoming popular as well. Woebot is a cognitive-behavioral therapy–based chat bot designed by a Stanford psychologist that can be accessed through Facebook Messenger. In a 2-week study, 70 young adults (age 18 to 28) with depression were randomly assigned to use Woebot or to read mental health e-books.³³ Participants who used Woebot experienced a significant reduction in depressive symptoms as measured by change in score on the Patient Health Questionnaire-9, while those assigned to the reading group did not.³³

Other researchers have focused on identifying patterns of inattention, hyperactivity, and impulsivity in children using AI technologies such as computer vision, machine learning, and data mining. For example, researchers at the University of Texas at Arlington and Yale University are analyzing data from watching children perform certain tasks involving attention, decision making, and emotion management.³⁴ There have been several advances in using AI to note abnormalities in a child’s gaze pattern that might suggest autism.³⁵

Continue to: A project at...

A project at the University of Southern California called SimSensei/Multisense uses software to track real-time behavior descriptors such as facial expressions, body postures, and acoustic features that can help identify psychological distress.³⁶ This software is combined with a virtual human platform that communicates with the patient as a therapist would.³⁶

The future of AI in health care appears to have great possibilities. Putting aside irrational fears of being replaced by computers one day, AI may someday be highly transformative, leading to vast improvements in patient care.

Bottom Line

Artificial intelligence (AI) —the development of computer systems able to perform tasks that normally require human intelligence—is being developed for use in a wide range of medical specialties. Potential uses in psychiatry include predicting a patient’s risk for suicide or psychosis. Privacy concerns, ethical issues, and the potential for medical errors are among the challenges of AI use in psychiatry.

Related Resources

• Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Mol Psychiatry. 2019. doi:10.1038/s41380-019-0365-9.
• Kretzschmar K, Tyroll H, Pavarini G, et al; NeurOx Young People’s Advisory Group. Can your phone be your therapist? Young people’s ethical perspectives on the use of fully automated conversational agents (chatbots) in mental health support. Biomed Inform Insights. 2019;11:1178222619829083. doi: 10.1177/1178222619829083.

“A leader is someone who helps improve the lives of other people or improve the system they live under.”

— Sam Houston

If my motivation to become a doctor was ever the supposed glamour and prestige conferred once “MD” is added to your name, that delusion was quickly wiped away on my first day of residency; not at work, but on my way there.

I live in New York City—a city that relies on buses and subways, where the wealthy and elite go to work using the same modes of transportation as everyone else. Unfortunately, because the shelter system in New York isn’t nearly large enough to accommodate the vast homeless population, many homeless people sleep in the subway at night. It’s not uncommon to see a still-sleeping homeless person on the subway in the early hours of the morning, and I encountered one on my first official day of work as a doctor.

There I was, dressed for the occasion in a new, freshly ironed white button-down shirt and black slacks. There he was, haggard, disheveled, and smelling of alcohol, lying on a subway bench with an empty bottle of vodka tucked into his pants pocket. Out of both pity and fear of what he might do if someone attempted to wake him, people allowed him to sleep, and politely stood around him as the train proceeded on its route. The homeless man had his legs tucked in the fetal position, and I saw there was enough space on the bench for someone to sit. I wondered why nobody else chose to use that space by his feet, and I saw no harm in sitting there, so I did.

Within seconds of sitting down, the man extended one of his legs and kicked me in the chest while still asleep. Not hard enough to cause pain or injury, but enough to leave a dirty boot print on my shirt. I had to wear that shirt for the rest of the day, and so I spent my first day of residency explaining to hospital staff and patients alike how I was branded by a drunk homeless man on the subway as he slept.

As time wore on in my first year of residency, I learned that encounters with individuals like these were not rare. The majority of the patients I see are people like that man on the subway. “I sleep on the subway” is often the answer when I ask a patient about their living conditions. “I’m on public assistance” is what I hear when questioning what a patient does for money. “I don’t have money to take the bus” is a typical explanation for why they missed their doctor’s appointments and ran out of medicine. And, sadly, “Because I’m lonely” is the main excuse for why patients engage in self-defeating habits such as drug and alcohol abuse.

I didn’t anticipate this part of psychiatry when I applied for residency in this specialty. My notion of this profession was far more romanticized. I was enthralled with the science of neurotransmitters, the parameters of DSM criteria, the interpersonal skills required to elicit information from a patient during an interview, the deliberation in arriving at a diagnosis, and the ever-changing nature of psychopharmacology. That’s the psychiatry I expected to learn when I got on the subway for my first day of residency. It wasn’t until later that I truly considered the human toll that psychiatric illness takes on the individual who suffers from it. To that person, the science behind their illness and the suffering they endure isn’t romantic at all; it’s a burden to be lifted.

Continue to: We use the term...

We use the term “underserved” to identify challenging patient populations, but there are categories of patients that fall below the threshold of merely underserved. I am mortified to know that one-third of homeless people in the United States have a serious and untreated mental illness. Individuals discharged from psychiatric hospitals are 3 times more likely to obtain food from garbage. They are also far more likely to be the victim of a crime than perpetrators of it. As I’ve discovered since starting residency, if a patient doesn’t have a place to live, food to eat, and some semblance of a support system, then it’s often meaningless for them to take pills, regardless of how those pills work in theory.

No definition of sound mental health is complete unless it gives deference to those who lack basic human needs. This is a realization that was literally kicked into me, and one I hope will guide me in the years ahead.

“A leader is someone who helps improve the lives of other people or improve the system they live under.”

— Sam Houston

If my motivation to become a doctor was ever the supposed glamour and prestige conferred once “MD” is added to your name, that delusion was quickly wiped away on my first day of residency; not at work, but on my way there.

I live in New York City—a city that relies on buses and subways, where the wealthy and elite go to work using the same modes of transportation as everyone else. Unfortunately, because the shelter system in New York isn’t nearly large enough to accommodate the vast homeless population, many homeless people sleep in the subway at night. It’s not uncommon to see a still-sleeping homeless person on the subway in the early hours of the morning, and I encountered one on my first official day of work as a doctor.

There I was, dressed for the occasion in a new, freshly ironed white button-down shirt and black slacks. There he was, haggard, disheveled, and smelling of alcohol, lying on a subway bench with an empty bottle of vodka tucked into his pants pocket. Out of both pity and fear of what he might do if someone attempted to wake him, people allowed him to sleep, and politely stood around him as the train proceeded on its route. The homeless man had his legs tucked in the fetal position, and I saw there was enough space on the bench for someone to sit. I wondered why nobody else chose to use that space by his feet, and I saw no harm in sitting there, so I did.

Within seconds of sitting down, the man extended one of his legs and kicked me in the chest while still asleep. Not hard enough to cause pain or injury, but enough to leave a dirty boot print on my shirt. I had to wear that shirt for the rest of the day, and so I spent my first day of residency explaining to hospital staff and patients alike how I was branded by a drunk homeless man on the subway as he slept.

As time wore on in my first year of residency, I learned that encounters with individuals like these were not rare. The majority of the patients I see are people like that man on the subway. “I sleep on the subway” is often the answer when I ask a patient about their living conditions. “I’m on public assistance” is what I hear when questioning what a patient does for money. “I don’t have money to take the bus” is a typical explanation for why they missed their doctor’s appointments and ran out of medicine. And, sadly, “Because I’m lonely” is the main excuse for why patients engage in self-defeating habits such as drug and alcohol abuse.

I didn’t anticipate this part of psychiatry when I applied for residency in this specialty. My notion of this profession was far more romanticized. I was enthralled with the science of neurotransmitters, the parameters of DSM criteria, the interpersonal skills required to elicit information from a patient during an interview, the deliberation in arriving at a diagnosis, and the ever-changing nature of psychopharmacology. That’s the psychiatry I expected to learn when I got on the subway for my first day of residency. It wasn’t until later that I truly considered the human toll that psychiatric illness takes on the individual who suffers from it. To that person, the science behind their illness and the suffering they endure isn’t romantic at all; it’s a burden to be lifted.

Continue to: We use the term...

We use the term “underserved” to identify challenging patient populations, but there are categories of patients that fall below the threshold of merely underserved. I am mortified to know that one-third of homeless people in the United States have a serious and untreated mental illness. Individuals discharged from psychiatric hospitals are 3 times more likely to obtain food from garbage. They are also far more likely to be the victim of a crime than perpetrators of it. As I’ve discovered since starting residency, if a patient doesn’t have a place to live, food to eat, and some semblance of a support system, then it’s often meaningless for them to take pills, regardless of how those pills work in theory.

No definition of sound mental health is complete unless it gives deference to those who lack basic human needs. This is a realization that was literally kicked into me, and one I hope will guide me in the years ahead.

“A leader is someone who helps improve the lives of other people or improve the system they live under.”

— Sam Houston

If my motivation to become a doctor was ever the supposed glamour and prestige conferred once “MD” is added to your name, that delusion was quickly wiped away on my first day of residency; not at work, but on my way there.

I live in New York City—a city that relies on buses and subways, where the wealthy and elite go to work using the same modes of transportation as everyone else. Unfortunately, because the shelter system in New York isn’t nearly large enough to accommodate the vast homeless population, many homeless people sleep in the subway at night. It’s not uncommon to see a still-sleeping homeless person on the subway in the early hours of the morning, and I encountered one on my first official day of work as a doctor.

There I was, dressed for the occasion in a new, freshly ironed white button-down shirt and black slacks. There he was, haggard, disheveled, and smelling of alcohol, lying on a subway bench with an empty bottle of vodka tucked into his pants pocket. Out of both pity and fear of what he might do if someone attempted to wake him, people allowed him to sleep, and politely stood around him as the train proceeded on its route. The homeless man had his legs tucked in the fetal position, and I saw there was enough space on the bench for someone to sit. I wondered why nobody else chose to use that space by his feet, and I saw no harm in sitting there, so I did.

Within seconds of sitting down, the man extended one of his legs and kicked me in the chest while still asleep. Not hard enough to cause pain or injury, but enough to leave a dirty boot print on my shirt. I had to wear that shirt for the rest of the day, and so I spent my first day of residency explaining to hospital staff and patients alike how I was branded by a drunk homeless man on the subway as he slept.

As time wore on in my first year of residency, I learned that encounters with individuals like these were not rare. The majority of the patients I see are people like that man on the subway. “I sleep on the subway” is often the answer when I ask a patient about their living conditions. “I’m on public assistance” is what I hear when questioning what a patient does for money. “I don’t have money to take the bus” is a typical explanation for why they missed their doctor’s appointments and ran out of medicine. And, sadly, “Because I’m lonely” is the main excuse for why patients engage in self-defeating habits such as drug and alcohol abuse.

I didn’t anticipate this part of psychiatry when I applied for residency in this specialty. My notion of this profession was far more romanticized. I was enthralled with the science of neurotransmitters, the parameters of DSM criteria, the interpersonal skills required to elicit information from a patient during an interview, the deliberation in arriving at a diagnosis, and the ever-changing nature of psychopharmacology. That’s the psychiatry I expected to learn when I got on the subway for my first day of residency. It wasn’t until later that I truly considered the human toll that psychiatric illness takes on the individual who suffers from it. To that person, the science behind their illness and the suffering they endure isn’t romantic at all; it’s a burden to be lifted.

Continue to: We use the term...

We use the term “underserved” to identify challenging patient populations, but there are categories of patients that fall below the threshold of merely underserved. I am mortified to know that one-third of homeless people in the United States have a serious and untreated mental illness. Individuals discharged from psychiatric hospitals are 3 times more likely to obtain food from garbage. They are also far more likely to be the victim of a crime than perpetrators of it. As I’ve discovered since starting residency, if a patient doesn’t have a place to live, food to eat, and some semblance of a support system, then it’s often meaningless for them to take pills, regardless of how those pills work in theory.

No definition of sound mental health is complete unless it gives deference to those who lack basic human needs. This is a realization that was literally kicked into me, and one I hope will guide me in the years ahead.