ObGyns are mindfully choosing their practice environments. The trend, as reported by the American College of Obstetricians and Gynecologists (ACOG),¹ shows movement from private practice to employment: an increasing number of ObGyns have joined large practices and are employed. Overall, fewer than half of US physicians owned their medical practice in 2016, reported the American Medical Association (AMA).² This is the first time that the majority of physicians are not practice owners.

Although employed ObGyns earn 9% less than self-employed ObGyns, ($276,000 vs $300,000, respectively), trading a higher salary for less time spent on administrative tasks seems to be worth the pay cut, reports Medscape. Employed ObGyns reported receiving additional benefits that might not have been available to self-employed ObGyns: professional liability coverage, employer-subsidized health and dental insurance, paid time off, and a retirement plan with employer match.³

What matters to ObGyns when choosing a practice setting?

Several decisions about practice setting need to be made at the beginning and throughout a career, among them the type of practice, desired salary, work-life balance, (the latter 2 may be influenced by practice type), and location.

Type of practice

“Patients benefit when physicians practice in settings they find professionally and personally rewarding,” said AMA President Andrew W. Gurman, MD. “The AMA is committed to helping physicians navigate their practice options and offers innovative strategies and resources to ensure physicians in all practice sizes and setting can thrive in the changing health environment.”²

More and more, that environment is a practice wholly owned by physicians. The AMA reports that in 2016, 55.8% of physicians worked in such a practice (including physicians who have an ownership stake in the practice, those who are employed by the practice, and those who are independent contractors).² An approximate 13.8% of physicians worked at practices with more than 50 physicians in 2016. The majority (57.8%), however, practiced in groups with 10 or fewer physicians. The most common practice type was the single-specialty group (42.8%), followed by the multispecialty group practice (24.6%).²

Paying physicians a salary instead of compensating them based on volume may improve physician satisfaction—it removes the need to deal with complex fee-for-service systems, say Ian Larkin, PhD, and George Loewenstein, PhD. In fee-for-service payment arrangements, physicians may be encouraged to order more tests and procedures because doing so may increase income. A better strategy, say Larkin and Loewenstein, is to switch to a straight salary system. Known for their quality of care and comparatively low costs, the Mayo Clinic, Cleveland Clinic, and Kaiser Permanente have successfully implemented this payment system.⁴

Related article:
ObGyn salaries jumped in the last year

Desired salary

The mean income for ObGyns rose by 3% in 2016 over 2015 ($286,000 compared with $277,000), according to Medscape.⁵ This jump follows a gradual increase over the last few years ($249,000 in 2014; $243,000 in 2013; $242,000 in 2012; $220,000 in 2011).^1,5,6

The highest earnings among all physicians were orthopedists ($489,000), plastic surgeons ($440,000), and cardiologists ($410,000). Pediatricians were the lowest paid physicians at $202,000.³

Fair compensation. Fewer than half (48%) of ObGyns who completed the Medscape survey felt they were fairly compensated in 2016, and 41% of those who were dissatisfied with their compensation believed they deserved to be earning between 11% and 25% more. When asked if they would still choose medicine, 72% of ObGyns answered affirmatively. Of those who would choose medicine again, 76% would choose obstetrics and gynecology once more.³

Gender differences. As in years past, full-time male ObGyns reported higher earnings (13%) than female ObGyns ($306,000 vs $270,000, respectively; (FIGURE 1).^3,5,7,8

Among ObGyns who responded to the 2017 Medscape survey, 14% of women and 10% of men indicated that they work part-time.³ Last year, 13% of female ObGyns reported part-time employment versus 16% of male ObGyns.⁶

Among the ObGyns who answered the 2017 survey, there was a gender gap in participation related to race. Although more men than women responded to the survey, more women than men ObGyns among black/African American (women, 78%), Asian (women, 69%), and white/Caucasian (women, 53%) groups responded. Men outweighed women only among Hispanic/Latino ObGyns (60%) who answered the survey.³

Read about work-life balance, job satisfaction, and burnout

Work-life balance

ACOG predicts that mid-career and younger ObGyns will focus on work-life balance issues. Practice sites (ambulatory, hospital, or a combination) that offer part-time schedules or extra time for nonprofessional matters are becoming the most desirable to these practitioners.¹

What satisfies and dissatisfies ObGyns? ObGyns reported to Medscape that their relationships with patients (41% of respondents) was the most rewarding part of their job (FIGURE 2).³

There are many job aspects that dissatisfy ObGyns, including^1,3,9:

• too many bureaucratic tasks
• the short time allotted for each patient office visit
• electronic health records (EHR) and increased computerization
• not feeling appreciated or properly compensated
• spending too many hours at work
• the impact of regulatory changes on clinical practice.

Bureaucratic tasks remain a primary cause for burnout among all physicians.¹⁰ This year, 56% of all physicians reported spending 10 hours or more per week on paperwork and administrative tasks, up from 35% in the 2014 report. More than half (54%) of ObGyns reported spending 10 hours or more on paperwork.³ For every hour of face-to-face patient time, physicians spent nearly 2 additional hours on their EHR and administration tasks.⁹

Time with patients. Medscape reported that 38% of ObGyns spent more than 45 hours per week with patients (FIGURE 3).

ACOG notes that ObGyns are increasingly referring patients to subspecialists, which frustrates patients and increases their costs.¹

ObGyns rank high in burnout rates. Burnout rates for physicians are twice that of other working adults.¹ ObGyns rank second (56%) in burn out (Emergency Medicine, 59%).¹⁰ When Medscape survey respondents were asked to grade their burnout level from 1 to 7 (1 = “It does not interfere with my life;” 7 = “It is so severe that I am thinking of leaving medicine altogether”), ObGyns ranked their burnout level at 4.3.¹⁰ Female physicians reported a higher percentage of burnout than their male colleagues (55% vs 45%, respectively).¹⁰ An estimated 40% to 75% of ObGyns experienced some level of burnout.¹

According to ACOG, the specialty is included among the “noncontrollable” lifestyle specialties, especially for those aged 50 years or younger. Many Millennials (born 1980 to 2000) do not view their work and professional achievement as central to their lives; ObGyns aged younger than 35 years want to work fewer hours per week compared with their older colleagues, says ACOG. However, when this option is unavailable, an increasing number of Millennials report lowered job satisfaction.¹

Related article:
What can administrators and ObGyns do together to reduce physician burnout?
 

Mindfulness about quality of life. The relationship of burnout to quality of life issues is gaining in awareness. In a recent OBG Management article, Lucia DiVenere, MA, noted that, “Being mindful of wellness strategies and practice efficiencies can help ObGyns avoid burnout’s deleterious effects—and thrive both personally and professionally.”¹¹

“We need to stop blaming individuals and treat physician burnout as a system issue…If it affects half our physicians, it is indirectly affecting half our patients,” notes Tait Shanafelt, MD, a hematologist and physician-burnout researcher at the Mayo Clinic.⁹ He says that burnout relates to a physician’s “professional spirit of life, and it primarily affects individuals whose work involves an intense interaction with people.”⁹

The Mayo Clinic in Minneapolis, Minnesota, has taken a lead in developing a space for their physicians to “reset” by offering a room where health professionals can retreat if they need a moment to recover from a traumatic event.⁹

Read about what factors attract ObGyns to specific locations

Location, location, location

Specific areas of the country are more attractive for their higher compensation rates. The highest average compensation was reported by ObGyns in the North Central area ($339,000), West ($301,000), and Great Lakes ($297,000) regions, while the lowest compensation rates were found in the Northwest ($260,000), Southwest ($268,000), and South Central ($275,000) areas.³

Key factors, such as healthy patient populations, higher rates of health insurance coverage, and lower stress levels attract physicians (FIGURE 4). Minnesota ranked the #1 best place to practice because it has the 4th healthiest population, 2nd highest rate of employer-sponsored health insurance, the 17th lowest number of malpractice lawsuits, and a medical board that is the 3rd least harsh in the nation.¹² Unfortunate situations such as the highest malpractice rates per capita, least healthy population, 8th lowest rate of employer-sponsored health insurance, and the 9th lowest compensation rate for physicians make Louisiana the worst place to practice in 2017.¹²

Supply and demand creates substantial geographic imbalances in the number of ObGyns in the United States. ACOG pro-jects that the need for ObGyns will increase nationally by 6% in the next 10 years, although demand will vary geographically from a 27% increase in Nevada to an 11% decrease in West Virginia.¹ Especially vulnerable states (Arizona, Washington, Utah, Idaho) currently have an insufficient supply of ObGyns and are projected to see an increased future demand. Florida, Texas, North Carolina, and Nevada will be at risk, according to ACOG, because the adult female population is expected to increase.¹

2017 Medscape survey demographics

The Medscape Compensation Report 2017 is a based on the responses of 19,270 physicians across 27+ specialties, 5% of whom were ObGyns. Data were collected in an online survey conducted from December 20, 2016, to March 7, 2017.³

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

ObGyns are mindfully choosing their practice environments. The trend, as reported by the American College of Obstetricians and Gynecologists (ACOG),¹ shows movement from private practice to employment: an increasing number of ObGyns have joined large practices and are employed. Overall, fewer than half of US physicians owned their medical practice in 2016, reported the American Medical Association (AMA).² This is the first time that the majority of physicians are not practice owners.

Although employed ObGyns earn 9% less than self-employed ObGyns, ($276,000 vs $300,000, respectively), trading a higher salary for less time spent on administrative tasks seems to be worth the pay cut, reports Medscape. Employed ObGyns reported receiving additional benefits that might not have been available to self-employed ObGyns: professional liability coverage, employer-subsidized health and dental insurance, paid time off, and a retirement plan with employer match.³

What matters to ObGyns when choosing a practice setting?

Several decisions about practice setting need to be made at the beginning and throughout a career, among them the type of practice, desired salary, work-life balance, (the latter 2 may be influenced by practice type), and location.

Type of practice

“Patients benefit when physicians practice in settings they find professionally and personally rewarding,” said AMA President Andrew W. Gurman, MD. “The AMA is committed to helping physicians navigate their practice options and offers innovative strategies and resources to ensure physicians in all practice sizes and setting can thrive in the changing health environment.”²

More and more, that environment is a practice wholly owned by physicians. The AMA reports that in 2016, 55.8% of physicians worked in such a practice (including physicians who have an ownership stake in the practice, those who are employed by the practice, and those who are independent contractors).² An approximate 13.8% of physicians worked at practices with more than 50 physicians in 2016. The majority (57.8%), however, practiced in groups with 10 or fewer physicians. The most common practice type was the single-specialty group (42.8%), followed by the multispecialty group practice (24.6%).²

Paying physicians a salary instead of compensating them based on volume may improve physician satisfaction—it removes the need to deal with complex fee-for-service systems, say Ian Larkin, PhD, and George Loewenstein, PhD. In fee-for-service payment arrangements, physicians may be encouraged to order more tests and procedures because doing so may increase income. A better strategy, say Larkin and Loewenstein, is to switch to a straight salary system. Known for their quality of care and comparatively low costs, the Mayo Clinic, Cleveland Clinic, and Kaiser Permanente have successfully implemented this payment system.⁴

Related article:
ObGyn salaries jumped in the last year

Desired salary

The mean income for ObGyns rose by 3% in 2016 over 2015 ($286,000 compared with $277,000), according to Medscape.⁵ This jump follows a gradual increase over the last few years ($249,000 in 2014; $243,000 in 2013; $242,000 in 2012; $220,000 in 2011).^1,5,6

The highest earnings among all physicians were orthopedists ($489,000), plastic surgeons ($440,000), and cardiologists ($410,000). Pediatricians were the lowest paid physicians at $202,000.³

Fair compensation. Fewer than half (48%) of ObGyns who completed the Medscape survey felt they were fairly compensated in 2016, and 41% of those who were dissatisfied with their compensation believed they deserved to be earning between 11% and 25% more. When asked if they would still choose medicine, 72% of ObGyns answered affirmatively. Of those who would choose medicine again, 76% would choose obstetrics and gynecology once more.³

Gender differences. As in years past, full-time male ObGyns reported higher earnings (13%) than female ObGyns ($306,000 vs $270,000, respectively; (FIGURE 1).^3,5,7,8

Among ObGyns who responded to the 2017 Medscape survey, 14% of women and 10% of men indicated that they work part-time.³ Last year, 13% of female ObGyns reported part-time employment versus 16% of male ObGyns.⁶

Among the ObGyns who answered the 2017 survey, there was a gender gap in participation related to race. Although more men than women responded to the survey, more women than men ObGyns among black/African American (women, 78%), Asian (women, 69%), and white/Caucasian (women, 53%) groups responded. Men outweighed women only among Hispanic/Latino ObGyns (60%) who answered the survey.³

Read about work-life balance, job satisfaction, and burnout

Work-life balance

ACOG predicts that mid-career and younger ObGyns will focus on work-life balance issues. Practice sites (ambulatory, hospital, or a combination) that offer part-time schedules or extra time for nonprofessional matters are becoming the most desirable to these practitioners.¹

What satisfies and dissatisfies ObGyns? ObGyns reported to Medscape that their relationships with patients (41% of respondents) was the most rewarding part of their job (FIGURE 2).³

There are many job aspects that dissatisfy ObGyns, including^1,3,9:

• too many bureaucratic tasks
• the short time allotted for each patient office visit
• electronic health records (EHR) and increased computerization
• not feeling appreciated or properly compensated
• spending too many hours at work
• the impact of regulatory changes on clinical practice.

Bureaucratic tasks remain a primary cause for burnout among all physicians.¹⁰ This year, 56% of all physicians reported spending 10 hours or more per week on paperwork and administrative tasks, up from 35% in the 2014 report. More than half (54%) of ObGyns reported spending 10 hours or more on paperwork.³ For every hour of face-to-face patient time, physicians spent nearly 2 additional hours on their EHR and administration tasks.⁹

Time with patients. Medscape reported that 38% of ObGyns spent more than 45 hours per week with patients (FIGURE 3).

ACOG notes that ObGyns are increasingly referring patients to subspecialists, which frustrates patients and increases their costs.¹

ObGyns rank high in burnout rates. Burnout rates for physicians are twice that of other working adults.¹ ObGyns rank second (56%) in burn out (Emergency Medicine, 59%).¹⁰ When Medscape survey respondents were asked to grade their burnout level from 1 to 7 (1 = “It does not interfere with my life;” 7 = “It is so severe that I am thinking of leaving medicine altogether”), ObGyns ranked their burnout level at 4.3.¹⁰ Female physicians reported a higher percentage of burnout than their male colleagues (55% vs 45%, respectively).¹⁰ An estimated 40% to 75% of ObGyns experienced some level of burnout.¹

According to ACOG, the specialty is included among the “noncontrollable” lifestyle specialties, especially for those aged 50 years or younger. Many Millennials (born 1980 to 2000) do not view their work and professional achievement as central to their lives; ObGyns aged younger than 35 years want to work fewer hours per week compared with their older colleagues, says ACOG. However, when this option is unavailable, an increasing number of Millennials report lowered job satisfaction.¹

Related article:
What can administrators and ObGyns do together to reduce physician burnout?
 

Mindfulness about quality of life. The relationship of burnout to quality of life issues is gaining in awareness. In a recent OBG Management article, Lucia DiVenere, MA, noted that, “Being mindful of wellness strategies and practice efficiencies can help ObGyns avoid burnout’s deleterious effects—and thrive both personally and professionally.”¹¹

“We need to stop blaming individuals and treat physician burnout as a system issue…If it affects half our physicians, it is indirectly affecting half our patients,” notes Tait Shanafelt, MD, a hematologist and physician-burnout researcher at the Mayo Clinic.⁹ He says that burnout relates to a physician’s “professional spirit of life, and it primarily affects individuals whose work involves an intense interaction with people.”⁹

The Mayo Clinic in Minneapolis, Minnesota, has taken a lead in developing a space for their physicians to “reset” by offering a room where health professionals can retreat if they need a moment to recover from a traumatic event.⁹

Read about what factors attract ObGyns to specific locations

Location, location, location

Specific areas of the country are more attractive for their higher compensation rates. The highest average compensation was reported by ObGyns in the North Central area ($339,000), West ($301,000), and Great Lakes ($297,000) regions, while the lowest compensation rates were found in the Northwest ($260,000), Southwest ($268,000), and South Central ($275,000) areas.³

Key factors, such as healthy patient populations, higher rates of health insurance coverage, and lower stress levels attract physicians (FIGURE 4). Minnesota ranked the #1 best place to practice because it has the 4th healthiest population, 2nd highest rate of employer-sponsored health insurance, the 17th lowest number of malpractice lawsuits, and a medical board that is the 3rd least harsh in the nation.¹² Unfortunate situations such as the highest malpractice rates per capita, least healthy population, 8th lowest rate of employer-sponsored health insurance, and the 9th lowest compensation rate for physicians make Louisiana the worst place to practice in 2017.¹²

Supply and demand creates substantial geographic imbalances in the number of ObGyns in the United States. ACOG pro-jects that the need for ObGyns will increase nationally by 6% in the next 10 years, although demand will vary geographically from a 27% increase in Nevada to an 11% decrease in West Virginia.¹ Especially vulnerable states (Arizona, Washington, Utah, Idaho) currently have an insufficient supply of ObGyns and are projected to see an increased future demand. Florida, Texas, North Carolina, and Nevada will be at risk, according to ACOG, because the adult female population is expected to increase.¹

2017 Medscape survey demographics

The Medscape Compensation Report 2017 is a based on the responses of 19,270 physicians across 27+ specialties, 5% of whom were ObGyns. Data were collected in an online survey conducted from December 20, 2016, to March 7, 2017.³

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

ObGyns are mindfully choosing their practice environments. The trend, as reported by the American College of Obstetricians and Gynecologists (ACOG),¹ shows movement from private practice to employment: an increasing number of ObGyns have joined large practices and are employed. Overall, fewer than half of US physicians owned their medical practice in 2016, reported the American Medical Association (AMA).² This is the first time that the majority of physicians are not practice owners.

Although employed ObGyns earn 9% less than self-employed ObGyns, ($276,000 vs $300,000, respectively), trading a higher salary for less time spent on administrative tasks seems to be worth the pay cut, reports Medscape. Employed ObGyns reported receiving additional benefits that might not have been available to self-employed ObGyns: professional liability coverage, employer-subsidized health and dental insurance, paid time off, and a retirement plan with employer match.³

What matters to ObGyns when choosing a practice setting?

Several decisions about practice setting need to be made at the beginning and throughout a career, among them the type of practice, desired salary, work-life balance, (the latter 2 may be influenced by practice type), and location.

Type of practice

“Patients benefit when physicians practice in settings they find professionally and personally rewarding,” said AMA President Andrew W. Gurman, MD. “The AMA is committed to helping physicians navigate their practice options and offers innovative strategies and resources to ensure physicians in all practice sizes and setting can thrive in the changing health environment.”²

More and more, that environment is a practice wholly owned by physicians. The AMA reports that in 2016, 55.8% of physicians worked in such a practice (including physicians who have an ownership stake in the practice, those who are employed by the practice, and those who are independent contractors).² An approximate 13.8% of physicians worked at practices with more than 50 physicians in 2016. The majority (57.8%), however, practiced in groups with 10 or fewer physicians. The most common practice type was the single-specialty group (42.8%), followed by the multispecialty group practice (24.6%).²

Paying physicians a salary instead of compensating them based on volume may improve physician satisfaction—it removes the need to deal with complex fee-for-service systems, say Ian Larkin, PhD, and George Loewenstein, PhD. In fee-for-service payment arrangements, physicians may be encouraged to order more tests and procedures because doing so may increase income. A better strategy, say Larkin and Loewenstein, is to switch to a straight salary system. Known for their quality of care and comparatively low costs, the Mayo Clinic, Cleveland Clinic, and Kaiser Permanente have successfully implemented this payment system.⁴

Related article:
ObGyn salaries jumped in the last year

Desired salary

The mean income for ObGyns rose by 3% in 2016 over 2015 ($286,000 compared with $277,000), according to Medscape.⁵ This jump follows a gradual increase over the last few years ($249,000 in 2014; $243,000 in 2013; $242,000 in 2012; $220,000 in 2011).^1,5,6

The highest earnings among all physicians were orthopedists ($489,000), plastic surgeons ($440,000), and cardiologists ($410,000). Pediatricians were the lowest paid physicians at $202,000.³

Fair compensation. Fewer than half (48%) of ObGyns who completed the Medscape survey felt they were fairly compensated in 2016, and 41% of those who were dissatisfied with their compensation believed they deserved to be earning between 11% and 25% more. When asked if they would still choose medicine, 72% of ObGyns answered affirmatively. Of those who would choose medicine again, 76% would choose obstetrics and gynecology once more.³

Gender differences. As in years past, full-time male ObGyns reported higher earnings (13%) than female ObGyns ($306,000 vs $270,000, respectively; (FIGURE 1).^3,5,7,8

Among ObGyns who responded to the 2017 Medscape survey, 14% of women and 10% of men indicated that they work part-time.³ Last year, 13% of female ObGyns reported part-time employment versus 16% of male ObGyns.⁶

Among the ObGyns who answered the 2017 survey, there was a gender gap in participation related to race. Although more men than women responded to the survey, more women than men ObGyns among black/African American (women, 78%), Asian (women, 69%), and white/Caucasian (women, 53%) groups responded. Men outweighed women only among Hispanic/Latino ObGyns (60%) who answered the survey.³

Read about work-life balance, job satisfaction, and burnout

Work-life balance

ACOG predicts that mid-career and younger ObGyns will focus on work-life balance issues. Practice sites (ambulatory, hospital, or a combination) that offer part-time schedules or extra time for nonprofessional matters are becoming the most desirable to these practitioners.¹

What satisfies and dissatisfies ObGyns? ObGyns reported to Medscape that their relationships with patients (41% of respondents) was the most rewarding part of their job (FIGURE 2).³

There are many job aspects that dissatisfy ObGyns, including^1,3,9:

• too many bureaucratic tasks
• the short time allotted for each patient office visit
• electronic health records (EHR) and increased computerization
• not feeling appreciated or properly compensated
• spending too many hours at work
• the impact of regulatory changes on clinical practice.

Bureaucratic tasks remain a primary cause for burnout among all physicians.¹⁰ This year, 56% of all physicians reported spending 10 hours or more per week on paperwork and administrative tasks, up from 35% in the 2014 report. More than half (54%) of ObGyns reported spending 10 hours or more on paperwork.³ For every hour of face-to-face patient time, physicians spent nearly 2 additional hours on their EHR and administration tasks.⁹

Time with patients. Medscape reported that 38% of ObGyns spent more than 45 hours per week with patients (FIGURE 3).

ACOG notes that ObGyns are increasingly referring patients to subspecialists, which frustrates patients and increases their costs.¹

ObGyns rank high in burnout rates. Burnout rates for physicians are twice that of other working adults.¹ ObGyns rank second (56%) in burn out (Emergency Medicine, 59%).¹⁰ When Medscape survey respondents were asked to grade their burnout level from 1 to 7 (1 = “It does not interfere with my life;” 7 = “It is so severe that I am thinking of leaving medicine altogether”), ObGyns ranked their burnout level at 4.3.¹⁰ Female physicians reported a higher percentage of burnout than their male colleagues (55% vs 45%, respectively).¹⁰ An estimated 40% to 75% of ObGyns experienced some level of burnout.¹

According to ACOG, the specialty is included among the “noncontrollable” lifestyle specialties, especially for those aged 50 years or younger. Many Millennials (born 1980 to 2000) do not view their work and professional achievement as central to their lives; ObGyns aged younger than 35 years want to work fewer hours per week compared with their older colleagues, says ACOG. However, when this option is unavailable, an increasing number of Millennials report lowered job satisfaction.¹

Related article:
What can administrators and ObGyns do together to reduce physician burnout?
 

Mindfulness about quality of life. The relationship of burnout to quality of life issues is gaining in awareness. In a recent OBG Management article, Lucia DiVenere, MA, noted that, “Being mindful of wellness strategies and practice efficiencies can help ObGyns avoid burnout’s deleterious effects—and thrive both personally and professionally.”¹¹

“We need to stop blaming individuals and treat physician burnout as a system issue…If it affects half our physicians, it is indirectly affecting half our patients,” notes Tait Shanafelt, MD, a hematologist and physician-burnout researcher at the Mayo Clinic.⁹ He says that burnout relates to a physician’s “professional spirit of life, and it primarily affects individuals whose work involves an intense interaction with people.”⁹

The Mayo Clinic in Minneapolis, Minnesota, has taken a lead in developing a space for their physicians to “reset” by offering a room where health professionals can retreat if they need a moment to recover from a traumatic event.⁹

Read about what factors attract ObGyns to specific locations

Location, location, location

Specific areas of the country are more attractive for their higher compensation rates. The highest average compensation was reported by ObGyns in the North Central area ($339,000), West ($301,000), and Great Lakes ($297,000) regions, while the lowest compensation rates were found in the Northwest ($260,000), Southwest ($268,000), and South Central ($275,000) areas.³

Key factors, such as healthy patient populations, higher rates of health insurance coverage, and lower stress levels attract physicians (FIGURE 4). Minnesota ranked the #1 best place to practice because it has the 4th healthiest population, 2nd highest rate of employer-sponsored health insurance, the 17th lowest number of malpractice lawsuits, and a medical board that is the 3rd least harsh in the nation.¹² Unfortunate situations such as the highest malpractice rates per capita, least healthy population, 8th lowest rate of employer-sponsored health insurance, and the 9th lowest compensation rate for physicians make Louisiana the worst place to practice in 2017.¹²

Supply and demand creates substantial geographic imbalances in the number of ObGyns in the United States. ACOG pro-jects that the need for ObGyns will increase nationally by 6% in the next 10 years, although demand will vary geographically from a 27% increase in Nevada to an 11% decrease in West Virginia.¹ Especially vulnerable states (Arizona, Washington, Utah, Idaho) currently have an insufficient supply of ObGyns and are projected to see an increased future demand. Florida, Texas, North Carolina, and Nevada will be at risk, according to ACOG, because the adult female population is expected to increase.¹

2017 Medscape survey demographics

The Medscape Compensation Report 2017 is a based on the responses of 19,270 physicians across 27+ specialties, 5% of whom were ObGyns. Data were collected in an online survey conducted from December 20, 2016, to March 7, 2017.³

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

Skilled nursing facilities (SNFs) play a crucial role in the hospital readmission process.Approximately 1 in 4 Medicare beneficiaries discharged from an acute care hospital is admitted to a SNF instead of returning directly home. Of these patients, 1 in 4 will be readmitted within 30 days,¹ a rate significantly higher than the readmission rate of the inpatient population as a whole.² The 2014 Protecting Access to Medicare Act created a value-based purchasing program that will use quality measures to steer funds to, or away from, individual SNFs. When the program takes effect in 2018, the Centers for Medicare & Medicaid Services will use SNFs’ 30-day all-cause readmission rate to determine which SNFs receive payments and which receive penalties.³ The Affordable Care Act, passed in 2010, has also established penalties for hospitals with higher than expected readmission rates for Medicare patients.⁴

Despite this intensifying regulatory focus, relatively little is known about the factors that drive readmissions from SNFs. A prospective review of data from SNFs in 4 states has shown that SNFs staffed by nurse practitioners or physician assistants and those equipped to provide intravenous therapy were less likely to transfer patients to the hospital for ambulatory care-sensitive diagnoses.⁵ Qualitative studies have provided useful insight into the causes of SNF-to-hospital transfers but have not focused on 30-day readmissions.^6,7 A single survey-based study has examined the causes of SNF-to-hospital readmissions.⁸ However, survey-based methodologies have limited ability to capture the complex perspectives of SNF clinicians, who play a critical role in determining which SNF patients require evaluation or treatment in an acute care setting.

To address this gap in knowledge about factors contributing to SNF readmissions, we conducted a qualitative study examining SNF clinicians’ perspectives on patients readmitted to the hospital within 30 days of discharge. We used a structured interview tool to explore the root causes of readmission with frontline SNF staff, with the goal of using this knowledge to inform future hospital quality improvement (QI) efforts.

Case Identification

Hospital data-tracking software (Allscripts) was used to identify patients who experienced a 30-day, unplanned readmission from SNFs to an academic medical center. We restricted our search to patients whose index admission and readmission were to the medical center’s inpatient general medicine service. A study team member (BWC) monitored the dataset on a weekly basis and contacted SNF clinicians by e-mail and telephone to arrange interviews at times of mutual convenience. To mitigate against recall bias, interviews were conducted within 30 days of the readmission in question. A total of 32 cases were identified. No SNF clinicians refused a request for interview. For 8 of these cases, it was not possible to find a time of mutual convenience within the specified 30-day window. The remaining 24 cases involved patients from 15 SNFs across Connecticut. Interviews were conducted from August 2015 to November 2015.

The project was reviewed by our institution’s Human Investigation Committee and was exempted from Institutional Review Board review.

Study Participants

Interviews were conducted on-site at SNFs with groups of 1 to 4 SNF clinicians and administrators. SNF participants were informed of interviewer credentials and the study’s QI goals prior to participation. Participation was voluntary and did not affect the clinician’s relationship with the hospital or the SNF. Participants were not paid.

Interventions to Reduce Acute Care Transfers (INTERACT) is a QI program that includes training for clinicians, communication tools, and advance care planning tools.⁹ INTERACT is currently used in 138 Connecticut SNFs as part of a statewide QI effort funded by the Connecticut State Department of Public Health. In prospective QI studies,^10,11 implementation of INTERACT has been associated with decreased transfers from SNFs to acute care hospitals. The INTERACT Quality Improvement Tool, one part of the INTERACT bundle of interventions, is a 26-item questionnaire used to identify root causes of transfers from SNFs to acute care hospitals. It includes both checklists and open-ended questions about patient factors, SNF procedures, and SNF clinical decision-making.

We used the INTERACT QI Tool¹² to conduct structured interviews with nurses and administrators at SNFs. Interviewers used a hard copy of the tool to maintain field notes, and all parts of the questionnaire were completed in each interview. Although the questionnaire elicits baseline demographic and medical information, such as the patient’s age and vital signs prior to readmission, the majority of each interview was dedicated to discussion of the open-ended questions in Table 1. Upon completion of the INTERACT QI Tool, the interviewer asked 2 open-ended questions about reducing readmissions and 4 closed-ended questions regarding SNF admission procedures. (Table 1) The supplemental questions were added after preliminary interviews with SNF clinicians revealed concerns about the SNF referral process and about communication between the hospital, emergency department (ED) and SNFs—issues not included in the INTERACT questionnaire. Interviewers used phatic communication, probing questions, and follow-up questions to elicit detailed information from participants, and participant responses were not limited to topics in the questionnaire and the list of supplemental questions.

Interviews were conducted by a hospital clinical integration coordinator, social worker, and a physician (KB, MCB, BWC). All interviewers received formal training in qualitative research methods prior to the study.

All interviews were audio recorded, with permission from the participants, and were professionally transcribed. Field notes were maintained to ensure accuracy of INTERACT QI Tool data. Participant interviews covered no more than two cases per session and lasted from 18 to 71 minutes (mean duration, 38 minutes).

Analysis

Analysis of transcripts was inductive and informed by grounded theory methodology, in which data is reviewed for repeating ideas, which are then analyzed and grouped to develop a theoretical understanding of the phenomenon under investigation.^13,14

A preliminary codebook was developed using transcripts of the first 11 interviews. All statements relevant to the readmission process were extracted from the raw interview transcript and collected into a single list. This list was then reviewed for statements sharing a particular idea or concern. Such statements were grouped together under the heading of a repeating idea, and each repeating idea was assigned a code. Using this codebook, each transcript was independently reviewed and coded by three study team members with formal training in inductive qualitative analysis (KB, KTM, BWC). Reviewers assigned codes to sections of relevant text. Discrepancies in code assignment were discussed among the 3 analysts until consensus was reached. Using the method of constant comparison described in grounded theory,the codebook was updated continuously as the process of coding transcripts proceeded.¹² Changes to the codebook were discussed among the coding team until consensus was achieved. The process of data acquisition and coding continued until theoretical saturation was reached. Themes relating to underlying factors associated with readmissions were then identified based on shared properties among repeating ideas. ATLAS.ti (Scientific Software, Berlin, Germany, Version 7) was used to facilitate data organization and retrieval.

The SNFs in our study included 12 for-profit and 3 non-profit facilities. The number of licensed beds in each facility ranged from 73 to 360, with a mean of 148 beds. The SNFs had CMS Nursing Home Compare ratings ranging from 1 star, the lowest possible rating, to 5 stars, the highest possible,¹⁵ with a mean rating of 2.9 stars. Our analysis did not reveal differences in perceived contributions to readmissions from large vs. small or highly rated vs poorly rated SNFs.

Emergent transitions: Lack of coordination between ED and SNF

SNF clinicians frequently encountered situations in which a relatively stable patient was readmitted to the hospital after being transferred to the ED, despite the fact that SNF clinicians believed the patient should have returned to the SNF once a specific test was performed or service rendered at the ED. Commonly cited clinical scenarios that resulted in such readmissions included placement of urinary catheters and evaluation for cystitis. An assistant director of nursing reported that “the ER doesn’t want to hear my side of the story,” making it difficult for her to provide information that would prevent such readmissions. Other SNF clinicians reported similar difficulties in communicating with ED clinicians.

Code status: Incompletely addressed goals of care

The SNF clinicians in our study described cases in which patients with end-stage lung disease and disseminated cancer were readmitted to the hospital, despite SNF efforts to prevent readmission and provide palliative care within the SNF. For example, a SNF advanced practice nurse described a case in which a patient with widely metastatic cancer requested readmission to the hospital for treatment of deep vein thrombosis, despite longstanding recommendations from SNF staff that the patient forego hospitalization and enroll in hospice care. After discussion of code status and goals of care with hospital clinicians, the patient chose to enroll in hospice care and not to continue anticoagulation. SNF clinicians often perceived that, in the words of one administrator, “the palliative talks in the hospital outweigh our talks by a lot.” Numerous SNF clinicians believed that in-depth clarification of goals of care prior to discharge could prevent some readmissions.

Wrong patient, wrong place: Mismatch between clinical needs and SNF capabilities

One director of nursing stated that “[when] you read a referral, there’s a huge difference sometimes between what you read and what you see.” SNF clinicians reported that this discrepancy between clinical report and clinical reality often leads to patients being placed in SNFs that are unequipped to care for them. Many patients were perceived as being too ill for discharge from the acute-care setting in the first place. A nurse manager described this as a pattern of “pushing patients out of the hospital.” However, mismatches in clinical disposition were also seen as contributing to readmissions for medically stable patients, such as those with dementia, for whom SNFs frequently lack adequate staffing and physical safeguards.

Missing links: Important clinical information not effectively communicated by hospital

SNF clinicians described numerous challenges in formulating plans of care based on hospital discharge documentation. Discrepancies between discharge summaries and patient instructions were perceived as common and potential causes of readmissions. For patients discharged from the academic medical center in this study, medication instructions are included in both the discharge summary sent to the SNF and in a patient instruction packet. Several SNF clinicians said that it was common for a course of antibiotics to be listed on the discharge summary but not the patient instruction packet, or vice versa. SNF clinicians, who usually lack access to the hospital’s electronic medical record, have limited means for determining the correct document. Other important clinical data points, such as intermittent intravenous (IV) furosemide dosing and suppressive antibiotic regimens, were omitted from discharge paperwork altogether. SNF clinicians had difficulty reaching hospital clinicians who could clarify these clinical questions. “Good luck finding the person that took care of [the patient] three days before,” said one director of nursing.

Change starts at home: Challenges in SNF processes and culture

Many clinicians in our study reported that their facilities had recently added clinical capabilities in an effort to care for patients with complex medical problems. For example, to prevent transfers of patients with decompensated heart failure, several facilities in our study had recently obtained certification to give IV diuretics. However, as one director of nursing stated, these efforts require “buy-in” from doctors to decrease readmissions. That buy-in has not always been forthcoming. SNF clinicians also reported difficulty convincing patients and families that their facilities are capable of providing care that, in the past, might only have been available in acute-care settings.

These themes, along with associated sub-themes and representative quotations, are shown above (Table 4).

Our study suggests that the interaction between EDs and SNFs is an important and understudied domain in the spectrum of events leading to readmission. Prior studies have documented inadequacies in patient information provided by SNFs to EDs.^16,17 Efforts to improve SNF-to-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^18,19 However, many of the clinicians in our study reported taking proactive steps to communicate with ED clinicians. These efforts encountered logistical and cultural barriers, with information that might have prevented readmission failing to reach ED providers. Many of the SNF clinicians in our study perceived this failure as a common cause of readmission, especially for relatively stable SNF patients.

Previous studies have pointed to a role for goals of care discussions in reducing hospital readmissions.²⁰ Our data underscore an important qualification to these findings: Location matters. The SNF clinicians in our study reported frequent and detailed goals of care discussions with their patients. However, they also reported that goals of care discussions held in the subacute setting carried less weight with patients and families than discussions held in the hospital. SNF clinicians described a number of cases in which patients were willing to adjust code status or goals of care only after being readmitted to the hospital.

Our study also points to the implications of existing research showing that patients are discharged from acute care hospitals “quicker and sicker” than they had been prior to the 1983 adoption of Medicare’s prospective payment system.²¹ Specifically, the SNF clinicians we interviewed perceived a strong link between patient acuity at the time of transfer and SNFs’ persistently high readmission rates. As SNFs have worked to expand their clinical capabilities, they struggle to win buy-in from physicians and families, many of whom view SNFs as incapable of managing acute illness. Many SNF clinicians also pointed to deficiencies in their own referral and admission processes as a recurring cause of readmissions. For example, several patients in our analysis suffered from dementia. Although these patients were stable enough to leave the acute care setting, the SNF clinicians responsible for their readmissions felt that their SNFs were not well-equipped to care for patients with dementia and that the patients should instead have been transferred to facilities with more robust resources for dementia care.

Finally, our findings highlight a fundamental tension between hospitals and SNFs: Which facility ought to shoulder the responsibility and cost for services that may prevent a readmission—the hospital or the SNF? For example, does responsibility for coordinating subspecialist evaluation of a patient’s chronic condition fall to the hospital or to the SNF? If such an evaluation is undertaken during a hospitalization, it prolongs the patient’s hospital stay and happens at the hospital’s expense. If the patient is discharged to a SNF and sees the subspecialist in clinic, then the SNF must pay for transportation to and from the clinic appointment. SNF clinicians expressed near unanimity that fragmented models of care and high barriers to communication made it difficult to design solutions to these dilemmas.

Strengths and limitations

To our knowledge, this is the first interview-based study examining SNF clinicians’ perspectives on unplanned, 30-day hospital readmissions. We gathered information from clinicians with a range of clinical experience, all of whom had cared directly for the patient who had been readmitted. Our data came from clinicians at 15 SNFs of varying sizes and quality ratings, allowing us to identify a broad range of factors contributing to readmissions.

Because this study relied on qualitative methods, it should be viewed as hypothesis-generating rather than hypothesis-confirming. Further research is needed to determine whether variables related to the themes above are causally linked to SNF readmissions. We identified cases for review using convenience sampling of a cohort of readmitted patients at a single tertiary-care hospital, and all participating SNFs were located in Connecticut. These factors may limit the generalizability of our findings. Although the clinicians we interviewed occupied diverse roles within their respective SNFs, our sample did not include direct-care staff without managerial responsibility, such as certified nursing assistants or licensed practical nurses. This prevented our study from identifying themes into which managers would have limited insight, especially those involving cultural and management practices leading to poor communication between them and their staff. Because our study examines cases in which discharge and readmission were to a general medicine service, it may not describe factors relevant to patients discharged from subspecialist or surgical services.

Implications for future QI efforts and research

Several clinicians we interviewed suggested that readmissions might be reduced by dedicating the services of a hospital professional, such as a nurse or case manager, to monitoring the clinical course of medically complex patients after discharge. A dedicated “transition coach” could clarify deficiencies in discharge paperwork, facilitate necessary follow-up appointments, liaise with staff at both the hospital and the SNF, or coordinate acquisition of necessary equipment. Prospective trials have demonstrated that such interventions can decrease readmission rates among hospitalized patients,^22,23 but formal studies have not been carried out among cohorts of SNF patients.

Prior efforts to improve SNF-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^24,25 The experiences of SNF clinicians in our study suggest that important information also fails to make its way from ED providers to SNFs and that this failure results in unnecessary readmissions of relatively stable SNF patients. Thus, hospitals may be able to prevent SNF readmissions by creating lines of communication between EDs and SNFs and by ensuring that ED physicians and mid-level providers are familiar with the clinical capabilities of local SNFs.

Future research and QI work should also investigate approaches to care coordination that ensure that complex patients are placed in SNFs with resources adequate to address their comorbidities. Potential interventions might include increased use of SNF “liaisons,” who would evaluate patients in-person prior to approving transfer to a given SNF. As has been previously suggested,²⁶ hospitals might also reduce readmissions by narrowing the pool of facilities to which they transfer patients, thereby building more robust, interconnected relationships with a smaller number of SNFs.

SNF clinicians identified areas for improvement at almost every point in the chain of events spanning hospitalization, discharge, and transfer. Among the most frequently cited contributors to readmissions were clinical instability at the time of discharge and omission of clinically important information from discharge documentation. Improved communication between hospitals, ED clinicians, and SNFs, as well as more thoroughly defined goals of care at the time of discharge, were seen as promising ways of decreasing readmissions. Successful interventions for reducing readmissions from SNFs will likely require multifaceted approaches to these problems.

Disclosure: This research was supported by a grant (#P30HS023554-01) from the Agency for Healthcare Research and Quality (AHRQ) and received support from Yale New Haven Hospital and the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (#P30AG021342 NIH/NIA).

Skilled nursing facilities (SNFs) play a crucial role in the hospital readmission process.Approximately 1 in 4 Medicare beneficiaries discharged from an acute care hospital is admitted to a SNF instead of returning directly home. Of these patients, 1 in 4 will be readmitted within 30 days,¹ a rate significantly higher than the readmission rate of the inpatient population as a whole.² The 2014 Protecting Access to Medicare Act created a value-based purchasing program that will use quality measures to steer funds to, or away from, individual SNFs. When the program takes effect in 2018, the Centers for Medicare & Medicaid Services will use SNFs’ 30-day all-cause readmission rate to determine which SNFs receive payments and which receive penalties.³ The Affordable Care Act, passed in 2010, has also established penalties for hospitals with higher than expected readmission rates for Medicare patients.⁴

Despite this intensifying regulatory focus, relatively little is known about the factors that drive readmissions from SNFs. A prospective review of data from SNFs in 4 states has shown that SNFs staffed by nurse practitioners or physician assistants and those equipped to provide intravenous therapy were less likely to transfer patients to the hospital for ambulatory care-sensitive diagnoses.⁵ Qualitative studies have provided useful insight into the causes of SNF-to-hospital transfers but have not focused on 30-day readmissions.^6,7 A single survey-based study has examined the causes of SNF-to-hospital readmissions.⁸ However, survey-based methodologies have limited ability to capture the complex perspectives of SNF clinicians, who play a critical role in determining which SNF patients require evaluation or treatment in an acute care setting.

To address this gap in knowledge about factors contributing to SNF readmissions, we conducted a qualitative study examining SNF clinicians’ perspectives on patients readmitted to the hospital within 30 days of discharge. We used a structured interview tool to explore the root causes of readmission with frontline SNF staff, with the goal of using this knowledge to inform future hospital quality improvement (QI) efforts.

Case Identification

Hospital data-tracking software (Allscripts) was used to identify patients who experienced a 30-day, unplanned readmission from SNFs to an academic medical center. We restricted our search to patients whose index admission and readmission were to the medical center’s inpatient general medicine service. A study team member (BWC) monitored the dataset on a weekly basis and contacted SNF clinicians by e-mail and telephone to arrange interviews at times of mutual convenience. To mitigate against recall bias, interviews were conducted within 30 days of the readmission in question. A total of 32 cases were identified. No SNF clinicians refused a request for interview. For 8 of these cases, it was not possible to find a time of mutual convenience within the specified 30-day window. The remaining 24 cases involved patients from 15 SNFs across Connecticut. Interviews were conducted from August 2015 to November 2015.

The project was reviewed by our institution’s Human Investigation Committee and was exempted from Institutional Review Board review.

Study Participants

Interviews were conducted on-site at SNFs with groups of 1 to 4 SNF clinicians and administrators. SNF participants were informed of interviewer credentials and the study’s QI goals prior to participation. Participation was voluntary and did not affect the clinician’s relationship with the hospital or the SNF. Participants were not paid.

Interventions to Reduce Acute Care Transfers (INTERACT) is a QI program that includes training for clinicians, communication tools, and advance care planning tools.⁹ INTERACT is currently used in 138 Connecticut SNFs as part of a statewide QI effort funded by the Connecticut State Department of Public Health. In prospective QI studies,^10,11 implementation of INTERACT has been associated with decreased transfers from SNFs to acute care hospitals. The INTERACT Quality Improvement Tool, one part of the INTERACT bundle of interventions, is a 26-item questionnaire used to identify root causes of transfers from SNFs to acute care hospitals. It includes both checklists and open-ended questions about patient factors, SNF procedures, and SNF clinical decision-making.

We used the INTERACT QI Tool¹² to conduct structured interviews with nurses and administrators at SNFs. Interviewers used a hard copy of the tool to maintain field notes, and all parts of the questionnaire were completed in each interview. Although the questionnaire elicits baseline demographic and medical information, such as the patient’s age and vital signs prior to readmission, the majority of each interview was dedicated to discussion of the open-ended questions in Table 1. Upon completion of the INTERACT QI Tool, the interviewer asked 2 open-ended questions about reducing readmissions and 4 closed-ended questions regarding SNF admission procedures. (Table 1) The supplemental questions were added after preliminary interviews with SNF clinicians revealed concerns about the SNF referral process and about communication between the hospital, emergency department (ED) and SNFs—issues not included in the INTERACT questionnaire. Interviewers used phatic communication, probing questions, and follow-up questions to elicit detailed information from participants, and participant responses were not limited to topics in the questionnaire and the list of supplemental questions.

Interviews were conducted by a hospital clinical integration coordinator, social worker, and a physician (KB, MCB, BWC). All interviewers received formal training in qualitative research methods prior to the study.

All interviews were audio recorded, with permission from the participants, and were professionally transcribed. Field notes were maintained to ensure accuracy of INTERACT QI Tool data. Participant interviews covered no more than two cases per session and lasted from 18 to 71 minutes (mean duration, 38 minutes).

Analysis

Analysis of transcripts was inductive and informed by grounded theory methodology, in which data is reviewed for repeating ideas, which are then analyzed and grouped to develop a theoretical understanding of the phenomenon under investigation.^13,14

A preliminary codebook was developed using transcripts of the first 11 interviews. All statements relevant to the readmission process were extracted from the raw interview transcript and collected into a single list. This list was then reviewed for statements sharing a particular idea or concern. Such statements were grouped together under the heading of a repeating idea, and each repeating idea was assigned a code. Using this codebook, each transcript was independently reviewed and coded by three study team members with formal training in inductive qualitative analysis (KB, KTM, BWC). Reviewers assigned codes to sections of relevant text. Discrepancies in code assignment were discussed among the 3 analysts until consensus was reached. Using the method of constant comparison described in grounded theory,the codebook was updated continuously as the process of coding transcripts proceeded.¹² Changes to the codebook were discussed among the coding team until consensus was achieved. The process of data acquisition and coding continued until theoretical saturation was reached. Themes relating to underlying factors associated with readmissions were then identified based on shared properties among repeating ideas. ATLAS.ti (Scientific Software, Berlin, Germany, Version 7) was used to facilitate data organization and retrieval.

The SNFs in our study included 12 for-profit and 3 non-profit facilities. The number of licensed beds in each facility ranged from 73 to 360, with a mean of 148 beds. The SNFs had CMS Nursing Home Compare ratings ranging from 1 star, the lowest possible rating, to 5 stars, the highest possible,¹⁵ with a mean rating of 2.9 stars. Our analysis did not reveal differences in perceived contributions to readmissions from large vs. small or highly rated vs poorly rated SNFs.

Emergent transitions: Lack of coordination between ED and SNF

SNF clinicians frequently encountered situations in which a relatively stable patient was readmitted to the hospital after being transferred to the ED, despite the fact that SNF clinicians believed the patient should have returned to the SNF once a specific test was performed or service rendered at the ED. Commonly cited clinical scenarios that resulted in such readmissions included placement of urinary catheters and evaluation for cystitis. An assistant director of nursing reported that “the ER doesn’t want to hear my side of the story,” making it difficult for her to provide information that would prevent such readmissions. Other SNF clinicians reported similar difficulties in communicating with ED clinicians.

Code status: Incompletely addressed goals of care

The SNF clinicians in our study described cases in which patients with end-stage lung disease and disseminated cancer were readmitted to the hospital, despite SNF efforts to prevent readmission and provide palliative care within the SNF. For example, a SNF advanced practice nurse described a case in which a patient with widely metastatic cancer requested readmission to the hospital for treatment of deep vein thrombosis, despite longstanding recommendations from SNF staff that the patient forego hospitalization and enroll in hospice care. After discussion of code status and goals of care with hospital clinicians, the patient chose to enroll in hospice care and not to continue anticoagulation. SNF clinicians often perceived that, in the words of one administrator, “the palliative talks in the hospital outweigh our talks by a lot.” Numerous SNF clinicians believed that in-depth clarification of goals of care prior to discharge could prevent some readmissions.

Wrong patient, wrong place: Mismatch between clinical needs and SNF capabilities

One director of nursing stated that “[when] you read a referral, there’s a huge difference sometimes between what you read and what you see.” SNF clinicians reported that this discrepancy between clinical report and clinical reality often leads to patients being placed in SNFs that are unequipped to care for them. Many patients were perceived as being too ill for discharge from the acute-care setting in the first place. A nurse manager described this as a pattern of “pushing patients out of the hospital.” However, mismatches in clinical disposition were also seen as contributing to readmissions for medically stable patients, such as those with dementia, for whom SNFs frequently lack adequate staffing and physical safeguards.

Missing links: Important clinical information not effectively communicated by hospital

SNF clinicians described numerous challenges in formulating plans of care based on hospital discharge documentation. Discrepancies between discharge summaries and patient instructions were perceived as common and potential causes of readmissions. For patients discharged from the academic medical center in this study, medication instructions are included in both the discharge summary sent to the SNF and in a patient instruction packet. Several SNF clinicians said that it was common for a course of antibiotics to be listed on the discharge summary but not the patient instruction packet, or vice versa. SNF clinicians, who usually lack access to the hospital’s electronic medical record, have limited means for determining the correct document. Other important clinical data points, such as intermittent intravenous (IV) furosemide dosing and suppressive antibiotic regimens, were omitted from discharge paperwork altogether. SNF clinicians had difficulty reaching hospital clinicians who could clarify these clinical questions. “Good luck finding the person that took care of [the patient] three days before,” said one director of nursing.

Change starts at home: Challenges in SNF processes and culture

Many clinicians in our study reported that their facilities had recently added clinical capabilities in an effort to care for patients with complex medical problems. For example, to prevent transfers of patients with decompensated heart failure, several facilities in our study had recently obtained certification to give IV diuretics. However, as one director of nursing stated, these efforts require “buy-in” from doctors to decrease readmissions. That buy-in has not always been forthcoming. SNF clinicians also reported difficulty convincing patients and families that their facilities are capable of providing care that, in the past, might only have been available in acute-care settings.

These themes, along with associated sub-themes and representative quotations, are shown above (Table 4).

Our study suggests that the interaction between EDs and SNFs is an important and understudied domain in the spectrum of events leading to readmission. Prior studies have documented inadequacies in patient information provided by SNFs to EDs.^16,17 Efforts to improve SNF-to-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^18,19 However, many of the clinicians in our study reported taking proactive steps to communicate with ED clinicians. These efforts encountered logistical and cultural barriers, with information that might have prevented readmission failing to reach ED providers. Many of the SNF clinicians in our study perceived this failure as a common cause of readmission, especially for relatively stable SNF patients.

Previous studies have pointed to a role for goals of care discussions in reducing hospital readmissions.²⁰ Our data underscore an important qualification to these findings: Location matters. The SNF clinicians in our study reported frequent and detailed goals of care discussions with their patients. However, they also reported that goals of care discussions held in the subacute setting carried less weight with patients and families than discussions held in the hospital. SNF clinicians described a number of cases in which patients were willing to adjust code status or goals of care only after being readmitted to the hospital.

Our study also points to the implications of existing research showing that patients are discharged from acute care hospitals “quicker and sicker” than they had been prior to the 1983 adoption of Medicare’s prospective payment system.²¹ Specifically, the SNF clinicians we interviewed perceived a strong link between patient acuity at the time of transfer and SNFs’ persistently high readmission rates. As SNFs have worked to expand their clinical capabilities, they struggle to win buy-in from physicians and families, many of whom view SNFs as incapable of managing acute illness. Many SNF clinicians also pointed to deficiencies in their own referral and admission processes as a recurring cause of readmissions. For example, several patients in our analysis suffered from dementia. Although these patients were stable enough to leave the acute care setting, the SNF clinicians responsible for their readmissions felt that their SNFs were not well-equipped to care for patients with dementia and that the patients should instead have been transferred to facilities with more robust resources for dementia care.

Finally, our findings highlight a fundamental tension between hospitals and SNFs: Which facility ought to shoulder the responsibility and cost for services that may prevent a readmission—the hospital or the SNF? For example, does responsibility for coordinating subspecialist evaluation of a patient’s chronic condition fall to the hospital or to the SNF? If such an evaluation is undertaken during a hospitalization, it prolongs the patient’s hospital stay and happens at the hospital’s expense. If the patient is discharged to a SNF and sees the subspecialist in clinic, then the SNF must pay for transportation to and from the clinic appointment. SNF clinicians expressed near unanimity that fragmented models of care and high barriers to communication made it difficult to design solutions to these dilemmas.

Strengths and limitations

To our knowledge, this is the first interview-based study examining SNF clinicians’ perspectives on unplanned, 30-day hospital readmissions. We gathered information from clinicians with a range of clinical experience, all of whom had cared directly for the patient who had been readmitted. Our data came from clinicians at 15 SNFs of varying sizes and quality ratings, allowing us to identify a broad range of factors contributing to readmissions.

Because this study relied on qualitative methods, it should be viewed as hypothesis-generating rather than hypothesis-confirming. Further research is needed to determine whether variables related to the themes above are causally linked to SNF readmissions. We identified cases for review using convenience sampling of a cohort of readmitted patients at a single tertiary-care hospital, and all participating SNFs were located in Connecticut. These factors may limit the generalizability of our findings. Although the clinicians we interviewed occupied diverse roles within their respective SNFs, our sample did not include direct-care staff without managerial responsibility, such as certified nursing assistants or licensed practical nurses. This prevented our study from identifying themes into which managers would have limited insight, especially those involving cultural and management practices leading to poor communication between them and their staff. Because our study examines cases in which discharge and readmission were to a general medicine service, it may not describe factors relevant to patients discharged from subspecialist or surgical services.

Implications for future QI efforts and research

Several clinicians we interviewed suggested that readmissions might be reduced by dedicating the services of a hospital professional, such as a nurse or case manager, to monitoring the clinical course of medically complex patients after discharge. A dedicated “transition coach” could clarify deficiencies in discharge paperwork, facilitate necessary follow-up appointments, liaise with staff at both the hospital and the SNF, or coordinate acquisition of necessary equipment. Prospective trials have demonstrated that such interventions can decrease readmission rates among hospitalized patients,^22,23 but formal studies have not been carried out among cohorts of SNF patients.

Prior efforts to improve SNF-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^24,25 The experiences of SNF clinicians in our study suggest that important information also fails to make its way from ED providers to SNFs and that this failure results in unnecessary readmissions of relatively stable SNF patients. Thus, hospitals may be able to prevent SNF readmissions by creating lines of communication between EDs and SNFs and by ensuring that ED physicians and mid-level providers are familiar with the clinical capabilities of local SNFs.

Future research and QI work should also investigate approaches to care coordination that ensure that complex patients are placed in SNFs with resources adequate to address their comorbidities. Potential interventions might include increased use of SNF “liaisons,” who would evaluate patients in-person prior to approving transfer to a given SNF. As has been previously suggested,²⁶ hospitals might also reduce readmissions by narrowing the pool of facilities to which they transfer patients, thereby building more robust, interconnected relationships with a smaller number of SNFs.

SNF clinicians identified areas for improvement at almost every point in the chain of events spanning hospitalization, discharge, and transfer. Among the most frequently cited contributors to readmissions were clinical instability at the time of discharge and omission of clinically important information from discharge documentation. Improved communication between hospitals, ED clinicians, and SNFs, as well as more thoroughly defined goals of care at the time of discharge, were seen as promising ways of decreasing readmissions. Successful interventions for reducing readmissions from SNFs will likely require multifaceted approaches to these problems.

Disclosure: This research was supported by a grant (#P30HS023554-01) from the Agency for Healthcare Research and Quality (AHRQ) and received support from Yale New Haven Hospital and the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (#P30AG021342 NIH/NIA).

Skilled nursing facilities (SNFs) play a crucial role in the hospital readmission process.Approximately 1 in 4 Medicare beneficiaries discharged from an acute care hospital is admitted to a SNF instead of returning directly home. Of these patients, 1 in 4 will be readmitted within 30 days,¹ a rate significantly higher than the readmission rate of the inpatient population as a whole.² The 2014 Protecting Access to Medicare Act created a value-based purchasing program that will use quality measures to steer funds to, or away from, individual SNFs. When the program takes effect in 2018, the Centers for Medicare & Medicaid Services will use SNFs’ 30-day all-cause readmission rate to determine which SNFs receive payments and which receive penalties.³ The Affordable Care Act, passed in 2010, has also established penalties for hospitals with higher than expected readmission rates for Medicare patients.⁴

Despite this intensifying regulatory focus, relatively little is known about the factors that drive readmissions from SNFs. A prospective review of data from SNFs in 4 states has shown that SNFs staffed by nurse practitioners or physician assistants and those equipped to provide intravenous therapy were less likely to transfer patients to the hospital for ambulatory care-sensitive diagnoses.⁵ Qualitative studies have provided useful insight into the causes of SNF-to-hospital transfers but have not focused on 30-day readmissions.^6,7 A single survey-based study has examined the causes of SNF-to-hospital readmissions.⁸ However, survey-based methodologies have limited ability to capture the complex perspectives of SNF clinicians, who play a critical role in determining which SNF patients require evaluation or treatment in an acute care setting.

To address this gap in knowledge about factors contributing to SNF readmissions, we conducted a qualitative study examining SNF clinicians’ perspectives on patients readmitted to the hospital within 30 days of discharge. We used a structured interview tool to explore the root causes of readmission with frontline SNF staff, with the goal of using this knowledge to inform future hospital quality improvement (QI) efforts.

Case Identification

Hospital data-tracking software (Allscripts) was used to identify patients who experienced a 30-day, unplanned readmission from SNFs to an academic medical center. We restricted our search to patients whose index admission and readmission were to the medical center’s inpatient general medicine service. A study team member (BWC) monitored the dataset on a weekly basis and contacted SNF clinicians by e-mail and telephone to arrange interviews at times of mutual convenience. To mitigate against recall bias, interviews were conducted within 30 days of the readmission in question. A total of 32 cases were identified. No SNF clinicians refused a request for interview. For 8 of these cases, it was not possible to find a time of mutual convenience within the specified 30-day window. The remaining 24 cases involved patients from 15 SNFs across Connecticut. Interviews were conducted from August 2015 to November 2015.

The project was reviewed by our institution’s Human Investigation Committee and was exempted from Institutional Review Board review.

Study Participants

Interviews were conducted on-site at SNFs with groups of 1 to 4 SNF clinicians and administrators. SNF participants were informed of interviewer credentials and the study’s QI goals prior to participation. Participation was voluntary and did not affect the clinician’s relationship with the hospital or the SNF. Participants were not paid.

Interventions to Reduce Acute Care Transfers (INTERACT) is a QI program that includes training for clinicians, communication tools, and advance care planning tools.⁹ INTERACT is currently used in 138 Connecticut SNFs as part of a statewide QI effort funded by the Connecticut State Department of Public Health. In prospective QI studies,^10,11 implementation of INTERACT has been associated with decreased transfers from SNFs to acute care hospitals. The INTERACT Quality Improvement Tool, one part of the INTERACT bundle of interventions, is a 26-item questionnaire used to identify root causes of transfers from SNFs to acute care hospitals. It includes both checklists and open-ended questions about patient factors, SNF procedures, and SNF clinical decision-making.

We used the INTERACT QI Tool¹² to conduct structured interviews with nurses and administrators at SNFs. Interviewers used a hard copy of the tool to maintain field notes, and all parts of the questionnaire were completed in each interview. Although the questionnaire elicits baseline demographic and medical information, such as the patient’s age and vital signs prior to readmission, the majority of each interview was dedicated to discussion of the open-ended questions in Table 1. Upon completion of the INTERACT QI Tool, the interviewer asked 2 open-ended questions about reducing readmissions and 4 closed-ended questions regarding SNF admission procedures. (Table 1) The supplemental questions were added after preliminary interviews with SNF clinicians revealed concerns about the SNF referral process and about communication between the hospital, emergency department (ED) and SNFs—issues not included in the INTERACT questionnaire. Interviewers used phatic communication, probing questions, and follow-up questions to elicit detailed information from participants, and participant responses were not limited to topics in the questionnaire and the list of supplemental questions.

Interviews were conducted by a hospital clinical integration coordinator, social worker, and a physician (KB, MCB, BWC). All interviewers received formal training in qualitative research methods prior to the study.

All interviews were audio recorded, with permission from the participants, and were professionally transcribed. Field notes were maintained to ensure accuracy of INTERACT QI Tool data. Participant interviews covered no more than two cases per session and lasted from 18 to 71 minutes (mean duration, 38 minutes).

Analysis

Analysis of transcripts was inductive and informed by grounded theory methodology, in which data is reviewed for repeating ideas, which are then analyzed and grouped to develop a theoretical understanding of the phenomenon under investigation.^13,14

A preliminary codebook was developed using transcripts of the first 11 interviews. All statements relevant to the readmission process were extracted from the raw interview transcript and collected into a single list. This list was then reviewed for statements sharing a particular idea or concern. Such statements were grouped together under the heading of a repeating idea, and each repeating idea was assigned a code. Using this codebook, each transcript was independently reviewed and coded by three study team members with formal training in inductive qualitative analysis (KB, KTM, BWC). Reviewers assigned codes to sections of relevant text. Discrepancies in code assignment were discussed among the 3 analysts until consensus was reached. Using the method of constant comparison described in grounded theory,the codebook was updated continuously as the process of coding transcripts proceeded.¹² Changes to the codebook were discussed among the coding team until consensus was achieved. The process of data acquisition and coding continued until theoretical saturation was reached. Themes relating to underlying factors associated with readmissions were then identified based on shared properties among repeating ideas. ATLAS.ti (Scientific Software, Berlin, Germany, Version 7) was used to facilitate data organization and retrieval.

The SNFs in our study included 12 for-profit and 3 non-profit facilities. The number of licensed beds in each facility ranged from 73 to 360, with a mean of 148 beds. The SNFs had CMS Nursing Home Compare ratings ranging from 1 star, the lowest possible rating, to 5 stars, the highest possible,¹⁵ with a mean rating of 2.9 stars. Our analysis did not reveal differences in perceived contributions to readmissions from large vs. small or highly rated vs poorly rated SNFs.

Emergent transitions: Lack of coordination between ED and SNF

SNF clinicians frequently encountered situations in which a relatively stable patient was readmitted to the hospital after being transferred to the ED, despite the fact that SNF clinicians believed the patient should have returned to the SNF once a specific test was performed or service rendered at the ED. Commonly cited clinical scenarios that resulted in such readmissions included placement of urinary catheters and evaluation for cystitis. An assistant director of nursing reported that “the ER doesn’t want to hear my side of the story,” making it difficult for her to provide information that would prevent such readmissions. Other SNF clinicians reported similar difficulties in communicating with ED clinicians.

Code status: Incompletely addressed goals of care

The SNF clinicians in our study described cases in which patients with end-stage lung disease and disseminated cancer were readmitted to the hospital, despite SNF efforts to prevent readmission and provide palliative care within the SNF. For example, a SNF advanced practice nurse described a case in which a patient with widely metastatic cancer requested readmission to the hospital for treatment of deep vein thrombosis, despite longstanding recommendations from SNF staff that the patient forego hospitalization and enroll in hospice care. After discussion of code status and goals of care with hospital clinicians, the patient chose to enroll in hospice care and not to continue anticoagulation. SNF clinicians often perceived that, in the words of one administrator, “the palliative talks in the hospital outweigh our talks by a lot.” Numerous SNF clinicians believed that in-depth clarification of goals of care prior to discharge could prevent some readmissions.

Wrong patient, wrong place: Mismatch between clinical needs and SNF capabilities

One director of nursing stated that “[when] you read a referral, there’s a huge difference sometimes between what you read and what you see.” SNF clinicians reported that this discrepancy between clinical report and clinical reality often leads to patients being placed in SNFs that are unequipped to care for them. Many patients were perceived as being too ill for discharge from the acute-care setting in the first place. A nurse manager described this as a pattern of “pushing patients out of the hospital.” However, mismatches in clinical disposition were also seen as contributing to readmissions for medically stable patients, such as those with dementia, for whom SNFs frequently lack adequate staffing and physical safeguards.

Missing links: Important clinical information not effectively communicated by hospital

SNF clinicians described numerous challenges in formulating plans of care based on hospital discharge documentation. Discrepancies between discharge summaries and patient instructions were perceived as common and potential causes of readmissions. For patients discharged from the academic medical center in this study, medication instructions are included in both the discharge summary sent to the SNF and in a patient instruction packet. Several SNF clinicians said that it was common for a course of antibiotics to be listed on the discharge summary but not the patient instruction packet, or vice versa. SNF clinicians, who usually lack access to the hospital’s electronic medical record, have limited means for determining the correct document. Other important clinical data points, such as intermittent intravenous (IV) furosemide dosing and suppressive antibiotic regimens, were omitted from discharge paperwork altogether. SNF clinicians had difficulty reaching hospital clinicians who could clarify these clinical questions. “Good luck finding the person that took care of [the patient] three days before,” said one director of nursing.

Change starts at home: Challenges in SNF processes and culture

Many clinicians in our study reported that their facilities had recently added clinical capabilities in an effort to care for patients with complex medical problems. For example, to prevent transfers of patients with decompensated heart failure, several facilities in our study had recently obtained certification to give IV diuretics. However, as one director of nursing stated, these efforts require “buy-in” from doctors to decrease readmissions. That buy-in has not always been forthcoming. SNF clinicians also reported difficulty convincing patients and families that their facilities are capable of providing care that, in the past, might only have been available in acute-care settings.

These themes, along with associated sub-themes and representative quotations, are shown above (Table 4).

Our study suggests that the interaction between EDs and SNFs is an important and understudied domain in the spectrum of events leading to readmission. Prior studies have documented inadequacies in patient information provided by SNFs to EDs.^16,17 Efforts to improve SNF-to-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^18,19 However, many of the clinicians in our study reported taking proactive steps to communicate with ED clinicians. These efforts encountered logistical and cultural barriers, with information that might have prevented readmission failing to reach ED providers. Many of the SNF clinicians in our study perceived this failure as a common cause of readmission, especially for relatively stable SNF patients.

Previous studies have pointed to a role for goals of care discussions in reducing hospital readmissions.²⁰ Our data underscore an important qualification to these findings: Location matters. The SNF clinicians in our study reported frequent and detailed goals of care discussions with their patients. However, they also reported that goals of care discussions held in the subacute setting carried less weight with patients and families than discussions held in the hospital. SNF clinicians described a number of cases in which patients were willing to adjust code status or goals of care only after being readmitted to the hospital.

Our study also points to the implications of existing research showing that patients are discharged from acute care hospitals “quicker and sicker” than they had been prior to the 1983 adoption of Medicare’s prospective payment system.²¹ Specifically, the SNF clinicians we interviewed perceived a strong link between patient acuity at the time of transfer and SNFs’ persistently high readmission rates. As SNFs have worked to expand their clinical capabilities, they struggle to win buy-in from physicians and families, many of whom view SNFs as incapable of managing acute illness. Many SNF clinicians also pointed to deficiencies in their own referral and admission processes as a recurring cause of readmissions. For example, several patients in our analysis suffered from dementia. Although these patients were stable enough to leave the acute care setting, the SNF clinicians responsible for their readmissions felt that their SNFs were not well-equipped to care for patients with dementia and that the patients should instead have been transferred to facilities with more robust resources for dementia care.

Finally, our findings highlight a fundamental tension between hospitals and SNFs: Which facility ought to shoulder the responsibility and cost for services that may prevent a readmission—the hospital or the SNF? For example, does responsibility for coordinating subspecialist evaluation of a patient’s chronic condition fall to the hospital or to the SNF? If such an evaluation is undertaken during a hospitalization, it prolongs the patient’s hospital stay and happens at the hospital’s expense. If the patient is discharged to a SNF and sees the subspecialist in clinic, then the SNF must pay for transportation to and from the clinic appointment. SNF clinicians expressed near unanimity that fragmented models of care and high barriers to communication made it difficult to design solutions to these dilemmas.

Strengths and limitations

To our knowledge, this is the first interview-based study examining SNF clinicians’ perspectives on unplanned, 30-day hospital readmissions. We gathered information from clinicians with a range of clinical experience, all of whom had cared directly for the patient who had been readmitted. Our data came from clinicians at 15 SNFs of varying sizes and quality ratings, allowing us to identify a broad range of factors contributing to readmissions.

Because this study relied on qualitative methods, it should be viewed as hypothesis-generating rather than hypothesis-confirming. Further research is needed to determine whether variables related to the themes above are causally linked to SNF readmissions. We identified cases for review using convenience sampling of a cohort of readmitted patients at a single tertiary-care hospital, and all participating SNFs were located in Connecticut. These factors may limit the generalizability of our findings. Although the clinicians we interviewed occupied diverse roles within their respective SNFs, our sample did not include direct-care staff without managerial responsibility, such as certified nursing assistants or licensed practical nurses. This prevented our study from identifying themes into which managers would have limited insight, especially those involving cultural and management practices leading to poor communication between them and their staff. Because our study examines cases in which discharge and readmission were to a general medicine service, it may not describe factors relevant to patients discharged from subspecialist or surgical services.

Implications for future QI efforts and research

Several clinicians we interviewed suggested that readmissions might be reduced by dedicating the services of a hospital professional, such as a nurse or case manager, to monitoring the clinical course of medically complex patients after discharge. A dedicated “transition coach” could clarify deficiencies in discharge paperwork, facilitate necessary follow-up appointments, liaise with staff at both the hospital and the SNF, or coordinate acquisition of necessary equipment. Prospective trials have demonstrated that such interventions can decrease readmission rates among hospitalized patients,^22,23 but formal studies have not been carried out among cohorts of SNF patients.

Prior efforts to improve SNF-ED information sharing have focused on making sure that ED clinicians have important baseline information about patients transferred from a SNF.^24,25 The experiences of SNF clinicians in our study suggest that important information also fails to make its way from ED providers to SNFs and that this failure results in unnecessary readmissions of relatively stable SNF patients. Thus, hospitals may be able to prevent SNF readmissions by creating lines of communication between EDs and SNFs and by ensuring that ED physicians and mid-level providers are familiar with the clinical capabilities of local SNFs.

Future research and QI work should also investigate approaches to care coordination that ensure that complex patients are placed in SNFs with resources adequate to address their comorbidities. Potential interventions might include increased use of SNF “liaisons,” who would evaluate patients in-person prior to approving transfer to a given SNF. As has been previously suggested,²⁶ hospitals might also reduce readmissions by narrowing the pool of facilities to which they transfer patients, thereby building more robust, interconnected relationships with a smaller number of SNFs.

SNF clinicians identified areas for improvement at almost every point in the chain of events spanning hospitalization, discharge, and transfer. Among the most frequently cited contributors to readmissions were clinical instability at the time of discharge and omission of clinically important information from discharge documentation. Improved communication between hospitals, ED clinicians, and SNFs, as well as more thoroughly defined goals of care at the time of discharge, were seen as promising ways of decreasing readmissions. Successful interventions for reducing readmissions from SNFs will likely require multifaceted approaches to these problems.

Disclosure: This research was supported by a grant (#P30HS023554-01) from the Agency for Healthcare Research and Quality (AHRQ) and received support from Yale New Haven Hospital and the Claude D. Pepper Older Americans Independence Center at Yale University School of Medicine (#P30AG021342 NIH/NIA).

BOSTON—At Island Neurological Associates in Plainview, New York, researchers uncovered a prevalence of chronic migraine among their population of patients with multiple sclerosis (MS) that was higher than would be expected in the general population. They reported their results at the 59th Annual Scientific Meeting of the American Headache Society. “Since migraine as a whole is generally accepted to occur in about 12% of the population, it appears that our MS patient prevalence of 21% significantly exceeds this [prevalence],” said Ira Turner, MD, a headache subspecialist at the Long Island facility. Similarly, “chronic migraine is thought to occur in 1% to 2% of the general population, but [it occurs] in 7% of our MS population,” Dr. Turner said.

Observing that MS and migraine are both chronic neurologic conditions in which inflammatory processes play an important role, Dr. Turner and colleagues sought evidence for increased migraine prevalence in the MS population. “Anecdotally, it has been our experience that there is a comorbidity of headache disorders in our MS patient population,” Dr. Turner said.

The investigators conducted a retrospective review of the electronic medical record (EMR) system at their community-based Comprehensive MS Center and Center for Headache Care and Research. They reviewed the EMR for all patients with a diagnosis of any form of MS. The EMR was then queried to determine which of the patients with MS had any headache diagnosis listed as a comorbidity. Those headache diagnoses were then reviewed and separated into those that met ICHD-3 beta criteria for chronic migraine, episodic migraine with aura, episodic migraine without aura, episodic cluster headache, chronic cluster headache, tension-type headache, or a nonspecific diagnosis of headache.

The researchers found 610 active patients with a diagnosis of MS. Of these, 139 (23%) also had a headache diagnosis listed in the EMR as a comorbidity. Migraine without aura was coded in 62 patients (10%), migraine with aura in 26 (4%), and chronic migraine in 45 (7%). Combining these diagnoses yielded a prevalence of comorbid migraine of 21% in the MS population studied. Episodic cluster headache was diagnosed in one patient, tension-type headache in two patients, and nonspecific headache in four patients. The prevalence of these three diagnoses was less than 1% each.

“While there is a potential bias caused by our practice having both an MS center and a headache center, this increased prevalence seems to be of great interest and would appear to warrant further investigation,” Dr. Turner said.

—Glenn S. Williams

BOSTON—At Island Neurological Associates in Plainview, New York, researchers uncovered a prevalence of chronic migraine among their population of patients with multiple sclerosis (MS) that was higher than would be expected in the general population. They reported their results at the 59th Annual Scientific Meeting of the American Headache Society. “Since migraine as a whole is generally accepted to occur in about 12% of the population, it appears that our MS patient prevalence of 21% significantly exceeds this [prevalence],” said Ira Turner, MD, a headache subspecialist at the Long Island facility. Similarly, “chronic migraine is thought to occur in 1% to 2% of the general population, but [it occurs] in 7% of our MS population,” Dr. Turner said.

Observing that MS and migraine are both chronic neurologic conditions in which inflammatory processes play an important role, Dr. Turner and colleagues sought evidence for increased migraine prevalence in the MS population. “Anecdotally, it has been our experience that there is a comorbidity of headache disorders in our MS patient population,” Dr. Turner said.

The investigators conducted a retrospective review of the electronic medical record (EMR) system at their community-based Comprehensive MS Center and Center for Headache Care and Research. They reviewed the EMR for all patients with a diagnosis of any form of MS. The EMR was then queried to determine which of the patients with MS had any headache diagnosis listed as a comorbidity. Those headache diagnoses were then reviewed and separated into those that met ICHD-3 beta criteria for chronic migraine, episodic migraine with aura, episodic migraine without aura, episodic cluster headache, chronic cluster headache, tension-type headache, or a nonspecific diagnosis of headache.

The researchers found 610 active patients with a diagnosis of MS. Of these, 139 (23%) also had a headache diagnosis listed in the EMR as a comorbidity. Migraine without aura was coded in 62 patients (10%), migraine with aura in 26 (4%), and chronic migraine in 45 (7%). Combining these diagnoses yielded a prevalence of comorbid migraine of 21% in the MS population studied. Episodic cluster headache was diagnosed in one patient, tension-type headache in two patients, and nonspecific headache in four patients. The prevalence of these three diagnoses was less than 1% each.

“While there is a potential bias caused by our practice having both an MS center and a headache center, this increased prevalence seems to be of great interest and would appear to warrant further investigation,” Dr. Turner said.

—Glenn S. Williams

BOSTON—At Island Neurological Associates in Plainview, New York, researchers uncovered a prevalence of chronic migraine among their population of patients with multiple sclerosis (MS) that was higher than would be expected in the general population. They reported their results at the 59th Annual Scientific Meeting of the American Headache Society. “Since migraine as a whole is generally accepted to occur in about 12% of the population, it appears that our MS patient prevalence of 21% significantly exceeds this [prevalence],” said Ira Turner, MD, a headache subspecialist at the Long Island facility. Similarly, “chronic migraine is thought to occur in 1% to 2% of the general population, but [it occurs] in 7% of our MS population,” Dr. Turner said.

Observing that MS and migraine are both chronic neurologic conditions in which inflammatory processes play an important role, Dr. Turner and colleagues sought evidence for increased migraine prevalence in the MS population. “Anecdotally, it has been our experience that there is a comorbidity of headache disorders in our MS patient population,” Dr. Turner said.

The investigators conducted a retrospective review of the electronic medical record (EMR) system at their community-based Comprehensive MS Center and Center for Headache Care and Research. They reviewed the EMR for all patients with a diagnosis of any form of MS. The EMR was then queried to determine which of the patients with MS had any headache diagnosis listed as a comorbidity. Those headache diagnoses were then reviewed and separated into those that met ICHD-3 beta criteria for chronic migraine, episodic migraine with aura, episodic migraine without aura, episodic cluster headache, chronic cluster headache, tension-type headache, or a nonspecific diagnosis of headache.

The researchers found 610 active patients with a diagnosis of MS. Of these, 139 (23%) also had a headache diagnosis listed in the EMR as a comorbidity. Migraine without aura was coded in 62 patients (10%), migraine with aura in 26 (4%), and chronic migraine in 45 (7%). Combining these diagnoses yielded a prevalence of comorbid migraine of 21% in the MS population studied. Episodic cluster headache was diagnosed in one patient, tension-type headache in two patients, and nonspecific headache in four patients. The prevalence of these three diagnoses was less than 1% each.

“While there is a potential bias caused by our practice having both an MS center and a headache center, this increased prevalence seems to be of great interest and would appear to warrant further investigation,” Dr. Turner said.

—Glenn S. Williams

Respiratory illness (RI) is one of the most common reasons for pediatric hospitalization.¹ Examples of RI include acute illness, such as bronchiolitis, bacterial pneumonia, and asthma, as well as chronic conditions, such as obstructive sleep apnea and chronic respiratory insufficiency. Hospital care for RI includes monitoring and treatment to optimize oxygenation, ventilation, hydration, and other body functions. Most previously healthy children hospitalized with RI stay in the hospital for a limited duration (eg, a few days) because the severity of their illness is short lived and they quickly return to their previous healthy status.² However, hospital care is increasing for children with fragile and tenuous health due to complex medical conditions.³ RI is a common reason for hospitalization among these children as well and recovery of respiratory health and function can be slow and protracted for some of them.⁴ Weeks, months, or longer periods of time may be necessary for the children to return to their previous respiratory baseline health and function after hospital discharge; other children may not return to their baseline.^5,6

Hospitalized older adults with high-severity RI are routinely streamlined for transfer to post-acute facility care (PAC) shortly (eg, a few days) after acute-care hospitalization. Nearly 70% of elderly Medicare beneficiaries use PAC following a brief length of stay (LOS) in the acute-care hospital.⁷ It is believed that PAC helps optimize the patients’ health and functional status and relieves the family caregiving burden that would have occurred at home.^8-10 PAC use also helps to shorten acute-care hospitalization for RI while avoiding readmission.^8-10 In contrast with adult patients, use of PAC for hospitalized children is not routine.¹¹ While PAC use in children is infrequent, RI is one of the most common reasons for acute admission among children who use it.¹²

For some children with RI, PAC might be positioned to offer a safe, therapeutic, and high-value setting for pulmonary rehabilitation, as well as related medical, nutritional, functional, and family cares.⁶ PAC, by design, could possibly help some of the children transition back into their homes and communities. As studies continue to emerge that assess the value of PAC in children, it is important to learn more about the use of PAC in children hospitalized with RI. The objectives were to (1) assess which children admitted with RI are the most likely to use PAC services for recovery and (2) estimate how many hospitalized children not using PAC had the same characteristics as those who did.

Study Design, Setting, and Population

We conducted a retrospective cohort analysis of 609,800 hospitalizations for RI occurring from January 1, 2010 to December 31, 2015, in 43 freestanding children’s hospitals in the Pediatric Health Information Systems (PHIS) dataset. All hospitals participating in PHIS are members of the Children’s Hospital Association.¹³ The Boston Children’s Hospital Institutional Review Board approved this study with a waiver for informed consent.

RI was identified using the Agency for Healthcare Research and Quality (AHRQ) Clinical Classification System (CCS).¹⁴ Using diagnosis CCS category 8 (“Diseases of the Respiratory System”) and the procedure CCS category 6 (“Operations on the Respiratory System”), we identified all hospitalizations from the participating hospitals with a principal diagnosis or procedure International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for an RI.

Main Outcome Measure

Discharge disposition following the acute-care hospitalization for RI was the main outcome measure. We used PHIS uniform disposition coding to classify the discharge disposition as transfer to PAC (ie, rehabilitation facility, skilled nursing facility, etc.) vs all other dispositions (ie, routine to home, against medical advice, etc.).¹² The PAC disposition category was derived from the Centers for Medicare & Medicaid Services Patient Discharge Status Codes and Hospital Transfer Policies as informed by the National Uniform Billing Committee Official UB-04 Data Specifications Manual, 2008. PAC transfer included disposition to external PAC facilities, as well as to internal, embedded PAC units residing in a few of the acute-care children’s hospitals included in the cohort.

Demographic and Clinical Characteristics

We assessed patient demographic and clinical characteristics that might correlate with PAC use following acute-care hospitalization for RI. Demographic characteristics included gender, age at admission in years, payer (public, private, and other), and race/ethnicity (Hispanic, non-Hispanic black, non-Hispanic white, other).

Clinical characteristics included chronic conditions (type and number) and assistance with medical technology. Chronic condition and medical technology characteristics were assessed with ICD-9-CM diagnosis codes. PHIS contain up to 41 ICD-9-CM diagnosis codes per hospital discharge record. To identify the presence and number of chronic conditions, we used the AHRQ Chronic Condition Indicator system, which categorizes over 14,000 ICD-9-CM diagnosis codes into chronic vs non-chronic conditions.^14,15 Children hospitalized with a chronic condition were further classified as having a complex chronic condition (CCC) using Feudtner and colleagues’ ICD-9-CM diagnosis classification scheme.¹⁶ CCCs represent defined diagnosis groupings expected to last longer than 12 months and involving either a single organ system, severe enough to require specialty pediatric care and hospitalization, or multiple organ systems.^17,18 Hospitalized children who were assisted with medical technology were identified with ICD-9-CM codes indicating the use of a medical device to manage and treat a chronic illness (eg, ventricular shunt to treat hydrocephalus) or to maintain basic body functions necessary for sustaining life (eg, a tracheostomy tube for breathing).^19,20 We distinguished children undergoing tracheotomy during hospitalization using ICD-9-CM procedure codes 31.1 and 31.2.

Acute-Care Hospitalization Characteristics

We also assessed the relationship between acute-care hospitalization characteristics and use of PAC after discharge, including US census region, LOS, use of intensive care, number of medication classes administered, and use of enhanced respiratory support. Enhanced respiratory support was defined as use of continuous or bilevel positive airway pressure (CPAP or BiPAP) or mechanical ventilation during the acute-care hospitalization for RI. These respiratory supports were identified using billing data in PHIS.

Statistical Analysis

In bivariable analysis, we compared demographic, clinical, and hospitalization characteristics of hospitalized children with vs without discharge to PAC using Rao-Scott chi-square tests and Wilcoxon rank-sum tests as appropriate. In multivariable analysis, we derived a generalized linear mix effects model with fixed effects for demographic, clinical, and hospitalization characteristics that were associated with PAC at P < 0.1 in bivariable analysis (ie, age, gender, race/ethnicity, payer, medical technology, use of intensive care unit [ICU], use of positive pressure or mechanical ventilation, hospital region, LOS, new tracheostomy, existing tracheostomy, other technologies, number of medications, number of chronic conditions [of any complexity], and type of complex chronic conditions). We controlled for clustering of patients within hospitals by including a random intercept for each hospital. We also assessed combinations of patient characteristics on the likelihood of PAC use with classification and regression tree (CART) modeling. Using CART, we determined which characteristic combinations were associated with the highest and lowest use of PAC using binary split and post-pruning, goodness of fit rules.²¹ All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, NC), and R v.3.2 (R Foundation for Statistical Computing, Vienna, Austria) using the “party” package. The threshold for statistical significance was set at P < 0.05.

Of the 609,800 hospitalizations for RI, PAC use after discharge occurred for 2660 (0.4%). RI discharges to PAC accounted for 2.1% (n = 67,405) of hospital days and 2.7% ($280 million) of hospital cost of all RI hospitalizations. For discharges to PAC, the most common RI were pneumonia (29.1% [n = 773]), respiratory failure or insufficiency (unspecified reason; 22.0% [n = 584]), and upper respiratory infection (12.2% [n = 323]).

Demographic Characteristics

Median age at acute-care admission was higher for PAC vs non-PAC discharges (6 years [interquartile range {IQR} 1-15] vs 2 years [0-7], P < 0.001; Table 1). Hispanic patients accounted for a smaller percentage of RI discharges to PAC vs non-PAC (14.1% vs 21.8%, P < 0.001) and a higher percentage to PAC were for patients with public insurance (75.9% vs 62.5, P < 0.001; Table 1).

Clinical Characteristics

A greater percentage of RI hospitalizations discharged to PAC vs not-PAC had ≥1 CCC (94.9% vs 33.5%), including a neuromuscular CCC (57.5% vs 8.9%) or respiratory CCC (62.5% vs 12.0%), P < 0.001 for all (Table 2). A greater percentage discharged to PAC was assisted with medical technology (83.2% vs 15.1%), including respiratory technology (eg, tracheostomy; 53.8% vs 5.4%) and gastrointestinal technology (eg, gastrostomy; 71.9% vs 11.8%), P < 0.001 for all. Of the children with respiratory technology, 14.8% (n = 394) underwent tracheotomy during the acute-care hospitalization. Children discharged to PAC had a higher percentage of multiple chronic conditions. For example, the percentages of children discharged to PAC vs not with ≥7 conditions were 54.5% vs 7.0% (P < 0.001; Table 2). The most common chronic conditions experienced by children discharged to PAC included epilepsy (41.2%), gastroesophageal reflux (36.6%), cerebral palsy (28.2%), and asthma (18.2%).

Hospitalization Characteristics

Acute-care RI hospitalization median LOS was longer for discharges to PAC vs non-PAC (10 days [IQR 4-27] vs 2 days [IQR 1-4], P < 0.001; Table 1). A greater percentage of discharges to PAC were administered medications from multiple classes during the acute-care RI admission (eg, 54.8% vs 13.4% used medications from ≥7 classes, P < 0.001). A greater percentage of discharges to PAC used intensive care services during the acute-care admission (65.6% vs 22.4%, P < 0.001). A greater percentage of discharges to PAC received CPAP (10.6 vs 5.0%), BiPAP (19.8% vs 11.4%), or mechanical ventilation (52.7% vs 9.1%) during the acute-care RI hospitalization (P < 0.001 for all; Table 1).

Multivariable Analysis of the Likelihood of Post-Acute Care Use Following Discharge

In multivariable analysis, the patient characteristics associated with the highest likelihood of discharge to PAC included ≥11 vs no chronic conditions (odds ratio [OR] 11.8 , 95% CI, 8.0-17.2), ≥9 classes vs no classes of medications administered during the acute-care hospitalization (OR 4.8 , 95% CI, 1.8-13.0), and existing tracheostomy (OR 3.0, [95% CI, 2.6-3.5; Figure 2 and eTable). Patient characteristics associated with a more modest likelihood of discharge to PAC included public vs private insurance (OR 1.8, 95% CI, 1.6-2.0), neuromuscular complex chronic condition (OR 1.6, 95% CI, 1.5-1.8), new tracheostomy (OR 1.9, 95% CI, 1.7-2.2), and use of any enhanced respiratory support (ie, CPAP/BiPAP/mechanical ventilation) during the acute-care hospitalization (OR 1.4, 95% CI, 1.3-1.6; Figure 2 and Supplementary Table).

Classification and Regression Tree Analysis

In the CART analysis, the highest percentage (6.3%) of children hospitalized with RI who were discharged to PAC had the following combination of characteristics: ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Median (IQR) length of acute-care LOS for children with these attributes who were transferred to PAC was 19 (IQR 8-56; range 1-1005) days; LOS remained long (median 13 days [IQR 6-41, range 1-1413]) for children with the same attributes not transferred to PAC (n = 9448). Between these children transferred vs not to PAC, 79.3% vs 65.9% received ICU services; 74.4% vs 73.5% received CPAP, BiPAP, or mechanical ventilation; and 31.0% vs 22.7% underwent tracheotomy during the acute-care hospitalization. Of these children who were not transferred to PAC, 18.9% were discharged to home nursing services.

The findings from the present study suggest that patients with RI hospitalization in children’s hospitals who use PAC are medically complex, with high rates of multiple chronic conditions—including cerebral palsy, asthma, chronic respiratory insufficiency, dysphagia, epilepsy, and gastroesophageal reflux—and high rates of technology assistance including enterostomy and tracheostomy. The characteristics of patients most likely to use PAC include long LOS, a large number of chronic conditions, many types of medications administered during the acute-care hospitalization, respiratory technology use, and an underlying neuromuscular condition. Specifically, the highest percentage of children hospitalized with RI who were discharged to PAC had ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Our analysis suggests that there may be a large population of children with these same characteristics who experienced a prolonged LOS but were not transferred to PAC.

Acknowledgments

Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01), the Lucile Packard Foundation for Children’s Health, and Franciscan Hospital for Children. The funders were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclosure: The authors have no financial relationships relevant to this article to disclose.

Respiratory illness (RI) is one of the most common reasons for pediatric hospitalization.¹ Examples of RI include acute illness, such as bronchiolitis, bacterial pneumonia, and asthma, as well as chronic conditions, such as obstructive sleep apnea and chronic respiratory insufficiency. Hospital care for RI includes monitoring and treatment to optimize oxygenation, ventilation, hydration, and other body functions. Most previously healthy children hospitalized with RI stay in the hospital for a limited duration (eg, a few days) because the severity of their illness is short lived and they quickly return to their previous healthy status.² However, hospital care is increasing for children with fragile and tenuous health due to complex medical conditions.³ RI is a common reason for hospitalization among these children as well and recovery of respiratory health and function can be slow and protracted for some of them.⁴ Weeks, months, or longer periods of time may be necessary for the children to return to their previous respiratory baseline health and function after hospital discharge; other children may not return to their baseline.^5,6

Hospitalized older adults with high-severity RI are routinely streamlined for transfer to post-acute facility care (PAC) shortly (eg, a few days) after acute-care hospitalization. Nearly 70% of elderly Medicare beneficiaries use PAC following a brief length of stay (LOS) in the acute-care hospital.⁷ It is believed that PAC helps optimize the patients’ health and functional status and relieves the family caregiving burden that would have occurred at home.^8-10 PAC use also helps to shorten acute-care hospitalization for RI while avoiding readmission.^8-10 In contrast with adult patients, use of PAC for hospitalized children is not routine.¹¹ While PAC use in children is infrequent, RI is one of the most common reasons for acute admission among children who use it.¹²

For some children with RI, PAC might be positioned to offer a safe, therapeutic, and high-value setting for pulmonary rehabilitation, as well as related medical, nutritional, functional, and family cares.⁶ PAC, by design, could possibly help some of the children transition back into their homes and communities. As studies continue to emerge that assess the value of PAC in children, it is important to learn more about the use of PAC in children hospitalized with RI. The objectives were to (1) assess which children admitted with RI are the most likely to use PAC services for recovery and (2) estimate how many hospitalized children not using PAC had the same characteristics as those who did.

Study Design, Setting, and Population

We conducted a retrospective cohort analysis of 609,800 hospitalizations for RI occurring from January 1, 2010 to December 31, 2015, in 43 freestanding children’s hospitals in the Pediatric Health Information Systems (PHIS) dataset. All hospitals participating in PHIS are members of the Children’s Hospital Association.¹³ The Boston Children’s Hospital Institutional Review Board approved this study with a waiver for informed consent.

RI was identified using the Agency for Healthcare Research and Quality (AHRQ) Clinical Classification System (CCS).¹⁴ Using diagnosis CCS category 8 (“Diseases of the Respiratory System”) and the procedure CCS category 6 (“Operations on the Respiratory System”), we identified all hospitalizations from the participating hospitals with a principal diagnosis or procedure International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for an RI.

Main Outcome Measure

Discharge disposition following the acute-care hospitalization for RI was the main outcome measure. We used PHIS uniform disposition coding to classify the discharge disposition as transfer to PAC (ie, rehabilitation facility, skilled nursing facility, etc.) vs all other dispositions (ie, routine to home, against medical advice, etc.).¹² The PAC disposition category was derived from the Centers for Medicare & Medicaid Services Patient Discharge Status Codes and Hospital Transfer Policies as informed by the National Uniform Billing Committee Official UB-04 Data Specifications Manual, 2008. PAC transfer included disposition to external PAC facilities, as well as to internal, embedded PAC units residing in a few of the acute-care children’s hospitals included in the cohort.

Demographic and Clinical Characteristics

We assessed patient demographic and clinical characteristics that might correlate with PAC use following acute-care hospitalization for RI. Demographic characteristics included gender, age at admission in years, payer (public, private, and other), and race/ethnicity (Hispanic, non-Hispanic black, non-Hispanic white, other).

Clinical characteristics included chronic conditions (type and number) and assistance with medical technology. Chronic condition and medical technology characteristics were assessed with ICD-9-CM diagnosis codes. PHIS contain up to 41 ICD-9-CM diagnosis codes per hospital discharge record. To identify the presence and number of chronic conditions, we used the AHRQ Chronic Condition Indicator system, which categorizes over 14,000 ICD-9-CM diagnosis codes into chronic vs non-chronic conditions.^14,15 Children hospitalized with a chronic condition were further classified as having a complex chronic condition (CCC) using Feudtner and colleagues’ ICD-9-CM diagnosis classification scheme.¹⁶ CCCs represent defined diagnosis groupings expected to last longer than 12 months and involving either a single organ system, severe enough to require specialty pediatric care and hospitalization, or multiple organ systems.^17,18 Hospitalized children who were assisted with medical technology were identified with ICD-9-CM codes indicating the use of a medical device to manage and treat a chronic illness (eg, ventricular shunt to treat hydrocephalus) or to maintain basic body functions necessary for sustaining life (eg, a tracheostomy tube for breathing).^19,20 We distinguished children undergoing tracheotomy during hospitalization using ICD-9-CM procedure codes 31.1 and 31.2.

Acute-Care Hospitalization Characteristics

We also assessed the relationship between acute-care hospitalization characteristics and use of PAC after discharge, including US census region, LOS, use of intensive care, number of medication classes administered, and use of enhanced respiratory support. Enhanced respiratory support was defined as use of continuous or bilevel positive airway pressure (CPAP or BiPAP) or mechanical ventilation during the acute-care hospitalization for RI. These respiratory supports were identified using billing data in PHIS.

Statistical Analysis

In bivariable analysis, we compared demographic, clinical, and hospitalization characteristics of hospitalized children with vs without discharge to PAC using Rao-Scott chi-square tests and Wilcoxon rank-sum tests as appropriate. In multivariable analysis, we derived a generalized linear mix effects model with fixed effects for demographic, clinical, and hospitalization characteristics that were associated with PAC at P < 0.1 in bivariable analysis (ie, age, gender, race/ethnicity, payer, medical technology, use of intensive care unit [ICU], use of positive pressure or mechanical ventilation, hospital region, LOS, new tracheostomy, existing tracheostomy, other technologies, number of medications, number of chronic conditions [of any complexity], and type of complex chronic conditions). We controlled for clustering of patients within hospitals by including a random intercept for each hospital. We also assessed combinations of patient characteristics on the likelihood of PAC use with classification and regression tree (CART) modeling. Using CART, we determined which characteristic combinations were associated with the highest and lowest use of PAC using binary split and post-pruning, goodness of fit rules.²¹ All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, NC), and R v.3.2 (R Foundation for Statistical Computing, Vienna, Austria) using the “party” package. The threshold for statistical significance was set at P < 0.05.

Of the 609,800 hospitalizations for RI, PAC use after discharge occurred for 2660 (0.4%). RI discharges to PAC accounted for 2.1% (n = 67,405) of hospital days and 2.7% ($280 million) of hospital cost of all RI hospitalizations. For discharges to PAC, the most common RI were pneumonia (29.1% [n = 773]), respiratory failure or insufficiency (unspecified reason; 22.0% [n = 584]), and upper respiratory infection (12.2% [n = 323]).

Demographic Characteristics

Median age at acute-care admission was higher for PAC vs non-PAC discharges (6 years [interquartile range {IQR} 1-15] vs 2 years [0-7], P < 0.001; Table 1). Hispanic patients accounted for a smaller percentage of RI discharges to PAC vs non-PAC (14.1% vs 21.8%, P < 0.001) and a higher percentage to PAC were for patients with public insurance (75.9% vs 62.5, P < 0.001; Table 1).

Clinical Characteristics

A greater percentage of RI hospitalizations discharged to PAC vs not-PAC had ≥1 CCC (94.9% vs 33.5%), including a neuromuscular CCC (57.5% vs 8.9%) or respiratory CCC (62.5% vs 12.0%), P < 0.001 for all (Table 2). A greater percentage discharged to PAC was assisted with medical technology (83.2% vs 15.1%), including respiratory technology (eg, tracheostomy; 53.8% vs 5.4%) and gastrointestinal technology (eg, gastrostomy; 71.9% vs 11.8%), P < 0.001 for all. Of the children with respiratory technology, 14.8% (n = 394) underwent tracheotomy during the acute-care hospitalization. Children discharged to PAC had a higher percentage of multiple chronic conditions. For example, the percentages of children discharged to PAC vs not with ≥7 conditions were 54.5% vs 7.0% (P < 0.001; Table 2). The most common chronic conditions experienced by children discharged to PAC included epilepsy (41.2%), gastroesophageal reflux (36.6%), cerebral palsy (28.2%), and asthma (18.2%).

Hospitalization Characteristics

Acute-care RI hospitalization median LOS was longer for discharges to PAC vs non-PAC (10 days [IQR 4-27] vs 2 days [IQR 1-4], P < 0.001; Table 1). A greater percentage of discharges to PAC were administered medications from multiple classes during the acute-care RI admission (eg, 54.8% vs 13.4% used medications from ≥7 classes, P < 0.001). A greater percentage of discharges to PAC used intensive care services during the acute-care admission (65.6% vs 22.4%, P < 0.001). A greater percentage of discharges to PAC received CPAP (10.6 vs 5.0%), BiPAP (19.8% vs 11.4%), or mechanical ventilation (52.7% vs 9.1%) during the acute-care RI hospitalization (P < 0.001 for all; Table 1).

Multivariable Analysis of the Likelihood of Post-Acute Care Use Following Discharge

In multivariable analysis, the patient characteristics associated with the highest likelihood of discharge to PAC included ≥11 vs no chronic conditions (odds ratio [OR] 11.8 , 95% CI, 8.0-17.2), ≥9 classes vs no classes of medications administered during the acute-care hospitalization (OR 4.8 , 95% CI, 1.8-13.0), and existing tracheostomy (OR 3.0, [95% CI, 2.6-3.5; Figure 2 and eTable). Patient characteristics associated with a more modest likelihood of discharge to PAC included public vs private insurance (OR 1.8, 95% CI, 1.6-2.0), neuromuscular complex chronic condition (OR 1.6, 95% CI, 1.5-1.8), new tracheostomy (OR 1.9, 95% CI, 1.7-2.2), and use of any enhanced respiratory support (ie, CPAP/BiPAP/mechanical ventilation) during the acute-care hospitalization (OR 1.4, 95% CI, 1.3-1.6; Figure 2 and Supplementary Table).

Classification and Regression Tree Analysis

In the CART analysis, the highest percentage (6.3%) of children hospitalized with RI who were discharged to PAC had the following combination of characteristics: ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Median (IQR) length of acute-care LOS for children with these attributes who were transferred to PAC was 19 (IQR 8-56; range 1-1005) days; LOS remained long (median 13 days [IQR 6-41, range 1-1413]) for children with the same attributes not transferred to PAC (n = 9448). Between these children transferred vs not to PAC, 79.3% vs 65.9% received ICU services; 74.4% vs 73.5% received CPAP, BiPAP, or mechanical ventilation; and 31.0% vs 22.7% underwent tracheotomy during the acute-care hospitalization. Of these children who were not transferred to PAC, 18.9% were discharged to home nursing services.

The findings from the present study suggest that patients with RI hospitalization in children’s hospitals who use PAC are medically complex, with high rates of multiple chronic conditions—including cerebral palsy, asthma, chronic respiratory insufficiency, dysphagia, epilepsy, and gastroesophageal reflux—and high rates of technology assistance including enterostomy and tracheostomy. The characteristics of patients most likely to use PAC include long LOS, a large number of chronic conditions, many types of medications administered during the acute-care hospitalization, respiratory technology use, and an underlying neuromuscular condition. Specifically, the highest percentage of children hospitalized with RI who were discharged to PAC had ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Our analysis suggests that there may be a large population of children with these same characteristics who experienced a prolonged LOS but were not transferred to PAC.

Acknowledgments

Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01), the Lucile Packard Foundation for Children’s Health, and Franciscan Hospital for Children. The funders were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclosure: The authors have no financial relationships relevant to this article to disclose.

Respiratory illness (RI) is one of the most common reasons for pediatric hospitalization.¹ Examples of RI include acute illness, such as bronchiolitis, bacterial pneumonia, and asthma, as well as chronic conditions, such as obstructive sleep apnea and chronic respiratory insufficiency. Hospital care for RI includes monitoring and treatment to optimize oxygenation, ventilation, hydration, and other body functions. Most previously healthy children hospitalized with RI stay in the hospital for a limited duration (eg, a few days) because the severity of their illness is short lived and they quickly return to their previous healthy status.² However, hospital care is increasing for children with fragile and tenuous health due to complex medical conditions.³ RI is a common reason for hospitalization among these children as well and recovery of respiratory health and function can be slow and protracted for some of them.⁴ Weeks, months, or longer periods of time may be necessary for the children to return to their previous respiratory baseline health and function after hospital discharge; other children may not return to their baseline.^5,6

Hospitalized older adults with high-severity RI are routinely streamlined for transfer to post-acute facility care (PAC) shortly (eg, a few days) after acute-care hospitalization. Nearly 70% of elderly Medicare beneficiaries use PAC following a brief length of stay (LOS) in the acute-care hospital.⁷ It is believed that PAC helps optimize the patients’ health and functional status and relieves the family caregiving burden that would have occurred at home.^8-10 PAC use also helps to shorten acute-care hospitalization for RI while avoiding readmission.^8-10 In contrast with adult patients, use of PAC for hospitalized children is not routine.¹¹ While PAC use in children is infrequent, RI is one of the most common reasons for acute admission among children who use it.¹²

For some children with RI, PAC might be positioned to offer a safe, therapeutic, and high-value setting for pulmonary rehabilitation, as well as related medical, nutritional, functional, and family cares.⁶ PAC, by design, could possibly help some of the children transition back into their homes and communities. As studies continue to emerge that assess the value of PAC in children, it is important to learn more about the use of PAC in children hospitalized with RI. The objectives were to (1) assess which children admitted with RI are the most likely to use PAC services for recovery and (2) estimate how many hospitalized children not using PAC had the same characteristics as those who did.

Study Design, Setting, and Population

We conducted a retrospective cohort analysis of 609,800 hospitalizations for RI occurring from January 1, 2010 to December 31, 2015, in 43 freestanding children’s hospitals in the Pediatric Health Information Systems (PHIS) dataset. All hospitals participating in PHIS are members of the Children’s Hospital Association.¹³ The Boston Children’s Hospital Institutional Review Board approved this study with a waiver for informed consent.

RI was identified using the Agency for Healthcare Research and Quality (AHRQ) Clinical Classification System (CCS).¹⁴ Using diagnosis CCS category 8 (“Diseases of the Respiratory System”) and the procedure CCS category 6 (“Operations on the Respiratory System”), we identified all hospitalizations from the participating hospitals with a principal diagnosis or procedure International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for an RI.

Main Outcome Measure

Discharge disposition following the acute-care hospitalization for RI was the main outcome measure. We used PHIS uniform disposition coding to classify the discharge disposition as transfer to PAC (ie, rehabilitation facility, skilled nursing facility, etc.) vs all other dispositions (ie, routine to home, against medical advice, etc.).¹² The PAC disposition category was derived from the Centers for Medicare & Medicaid Services Patient Discharge Status Codes and Hospital Transfer Policies as informed by the National Uniform Billing Committee Official UB-04 Data Specifications Manual, 2008. PAC transfer included disposition to external PAC facilities, as well as to internal, embedded PAC units residing in a few of the acute-care children’s hospitals included in the cohort.

Demographic and Clinical Characteristics

We assessed patient demographic and clinical characteristics that might correlate with PAC use following acute-care hospitalization for RI. Demographic characteristics included gender, age at admission in years, payer (public, private, and other), and race/ethnicity (Hispanic, non-Hispanic black, non-Hispanic white, other).

Clinical characteristics included chronic conditions (type and number) and assistance with medical technology. Chronic condition and medical technology characteristics were assessed with ICD-9-CM diagnosis codes. PHIS contain up to 41 ICD-9-CM diagnosis codes per hospital discharge record. To identify the presence and number of chronic conditions, we used the AHRQ Chronic Condition Indicator system, which categorizes over 14,000 ICD-9-CM diagnosis codes into chronic vs non-chronic conditions.^14,15 Children hospitalized with a chronic condition were further classified as having a complex chronic condition (CCC) using Feudtner and colleagues’ ICD-9-CM diagnosis classification scheme.¹⁶ CCCs represent defined diagnosis groupings expected to last longer than 12 months and involving either a single organ system, severe enough to require specialty pediatric care and hospitalization, or multiple organ systems.^17,18 Hospitalized children who were assisted with medical technology were identified with ICD-9-CM codes indicating the use of a medical device to manage and treat a chronic illness (eg, ventricular shunt to treat hydrocephalus) or to maintain basic body functions necessary for sustaining life (eg, a tracheostomy tube for breathing).^19,20 We distinguished children undergoing tracheotomy during hospitalization using ICD-9-CM procedure codes 31.1 and 31.2.

Acute-Care Hospitalization Characteristics

We also assessed the relationship between acute-care hospitalization characteristics and use of PAC after discharge, including US census region, LOS, use of intensive care, number of medication classes administered, and use of enhanced respiratory support. Enhanced respiratory support was defined as use of continuous or bilevel positive airway pressure (CPAP or BiPAP) or mechanical ventilation during the acute-care hospitalization for RI. These respiratory supports were identified using billing data in PHIS.

Statistical Analysis

In bivariable analysis, we compared demographic, clinical, and hospitalization characteristics of hospitalized children with vs without discharge to PAC using Rao-Scott chi-square tests and Wilcoxon rank-sum tests as appropriate. In multivariable analysis, we derived a generalized linear mix effects model with fixed effects for demographic, clinical, and hospitalization characteristics that were associated with PAC at P < 0.1 in bivariable analysis (ie, age, gender, race/ethnicity, payer, medical technology, use of intensive care unit [ICU], use of positive pressure or mechanical ventilation, hospital region, LOS, new tracheostomy, existing tracheostomy, other technologies, number of medications, number of chronic conditions [of any complexity], and type of complex chronic conditions). We controlled for clustering of patients within hospitals by including a random intercept for each hospital. We also assessed combinations of patient characteristics on the likelihood of PAC use with classification and regression tree (CART) modeling. Using CART, we determined which characteristic combinations were associated with the highest and lowest use of PAC using binary split and post-pruning, goodness of fit rules.²¹ All statistical analyses were performed using SAS v.9.4 (SAS Institute, Cary, NC), and R v.3.2 (R Foundation for Statistical Computing, Vienna, Austria) using the “party” package. The threshold for statistical significance was set at P < 0.05.

Of the 609,800 hospitalizations for RI, PAC use after discharge occurred for 2660 (0.4%). RI discharges to PAC accounted for 2.1% (n = 67,405) of hospital days and 2.7% ($280 million) of hospital cost of all RI hospitalizations. For discharges to PAC, the most common RI were pneumonia (29.1% [n = 773]), respiratory failure or insufficiency (unspecified reason; 22.0% [n = 584]), and upper respiratory infection (12.2% [n = 323]).

Demographic Characteristics

Median age at acute-care admission was higher for PAC vs non-PAC discharges (6 years [interquartile range {IQR} 1-15] vs 2 years [0-7], P < 0.001; Table 1). Hispanic patients accounted for a smaller percentage of RI discharges to PAC vs non-PAC (14.1% vs 21.8%, P < 0.001) and a higher percentage to PAC were for patients with public insurance (75.9% vs 62.5, P < 0.001; Table 1).

Clinical Characteristics

A greater percentage of RI hospitalizations discharged to PAC vs not-PAC had ≥1 CCC (94.9% vs 33.5%), including a neuromuscular CCC (57.5% vs 8.9%) or respiratory CCC (62.5% vs 12.0%), P < 0.001 for all (Table 2). A greater percentage discharged to PAC was assisted with medical technology (83.2% vs 15.1%), including respiratory technology (eg, tracheostomy; 53.8% vs 5.4%) and gastrointestinal technology (eg, gastrostomy; 71.9% vs 11.8%), P < 0.001 for all. Of the children with respiratory technology, 14.8% (n = 394) underwent tracheotomy during the acute-care hospitalization. Children discharged to PAC had a higher percentage of multiple chronic conditions. For example, the percentages of children discharged to PAC vs not with ≥7 conditions were 54.5% vs 7.0% (P < 0.001; Table 2). The most common chronic conditions experienced by children discharged to PAC included epilepsy (41.2%), gastroesophageal reflux (36.6%), cerebral palsy (28.2%), and asthma (18.2%).

Hospitalization Characteristics

Acute-care RI hospitalization median LOS was longer for discharges to PAC vs non-PAC (10 days [IQR 4-27] vs 2 days [IQR 1-4], P < 0.001; Table 1). A greater percentage of discharges to PAC were administered medications from multiple classes during the acute-care RI admission (eg, 54.8% vs 13.4% used medications from ≥7 classes, P < 0.001). A greater percentage of discharges to PAC used intensive care services during the acute-care admission (65.6% vs 22.4%, P < 0.001). A greater percentage of discharges to PAC received CPAP (10.6 vs 5.0%), BiPAP (19.8% vs 11.4%), or mechanical ventilation (52.7% vs 9.1%) during the acute-care RI hospitalization (P < 0.001 for all; Table 1).

Multivariable Analysis of the Likelihood of Post-Acute Care Use Following Discharge

In multivariable analysis, the patient characteristics associated with the highest likelihood of discharge to PAC included ≥11 vs no chronic conditions (odds ratio [OR] 11.8 , 95% CI, 8.0-17.2), ≥9 classes vs no classes of medications administered during the acute-care hospitalization (OR 4.8 , 95% CI, 1.8-13.0), and existing tracheostomy (OR 3.0, [95% CI, 2.6-3.5; Figure 2 and eTable). Patient characteristics associated with a more modest likelihood of discharge to PAC included public vs private insurance (OR 1.8, 95% CI, 1.6-2.0), neuromuscular complex chronic condition (OR 1.6, 95% CI, 1.5-1.8), new tracheostomy (OR 1.9, 95% CI, 1.7-2.2), and use of any enhanced respiratory support (ie, CPAP/BiPAP/mechanical ventilation) during the acute-care hospitalization (OR 1.4, 95% CI, 1.3-1.6; Figure 2 and Supplementary Table).

Classification and Regression Tree Analysis

In the CART analysis, the highest percentage (6.3%) of children hospitalized with RI who were discharged to PAC had the following combination of characteristics: ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Median (IQR) length of acute-care LOS for children with these attributes who were transferred to PAC was 19 (IQR 8-56; range 1-1005) days; LOS remained long (median 13 days [IQR 6-41, range 1-1413]) for children with the same attributes not transferred to PAC (n = 9448). Between these children transferred vs not to PAC, 79.3% vs 65.9% received ICU services; 74.4% vs 73.5% received CPAP, BiPAP, or mechanical ventilation; and 31.0% vs 22.7% underwent tracheotomy during the acute-care hospitalization. Of these children who were not transferred to PAC, 18.9% were discharged to home nursing services.

The findings from the present study suggest that patients with RI hospitalization in children’s hospitals who use PAC are medically complex, with high rates of multiple chronic conditions—including cerebral palsy, asthma, chronic respiratory insufficiency, dysphagia, epilepsy, and gastroesophageal reflux—and high rates of technology assistance including enterostomy and tracheostomy. The characteristics of patients most likely to use PAC include long LOS, a large number of chronic conditions, many types of medications administered during the acute-care hospitalization, respiratory technology use, and an underlying neuromuscular condition. Specifically, the highest percentage of children hospitalized with RI who were discharged to PAC had ≥6 chronic conditions, ≥7 classes of medications administered, and respiratory technology. Our analysis suggests that there may be a large population of children with these same characteristics who experienced a prolonged LOS but were not transferred to PAC.

Acknowledgments

Dr. Berry was supported by the Agency for Healthcare Research and Quality (R21 HS023092-01), the Lucile Packard Foundation for Children’s Health, and Franciscan Hospital for Children. The funders were not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclosure: The authors have no financial relationships relevant to this article to disclose.

Given growing incentives to reduce readmission rates, predischarge checklists and bundles have recommended that inpatient providers schedule postdischarge follow-up visits (PDFVs) for their hospitalized patients.^1-4 PDFVs have been linked to lower readmission rates in patients with chronic conditions, including congestive heart failure, psychiatric illnesses, and chronic obstructive pulmonary disease.^5-8 In contrast, the impact of PDFVs on readmissions in hospitalized general medicine populations has been mixed.^9-12 Beyond the presence or absence of PDFVs, it may be a patient’s inability to keep scheduled PDFVs that contributes more strongly to preventable readmissions.¹¹

This challenge, dealing with the 12% to 37% of patients who miss their visits (“no-shows”), is not new.^13-17 In high-risk patient populations, such as those with substance abuse, diabetes, or human immunodeficiency virus, no-shows (NSs) have been linked to poorer short-term and long-term clinical outcomes.^16,18-20 Additionally, NSs pose a challenge for outpatient clinics and the healthcare system at large. The financial cost of NSs ranges from approximately $200 per patient in 2 analyses to $7 million in cumulative lost revenue per year at 1 large academic health system.^13,17,21 As such, increasing attendance at PDFVs is a potential target for improving both patient outcomes and clinic productivity.

Most prior PDFV research has focused on readmission risk rather than PDFV attendance as the primary outcome.^5-12 However, given the patient-oriented benefits of attending PDFVs and the clinic-oriented benefits of avoiding vacant time slots, NS PDFVs represent an important missed opportunity for our healthcare delivery system. To our knowledge, risk factors for PDFV nonattendance have not yet been systematically studied. The aim of our study was to analyze PDFV nonattendance, particularly NSs and same-day cancellations (SDCs), for hospitalizations and clinics within our healthcare system.

Study Design

We conducted an observational cohort study of adult patients from 10 medical units at the Hospital of the University of Pennsylvania (a 789-bed quaternary-care hospital within an urban, academic medical system) who were scheduled with at least 1 PDFV. Specifically, the patients included in our analysis were hospitalized on general internal medicine services or medical subspecialty services with discharge dates between April 1, 2014, and March 31, 2015. Hospitalizations included in our study had at least 1 PDFV scheduled with an outpatient provider affiliated with the University of Pennsylvania Health System (UPHS). PDFVs scheduled with unaffiliated providers were not examined.

Each PDFV was requested by a patient’s inpatient care team. Once the care team had determined that a PDFV was clinically warranted, a member of the team (generally a resident, advanced practice provider, medical student, or designee) either called the UPHS clinic to schedule an appointment time or e-mailed the outpatient UPHS provider directly to facilitate a more urgent PDFV appointment time. Once a PDFV time was confirmed, PDFV details (ie, date, time, location, and phone number) were electronically entered into the patient’s discharge instructions by the inpatient care team. At the time of discharge, nurses reviewed these instructions with their patients. All patients left the hospital with a physical copy of these instructions. As part of routine care at our institution, patients then received automated telephone reminders from their UPHS-affiliated outpatient clinic 48 hours prior to each PDFV.

Data Collection

Our study was determined to meet criteria for quality improvement by the University of Pennsylvania’s Institutional Review Board. We used our healthcare system’s integrated electronic medical record system to track the dates of initial PDFV requests, the dates of hospitalization, and actual PDFV dates. PDFVs were included if the appointment request was made while a patient was hospitalized, including the day of discharge. Our study methodology only allowed us to investigate PDFVs scheduled with UPHS outpatient providers. We did not review discharge instructions or survey non-UPHS clinics to quantify visits scheduled with other providers, for example, community health centers or external private practices.

Exclusion criteria included the following: (1) office visits with nonproviders, for example, scheduled diagnostic procedures or pharmacist appointments for warfarin dosing; (2) visits cancelled by inpatient providers prior to discharge; (3) visits for patients not otherwise eligible for UPHS outpatient care because of insurance reasons; and (4) visits scheduled for dates after a patient’s death. Our motivation for the third exclusion criterion was the infrequent and irregular process by which PDFVs were authorized for these patients. These patients and their characteristics are described in Supplementary Table 1 in more detail.

For each PDFV, we recorded age, gender, race, insurance status, driving distance, length of stay for index hospitalization, discharging service (general internal medicine vs subspecialty), postdischarge disposition (home, home with home care services such as nursing or physical therapy, or facility), the number of PDFVs scheduled per index hospitalization, PDFV specialty type (oncologic subspecialty, nononcologic medical subspecialty, nononcologic surgical subspecialty, primary care, or other specialty), PDFV season, and PDFV lead time (the number of days between the discharge date and PDFV). We consolidated oncologic specialties into 1 group given the integrated nature of our healthcare system’s comprehensive cancer center. “Other” PDFV specialty subtypes are described in Supplementary Table 2. Driving distances between patient postal codes and our hospital were calculated using Excel VBA Master (Salt Lake City, Utah) and were subsequently categorized into patient-level quartiles for further analysis. For cancelled PDFVs, we collected dates of cancellation relative to the date of the appointment itself.

Study Outcomes

The primary study outcome was PDFV attendance. Each PDFV’s status was categorized by outpatient clinic staff as attended, cancelled, or NS. For cancelled appointments, cancellation dates and reasons (if entered by clinic representatives) were collected. In keeping with prior studies investigating outpatient nonattendance,we calculated collective NS/SDC rates for the variables listed above.^17,22-25 We additionally calculated NS/SDC and attendance-as-scheduled rates stratified by the number of PDFVs per patient to assess for a “high-utilizer” effect with regard to PDFV attendance.

Statistical Analysis

We used multivariable mixed-effects regression with a logit link to assess associations between age, gender, race, insurance, driving distance quartile, length of stay, discharging service, postdischarge disposition, the number of PDFVs per hospitalization, PDFV specialty type, PDFV season, PDFV lead time, and our NS/SDC outcome. The mixed-effects approach was used to account for correlation structures induced by patients who had multiple visits and for patients with multiple hospitalizations. Specifically, our model specified 2 levels of nesting (PDFVs nested within each hospitalization, which were nested within each patient) to obtain appropriate standard error estimates for our adjusted odds ratios (ORs). Correlation matrices and multivariable variance inflation factors were used to assess collinearity among the predictor variables. These assessments did not indicate strong collinearity; hence, all predictors were included in the model. Only driving distance had a small amount of missing data (0.18% of driving distances were unavailable), so multiple imputation was not undertaken. Analyses were performed using R version 3.3.1 (R Foundation for Statistical Computing, Vienna, Austria).

Baseline Characteristics

During the 1-year study period, there were 11,829 discrete hospitalizations in medical units at our hospital. Of these hospitalizations, 6136 (52%) had at least 1 UPHS-affiliated PDFV meeting our inclusion and exclusion criteria, as detailed in the Figure. Across these hospitalizations, 9258 PDFVs were scheduled on behalf of 4653 patients. Demographic characteristics for these patients, hospitalizations, and visits are detailed in Table 1. The median age of patients in our cohort was 61 years old (interquartile range [IQR] 49-70, range 18-101). The median driving distance was 17 miles (IQR 4.3-38.8, range 0-2891). For hospitalizations, the median length of stay was 5 days (IQR 3-10, range 0-97). The median PDFV lead time, which is defined as the number of days between discharge and PDFV, was 12 days (IQR 6-23, range 1-60). Overall, 41% of patients (n = 1927) attended all of their PDFVs as scheduled; Supplementary Figure 1 lists patient-level PDFV attendance-as-scheduled percentages in more detail.

Incidence of NSs and SDCs

Twenty-five percent of PDFVs (n = 2303) were ultimately NS/SDCs; this included 1658 NSs (18% of all appointments) and 645 SDCs (7% of all appointments). Fifty-two percent of PDFVs (n = 4847) were kept as scheduled, while 23% (n = 2108) were cancelled before the day of the visit. Of the 2558 cancellations with valid cancellation dates, 49% (n = 1252) were cancelled 2 or fewer days beforehand, as shown in Supplementary Figure 2.

When comparing PDFV characteristics themselves, oncologic visits had the lowest NS/SDC incidence of any group analyzed in our study. This may be related to the inherent life-altering nature of a cancer diagnosis or our cancer center’s use of patient navigators.^23,30 In contrast, primary care clinics suffered from NS/SDC rates approaching 40%, which is a concerning finding given the importance of primary care coordination in the posthospitalization period.^9,31 Why are primary care appointments so commonly missed? Some studies suggest that forgetting about a primary care appointment is a leading reason.^15,32,33 For PDFVs, this phenomenon may be augmented because the visits are not scheduled by patients themselves. Additionally, patients may paradoxically undervalue the benefit of an all-encompassing primary care visit, compared to a PDFV focused on a specific problem, (eg, a cardiology follow-up appointment for a patient with congestive heart failure). In particular, patients with limited health literacy may potentially undervalue the capabilities of their primary care clinics.^34,35

The low absolute number of primary care PDFVs (only 8% of all visits) scheduled for patients at our hospital was an unexpected finding. This low percentage is likely a function of the patient population hospitalized at our large, urban quaternary-care facility. First, an unknown number of patients may have had PDFVs manually scheduled with primary care providers external to our health system; these PDFVs were not captured within our study. Second, 71% of the hospitalizations in our study occurred in subspecialty services, for which specific primary care follow-up may not be as urgent. Supporting this fact, further analysis of the 6136 hospitalizations in our study (data not shown) revealed that 28% of the hospitalizations in general internal medicine were scheduled with at least 1 primary care PDFV as opposed to only 5% of subspecialty-service hospitalizations.

In contrast to several previous studies of outpatient nonattendance,we did not find that visits scheduled for time points further in the future were more likely to be missed.^{14,24,25,36,37} Unlike other appointments, it may be that PDFV lead time does not affect attendance because of the unique manner in which PDFV times are scheduled and conveyed to patients. Unlike other appointments, patients do not schedule PDFVs themselves but instead learn about their PDFV dates as part of a large set of discharge instructions. This practice may result in poor recall of PDFV dates in recently hospitalized patients³⁸, regardless of the lead time between discharge and the visit itself.

Supplementary Table 1 details a 51% NS/SDC rate for the small number of PDFVs (n = 65) that were excluded a priori from our analysis because of general ineligibility for UPHS outpatient care. We specifically chose to exclude this population because of the infrequent and irregular process by which these PDFVs were authorized on a case-by-case basis, typically via active engagement by our hospital’s social work department. We did not study this population further but postulate that the 51% NS/SDC rate may reflect other social determinants of health that contribute to appointment nonadherence in a predominantly uninsured population.

Beyond their effect on patient outcomes, improving PDFV-related processes has the potential to boost both inpatient and outpatient provider satisfaction. From the standpoint of frontline inpatient providers (often resident physicians), calling outpatient clinics to request PDFVs is viewed as 1 of the top 5 administrative tasks that interfere with house staff education.³⁹ Future interventions that involve patients in the PDFV scheduling process may improve inpatient workflow while simultaneously engaging patients in their own care. For example, asking clinic representatives to directly schedule PDFVs with hospitalized patients, either by phone or in person, has been shown in pilot studies to improve PDFV attendance and decrease readmissions.^40-42 Conversely, NS/SDC visits harm outpatient provider productivity and decrease provider availability for other patients.^13,17,43 Strategies to mitigate the impact of unfilled appointment slots (eg, deliberately overbooking time slots in advance) carry their own risks, including provider burnout.⁴⁴ As such, preventing NSs may be superior to curing their adverse impacts. Many such strategies exist in the ambulatory setting,^13,43,45 for example, better communication with patients through texting or goal-directed, personalized phone reminders.^46-48Our study methodology has several limitations. Most importantly, we were unable to measure PDFVs made with providers unaffiliated with UPHS. As previously noted, our low proportion of primary care PDFVs may specifically reflect patients with primary care providers outside of our health system. Similarly, our low percentage of Medicaid patients receiving PDFVs may be related to follow-up visits with nonaffiliated community health centers. We were unable to measure patient acuity and health literacy as potential predictors of NS/SDC rates. Driving distances were calculated from patient postal codes to our hospital, not to individual outpatient clinics. However, the majority of our hospital-affiliated clinics are located adjacent to our hospital; additionally, we grouped driving distances into quartiles for our analysis. We had initially attempted to differentiate between clinic-initiated and patient-initiated cancellations, but unfortunately, we found that the data were too unreliable to be used for further analysis (outlined in Supplementary Table 3). Lastly, because we studied patients in medical units at a single large, urban, academic center, our results are not generalizable to other settings (eg, community hospitals, hospitals with smaller networks of outpatient providers, or patients being discharged from surgical services or observation units).

Given national efforts to enhance postdischarge transitions of care, we aimed to analyze attendance at provider-scheduled PDFV appointments. Our finding that 25% of PDFVs resulted in NS/SDCs raises both questions and opportunities for inpatient and outpatient providers. Further research is needed to understand why so many patients miss their PDFVs, and we should work as a field to develop creative solutions to improve PDFV scheduling and attendance.

Acknowledgments

The authors acknowledge Marie Synnestvedt, PhD, and Manik Chhabra, MD, for their assistance with data gathering and statistical analysis. They also acknowledge Allison DeKosky, MD, Michael Serpa, BS, Michael McFall, and Scott Schlegel, MBA, for their assistance with researching this topic. They did not receive external compensation for their assistance outside of their usual salary support.

DISCLOSURE

Nothing to report.

Given growing incentives to reduce readmission rates, predischarge checklists and bundles have recommended that inpatient providers schedule postdischarge follow-up visits (PDFVs) for their hospitalized patients.^1-4 PDFVs have been linked to lower readmission rates in patients with chronic conditions, including congestive heart failure, psychiatric illnesses, and chronic obstructive pulmonary disease.^5-8 In contrast, the impact of PDFVs on readmissions in hospitalized general medicine populations has been mixed.^9-12 Beyond the presence or absence of PDFVs, it may be a patient’s inability to keep scheduled PDFVs that contributes more strongly to preventable readmissions.¹¹

This challenge, dealing with the 12% to 37% of patients who miss their visits (“no-shows”), is not new.^13-17 In high-risk patient populations, such as those with substance abuse, diabetes, or human immunodeficiency virus, no-shows (NSs) have been linked to poorer short-term and long-term clinical outcomes.^16,18-20 Additionally, NSs pose a challenge for outpatient clinics and the healthcare system at large. The financial cost of NSs ranges from approximately $200 per patient in 2 analyses to $7 million in cumulative lost revenue per year at 1 large academic health system.^13,17,21 As such, increasing attendance at PDFVs is a potential target for improving both patient outcomes and clinic productivity.

Most prior PDFV research has focused on readmission risk rather than PDFV attendance as the primary outcome.^5-12 However, given the patient-oriented benefits of attending PDFVs and the clinic-oriented benefits of avoiding vacant time slots, NS PDFVs represent an important missed opportunity for our healthcare delivery system. To our knowledge, risk factors for PDFV nonattendance have not yet been systematically studied. The aim of our study was to analyze PDFV nonattendance, particularly NSs and same-day cancellations (SDCs), for hospitalizations and clinics within our healthcare system.

Study Design

We conducted an observational cohort study of adult patients from 10 medical units at the Hospital of the University of Pennsylvania (a 789-bed quaternary-care hospital within an urban, academic medical system) who were scheduled with at least 1 PDFV. Specifically, the patients included in our analysis were hospitalized on general internal medicine services or medical subspecialty services with discharge dates between April 1, 2014, and March 31, 2015. Hospitalizations included in our study had at least 1 PDFV scheduled with an outpatient provider affiliated with the University of Pennsylvania Health System (UPHS). PDFVs scheduled with unaffiliated providers were not examined.

Each PDFV was requested by a patient’s inpatient care team. Once the care team had determined that a PDFV was clinically warranted, a member of the team (generally a resident, advanced practice provider, medical student, or designee) either called the UPHS clinic to schedule an appointment time or e-mailed the outpatient UPHS provider directly to facilitate a more urgent PDFV appointment time. Once a PDFV time was confirmed, PDFV details (ie, date, time, location, and phone number) were electronically entered into the patient’s discharge instructions by the inpatient care team. At the time of discharge, nurses reviewed these instructions with their patients. All patients left the hospital with a physical copy of these instructions. As part of routine care at our institution, patients then received automated telephone reminders from their UPHS-affiliated outpatient clinic 48 hours prior to each PDFV.

Data Collection

Our study was determined to meet criteria for quality improvement by the University of Pennsylvania’s Institutional Review Board. We used our healthcare system’s integrated electronic medical record system to track the dates of initial PDFV requests, the dates of hospitalization, and actual PDFV dates. PDFVs were included if the appointment request was made while a patient was hospitalized, including the day of discharge. Our study methodology only allowed us to investigate PDFVs scheduled with UPHS outpatient providers. We did not review discharge instructions or survey non-UPHS clinics to quantify visits scheduled with other providers, for example, community health centers or external private practices.

Exclusion criteria included the following: (1) office visits with nonproviders, for example, scheduled diagnostic procedures or pharmacist appointments for warfarin dosing; (2) visits cancelled by inpatient providers prior to discharge; (3) visits for patients not otherwise eligible for UPHS outpatient care because of insurance reasons; and (4) visits scheduled for dates after a patient’s death. Our motivation for the third exclusion criterion was the infrequent and irregular process by which PDFVs were authorized for these patients. These patients and their characteristics are described in Supplementary Table 1 in more detail.

For each PDFV, we recorded age, gender, race, insurance status, driving distance, length of stay for index hospitalization, discharging service (general internal medicine vs subspecialty), postdischarge disposition (home, home with home care services such as nursing or physical therapy, or facility), the number of PDFVs scheduled per index hospitalization, PDFV specialty type (oncologic subspecialty, nononcologic medical subspecialty, nononcologic surgical subspecialty, primary care, or other specialty), PDFV season, and PDFV lead time (the number of days between the discharge date and PDFV). We consolidated oncologic specialties into 1 group given the integrated nature of our healthcare system’s comprehensive cancer center. “Other” PDFV specialty subtypes are described in Supplementary Table 2. Driving distances between patient postal codes and our hospital were calculated using Excel VBA Master (Salt Lake City, Utah) and were subsequently categorized into patient-level quartiles for further analysis. For cancelled PDFVs, we collected dates of cancellation relative to the date of the appointment itself.

Study Outcomes

The primary study outcome was PDFV attendance. Each PDFV’s status was categorized by outpatient clinic staff as attended, cancelled, or NS. For cancelled appointments, cancellation dates and reasons (if entered by clinic representatives) were collected. In keeping with prior studies investigating outpatient nonattendance,we calculated collective NS/SDC rates for the variables listed above.^17,22-25 We additionally calculated NS/SDC and attendance-as-scheduled rates stratified by the number of PDFVs per patient to assess for a “high-utilizer” effect with regard to PDFV attendance.

Statistical Analysis

We used multivariable mixed-effects regression with a logit link to assess associations between age, gender, race, insurance, driving distance quartile, length of stay, discharging service, postdischarge disposition, the number of PDFVs per hospitalization, PDFV specialty type, PDFV season, PDFV lead time, and our NS/SDC outcome. The mixed-effects approach was used to account for correlation structures induced by patients who had multiple visits and for patients with multiple hospitalizations. Specifically, our model specified 2 levels of nesting (PDFVs nested within each hospitalization, which were nested within each patient) to obtain appropriate standard error estimates for our adjusted odds ratios (ORs). Correlation matrices and multivariable variance inflation factors were used to assess collinearity among the predictor variables. These assessments did not indicate strong collinearity; hence, all predictors were included in the model. Only driving distance had a small amount of missing data (0.18% of driving distances were unavailable), so multiple imputation was not undertaken. Analyses were performed using R version 3.3.1 (R Foundation for Statistical Computing, Vienna, Austria).

Baseline Characteristics

During the 1-year study period, there were 11,829 discrete hospitalizations in medical units at our hospital. Of these hospitalizations, 6136 (52%) had at least 1 UPHS-affiliated PDFV meeting our inclusion and exclusion criteria, as detailed in the Figure. Across these hospitalizations, 9258 PDFVs were scheduled on behalf of 4653 patients. Demographic characteristics for these patients, hospitalizations, and visits are detailed in Table 1. The median age of patients in our cohort was 61 years old (interquartile range [IQR] 49-70, range 18-101). The median driving distance was 17 miles (IQR 4.3-38.8, range 0-2891). For hospitalizations, the median length of stay was 5 days (IQR 3-10, range 0-97). The median PDFV lead time, which is defined as the number of days between discharge and PDFV, was 12 days (IQR 6-23, range 1-60). Overall, 41% of patients (n = 1927) attended all of their PDFVs as scheduled; Supplementary Figure 1 lists patient-level PDFV attendance-as-scheduled percentages in more detail.

Incidence of NSs and SDCs

Twenty-five percent of PDFVs (n = 2303) were ultimately NS/SDCs; this included 1658 NSs (18% of all appointments) and 645 SDCs (7% of all appointments). Fifty-two percent of PDFVs (n = 4847) were kept as scheduled, while 23% (n = 2108) were cancelled before the day of the visit. Of the 2558 cancellations with valid cancellation dates, 49% (n = 1252) were cancelled 2 or fewer days beforehand, as shown in Supplementary Figure 2.

When comparing PDFV characteristics themselves, oncologic visits had the lowest NS/SDC incidence of any group analyzed in our study. This may be related to the inherent life-altering nature of a cancer diagnosis or our cancer center’s use of patient navigators.^23,30 In contrast, primary care clinics suffered from NS/SDC rates approaching 40%, which is a concerning finding given the importance of primary care coordination in the posthospitalization period.^9,31 Why are primary care appointments so commonly missed? Some studies suggest that forgetting about a primary care appointment is a leading reason.^15,32,33 For PDFVs, this phenomenon may be augmented because the visits are not scheduled by patients themselves. Additionally, patients may paradoxically undervalue the benefit of an all-encompassing primary care visit, compared to a PDFV focused on a specific problem, (eg, a cardiology follow-up appointment for a patient with congestive heart failure). In particular, patients with limited health literacy may potentially undervalue the capabilities of their primary care clinics.^34,35

The low absolute number of primary care PDFVs (only 8% of all visits) scheduled for patients at our hospital was an unexpected finding. This low percentage is likely a function of the patient population hospitalized at our large, urban quaternary-care facility. First, an unknown number of patients may have had PDFVs manually scheduled with primary care providers external to our health system; these PDFVs were not captured within our study. Second, 71% of the hospitalizations in our study occurred in subspecialty services, for which specific primary care follow-up may not be as urgent. Supporting this fact, further analysis of the 6136 hospitalizations in our study (data not shown) revealed that 28% of the hospitalizations in general internal medicine were scheduled with at least 1 primary care PDFV as opposed to only 5% of subspecialty-service hospitalizations.

In contrast to several previous studies of outpatient nonattendance,we did not find that visits scheduled for time points further in the future were more likely to be missed.^{14,24,25,36,37} Unlike other appointments, it may be that PDFV lead time does not affect attendance because of the unique manner in which PDFV times are scheduled and conveyed to patients. Unlike other appointments, patients do not schedule PDFVs themselves but instead learn about their PDFV dates as part of a large set of discharge instructions. This practice may result in poor recall of PDFV dates in recently hospitalized patients³⁸, regardless of the lead time between discharge and the visit itself.

Supplementary Table 1 details a 51% NS/SDC rate for the small number of PDFVs (n = 65) that were excluded a priori from our analysis because of general ineligibility for UPHS outpatient care. We specifically chose to exclude this population because of the infrequent and irregular process by which these PDFVs were authorized on a case-by-case basis, typically via active engagement by our hospital’s social work department. We did not study this population further but postulate that the 51% NS/SDC rate may reflect other social determinants of health that contribute to appointment nonadherence in a predominantly uninsured population.

Beyond their effect on patient outcomes, improving PDFV-related processes has the potential to boost both inpatient and outpatient provider satisfaction. From the standpoint of frontline inpatient providers (often resident physicians), calling outpatient clinics to request PDFVs is viewed as 1 of the top 5 administrative tasks that interfere with house staff education.³⁹ Future interventions that involve patients in the PDFV scheduling process may improve inpatient workflow while simultaneously engaging patients in their own care. For example, asking clinic representatives to directly schedule PDFVs with hospitalized patients, either by phone or in person, has been shown in pilot studies to improve PDFV attendance and decrease readmissions.^40-42 Conversely, NS/SDC visits harm outpatient provider productivity and decrease provider availability for other patients.^13,17,43 Strategies to mitigate the impact of unfilled appointment slots (eg, deliberately overbooking time slots in advance) carry their own risks, including provider burnout.⁴⁴ As such, preventing NSs may be superior to curing their adverse impacts. Many such strategies exist in the ambulatory setting,^13,43,45 for example, better communication with patients through texting or goal-directed, personalized phone reminders.^46-48Our study methodology has several limitations. Most importantly, we were unable to measure PDFVs made with providers unaffiliated with UPHS. As previously noted, our low proportion of primary care PDFVs may specifically reflect patients with primary care providers outside of our health system. Similarly, our low percentage of Medicaid patients receiving PDFVs may be related to follow-up visits with nonaffiliated community health centers. We were unable to measure patient acuity and health literacy as potential predictors of NS/SDC rates. Driving distances were calculated from patient postal codes to our hospital, not to individual outpatient clinics. However, the majority of our hospital-affiliated clinics are located adjacent to our hospital; additionally, we grouped driving distances into quartiles for our analysis. We had initially attempted to differentiate between clinic-initiated and patient-initiated cancellations, but unfortunately, we found that the data were too unreliable to be used for further analysis (outlined in Supplementary Table 3). Lastly, because we studied patients in medical units at a single large, urban, academic center, our results are not generalizable to other settings (eg, community hospitals, hospitals with smaller networks of outpatient providers, or patients being discharged from surgical services or observation units).

Given national efforts to enhance postdischarge transitions of care, we aimed to analyze attendance at provider-scheduled PDFV appointments. Our finding that 25% of PDFVs resulted in NS/SDCs raises both questions and opportunities for inpatient and outpatient providers. Further research is needed to understand why so many patients miss their PDFVs, and we should work as a field to develop creative solutions to improve PDFV scheduling and attendance.

Acknowledgments

The authors acknowledge Marie Synnestvedt, PhD, and Manik Chhabra, MD, for their assistance with data gathering and statistical analysis. They also acknowledge Allison DeKosky, MD, Michael Serpa, BS, Michael McFall, and Scott Schlegel, MBA, for their assistance with researching this topic. They did not receive external compensation for their assistance outside of their usual salary support.

DISCLOSURE

Nothing to report.

Given growing incentives to reduce readmission rates, predischarge checklists and bundles have recommended that inpatient providers schedule postdischarge follow-up visits (PDFVs) for their hospitalized patients.^1-4 PDFVs have been linked to lower readmission rates in patients with chronic conditions, including congestive heart failure, psychiatric illnesses, and chronic obstructive pulmonary disease.^5-8 In contrast, the impact of PDFVs on readmissions in hospitalized general medicine populations has been mixed.^9-12 Beyond the presence or absence of PDFVs, it may be a patient’s inability to keep scheduled PDFVs that contributes more strongly to preventable readmissions.¹¹

This challenge, dealing with the 12% to 37% of patients who miss their visits (“no-shows”), is not new.^13-17 In high-risk patient populations, such as those with substance abuse, diabetes, or human immunodeficiency virus, no-shows (NSs) have been linked to poorer short-term and long-term clinical outcomes.^16,18-20 Additionally, NSs pose a challenge for outpatient clinics and the healthcare system at large. The financial cost of NSs ranges from approximately $200 per patient in 2 analyses to $7 million in cumulative lost revenue per year at 1 large academic health system.^13,17,21 As such, increasing attendance at PDFVs is a potential target for improving both patient outcomes and clinic productivity.

Most prior PDFV research has focused on readmission risk rather than PDFV attendance as the primary outcome.^5-12 However, given the patient-oriented benefits of attending PDFVs and the clinic-oriented benefits of avoiding vacant time slots, NS PDFVs represent an important missed opportunity for our healthcare delivery system. To our knowledge, risk factors for PDFV nonattendance have not yet been systematically studied. The aim of our study was to analyze PDFV nonattendance, particularly NSs and same-day cancellations (SDCs), for hospitalizations and clinics within our healthcare system.

Study Design

We conducted an observational cohort study of adult patients from 10 medical units at the Hospital of the University of Pennsylvania (a 789-bed quaternary-care hospital within an urban, academic medical system) who were scheduled with at least 1 PDFV. Specifically, the patients included in our analysis were hospitalized on general internal medicine services or medical subspecialty services with discharge dates between April 1, 2014, and March 31, 2015. Hospitalizations included in our study had at least 1 PDFV scheduled with an outpatient provider affiliated with the University of Pennsylvania Health System (UPHS). PDFVs scheduled with unaffiliated providers were not examined.

Each PDFV was requested by a patient’s inpatient care team. Once the care team had determined that a PDFV was clinically warranted, a member of the team (generally a resident, advanced practice provider, medical student, or designee) either called the UPHS clinic to schedule an appointment time or e-mailed the outpatient UPHS provider directly to facilitate a more urgent PDFV appointment time. Once a PDFV time was confirmed, PDFV details (ie, date, time, location, and phone number) were electronically entered into the patient’s discharge instructions by the inpatient care team. At the time of discharge, nurses reviewed these instructions with their patients. All patients left the hospital with a physical copy of these instructions. As part of routine care at our institution, patients then received automated telephone reminders from their UPHS-affiliated outpatient clinic 48 hours prior to each PDFV.

Data Collection

Our study was determined to meet criteria for quality improvement by the University of Pennsylvania’s Institutional Review Board. We used our healthcare system’s integrated electronic medical record system to track the dates of initial PDFV requests, the dates of hospitalization, and actual PDFV dates. PDFVs were included if the appointment request was made while a patient was hospitalized, including the day of discharge. Our study methodology only allowed us to investigate PDFVs scheduled with UPHS outpatient providers. We did not review discharge instructions or survey non-UPHS clinics to quantify visits scheduled with other providers, for example, community health centers or external private practices.

Exclusion criteria included the following: (1) office visits with nonproviders, for example, scheduled diagnostic procedures or pharmacist appointments for warfarin dosing; (2) visits cancelled by inpatient providers prior to discharge; (3) visits for patients not otherwise eligible for UPHS outpatient care because of insurance reasons; and (4) visits scheduled for dates after a patient’s death. Our motivation for the third exclusion criterion was the infrequent and irregular process by which PDFVs were authorized for these patients. These patients and their characteristics are described in Supplementary Table 1 in more detail.

For each PDFV, we recorded age, gender, race, insurance status, driving distance, length of stay for index hospitalization, discharging service (general internal medicine vs subspecialty), postdischarge disposition (home, home with home care services such as nursing or physical therapy, or facility), the number of PDFVs scheduled per index hospitalization, PDFV specialty type (oncologic subspecialty, nononcologic medical subspecialty, nononcologic surgical subspecialty, primary care, or other specialty), PDFV season, and PDFV lead time (the number of days between the discharge date and PDFV). We consolidated oncologic specialties into 1 group given the integrated nature of our healthcare system’s comprehensive cancer center. “Other” PDFV specialty subtypes are described in Supplementary Table 2. Driving distances between patient postal codes and our hospital were calculated using Excel VBA Master (Salt Lake City, Utah) and were subsequently categorized into patient-level quartiles for further analysis. For cancelled PDFVs, we collected dates of cancellation relative to the date of the appointment itself.

Study Outcomes

The primary study outcome was PDFV attendance. Each PDFV’s status was categorized by outpatient clinic staff as attended, cancelled, or NS. For cancelled appointments, cancellation dates and reasons (if entered by clinic representatives) were collected. In keeping with prior studies investigating outpatient nonattendance,we calculated collective NS/SDC rates for the variables listed above.^17,22-25 We additionally calculated NS/SDC and attendance-as-scheduled rates stratified by the number of PDFVs per patient to assess for a “high-utilizer” effect with regard to PDFV attendance.

Statistical Analysis

We used multivariable mixed-effects regression with a logit link to assess associations between age, gender, race, insurance, driving distance quartile, length of stay, discharging service, postdischarge disposition, the number of PDFVs per hospitalization, PDFV specialty type, PDFV season, PDFV lead time, and our NS/SDC outcome. The mixed-effects approach was used to account for correlation structures induced by patients who had multiple visits and for patients with multiple hospitalizations. Specifically, our model specified 2 levels of nesting (PDFVs nested within each hospitalization, which were nested within each patient) to obtain appropriate standard error estimates for our adjusted odds ratios (ORs). Correlation matrices and multivariable variance inflation factors were used to assess collinearity among the predictor variables. These assessments did not indicate strong collinearity; hence, all predictors were included in the model. Only driving distance had a small amount of missing data (0.18% of driving distances were unavailable), so multiple imputation was not undertaken. Analyses were performed using R version 3.3.1 (R Foundation for Statistical Computing, Vienna, Austria).

Baseline Characteristics

During the 1-year study period, there were 11,829 discrete hospitalizations in medical units at our hospital. Of these hospitalizations, 6136 (52%) had at least 1 UPHS-affiliated PDFV meeting our inclusion and exclusion criteria, as detailed in the Figure. Across these hospitalizations, 9258 PDFVs were scheduled on behalf of 4653 patients. Demographic characteristics for these patients, hospitalizations, and visits are detailed in Table 1. The median age of patients in our cohort was 61 years old (interquartile range [IQR] 49-70, range 18-101). The median driving distance was 17 miles (IQR 4.3-38.8, range 0-2891). For hospitalizations, the median length of stay was 5 days (IQR 3-10, range 0-97). The median PDFV lead time, which is defined as the number of days between discharge and PDFV, was 12 days (IQR 6-23, range 1-60). Overall, 41% of patients (n = 1927) attended all of their PDFVs as scheduled; Supplementary Figure 1 lists patient-level PDFV attendance-as-scheduled percentages in more detail.

Incidence of NSs and SDCs

Twenty-five percent of PDFVs (n = 2303) were ultimately NS/SDCs; this included 1658 NSs (18% of all appointments) and 645 SDCs (7% of all appointments). Fifty-two percent of PDFVs (n = 4847) were kept as scheduled, while 23% (n = 2108) were cancelled before the day of the visit. Of the 2558 cancellations with valid cancellation dates, 49% (n = 1252) were cancelled 2 or fewer days beforehand, as shown in Supplementary Figure 2.

When comparing PDFV characteristics themselves, oncologic visits had the lowest NS/SDC incidence of any group analyzed in our study. This may be related to the inherent life-altering nature of a cancer diagnosis or our cancer center’s use of patient navigators.^23,30 In contrast, primary care clinics suffered from NS/SDC rates approaching 40%, which is a concerning finding given the importance of primary care coordination in the posthospitalization period.^9,31 Why are primary care appointments so commonly missed? Some studies suggest that forgetting about a primary care appointment is a leading reason.^15,32,33 For PDFVs, this phenomenon may be augmented because the visits are not scheduled by patients themselves. Additionally, patients may paradoxically undervalue the benefit of an all-encompassing primary care visit, compared to a PDFV focused on a specific problem, (eg, a cardiology follow-up appointment for a patient with congestive heart failure). In particular, patients with limited health literacy may potentially undervalue the capabilities of their primary care clinics.^34,35

The low absolute number of primary care PDFVs (only 8% of all visits) scheduled for patients at our hospital was an unexpected finding. This low percentage is likely a function of the patient population hospitalized at our large, urban quaternary-care facility. First, an unknown number of patients may have had PDFVs manually scheduled with primary care providers external to our health system; these PDFVs were not captured within our study. Second, 71% of the hospitalizations in our study occurred in subspecialty services, for which specific primary care follow-up may not be as urgent. Supporting this fact, further analysis of the 6136 hospitalizations in our study (data not shown) revealed that 28% of the hospitalizations in general internal medicine were scheduled with at least 1 primary care PDFV as opposed to only 5% of subspecialty-service hospitalizations.

In contrast to several previous studies of outpatient nonattendance,we did not find that visits scheduled for time points further in the future were more likely to be missed.^{14,24,25,36,37} Unlike other appointments, it may be that PDFV lead time does not affect attendance because of the unique manner in which PDFV times are scheduled and conveyed to patients. Unlike other appointments, patients do not schedule PDFVs themselves but instead learn about their PDFV dates as part of a large set of discharge instructions. This practice may result in poor recall of PDFV dates in recently hospitalized patients³⁸, regardless of the lead time between discharge and the visit itself.

Supplementary Table 1 details a 51% NS/SDC rate for the small number of PDFVs (n = 65) that were excluded a priori from our analysis because of general ineligibility for UPHS outpatient care. We specifically chose to exclude this population because of the infrequent and irregular process by which these PDFVs were authorized on a case-by-case basis, typically via active engagement by our hospital’s social work department. We did not study this population further but postulate that the 51% NS/SDC rate may reflect other social determinants of health that contribute to appointment nonadherence in a predominantly uninsured population.

Beyond their effect on patient outcomes, improving PDFV-related processes has the potential to boost both inpatient and outpatient provider satisfaction. From the standpoint of frontline inpatient providers (often resident physicians), calling outpatient clinics to request PDFVs is viewed as 1 of the top 5 administrative tasks that interfere with house staff education.³⁹ Future interventions that involve patients in the PDFV scheduling process may improve inpatient workflow while simultaneously engaging patients in their own care. For example, asking clinic representatives to directly schedule PDFVs with hospitalized patients, either by phone or in person, has been shown in pilot studies to improve PDFV attendance and decrease readmissions.^40-42 Conversely, NS/SDC visits harm outpatient provider productivity and decrease provider availability for other patients.^13,17,43 Strategies to mitigate the impact of unfilled appointment slots (eg, deliberately overbooking time slots in advance) carry their own risks, including provider burnout.⁴⁴ As such, preventing NSs may be superior to curing their adverse impacts. Many such strategies exist in the ambulatory setting,^13,43,45 for example, better communication with patients through texting or goal-directed, personalized phone reminders.^46-48Our study methodology has several limitations. Most importantly, we were unable to measure PDFVs made with providers unaffiliated with UPHS. As previously noted, our low proportion of primary care PDFVs may specifically reflect patients with primary care providers outside of our health system. Similarly, our low percentage of Medicaid patients receiving PDFVs may be related to follow-up visits with nonaffiliated community health centers. We were unable to measure patient acuity and health literacy as potential predictors of NS/SDC rates. Driving distances were calculated from patient postal codes to our hospital, not to individual outpatient clinics. However, the majority of our hospital-affiliated clinics are located adjacent to our hospital; additionally, we grouped driving distances into quartiles for our analysis. We had initially attempted to differentiate between clinic-initiated and patient-initiated cancellations, but unfortunately, we found that the data were too unreliable to be used for further analysis (outlined in Supplementary Table 3). Lastly, because we studied patients in medical units at a single large, urban, academic center, our results are not generalizable to other settings (eg, community hospitals, hospitals with smaller networks of outpatient providers, or patients being discharged from surgical services or observation units).

Given national efforts to enhance postdischarge transitions of care, we aimed to analyze attendance at provider-scheduled PDFV appointments. Our finding that 25% of PDFVs resulted in NS/SDCs raises both questions and opportunities for inpatient and outpatient providers. Further research is needed to understand why so many patients miss their PDFVs, and we should work as a field to develop creative solutions to improve PDFV scheduling and attendance.

Acknowledgments

The authors acknowledge Marie Synnestvedt, PhD, and Manik Chhabra, MD, for their assistance with data gathering and statistical analysis. They also acknowledge Allison DeKosky, MD, Michael Serpa, BS, Michael McFall, and Scott Schlegel, MBA, for their assistance with researching this topic. They did not receive external compensation for their assistance outside of their usual salary support.

DISCLOSURE

Nothing to report.

INTRODUCTION

According to Centers for Medicare & Medicaid Services (CMS), approximately 1 in 5 patients discharged from a hospital will be readmitted within 30 days.¹ The Hospital Readmission Reduction Program (HRRP) is designed to reduce readmission by withholding up to 3% of all Medicare reimbursement from hospitals with “excess” readmissions; however, absent from the HRRP is adjustment for socioeconomic status (SES), which CMS holds may undermine incentives to reduce health disparities and institutionalize lower standards for hospitals serving disadvantaged populations.²

Lack of SES adjustment has been criticized by those who point to evidence highlighting postdischarge environment and patient SES as drivers of readmission and suggest hospitals that serve low SES individuals will bear a disproportionate share of penalties.^3-6 Single-center,^3,7,8 regional,^9,10 and nationwide^6,11 studies highlight census tract level socioeconomic variables as predictive of readmission. Single-center studies, robust in controlling for confounders, including staffing, training, electronic medical record utilization, and transitional care processes, do not allow comparisons between hospitals, limiting utility in HRRP evaluation. Multicenter cohorts, on the other hand, allow for comparisons between high and low penalty hospitals, pioneered by Joynt et al¹² after the first round of HRRP penalties; yet this technique may not account for confounding caused by extensive demographic, socioeconomic, and hospital characteristic heterogeneity inherent in a national cohort. Analysis of the 2015 HRRP penalty data by Sjoding et al.⁶ revealed higher chronic obstructive pulmonary disease (COPD) readmission rates in the Mid-Atlantic, Midwest, and South relative to other regions; however, the magnitude of small-area variation and its relationship to population SES have yet to be characterized.

Therefore, we conducted a matched case-control design, whereby each maximum penalty hospital was matched to a nonpenalty hospital using key hospital characteristics. We then used geographic matching to isolate SES factors predictive of readmission within specific geographies in an effort to control for regional population differences. We hypothesized that, among both matched and localized hospital pairs, the disparities in population SES are the most significant predictors of a maximum penalty. Now in the 3rd year of the HRRP with approximately 75% of eligible hospitals to receive penalties worth an estimated $428 million in the 2015 fiscal year,¹³ we offer a small-area analysis of bipolar extremes to inform debate surrounding the HRRP with evidence regarding the causes and implications of readmission penalties.

Study Design and Sample

This study relies on a case-control design. The cases were defined as US hospitals to receive the maximum 3% HRRP penalty in fiscal year 2015. Controls were drawn from the cohort of hospitals potentially subject to HRRP penalties that received no readmission penalty in the 2015 fiscal year with at least 1 admission for any of the following conditions: heart failure (HF), acute myocardial infarction (AMI), pneumonia (PN), total knee arthroscopy or total hip arthroscopy (THA/TKA), or chronic obstructive pulmonary disease (COPD).

Data Sources

Penalty data were drawn from the 2015 master penalty file,¹⁴ which were accessed via CMS.gov. County-level demographic and socioeconomic data were gathered from the 2015 American Community Survey (ACS), a subsidiary of the US Census. Data on hospital characteristics, capacity, and regional healthcare utilization were drawn from 2012 Dartmouth Atlas,¹⁵ 2012 Medicare Cost Report,¹⁶ 2012 American Hospital Association Hospital Statistics Database, and 2014 Hospital Care Downloadable Database.

Hospital-level CMS data were linked to the master 2015 penalty file. Dartmouth Atlas data were subsequently linked to the file using the Dartmouth Atlas “Hospital to HSA/HRR Crosswalk” file (accessed via DartmouthAtlas.org.) Each hospital was assigned the profile of the hospital service area (HSA) and hospital referral region (HRR) in which it is located. An HSA is a geographic region defined by hospital admissions; the majority, but not entirety, of residents of a given HSA utilize the corresponding hospital. Similarly, an HRR is a geographic region defined by referrals for major cardiovascular and neurosurgery procedures. County-level socioeconomic data were linked to the dataset by county name; thus, hospital socioeconomic profiles are based on the county in which they are located.

Case-Control Matching

In the primary analysis, coarsened exact matching (CEM) matched controls to cases by potential confounding hospital characteristics, including the following: ownership, number of beds, case mix index (measure of acuity), ambulatory care visit rates within 14 days of discharge, and total number of penalty-eligible cases, including HF, AMI, COPD, PN, and THA/TKA.

In the secondary analysis, hospitals were geocoded by zip code. Geographic Information Systems mapping software (ESRI ArcGIS, Redlands, CA) relied upon Euclidean allocation distance spatial analysis^17,18 to match each maximum-penalty hospital to the nearest nonpenalty hospital. Each case was matched to a distinct control; duplicate controls were replaced with the nearest unmatched no-penalty hospital.

Statistical Analysis

Univariate analyses utilized unpaired Student t tests (primary analysis) and paired Student t tests (secondary analysis). The CEM algorithm matches by strata rather than pairs, precluding paired Student t tests in the primary analysis. Statistical analyses were conducted using STATA (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX).

Maximum Penalty and Nonpenalty Hospital Matching

Of 3383 hospitals eligible for the HRRP, 39 received the maximum penalty and 770 received no penalty. Thirty-eight control hospitals were identified using CEM algorithm; 1 maximum-penalty hospital could not be matched and was excluded from primary analy

Hospital Characteristics

Case and control profiles are presented in Table 1. Cases and controls were matched by characteristics which may impact readmission rates (Table 1). CEM yielded cohorts similar across a spectrum of metrics, and identical in terms of matching criteria including ownership, beds (quartile), case mix index (above median), ambulatory care visit within 14 days of discharge (above median), and total number of penalty-eligible cases (above median). Relative to no-penalty hospitals, maximum-penalty hospitals were more likely rural (n = 9 vs n = 2, P = 0.022) and have a less profitable operating margin (0.1% vs 6.9%), and location within HSAs with higher age, sex, and race adjusted hospital-wide mortality rate (5.3% vs 4.9%, P = 0.009) and higher rates of discharge for ambulatory care sensitive conditions (108 vs 63 discharges per 1000 Medicare enrollees).

Demographic and Socioeconomic Characteristics

As presented in Table 2, cases a

Secondary Analysis: Geographical Matching

Secondary analysis matched each maximum-penalty hospital to the nearest no-penalty hospital using a global information system vector analysis algorithm. As shown in the Figure, median distance between the case and the control was 42.5 miles (interquartile range: 25th percentile, 15.4 miles; 75th percentile, 98.4 miles). Seventeen pairs (44%) were in the same HRR, 6 of which were in the same HSA. Seven pairs (18%) were within the same county

Secondary Analysis: Economic and Demographic Profiles of Geographically Matched Pairs

Demographic and socioeconomic profiles are presented in Table 3. The cases and controls are in counties with similar age, sex, and ethnicity distributions. Relative to no-penalty hospitals, maximum-penalty hospitals are in counties with lower socioeconomic profiles, including increased rates of poverty (15.6% vs 19.2%, P = 0.007) and lower rates of high school (86.4% vs 82.1%, P = 0.005) or college graduation (22.3% vs 28.1%, P = 0.002). Seven pairs were in the same county; a sensitivity analysis excluding these hospitals revealed similarly lower SES profile in cases relative to controls (Supplementary Table 1).

Our analysis reveals that county-level socioeconomic profiles are predictors of maximum HRRP penalties. Specifically, after matching cases and controls on 5 hospital characteristics that may influence readmission, maximum-penalty hospitals were more likely to be in rural counties with higher rates of poverty and lower rates of education relative to no-penalty hospitals. We observed no difference between cases and controls with respect to age, sex, or ethnicity.

Our study complement

Maximum Penalties as a Function of Population Health

The Dartmouth Atlas of Healthcare measures health outcomes, which are regionally aggregated among local hospitals by either HSA or HRR; see Methods. Such small-area aggregation does not precisely reflect outcomes from a specific hospital, but rather it describes the health status of localities. Disparities in health outcomes exist between maximum-penalty and no-penalty HSAs. Complication rates were slightly higher in maximum penalty HSAs, consistent with studies highlighting complications as drivers of surgical readmissions.^22,23 Moreover, hospital-wide mortality rates were higher in maximum-penalty areas relative to nonpenalty HSAs (5.3 vs 4.9, P = 0.009).

Using national data, Krumholz et al. found no correlation between rates of readmission and mortality for HF, AMI, and PN²⁴, which is a phenomenon acknowledged by the Medicare Payment Advisory Commission (MedPac) in a 2013 report titled, “Refining the hospital readmission reduction program.”²⁵ In large national studies, others have shown low SES to be associated with elevated readmission but not mortality.^10,11 In contrast, we restricted our analysis to matched cohorts and are, to our knowledge, the first to present evidence of an association between readmission and hospital-wide mortality adjusted for age, sex, and ethnicity.

Our results suggest maximum readmission penalties are a function of population health and public health capacity. The rates of ambulatory care sensitive condition (ACSC) discharges were substantially higher in HSAs of maximum penalty hospitals relative to nonpenalty hospitals (108 vs 63 per 1000 Medicare enrollees, P < 0.001). ACSC discharges have been used to measure primary care quality for 30 years, with the assumption being that admission for chronic conditions, such as HF, can be prevented with effective primary care.^26,27 Moreover, patients discharged from maximum-penalty hospitals were more likely to have an emergency room visit within 30 days of discharge (20.8% vs 18.4%, P < 0.001). Higher rates of ACSCs and postdischarge emergency department visits suggest outpatient resources in maximum-penalty service areas struggle to manage the disease burden of high-risk populations. Geography may be a contributor; maximum-penalty hospitals were more likely to be rural than no-penalty hospitals (24% vs 5%, P = 0.022).

Our findings suggest hospitals providing care to vulnerable communities (defined by low income, low education, and high rates of ambulatory sensitive discharges) are disproportionately penalized. McHugh et al. revealed high nurse staffing levels to be protective against readmission penalties²⁸, yet high penalties to low-margin hospitals may encourage reduced rather than increased staff. It may be better policy to direct resources rather than penalties to underserved communities; our findings echo others with concern about disproportionate penalties to hospitals serving low SES patients.^2,5,6,29

Secondary Analysis: Geographic Matching

Geographic matching paired each maximum-penalty hospital to the nearest no-penalty hospital in an attempt to control for unmeasured regional factors that may confound an association between socioeconomic profile and health outcomes. For example, cost of living ^{30, 31} and obesity ^32,33 vary regionally. Our study was unequipped to assess potential regional confounders; we attempted to control for them with geographical matching.

Median distance between maximum-penalty and no-penalty hospitals was 42.5 miles. Seven pairs were located within the same county, thus both case and control were assigned the same socioeconomic profile. Despite close proximity and identical SES profile in 7 of 39 pairs, maximum-penalty hospitals were in counties with lower income and lower rates of education, strengthening the association between SES and maximum readmission penalties.

Implications and Future Directions

In response to criticism surrounding the HRRP, the National Quality Forum endorsed the general concept of SES adjustment for hospital quality measures.³⁴ Subsequently, in a briefing dated March 24, 2015, MedPAC, a government agency which provides Medicare policy analysis to Congress, proposed an SES adjustment methodology of “dividing hospitals into peer groups based on their overall share of low-income Medicare patients, and then setting a benchmark readmissions target for each peer group”;³⁵ in other words, lower standards for hospitals that serve low-income populations. MedPAC’s proposal will reduce penalties to “safety net” institutions, which is progress but not a solution. Although the HRRP appears to be working, according to the US Department of Health and Human Services, readmissions fell by 150,000 between January 2012 and February 2013,³⁶ we are concerned neither the HRRP nor the MedPac revision proposal considers geographic and environmental components of readmission. The HRRP promotes national improvement in exchange for regional regression.

Fair quality measures are key to value-based reimbursement models; yet, implicit in penalties for excess readmissions is the assumed ability to calculate hospital performance targets. Benchmarks for safety, timely care, and patient satisfaction can be uniform; rates of central line infections should not be influenced by patient mix. However, 9 of the 39 maximum-penalty hospitals under the HRRP are in rural Kentucky; one could hypothesize many reasons why rural Kentucky is a hotbed for excess readmission, including the regional production of tobacco and bourbon.

The fundamental question raised by our study is whether poor performance on quality measures is a function of underperforming hospitals or a manifestation of underserved communities. Moving forward, we encourage data systems and study designs that focus research on geospatial distribution of population health within the context of social and behavioral health determinants.³⁷ Small-area studies of factors that drive health outcomes will inform rational alignment of healthcare policies and resources (including penalties and incentives) with underlying population needs.

Strengths and Weaknesses

Matching is a strength of the study. Primary analysis matched case and controls by hospital characteristics, generating cohorts similar across a spectrum of hospital metrics. Therefore, variation in readmission rates was less likely confounded by hospital characteristics. The secondary analysis was matched by geography in an effort to adjust for unmeasured, regional factors, including obesity and cost of living that may confound an association between SES and health outcomes. Geographic matching adds strength to our assertion that SES drives distinction between maximum-penalty hospitals and nonpenalty hospitals.

One weakness was the regional unit of analysis for socioeconomic and Dartmouth Atlas data, which is not a precise profile of the corresponding hospital. Each hospital was assigned a county-level socioeconomic profile. A more robust methodology would analyze patient-level SES data; this was impractical given a cohort of 78 hospitals. Regional health outcomes data limits analysis of readmission penalties as a function of hospital quality. Instead, regional data facilitated associations between readmission and population health, consistent with the aim of our study.

We analyzed 116 of 3668 hospitals eligible for the HRRP (3.2%), limiting the generalizability of our findings. Eighty-four percent of hospitals in the primary analysis have below the median number of beds, and none of them are teaching hospitals. Our analysis, restricted to maximum-penalty and no-penalty cohorts, does not address potential association between submaximal readmission penalties and socioeconomics.

Both matching techniques potentially controlled for similar SES factors and skewed our results towards null, especially in terms of race and ethnicity. Geographic matching generated 7 pairs (18%) within in the same county; both maximum-penalty and no-penalty hospitals were assigned the same socioeconomic profile, as well as 6 pairs (15%) within the same HSA, and both cases and controls had identical Dartmouth Atlas health outcomes profiles. We retained these pairs in our analysis to avoid artificially inflating SES and population health differences between cohorts.

Thirty-nine hospitals received a maximum penalty in the 3rd year of the HRRP. Relative to geographically matched no-penalty hospitals, maximum-penalty hospitals were more likely to be rural and located in counties with less educational attainment, more poverty and more poorly controlled chronic disease. In contrast to nationwide studies, a matched analysis plan revealed no difference between the cohorts in terms of race and ethnicity and provided evidence that maximum penalty hospitals had higher rates of age-, sex-, and race-adjusted hospital-wide mortality.

Our results highlight potential consequences of nationally derived benchmarks for phenomena underpinned by social, behavioral, and environmental factors and raise the question of whether maximum HRRP penalties are a consequence of underperforming hospitals or a manifestation of underserved communities. We are encouraged by MedPAC’s proposal to stratify HRRP by SES, yet recommend further small-area geographic analyses to better align quality measures, penalties, and incentives with resources and needs of distinct populations.

Acknowledgments

The authors thank William Hisey, who laid the foundation for the analysis and without whom the project would not have been possible.

DISCLOSURE

The authors certify that none of the material in this manuscript has been previously published and that none of this material is currently under consideration for publication elsewhere. This project received no funding. None of the authors on this manuscript have any commercial relationships to disclose in relation to this manuscript. All authors have reviewed and approved this manuscript and have contributed significantly to the design, conduct, and/or analysis of the research. No authors have any financial interests to disclose. No authors have any potential conflicts of interest to disclose. No authors have financial or personal relationships with any of the subject material presented in the manuscript.

INTRODUCTION

According to Centers for Medicare & Medicaid Services (CMS), approximately 1 in 5 patients discharged from a hospital will be readmitted within 30 days.¹ The Hospital Readmission Reduction Program (HRRP) is designed to reduce readmission by withholding up to 3% of all Medicare reimbursement from hospitals with “excess” readmissions; however, absent from the HRRP is adjustment for socioeconomic status (SES), which CMS holds may undermine incentives to reduce health disparities and institutionalize lower standards for hospitals serving disadvantaged populations.²

Lack of SES adjustment has been criticized by those who point to evidence highlighting postdischarge environment and patient SES as drivers of readmission and suggest hospitals that serve low SES individuals will bear a disproportionate share of penalties.^3-6 Single-center,^3,7,8 regional,^9,10 and nationwide^6,11 studies highlight census tract level socioeconomic variables as predictive of readmission. Single-center studies, robust in controlling for confounders, including staffing, training, electronic medical record utilization, and transitional care processes, do not allow comparisons between hospitals, limiting utility in HRRP evaluation. Multicenter cohorts, on the other hand, allow for comparisons between high and low penalty hospitals, pioneered by Joynt et al¹² after the first round of HRRP penalties; yet this technique may not account for confounding caused by extensive demographic, socioeconomic, and hospital characteristic heterogeneity inherent in a national cohort. Analysis of the 2015 HRRP penalty data by Sjoding et al.⁶ revealed higher chronic obstructive pulmonary disease (COPD) readmission rates in the Mid-Atlantic, Midwest, and South relative to other regions; however, the magnitude of small-area variation and its relationship to population SES have yet to be characterized.

Therefore, we conducted a matched case-control design, whereby each maximum penalty hospital was matched to a nonpenalty hospital using key hospital characteristics. We then used geographic matching to isolate SES factors predictive of readmission within specific geographies in an effort to control for regional population differences. We hypothesized that, among both matched and localized hospital pairs, the disparities in population SES are the most significant predictors of a maximum penalty. Now in the 3rd year of the HRRP with approximately 75% of eligible hospitals to receive penalties worth an estimated $428 million in the 2015 fiscal year,¹³ we offer a small-area analysis of bipolar extremes to inform debate surrounding the HRRP with evidence regarding the causes and implications of readmission penalties.

Study Design and Sample

This study relies on a case-control design. The cases were defined as US hospitals to receive the maximum 3% HRRP penalty in fiscal year 2015. Controls were drawn from the cohort of hospitals potentially subject to HRRP penalties that received no readmission penalty in the 2015 fiscal year with at least 1 admission for any of the following conditions: heart failure (HF), acute myocardial infarction (AMI), pneumonia (PN), total knee arthroscopy or total hip arthroscopy (THA/TKA), or chronic obstructive pulmonary disease (COPD).

Data Sources

Penalty data were drawn from the 2015 master penalty file,¹⁴ which were accessed via CMS.gov. County-level demographic and socioeconomic data were gathered from the 2015 American Community Survey (ACS), a subsidiary of the US Census. Data on hospital characteristics, capacity, and regional healthcare utilization were drawn from 2012 Dartmouth Atlas,¹⁵ 2012 Medicare Cost Report,¹⁶ 2012 American Hospital Association Hospital Statistics Database, and 2014 Hospital Care Downloadable Database.

Hospital-level CMS data were linked to the master 2015 penalty file. Dartmouth Atlas data were subsequently linked to the file using the Dartmouth Atlas “Hospital to HSA/HRR Crosswalk” file (accessed via DartmouthAtlas.org.) Each hospital was assigned the profile of the hospital service area (HSA) and hospital referral region (HRR) in which it is located. An HSA is a geographic region defined by hospital admissions; the majority, but not entirety, of residents of a given HSA utilize the corresponding hospital. Similarly, an HRR is a geographic region defined by referrals for major cardiovascular and neurosurgery procedures. County-level socioeconomic data were linked to the dataset by county name; thus, hospital socioeconomic profiles are based on the county in which they are located.

Case-Control Matching

In the primary analysis, coarsened exact matching (CEM) matched controls to cases by potential confounding hospital characteristics, including the following: ownership, number of beds, case mix index (measure of acuity), ambulatory care visit rates within 14 days of discharge, and total number of penalty-eligible cases, including HF, AMI, COPD, PN, and THA/TKA.

In the secondary analysis, hospitals were geocoded by zip code. Geographic Information Systems mapping software (ESRI ArcGIS, Redlands, CA) relied upon Euclidean allocation distance spatial analysis^17,18 to match each maximum-penalty hospital to the nearest nonpenalty hospital. Each case was matched to a distinct control; duplicate controls were replaced with the nearest unmatched no-penalty hospital.

Statistical Analysis

Univariate analyses utilized unpaired Student t tests (primary analysis) and paired Student t tests (secondary analysis). The CEM algorithm matches by strata rather than pairs, precluding paired Student t tests in the primary analysis. Statistical analyses were conducted using STATA (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX).

Maximum Penalty and Nonpenalty Hospital Matching

Of 3383 hospitals eligible for the HRRP, 39 received the maximum penalty and 770 received no penalty. Thirty-eight control hospitals were identified using CEM algorithm; 1 maximum-penalty hospital could not be matched and was excluded from primary analy

Hospital Characteristics

Case and control profiles are presented in Table 1. Cases and controls were matched by characteristics which may impact readmission rates (Table 1). CEM yielded cohorts similar across a spectrum of metrics, and identical in terms of matching criteria including ownership, beds (quartile), case mix index (above median), ambulatory care visit within 14 days of discharge (above median), and total number of penalty-eligible cases (above median). Relative to no-penalty hospitals, maximum-penalty hospitals were more likely rural (n = 9 vs n = 2, P = 0.022) and have a less profitable operating margin (0.1% vs 6.9%), and location within HSAs with higher age, sex, and race adjusted hospital-wide mortality rate (5.3% vs 4.9%, P = 0.009) and higher rates of discharge for ambulatory care sensitive conditions (108 vs 63 discharges per 1000 Medicare enrollees).

Demographic and Socioeconomic Characteristics

As presented in Table 2, cases a

Secondary Analysis: Geographical Matching

Secondary analysis matched each maximum-penalty hospital to the nearest no-penalty hospital using a global information system vector analysis algorithm. As shown in the Figure, median distance between the case and the control was 42.5 miles (interquartile range: 25th percentile, 15.4 miles; 75th percentile, 98.4 miles). Seventeen pairs (44%) were in the same HRR, 6 of which were in the same HSA. Seven pairs (18%) were within the same county

Secondary Analysis: Economic and Demographic Profiles of Geographically Matched Pairs

Demographic and socioeconomic profiles are presented in Table 3. The cases and controls are in counties with similar age, sex, and ethnicity distributions. Relative to no-penalty hospitals, maximum-penalty hospitals are in counties with lower socioeconomic profiles, including increased rates of poverty (15.6% vs 19.2%, P = 0.007) and lower rates of high school (86.4% vs 82.1%, P = 0.005) or college graduation (22.3% vs 28.1%, P = 0.002). Seven pairs were in the same county; a sensitivity analysis excluding these hospitals revealed similarly lower SES profile in cases relative to controls (Supplementary Table 1).

Our analysis reveals that county-level socioeconomic profiles are predictors of maximum HRRP penalties. Specifically, after matching cases and controls on 5 hospital characteristics that may influence readmission, maximum-penalty hospitals were more likely to be in rural counties with higher rates of poverty and lower rates of education relative to no-penalty hospitals. We observed no difference between cases and controls with respect to age, sex, or ethnicity.

Our study complement

Maximum Penalties as a Function of Population Health

The Dartmouth Atlas of Healthcare measures health outcomes, which are regionally aggregated among local hospitals by either HSA or HRR; see Methods. Such small-area aggregation does not precisely reflect outcomes from a specific hospital, but rather it describes the health status of localities. Disparities in health outcomes exist between maximum-penalty and no-penalty HSAs. Complication rates were slightly higher in maximum penalty HSAs, consistent with studies highlighting complications as drivers of surgical readmissions.^22,23 Moreover, hospital-wide mortality rates were higher in maximum-penalty areas relative to nonpenalty HSAs (5.3 vs 4.9, P = 0.009).

Using national data, Krumholz et al. found no correlation between rates of readmission and mortality for HF, AMI, and PN²⁴, which is a phenomenon acknowledged by the Medicare Payment Advisory Commission (MedPac) in a 2013 report titled, “Refining the hospital readmission reduction program.”²⁵ In large national studies, others have shown low SES to be associated with elevated readmission but not mortality.^10,11 In contrast, we restricted our analysis to matched cohorts and are, to our knowledge, the first to present evidence of an association between readmission and hospital-wide mortality adjusted for age, sex, and ethnicity.

Our results suggest maximum readmission penalties are a function of population health and public health capacity. The rates of ambulatory care sensitive condition (ACSC) discharges were substantially higher in HSAs of maximum penalty hospitals relative to nonpenalty hospitals (108 vs 63 per 1000 Medicare enrollees, P < 0.001). ACSC discharges have been used to measure primary care quality for 30 years, with the assumption being that admission for chronic conditions, such as HF, can be prevented with effective primary care.^26,27 Moreover, patients discharged from maximum-penalty hospitals were more likely to have an emergency room visit within 30 days of discharge (20.8% vs 18.4%, P < 0.001). Higher rates of ACSCs and postdischarge emergency department visits suggest outpatient resources in maximum-penalty service areas struggle to manage the disease burden of high-risk populations. Geography may be a contributor; maximum-penalty hospitals were more likely to be rural than no-penalty hospitals (24% vs 5%, P = 0.022).

Our findings suggest hospitals providing care to vulnerable communities (defined by low income, low education, and high rates of ambulatory sensitive discharges) are disproportionately penalized. McHugh et al. revealed high nurse staffing levels to be protective against readmission penalties²⁸, yet high penalties to low-margin hospitals may encourage reduced rather than increased staff. It may be better policy to direct resources rather than penalties to underserved communities; our findings echo others with concern about disproportionate penalties to hospitals serving low SES patients.^2,5,6,29

Secondary Analysis: Geographic Matching

Geographic matching paired each maximum-penalty hospital to the nearest no-penalty hospital in an attempt to control for unmeasured regional factors that may confound an association between socioeconomic profile and health outcomes. For example, cost of living ^{30, 31} and obesity ^32,33 vary regionally. Our study was unequipped to assess potential regional confounders; we attempted to control for them with geographical matching.

Median distance between maximum-penalty and no-penalty hospitals was 42.5 miles. Seven pairs were located within the same county, thus both case and control were assigned the same socioeconomic profile. Despite close proximity and identical SES profile in 7 of 39 pairs, maximum-penalty hospitals were in counties with lower income and lower rates of education, strengthening the association between SES and maximum readmission penalties.

Implications and Future Directions

In response to criticism surrounding the HRRP, the National Quality Forum endorsed the general concept of SES adjustment for hospital quality measures.³⁴ Subsequently, in a briefing dated March 24, 2015, MedPAC, a government agency which provides Medicare policy analysis to Congress, proposed an SES adjustment methodology of “dividing hospitals into peer groups based on their overall share of low-income Medicare patients, and then setting a benchmark readmissions target for each peer group”;³⁵ in other words, lower standards for hospitals that serve low-income populations. MedPAC’s proposal will reduce penalties to “safety net” institutions, which is progress but not a solution. Although the HRRP appears to be working, according to the US Department of Health and Human Services, readmissions fell by 150,000 between January 2012 and February 2013,³⁶ we are concerned neither the HRRP nor the MedPac revision proposal considers geographic and environmental components of readmission. The HRRP promotes national improvement in exchange for regional regression.

Fair quality measures are key to value-based reimbursement models; yet, implicit in penalties for excess readmissions is the assumed ability to calculate hospital performance targets. Benchmarks for safety, timely care, and patient satisfaction can be uniform; rates of central line infections should not be influenced by patient mix. However, 9 of the 39 maximum-penalty hospitals under the HRRP are in rural Kentucky; one could hypothesize many reasons why rural Kentucky is a hotbed for excess readmission, including the regional production of tobacco and bourbon.

The fundamental question raised by our study is whether poor performance on quality measures is a function of underperforming hospitals or a manifestation of underserved communities. Moving forward, we encourage data systems and study designs that focus research on geospatial distribution of population health within the context of social and behavioral health determinants.³⁷ Small-area studies of factors that drive health outcomes will inform rational alignment of healthcare policies and resources (including penalties and incentives) with underlying population needs.

Strengths and Weaknesses

Matching is a strength of the study. Primary analysis matched case and controls by hospital characteristics, generating cohorts similar across a spectrum of hospital metrics. Therefore, variation in readmission rates was less likely confounded by hospital characteristics. The secondary analysis was matched by geography in an effort to adjust for unmeasured, regional factors, including obesity and cost of living that may confound an association between SES and health outcomes. Geographic matching adds strength to our assertion that SES drives distinction between maximum-penalty hospitals and nonpenalty hospitals.

One weakness was the regional unit of analysis for socioeconomic and Dartmouth Atlas data, which is not a precise profile of the corresponding hospital. Each hospital was assigned a county-level socioeconomic profile. A more robust methodology would analyze patient-level SES data; this was impractical given a cohort of 78 hospitals. Regional health outcomes data limits analysis of readmission penalties as a function of hospital quality. Instead, regional data facilitated associations between readmission and population health, consistent with the aim of our study.

We analyzed 116 of 3668 hospitals eligible for the HRRP (3.2%), limiting the generalizability of our findings. Eighty-four percent of hospitals in the primary analysis have below the median number of beds, and none of them are teaching hospitals. Our analysis, restricted to maximum-penalty and no-penalty cohorts, does not address potential association between submaximal readmission penalties and socioeconomics.

Both matching techniques potentially controlled for similar SES factors and skewed our results towards null, especially in terms of race and ethnicity. Geographic matching generated 7 pairs (18%) within in the same county; both maximum-penalty and no-penalty hospitals were assigned the same socioeconomic profile, as well as 6 pairs (15%) within the same HSA, and both cases and controls had identical Dartmouth Atlas health outcomes profiles. We retained these pairs in our analysis to avoid artificially inflating SES and population health differences between cohorts.

Thirty-nine hospitals received a maximum penalty in the 3rd year of the HRRP. Relative to geographically matched no-penalty hospitals, maximum-penalty hospitals were more likely to be rural and located in counties with less educational attainment, more poverty and more poorly controlled chronic disease. In contrast to nationwide studies, a matched analysis plan revealed no difference between the cohorts in terms of race and ethnicity and provided evidence that maximum penalty hospitals had higher rates of age-, sex-, and race-adjusted hospital-wide mortality.

Our results highlight potential consequences of nationally derived benchmarks for phenomena underpinned by social, behavioral, and environmental factors and raise the question of whether maximum HRRP penalties are a consequence of underperforming hospitals or a manifestation of underserved communities. We are encouraged by MedPAC’s proposal to stratify HRRP by SES, yet recommend further small-area geographic analyses to better align quality measures, penalties, and incentives with resources and needs of distinct populations.

Acknowledgments

The authors thank William Hisey, who laid the foundation for the analysis and without whom the project would not have been possible.

DISCLOSURE

The authors certify that none of the material in this manuscript has been previously published and that none of this material is currently under consideration for publication elsewhere. This project received no funding. None of the authors on this manuscript have any commercial relationships to disclose in relation to this manuscript. All authors have reviewed and approved this manuscript and have contributed significantly to the design, conduct, and/or analysis of the research. No authors have any financial interests to disclose. No authors have any potential conflicts of interest to disclose. No authors have financial or personal relationships with any of the subject material presented in the manuscript.

INTRODUCTION

According to Centers for Medicare & Medicaid Services (CMS), approximately 1 in 5 patients discharged from a hospital will be readmitted within 30 days.¹ The Hospital Readmission Reduction Program (HRRP) is designed to reduce readmission by withholding up to 3% of all Medicare reimbursement from hospitals with “excess” readmissions; however, absent from the HRRP is adjustment for socioeconomic status (SES), which CMS holds may undermine incentives to reduce health disparities and institutionalize lower standards for hospitals serving disadvantaged populations.²

Lack of SES adjustment has been criticized by those who point to evidence highlighting postdischarge environment and patient SES as drivers of readmission and suggest hospitals that serve low SES individuals will bear a disproportionate share of penalties.^3-6 Single-center,^3,7,8 regional,^9,10 and nationwide^6,11 studies highlight census tract level socioeconomic variables as predictive of readmission. Single-center studies, robust in controlling for confounders, including staffing, training, electronic medical record utilization, and transitional care processes, do not allow comparisons between hospitals, limiting utility in HRRP evaluation. Multicenter cohorts, on the other hand, allow for comparisons between high and low penalty hospitals, pioneered by Joynt et al¹² after the first round of HRRP penalties; yet this technique may not account for confounding caused by extensive demographic, socioeconomic, and hospital characteristic heterogeneity inherent in a national cohort. Analysis of the 2015 HRRP penalty data by Sjoding et al.⁶ revealed higher chronic obstructive pulmonary disease (COPD) readmission rates in the Mid-Atlantic, Midwest, and South relative to other regions; however, the magnitude of small-area variation and its relationship to population SES have yet to be characterized.

Therefore, we conducted a matched case-control design, whereby each maximum penalty hospital was matched to a nonpenalty hospital using key hospital characteristics. We then used geographic matching to isolate SES factors predictive of readmission within specific geographies in an effort to control for regional population differences. We hypothesized that, among both matched and localized hospital pairs, the disparities in population SES are the most significant predictors of a maximum penalty. Now in the 3rd year of the HRRP with approximately 75% of eligible hospitals to receive penalties worth an estimated $428 million in the 2015 fiscal year,¹³ we offer a small-area analysis of bipolar extremes to inform debate surrounding the HRRP with evidence regarding the causes and implications of readmission penalties.

Study Design and Sample

This study relies on a case-control design. The cases were defined as US hospitals to receive the maximum 3% HRRP penalty in fiscal year 2015. Controls were drawn from the cohort of hospitals potentially subject to HRRP penalties that received no readmission penalty in the 2015 fiscal year with at least 1 admission for any of the following conditions: heart failure (HF), acute myocardial infarction (AMI), pneumonia (PN), total knee arthroscopy or total hip arthroscopy (THA/TKA), or chronic obstructive pulmonary disease (COPD).

Data Sources

Penalty data were drawn from the 2015 master penalty file,¹⁴ which were accessed via CMS.gov. County-level demographic and socioeconomic data were gathered from the 2015 American Community Survey (ACS), a subsidiary of the US Census. Data on hospital characteristics, capacity, and regional healthcare utilization were drawn from 2012 Dartmouth Atlas,¹⁵ 2012 Medicare Cost Report,¹⁶ 2012 American Hospital Association Hospital Statistics Database, and 2014 Hospital Care Downloadable Database.

Hospital-level CMS data were linked to the master 2015 penalty file. Dartmouth Atlas data were subsequently linked to the file using the Dartmouth Atlas “Hospital to HSA/HRR Crosswalk” file (accessed via DartmouthAtlas.org.) Each hospital was assigned the profile of the hospital service area (HSA) and hospital referral region (HRR) in which it is located. An HSA is a geographic region defined by hospital admissions; the majority, but not entirety, of residents of a given HSA utilize the corresponding hospital. Similarly, an HRR is a geographic region defined by referrals for major cardiovascular and neurosurgery procedures. County-level socioeconomic data were linked to the dataset by county name; thus, hospital socioeconomic profiles are based on the county in which they are located.

Case-Control Matching

In the primary analysis, coarsened exact matching (CEM) matched controls to cases by potential confounding hospital characteristics, including the following: ownership, number of beds, case mix index (measure of acuity), ambulatory care visit rates within 14 days of discharge, and total number of penalty-eligible cases, including HF, AMI, COPD, PN, and THA/TKA.

In the secondary analysis, hospitals were geocoded by zip code. Geographic Information Systems mapping software (ESRI ArcGIS, Redlands, CA) relied upon Euclidean allocation distance spatial analysis^17,18 to match each maximum-penalty hospital to the nearest nonpenalty hospital. Each case was matched to a distinct control; duplicate controls were replaced with the nearest unmatched no-penalty hospital.

Statistical Analysis

Univariate analyses utilized unpaired Student t tests (primary analysis) and paired Student t tests (secondary analysis). The CEM algorithm matches by strata rather than pairs, precluding paired Student t tests in the primary analysis. Statistical analyses were conducted using STATA (StataCorp. 2013. Stata Statistical Software: Release 13. College Station, TX).

Maximum Penalty and Nonpenalty Hospital Matching

Of 3383 hospitals eligible for the HRRP, 39 received the maximum penalty and 770 received no penalty. Thirty-eight control hospitals were identified using CEM algorithm; 1 maximum-penalty hospital could not be matched and was excluded from primary analy

Hospital Characteristics

Case and control profiles are presented in Table 1. Cases and controls were matched by characteristics which may impact readmission rates (Table 1). CEM yielded cohorts similar across a spectrum of metrics, and identical in terms of matching criteria including ownership, beds (quartile), case mix index (above median), ambulatory care visit within 14 days of discharge (above median), and total number of penalty-eligible cases (above median). Relative to no-penalty hospitals, maximum-penalty hospitals were more likely rural (n = 9 vs n = 2, P = 0.022) and have a less profitable operating margin (0.1% vs 6.9%), and location within HSAs with higher age, sex, and race adjusted hospital-wide mortality rate (5.3% vs 4.9%, P = 0.009) and higher rates of discharge for ambulatory care sensitive conditions (108 vs 63 discharges per 1000 Medicare enrollees).

Demographic and Socioeconomic Characteristics

As presented in Table 2, cases a

Secondary Analysis: Geographical Matching

Secondary analysis matched each maximum-penalty hospital to the nearest no-penalty hospital using a global information system vector analysis algorithm. As shown in the Figure, median distance between the case and the control was 42.5 miles (interquartile range: 25th percentile, 15.4 miles; 75th percentile, 98.4 miles). Seventeen pairs (44%) were in the same HRR, 6 of which were in the same HSA. Seven pairs (18%) were within the same county

Secondary Analysis: Economic and Demographic Profiles of Geographically Matched Pairs

Demographic and socioeconomic profiles are presented in Table 3. The cases and controls are in counties with similar age, sex, and ethnicity distributions. Relative to no-penalty hospitals, maximum-penalty hospitals are in counties with lower socioeconomic profiles, including increased rates of poverty (15.6% vs 19.2%, P = 0.007) and lower rates of high school (86.4% vs 82.1%, P = 0.005) or college graduation (22.3% vs 28.1%, P = 0.002). Seven pairs were in the same county; a sensitivity analysis excluding these hospitals revealed similarly lower SES profile in cases relative to controls (Supplementary Table 1).

Our analysis reveals that county-level socioeconomic profiles are predictors of maximum HRRP penalties. Specifically, after matching cases and controls on 5 hospital characteristics that may influence readmission, maximum-penalty hospitals were more likely to be in rural counties with higher rates of poverty and lower rates of education relative to no-penalty hospitals. We observed no difference between cases and controls with respect to age, sex, or ethnicity.

Our study complement

Maximum Penalties as a Function of Population Health

The Dartmouth Atlas of Healthcare measures health outcomes, which are regionally aggregated among local hospitals by either HSA or HRR; see Methods. Such small-area aggregation does not precisely reflect outcomes from a specific hospital, but rather it describes the health status of localities. Disparities in health outcomes exist between maximum-penalty and no-penalty HSAs. Complication rates were slightly higher in maximum penalty HSAs, consistent with studies highlighting complications as drivers of surgical readmissions.^22,23 Moreover, hospital-wide mortality rates were higher in maximum-penalty areas relative to nonpenalty HSAs (5.3 vs 4.9, P = 0.009).

Using national data, Krumholz et al. found no correlation between rates of readmission and mortality for HF, AMI, and PN²⁴, which is a phenomenon acknowledged by the Medicare Payment Advisory Commission (MedPac) in a 2013 report titled, “Refining the hospital readmission reduction program.”²⁵ In large national studies, others have shown low SES to be associated with elevated readmission but not mortality.^10,11 In contrast, we restricted our analysis to matched cohorts and are, to our knowledge, the first to present evidence of an association between readmission and hospital-wide mortality adjusted for age, sex, and ethnicity.

Our results suggest maximum readmission penalties are a function of population health and public health capacity. The rates of ambulatory care sensitive condition (ACSC) discharges were substantially higher in HSAs of maximum penalty hospitals relative to nonpenalty hospitals (108 vs 63 per 1000 Medicare enrollees, P < 0.001). ACSC discharges have been used to measure primary care quality for 30 years, with the assumption being that admission for chronic conditions, such as HF, can be prevented with effective primary care.^26,27 Moreover, patients discharged from maximum-penalty hospitals were more likely to have an emergency room visit within 30 days of discharge (20.8% vs 18.4%, P < 0.001). Higher rates of ACSCs and postdischarge emergency department visits suggest outpatient resources in maximum-penalty service areas struggle to manage the disease burden of high-risk populations. Geography may be a contributor; maximum-penalty hospitals were more likely to be rural than no-penalty hospitals (24% vs 5%, P = 0.022).

Our findings suggest hospitals providing care to vulnerable communities (defined by low income, low education, and high rates of ambulatory sensitive discharges) are disproportionately penalized. McHugh et al. revealed high nurse staffing levels to be protective against readmission penalties²⁸, yet high penalties to low-margin hospitals may encourage reduced rather than increased staff. It may be better policy to direct resources rather than penalties to underserved communities; our findings echo others with concern about disproportionate penalties to hospitals serving low SES patients.^2,5,6,29

Secondary Analysis: Geographic Matching

Geographic matching paired each maximum-penalty hospital to the nearest no-penalty hospital in an attempt to control for unmeasured regional factors that may confound an association between socioeconomic profile and health outcomes. For example, cost of living ^{30, 31} and obesity ^32,33 vary regionally. Our study was unequipped to assess potential regional confounders; we attempted to control for them with geographical matching.

Median distance between maximum-penalty and no-penalty hospitals was 42.5 miles. Seven pairs were located within the same county, thus both case and control were assigned the same socioeconomic profile. Despite close proximity and identical SES profile in 7 of 39 pairs, maximum-penalty hospitals were in counties with lower income and lower rates of education, strengthening the association between SES and maximum readmission penalties.

Implications and Future Directions

In response to criticism surrounding the HRRP, the National Quality Forum endorsed the general concept of SES adjustment for hospital quality measures.³⁴ Subsequently, in a briefing dated March 24, 2015, MedPAC, a government agency which provides Medicare policy analysis to Congress, proposed an SES adjustment methodology of “dividing hospitals into peer groups based on their overall share of low-income Medicare patients, and then setting a benchmark readmissions target for each peer group”;³⁵ in other words, lower standards for hospitals that serve low-income populations. MedPAC’s proposal will reduce penalties to “safety net” institutions, which is progress but not a solution. Although the HRRP appears to be working, according to the US Department of Health and Human Services, readmissions fell by 150,000 between January 2012 and February 2013,³⁶ we are concerned neither the HRRP nor the MedPac revision proposal considers geographic and environmental components of readmission. The HRRP promotes national improvement in exchange for regional regression.

Fair quality measures are key to value-based reimbursement models; yet, implicit in penalties for excess readmissions is the assumed ability to calculate hospital performance targets. Benchmarks for safety, timely care, and patient satisfaction can be uniform; rates of central line infections should not be influenced by patient mix. However, 9 of the 39 maximum-penalty hospitals under the HRRP are in rural Kentucky; one could hypothesize many reasons why rural Kentucky is a hotbed for excess readmission, including the regional production of tobacco and bourbon.

The fundamental question raised by our study is whether poor performance on quality measures is a function of underperforming hospitals or a manifestation of underserved communities. Moving forward, we encourage data systems and study designs that focus research on geospatial distribution of population health within the context of social and behavioral health determinants.³⁷ Small-area studies of factors that drive health outcomes will inform rational alignment of healthcare policies and resources (including penalties and incentives) with underlying population needs.

Strengths and Weaknesses

Matching is a strength of the study. Primary analysis matched case and controls by hospital characteristics, generating cohorts similar across a spectrum of hospital metrics. Therefore, variation in readmission rates was less likely confounded by hospital characteristics. The secondary analysis was matched by geography in an effort to adjust for unmeasured, regional factors, including obesity and cost of living that may confound an association between SES and health outcomes. Geographic matching adds strength to our assertion that SES drives distinction between maximum-penalty hospitals and nonpenalty hospitals.

One weakness was the regional unit of analysis for socioeconomic and Dartmouth Atlas data, which is not a precise profile of the corresponding hospital. Each hospital was assigned a county-level socioeconomic profile. A more robust methodology would analyze patient-level SES data; this was impractical given a cohort of 78 hospitals. Regional health outcomes data limits analysis of readmission penalties as a function of hospital quality. Instead, regional data facilitated associations between readmission and population health, consistent with the aim of our study.

We analyzed 116 of 3668 hospitals eligible for the HRRP (3.2%), limiting the generalizability of our findings. Eighty-four percent of hospitals in the primary analysis have below the median number of beds, and none of them are teaching hospitals. Our analysis, restricted to maximum-penalty and no-penalty cohorts, does not address potential association between submaximal readmission penalties and socioeconomics.

Both matching techniques potentially controlled for similar SES factors and skewed our results towards null, especially in terms of race and ethnicity. Geographic matching generated 7 pairs (18%) within in the same county; both maximum-penalty and no-penalty hospitals were assigned the same socioeconomic profile, as well as 6 pairs (15%) within the same HSA, and both cases and controls had identical Dartmouth Atlas health outcomes profiles. We retained these pairs in our analysis to avoid artificially inflating SES and population health differences between cohorts.

Thirty-nine hospitals received a maximum penalty in the 3rd year of the HRRP. Relative to geographically matched no-penalty hospitals, maximum-penalty hospitals were more likely to be rural and located in counties with less educational attainment, more poverty and more poorly controlled chronic disease. In contrast to nationwide studies, a matched analysis plan revealed no difference between the cohorts in terms of race and ethnicity and provided evidence that maximum penalty hospitals had higher rates of age-, sex-, and race-adjusted hospital-wide mortality.

Our results highlight potential consequences of nationally derived benchmarks for phenomena underpinned by social, behavioral, and environmental factors and raise the question of whether maximum HRRP penalties are a consequence of underperforming hospitals or a manifestation of underserved communities. We are encouraged by MedPAC’s proposal to stratify HRRP by SES, yet recommend further small-area geographic analyses to better align quality measures, penalties, and incentives with resources and needs of distinct populations.

Acknowledgments

The authors thank William Hisey, who laid the foundation for the analysis and without whom the project would not have been possible.

DISCLOSURE

The authors certify that none of the material in this manuscript has been previously published and that none of this material is currently under consideration for publication elsewhere. This project received no funding. None of the authors on this manuscript have any commercial relationships to disclose in relation to this manuscript. All authors have reviewed and approved this manuscript and have contributed significantly to the design, conduct, and/or analysis of the research. No authors have any financial interests to disclose. No authors have any potential conflicts of interest to disclose. No authors have financial or personal relationships with any of the subject material presented in the manuscript.

Study Overview

Objective. To evaluate long-term adherence to antihypertensive therapy among patients on fixed-dose combination medication as well as antihypertensive monotherapy; and to identify demographic and clinical risk factors associated with selection of and adherence and persistence to antihypertensive medication therapy.

Design. Retrospective cohort study using claims data from a large nationwide insurer.

Setting and participants. The study population included patients older than age 18 who initiated antihypertensive medication between 1 January 2009 and 31 December 2012 and who were continually enrolled at least 180 days before and 365 days after the index date, defined as the date of initiation of antihypertensive therapy. Patients were excluded from the study if they had previously filled any antihypertensive medication at any time prior to the index date. Patients were categorized based on the number and type of antihypertensive medications (fixed-dose combination, defined as a single pill containing multiple medications; multi-pill combination, defined as 2 or more distinct antihypertensive tablets or capsules; or single therapy, defined as only 1 medication) using National Drug Codes (NDC). Study authors also measured patient baseline characteristics, such as age, region, gender, diagnoses as defined by ICD-9 codes, patient utilization characteristics (both outpatient visits and hospitalizations) and characteristics of the initiated medication, including patient copayment and number of days of medication supplied.

Main outcome measures. The primary outcome of inte-rest was persistence, defined as having supply for any antihypertensive medication that overlapped with the 365th day after initiation (index date), whether the initiated medication or other antihypertensive. Additional outcomes included adherence to at least 1 antihypertensive in the 12 months after initiation and refilling at least 1 antihypertensive medication. To determine adherence, the study authors calculated the proportion of days the patient had any antihypertensive available to them (proportion of days covered; PDC). PDC > 80% to at least 1 antihypertensive in the 12 months after initiation was defined as “fully adherent.”

Statistical analysis utilized modified multivariable Poisson regression models and sensitivity analyses were performed. The main study comparisons focused on patients initiating fixed-dose combination therapy and monotherapy because these groups were more comparable in terms of baseline characteristics and medications initiated than the multi-pill combination group.

Main results. The study sample consisted of 484,493 patients who initiated an oral antihypertensive, including 78,958 patient initiating fixed-dose combinations, 380,269 filled a single therapy, and 22,266 who initiated multi-pill combinations. The most frequently initiated fixed-dose combination was lisinopril-hydrochlorothiazide. Lisinopril, hydrochlorothiazide, and amlodipine with the most frequently initiated monotherapy. The mean age of the study population was 47.2 years and 51.8% were women. Patients initiating multiple pill combinations were older (mean age 52.5) and tended to be sicker with more comorbidities than fixed-dose combinations or monotherapy. Patients initiating fixed-dose combination had higher prescription copayments than patients using single medication (prescription copay $14.4 versus $9.6). Patients initiating fixed-dose combinations were 9% more likely to be persistent (relative risk [RR] 1.09, 95% CI 1.08–1.10) and 13% more likely to be adherent (RR 1.13, 95% CI 1.11–1.14) than those who started on a monotherapy. Refill rates were also slightly higher among fixed-dose combination initiators (RR 1.06, 95% CI 1.05-1.07).

Conclusion. Compared with monotherapy, fixed-dose combination therapy appears to improve adherence and persistence to antihypertensive medications.

Commentary

Approximately half of US of individuals with diagnosed hypertension obtain control of their condition based on currently defined targets [1]. The most effective approach to blood pressure management has been controversial. The JNC8 [2] guidelines liberalized blood pressure targets, while recent results from the SPRINT (systolic blood pressure intervention trial) [3] indicates that lower blood pressure targets are able to prevent hypertension-related complications without significant additional risk. Given these conflicts, there is clearly ambiguity in the most effective approach to initiating antihypertensive treatment. Prior studies have shown that fewer than 50% of patients continue to take their medications just 12 months after initiation [4,5].

Fixed-dose combination therapy for blood pressure management has been cited as better for adherence and is now making its way into clinical guidelines [6–8]. However, it should be noted that fixed-dose combination therapy for blood pressure management limits dosing flexibility. Dose titration may be needed, potentially leading to additional prescriptions, thus potentially complicating the drug regimen and adding additional cost. Complicating matters further, quality metrics and reporting requirements for hypertension require primary care providers to achieve blood pressure control while also ensuring patient adherence and concomitantly avoiding side effects related to medication therapy.

This study was conducted using claims data for commercially insured patients or those with Medicare Advan-tage and is unlikely to be representative of the entire population. Additionally, the study authors did not have detailed clinical information about patients, limiting the ability to understand the true clinical implications. Further, patients may have initiated medications for indications other than hypertension. In addition, causality cannot be established given the retrospective observational cohort nature of this study.

Applications for Clinical Practice

Primary care physicians face substantial challenges in the treatment of hypertension, including with respect to selection of initial medication therapy. Results from this study add to the evidence base that fixed-dose combination therapy is more effective in obtaining blood pressure control than monotherapy or multiple-pill therapy. Medication adherence in primary care practice is challenging. Strategies such as fixed-dose combination therapy are reasonable to employ to improve medication adherence; however, costs must be considered.

—Ajay Dharod, MD, Wake Forest School of Medicine, Winston-Salem, NC

Study Overview

Objective. To evaluate long-term adherence to antihypertensive therapy among patients on fixed-dose combination medication as well as antihypertensive monotherapy; and to identify demographic and clinical risk factors associated with selection of and adherence and persistence to antihypertensive medication therapy.

Design. Retrospective cohort study using claims data from a large nationwide insurer.

Setting and participants. The study population included patients older than age 18 who initiated antihypertensive medication between 1 January 2009 and 31 December 2012 and who were continually enrolled at least 180 days before and 365 days after the index date, defined as the date of initiation of antihypertensive therapy. Patients were excluded from the study if they had previously filled any antihypertensive medication at any time prior to the index date. Patients were categorized based on the number and type of antihypertensive medications (fixed-dose combination, defined as a single pill containing multiple medications; multi-pill combination, defined as 2 or more distinct antihypertensive tablets or capsules; or single therapy, defined as only 1 medication) using National Drug Codes (NDC). Study authors also measured patient baseline characteristics, such as age, region, gender, diagnoses as defined by ICD-9 codes, patient utilization characteristics (both outpatient visits and hospitalizations) and characteristics of the initiated medication, including patient copayment and number of days of medication supplied.

Main outcome measures. The primary outcome of inte-rest was persistence, defined as having supply for any antihypertensive medication that overlapped with the 365th day after initiation (index date), whether the initiated medication or other antihypertensive. Additional outcomes included adherence to at least 1 antihypertensive in the 12 months after initiation and refilling at least 1 antihypertensive medication. To determine adherence, the study authors calculated the proportion of days the patient had any antihypertensive available to them (proportion of days covered; PDC). PDC > 80% to at least 1 antihypertensive in the 12 months after initiation was defined as “fully adherent.”

Statistical analysis utilized modified multivariable Poisson regression models and sensitivity analyses were performed. The main study comparisons focused on patients initiating fixed-dose combination therapy and monotherapy because these groups were more comparable in terms of baseline characteristics and medications initiated than the multi-pill combination group.

Main results. The study sample consisted of 484,493 patients who initiated an oral antihypertensive, including 78,958 patient initiating fixed-dose combinations, 380,269 filled a single therapy, and 22,266 who initiated multi-pill combinations. The most frequently initiated fixed-dose combination was lisinopril-hydrochlorothiazide. Lisinopril, hydrochlorothiazide, and amlodipine with the most frequently initiated monotherapy. The mean age of the study population was 47.2 years and 51.8% were women. Patients initiating multiple pill combinations were older (mean age 52.5) and tended to be sicker with more comorbidities than fixed-dose combinations or monotherapy. Patients initiating fixed-dose combination had higher prescription copayments than patients using single medication (prescription copay $14.4 versus $9.6). Patients initiating fixed-dose combinations were 9% more likely to be persistent (relative risk [RR] 1.09, 95% CI 1.08–1.10) and 13% more likely to be adherent (RR 1.13, 95% CI 1.11–1.14) than those who started on a monotherapy. Refill rates were also slightly higher among fixed-dose combination initiators (RR 1.06, 95% CI 1.05-1.07).

Conclusion. Compared with monotherapy, fixed-dose combination therapy appears to improve adherence and persistence to antihypertensive medications.

Commentary

Approximately half of US of individuals with diagnosed hypertension obtain control of their condition based on currently defined targets [1]. The most effective approach to blood pressure management has been controversial. The JNC8 [2] guidelines liberalized blood pressure targets, while recent results from the SPRINT (systolic blood pressure intervention trial) [3] indicates that lower blood pressure targets are able to prevent hypertension-related complications without significant additional risk. Given these conflicts, there is clearly ambiguity in the most effective approach to initiating antihypertensive treatment. Prior studies have shown that fewer than 50% of patients continue to take their medications just 12 months after initiation [4,5].

Fixed-dose combination therapy for blood pressure management has been cited as better for adherence and is now making its way into clinical guidelines [6–8]. However, it should be noted that fixed-dose combination therapy for blood pressure management limits dosing flexibility. Dose titration may be needed, potentially leading to additional prescriptions, thus potentially complicating the drug regimen and adding additional cost. Complicating matters further, quality metrics and reporting requirements for hypertension require primary care providers to achieve blood pressure control while also ensuring patient adherence and concomitantly avoiding side effects related to medication therapy.

This study was conducted using claims data for commercially insured patients or those with Medicare Advan-tage and is unlikely to be representative of the entire population. Additionally, the study authors did not have detailed clinical information about patients, limiting the ability to understand the true clinical implications. Further, patients may have initiated medications for indications other than hypertension. In addition, causality cannot be established given the retrospective observational cohort nature of this study.

Applications for Clinical Practice

Primary care physicians face substantial challenges in the treatment of hypertension, including with respect to selection of initial medication therapy. Results from this study add to the evidence base that fixed-dose combination therapy is more effective in obtaining blood pressure control than monotherapy or multiple-pill therapy. Medication adherence in primary care practice is challenging. Strategies such as fixed-dose combination therapy are reasonable to employ to improve medication adherence; however, costs must be considered.

—Ajay Dharod, MD, Wake Forest School of Medicine, Winston-Salem, NC

Study Overview

Objective. To evaluate long-term adherence to antihypertensive therapy among patients on fixed-dose combination medication as well as antihypertensive monotherapy; and to identify demographic and clinical risk factors associated with selection of and adherence and persistence to antihypertensive medication therapy.

Design. Retrospective cohort study using claims data from a large nationwide insurer.

Setting and participants. The study population included patients older than age 18 who initiated antihypertensive medication between 1 January 2009 and 31 December 2012 and who were continually enrolled at least 180 days before and 365 days after the index date, defined as the date of initiation of antihypertensive therapy. Patients were excluded from the study if they had previously filled any antihypertensive medication at any time prior to the index date. Patients were categorized based on the number and type of antihypertensive medications (fixed-dose combination, defined as a single pill containing multiple medications; multi-pill combination, defined as 2 or more distinct antihypertensive tablets or capsules; or single therapy, defined as only 1 medication) using National Drug Codes (NDC). Study authors also measured patient baseline characteristics, such as age, region, gender, diagnoses as defined by ICD-9 codes, patient utilization characteristics (both outpatient visits and hospitalizations) and characteristics of the initiated medication, including patient copayment and number of days of medication supplied.

Main outcome measures. The primary outcome of inte-rest was persistence, defined as having supply for any antihypertensive medication that overlapped with the 365th day after initiation (index date), whether the initiated medication or other antihypertensive. Additional outcomes included adherence to at least 1 antihypertensive in the 12 months after initiation and refilling at least 1 antihypertensive medication. To determine adherence, the study authors calculated the proportion of days the patient had any antihypertensive available to them (proportion of days covered; PDC). PDC > 80% to at least 1 antihypertensive in the 12 months after initiation was defined as “fully adherent.”

Statistical analysis utilized modified multivariable Poisson regression models and sensitivity analyses were performed. The main study comparisons focused on patients initiating fixed-dose combination therapy and monotherapy because these groups were more comparable in terms of baseline characteristics and medications initiated than the multi-pill combination group.

Main results. The study sample consisted of 484,493 patients who initiated an oral antihypertensive, including 78,958 patient initiating fixed-dose combinations, 380,269 filled a single therapy, and 22,266 who initiated multi-pill combinations. The most frequently initiated fixed-dose combination was lisinopril-hydrochlorothiazide. Lisinopril, hydrochlorothiazide, and amlodipine with the most frequently initiated monotherapy. The mean age of the study population was 47.2 years and 51.8% were women. Patients initiating multiple pill combinations were older (mean age 52.5) and tended to be sicker with more comorbidities than fixed-dose combinations or monotherapy. Patients initiating fixed-dose combination had higher prescription copayments than patients using single medication (prescription copay $14.4 versus $9.6). Patients initiating fixed-dose combinations were 9% more likely to be persistent (relative risk [RR] 1.09, 95% CI 1.08–1.10) and 13% more likely to be adherent (RR 1.13, 95% CI 1.11–1.14) than those who started on a monotherapy. Refill rates were also slightly higher among fixed-dose combination initiators (RR 1.06, 95% CI 1.05-1.07).

Conclusion. Compared with monotherapy, fixed-dose combination therapy appears to improve adherence and persistence to antihypertensive medications.

Commentary

Approximately half of US of individuals with diagnosed hypertension obtain control of their condition based on currently defined targets [1]. The most effective approach to blood pressure management has been controversial. The JNC8 [2] guidelines liberalized blood pressure targets, while recent results from the SPRINT (systolic blood pressure intervention trial) [3] indicates that lower blood pressure targets are able to prevent hypertension-related complications without significant additional risk. Given these conflicts, there is clearly ambiguity in the most effective approach to initiating antihypertensive treatment. Prior studies have shown that fewer than 50% of patients continue to take their medications just 12 months after initiation [4,5].

Fixed-dose combination therapy for blood pressure management has been cited as better for adherence and is now making its way into clinical guidelines [6–8]. However, it should be noted that fixed-dose combination therapy for blood pressure management limits dosing flexibility. Dose titration may be needed, potentially leading to additional prescriptions, thus potentially complicating the drug regimen and adding additional cost. Complicating matters further, quality metrics and reporting requirements for hypertension require primary care providers to achieve blood pressure control while also ensuring patient adherence and concomitantly avoiding side effects related to medication therapy.

This study was conducted using claims data for commercially insured patients or those with Medicare Advan-tage and is unlikely to be representative of the entire population. Additionally, the study authors did not have detailed clinical information about patients, limiting the ability to understand the true clinical implications. Further, patients may have initiated medications for indications other than hypertension. In addition, causality cannot be established given the retrospective observational cohort nature of this study.

Applications for Clinical Practice

Primary care physicians face substantial challenges in the treatment of hypertension, including with respect to selection of initial medication therapy. Results from this study add to the evidence base that fixed-dose combination therapy is more effective in obtaining blood pressure control than monotherapy or multiple-pill therapy. Medication adherence in primary care practice is challenging. Strategies such as fixed-dose combination therapy are reasonable to employ to improve medication adherence; however, costs must be considered.

—Ajay Dharod, MD, Wake Forest School of Medicine, Winston-Salem, NC

For ObGyns, 2 intensely stressful career milestones are the day you start your practice and the day you decide to put it up for sale.

One of us, Dr. Baum, started a practice in 1976. At that time, many clinicians seemed to work right up until the day they died—in mid-examination or with scalpel in hand! Today, clinicians seriously contemplate leaving an active practice at age 55, 60, or, more traditionally, 65.

ObGyns in group practice, even those with only 1 or 2 partners, presumably have in place a well-thought-out and properly drafted contract with buyout and phase-down provisions. For members of a group practice, it is imperative to critically review and discuss contractual arrangements periodically and decide if they make sense as much now as they did at the start. ObGyns who continually revisit their contracts probably have an exit strategy that is fairly self-executing and effective and that will provide the seller with a seamless transition to retirement.

A solo ObGyn who is selling a practice has 3 basic options: find a successor physician, sell to a hospital or to a larger group, or close the practice.

Related article:
ObGyns’ choice of practice environment is a big deal

Preparing your practice for sale

Regardless of who will take over your practice, you need to prepare for its transition.

The most important aspect of selling your practice is knowing its finances and ensuring that they are in order. Any serious buyer will ask to examine your books, see how you are running the business, and assess its vitality and potential growth. Simply, a buyer will want to know where your revenue comes from and where it goes.

Your practice will be attractive to a buyer if it shows a stable or growing revenue base, an attractive payer mix, reasonable overhead, and personal income that is steady if not increasing. If your earning capacity is low or declining, you will need to explain why.

Timing is key

We strongly recommend beginning the process 3 to 5 years before your intended exit.

By starting early, up to 5 years in advance, you can maximize the likelihood that your practice will retain all or most of its value. Moreover, you can use the long lead time to thoroughly explore all available options and find a committed buyer.

Selling a practice can be a complicated affair, and many ObGyns do not have the requisite skills. So much of the success in selling depends on the specifics of the practice, the physician, and the market (the hospital and physician environment).

Identifying potential buyers

Other ObGyns. Recruiting an ObGyn to take over your practice seems to be the best option but can prove very difficult in today’s environment. Many younger clinicians are either joining large groups or becoming hospital employees.

Other physician groups. While working your way down your list of potential buyers, you should also be quietly, subtly, and tactfully assessing other practices, even your competitors, to see if any are candidates for merging with and/or acquiring yours and all your charts, records, and referring physicians.

Hospitals. In today’s health care environment, in which more than half of clinicians are becoming hospital employees, selling to your associated hospital may be a viable option.

Your practice is probably contributing millions of dollars in income to that hospital each year, and of course the hospital would like to maintain this revenue stream. You should consider talking to the hospital’s CEO or medical director.

Hospitals also know that, if you leave and the market cannot absorb the resulting increase in demand for care, patients may go elsewhere, to a competing hospital or outside the community. Rather than lose your market share, a hospital may consider the obvious solution: recruit a replacement ObGyn for your practice.

Your goal here is to negotiate an agreement in which your hospital will recruit a replacement ObGyn, provide financial support, and transition your practice to that ObGyn over a specified period.

The hospital could acquire your practice and either employ you during the transition or provide recruiting support and an income guarantee to help your practice pay the new physician’s salary. Whether to sell or remain independent is often driven by the needs and desires of the recruit. As the vast majority of clinicians coming out of training are seeking employment, in most cases the agreement will require a sale.

Selling to a hospital a few years before your retirement can be a plus. You might find employment a welcome respite from the daunting responsibility of managing your own practice. Life can become much less stressful as you introduce and transition your patients to the new ObGyn. You will be working less, taking fewer calls, and maintaining or even increasing your income, all without the burden of managing the practice.

Read about determining your practice’s value

Putting a monetary value on your practice

After deciding to sell your practice, you need to determine its value. Buying a practice may be the largest financial transaction a young ObGyn will ever make. For a retiring physician, valuation of a practice may reflect a career’s worth of “sweat equity.”

What is your practice worth?

All ObGyns believe their practice is worth far more than any young ObGyn or hospital is willing to pay for it. After all, you have spent a medical lifetime creating, building, and nurturing your practice. You have cared for several thousand patients, who have been loyal and may want to stay with the practice under its new ObGyn. So, how does a retiring physician put a value on his or her practice and then “cast the net” to the marketplace? How do you find a buyer who will pay the asking price and then help the practice make the transition from seller to buyer and continue to serve their patients?

The buyer’s perspective on value. In a pure sense, the value of any asset is what a potential buyer is willing to pay. From a value standpoint, the price that potential buyers are willing to pay varies by the specifics of the situation, regardless of what a valuation or practice appraisal might indicate.

For example, once your plan to retire becomes known, why would a young ObGyn agree to pay X dollars for all your medical records? After all, the potential buyer knows that your existing patients and your referral base will need to seek care from another ObGyn after you leave, and they will likely stay with the practice if they feel they will be treated well by the new clinician.

A hospital may take a similar tack but more often will be willing to pay fair market value for your practice. Hospitals, however, cannot legally pay more than fair market value as determined by an independent appraiser.

Related article:
Four pillars of a successful practice: 1. Keep your current patients happy

Valuation methods

The valuation of any business generally is approached in terms of market, assets, and income.

The market approach usually is taken only with regard to office real estate. Given the lack of reliable and comparable sales information, this approach is seldom used in the valuation of medical practices. If you own your office real estate, a real estate appraiser will establish its fair market value.

In the assets approach, the individual assets of a medical practice are valued on the basis of their current market values. These assets are either tangible or intangible.

Tangible assets can be seen and touched. Furniture, equipment, and office real estate are examples.

The fair market value of used furniture and equipment is most often determined by replacement cost. The value of these items is limited. Usually it starts at 50% of the cost of buying new furniture or equipment of the same utility. From there, the value is lowered on the basis of the age and condition of the items.

Often, the market value of major ObGyn office equipment, such as a DXA (dual-energy x-ray absorptiometry) scanner, is based on similar items for sale or recently sold in the used secondary equipment market.

Tangible assets may include accounts receivable (A/R). A/R represents uncollected payment for work performed. Most buyers want to avoid paying for A/R and assuming the risk of collections. Generally, you should expect to retain your A/R and pay a small or nominal fee to have the buyer handle the collections after you have retired.

Intangible assets are not physical. Examples include the physician’s name, phone number, reputation, referral base, trained staff, and medical records—in other words, what gets patients to keep coming back. Most physicians value these goodwill or “blue-sky” assets highly. Today, unfortunately, most sellers are unable to reap any financial benefit from their intangible assets.

The income approach is based on the premise that the value of any business is in the income it generates for its owner. In simple terms, value in the income approach is a multiple of the cash the business generates after expenses.

Read important keys to transitioning the practice

Transitioning the practice: Role of the seller and the buyer

First and very important is the contract agreement regarding the overlap period, when both the exiting ObGyn and the new ObGyn are at the practice. We suggest making the overlap a minimum of 6 months and a maximum of 1 year. During this period, the exiting physician can introduce the incoming physician to the patients. A face-to-face introduction can amount to an endorsement, which can ease a patient’s mind and help her decide to take on the new ObGyn and philosophy rather than search elsewhere for obstetric and gynecologic care. The new ObGyn also can use the overlap period to become familiar and comfortable with the staff and learn the process for physician and staff management of case flow, from scheduling and examination to insurance and patient follow-up.

We suggest that the exiting ObGyn send a farewell/welcome letter to patients and referring physicians. The letter should state the exiting ObGyn’s intention to leave (or retire from) the practice and should introduce the ObGyn who will be taking over.

The exiting ObGyn should also take the new ObGyn to meet the physicians who have been providing referrals over the years. We suggest visiting each referring physician’s office to make the introduction. Another good way to introduce a new ObGyn to referring physicians and other professionals—endocrinologists, cardiologists, nurses, pharmaceutical representatives—is to host an open house at your practice. Invite the staff members of the referring physicians as well, since they can be invaluable in making referrals.

We recommend that the exiting ObGyn spend the money to update all the practice’s stationery, brochures, and print materials and ensure they look professional. Note that it is not acceptable to place the new ObGyn’s name under the exiting ObGyn’s name. If the practice has a website, introduce the new physician there and make any necessary updates regarding office hours and accepted insurance plans.

If the exiting ObGyn’s practice lacks a robust Internet and social media presence, the new ObGyn should establish one. We recommend setting up an interactive website that patients can use to make appointments and pay bills. The website should have an email component that can be used to ask questions, raise concerns, and get answers. We also recommend opening Facebook, YouTube, and Twitter accounts for the practice and being active on these social media.

In our experience, smoothly transitioning practices can achieve patient retention rates as high as 90% to 95%. For practices without a plan, however, these rates may be as low as 50%, or worse. Therefore, work out a plan in advance, and include the steps described here, so that on arrival the new ObGyn can hit the ground running.

Acquiring a successful medical practice is doable and offers many advantages, such as autonomy and the ability to make business decisions affecting the practice. Despite all the changes happening in health care, we still think this is the best way to go.

Related article:
Four pillars of a successful practice: 4. Motivate your staff

Bottom line

Selling an ObGyn practice can be a daunting process. However, deciding to sell your practice, performing the valuation, and ensuring a smooth transition are part and parcel of making the transfer a success, equitable for both the buyer and the seller.

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

For ObGyns, 2 intensely stressful career milestones are the day you start your practice and the day you decide to put it up for sale.

One of us, Dr. Baum, started a practice in 1976. At that time, many clinicians seemed to work right up until the day they died—in mid-examination or with scalpel in hand! Today, clinicians seriously contemplate leaving an active practice at age 55, 60, or, more traditionally, 65.

ObGyns in group practice, even those with only 1 or 2 partners, presumably have in place a well-thought-out and properly drafted contract with buyout and phase-down provisions. For members of a group practice, it is imperative to critically review and discuss contractual arrangements periodically and decide if they make sense as much now as they did at the start. ObGyns who continually revisit their contracts probably have an exit strategy that is fairly self-executing and effective and that will provide the seller with a seamless transition to retirement.

A solo ObGyn who is selling a practice has 3 basic options: find a successor physician, sell to a hospital or to a larger group, or close the practice.

Related article:
ObGyns’ choice of practice environment is a big deal

Preparing your practice for sale

Regardless of who will take over your practice, you need to prepare for its transition.

The most important aspect of selling your practice is knowing its finances and ensuring that they are in order. Any serious buyer will ask to examine your books, see how you are running the business, and assess its vitality and potential growth. Simply, a buyer will want to know where your revenue comes from and where it goes.

Your practice will be attractive to a buyer if it shows a stable or growing revenue base, an attractive payer mix, reasonable overhead, and personal income that is steady if not increasing. If your earning capacity is low or declining, you will need to explain why.

Timing is key

We strongly recommend beginning the process 3 to 5 years before your intended exit.

By starting early, up to 5 years in advance, you can maximize the likelihood that your practice will retain all or most of its value. Moreover, you can use the long lead time to thoroughly explore all available options and find a committed buyer.

Selling a practice can be a complicated affair, and many ObGyns do not have the requisite skills. So much of the success in selling depends on the specifics of the practice, the physician, and the market (the hospital and physician environment).

Identifying potential buyers

Other ObGyns. Recruiting an ObGyn to take over your practice seems to be the best option but can prove very difficult in today’s environment. Many younger clinicians are either joining large groups or becoming hospital employees.

Other physician groups. While working your way down your list of potential buyers, you should also be quietly, subtly, and tactfully assessing other practices, even your competitors, to see if any are candidates for merging with and/or acquiring yours and all your charts, records, and referring physicians.

Hospitals. In today’s health care environment, in which more than half of clinicians are becoming hospital employees, selling to your associated hospital may be a viable option.

Your practice is probably contributing millions of dollars in income to that hospital each year, and of course the hospital would like to maintain this revenue stream. You should consider talking to the hospital’s CEO or medical director.

Hospitals also know that, if you leave and the market cannot absorb the resulting increase in demand for care, patients may go elsewhere, to a competing hospital or outside the community. Rather than lose your market share, a hospital may consider the obvious solution: recruit a replacement ObGyn for your practice.

Your goal here is to negotiate an agreement in which your hospital will recruit a replacement ObGyn, provide financial support, and transition your practice to that ObGyn over a specified period.

The hospital could acquire your practice and either employ you during the transition or provide recruiting support and an income guarantee to help your practice pay the new physician’s salary. Whether to sell or remain independent is often driven by the needs and desires of the recruit. As the vast majority of clinicians coming out of training are seeking employment, in most cases the agreement will require a sale.

Selling to a hospital a few years before your retirement can be a plus. You might find employment a welcome respite from the daunting responsibility of managing your own practice. Life can become much less stressful as you introduce and transition your patients to the new ObGyn. You will be working less, taking fewer calls, and maintaining or even increasing your income, all without the burden of managing the practice.

Read about determining your practice’s value

Putting a monetary value on your practice

After deciding to sell your practice, you need to determine its value. Buying a practice may be the largest financial transaction a young ObGyn will ever make. For a retiring physician, valuation of a practice may reflect a career’s worth of “sweat equity.”

What is your practice worth?

All ObGyns believe their practice is worth far more than any young ObGyn or hospital is willing to pay for it. After all, you have spent a medical lifetime creating, building, and nurturing your practice. You have cared for several thousand patients, who have been loyal and may want to stay with the practice under its new ObGyn. So, how does a retiring physician put a value on his or her practice and then “cast the net” to the marketplace? How do you find a buyer who will pay the asking price and then help the practice make the transition from seller to buyer and continue to serve their patients?

The buyer’s perspective on value. In a pure sense, the value of any asset is what a potential buyer is willing to pay. From a value standpoint, the price that potential buyers are willing to pay varies by the specifics of the situation, regardless of what a valuation or practice appraisal might indicate.

For example, once your plan to retire becomes known, why would a young ObGyn agree to pay X dollars for all your medical records? After all, the potential buyer knows that your existing patients and your referral base will need to seek care from another ObGyn after you leave, and they will likely stay with the practice if they feel they will be treated well by the new clinician.

A hospital may take a similar tack but more often will be willing to pay fair market value for your practice. Hospitals, however, cannot legally pay more than fair market value as determined by an independent appraiser.

Related article:
Four pillars of a successful practice: 1. Keep your current patients happy

Valuation methods

The valuation of any business generally is approached in terms of market, assets, and income.

The market approach usually is taken only with regard to office real estate. Given the lack of reliable and comparable sales information, this approach is seldom used in the valuation of medical practices. If you own your office real estate, a real estate appraiser will establish its fair market value.

In the assets approach, the individual assets of a medical practice are valued on the basis of their current market values. These assets are either tangible or intangible.

Tangible assets can be seen and touched. Furniture, equipment, and office real estate are examples.

The fair market value of used furniture and equipment is most often determined by replacement cost. The value of these items is limited. Usually it starts at 50% of the cost of buying new furniture or equipment of the same utility. From there, the value is lowered on the basis of the age and condition of the items.

Often, the market value of major ObGyn office equipment, such as a DXA (dual-energy x-ray absorptiometry) scanner, is based on similar items for sale or recently sold in the used secondary equipment market.

Tangible assets may include accounts receivable (A/R). A/R represents uncollected payment for work performed. Most buyers want to avoid paying for A/R and assuming the risk of collections. Generally, you should expect to retain your A/R and pay a small or nominal fee to have the buyer handle the collections after you have retired.

Intangible assets are not physical. Examples include the physician’s name, phone number, reputation, referral base, trained staff, and medical records—in other words, what gets patients to keep coming back. Most physicians value these goodwill or “blue-sky” assets highly. Today, unfortunately, most sellers are unable to reap any financial benefit from their intangible assets.

The income approach is based on the premise that the value of any business is in the income it generates for its owner. In simple terms, value in the income approach is a multiple of the cash the business generates after expenses.

Read important keys to transitioning the practice

Transitioning the practice: Role of the seller and the buyer

First and very important is the contract agreement regarding the overlap period, when both the exiting ObGyn and the new ObGyn are at the practice. We suggest making the overlap a minimum of 6 months and a maximum of 1 year. During this period, the exiting physician can introduce the incoming physician to the patients. A face-to-face introduction can amount to an endorsement, which can ease a patient’s mind and help her decide to take on the new ObGyn and philosophy rather than search elsewhere for obstetric and gynecologic care. The new ObGyn also can use the overlap period to become familiar and comfortable with the staff and learn the process for physician and staff management of case flow, from scheduling and examination to insurance and patient follow-up.

We suggest that the exiting ObGyn send a farewell/welcome letter to patients and referring physicians. The letter should state the exiting ObGyn’s intention to leave (or retire from) the practice and should introduce the ObGyn who will be taking over.

The exiting ObGyn should also take the new ObGyn to meet the physicians who have been providing referrals over the years. We suggest visiting each referring physician’s office to make the introduction. Another good way to introduce a new ObGyn to referring physicians and other professionals—endocrinologists, cardiologists, nurses, pharmaceutical representatives—is to host an open house at your practice. Invite the staff members of the referring physicians as well, since they can be invaluable in making referrals.

We recommend that the exiting ObGyn spend the money to update all the practice’s stationery, brochures, and print materials and ensure they look professional. Note that it is not acceptable to place the new ObGyn’s name under the exiting ObGyn’s name. If the practice has a website, introduce the new physician there and make any necessary updates regarding office hours and accepted insurance plans.

If the exiting ObGyn’s practice lacks a robust Internet and social media presence, the new ObGyn should establish one. We recommend setting up an interactive website that patients can use to make appointments and pay bills. The website should have an email component that can be used to ask questions, raise concerns, and get answers. We also recommend opening Facebook, YouTube, and Twitter accounts for the practice and being active on these social media.

In our experience, smoothly transitioning practices can achieve patient retention rates as high as 90% to 95%. For practices without a plan, however, these rates may be as low as 50%, or worse. Therefore, work out a plan in advance, and include the steps described here, so that on arrival the new ObGyn can hit the ground running.

Acquiring a successful medical practice is doable and offers many advantages, such as autonomy and the ability to make business decisions affecting the practice. Despite all the changes happening in health care, we still think this is the best way to go.

Related article:
Four pillars of a successful practice: 4. Motivate your staff

Bottom line

Selling an ObGyn practice can be a daunting process. However, deciding to sell your practice, performing the valuation, and ensuring a smooth transition are part and parcel of making the transfer a success, equitable for both the buyer and the seller.

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

For ObGyns, 2 intensely stressful career milestones are the day you start your practice and the day you decide to put it up for sale.

One of us, Dr. Baum, started a practice in 1976. At that time, many clinicians seemed to work right up until the day they died—in mid-examination or with scalpel in hand! Today, clinicians seriously contemplate leaving an active practice at age 55, 60, or, more traditionally, 65.

ObGyns in group practice, even those with only 1 or 2 partners, presumably have in place a well-thought-out and properly drafted contract with buyout and phase-down provisions. For members of a group practice, it is imperative to critically review and discuss contractual arrangements periodically and decide if they make sense as much now as they did at the start. ObGyns who continually revisit their contracts probably have an exit strategy that is fairly self-executing and effective and that will provide the seller with a seamless transition to retirement.

A solo ObGyn who is selling a practice has 3 basic options: find a successor physician, sell to a hospital or to a larger group, or close the practice.

Related article:
ObGyns’ choice of practice environment is a big deal

Preparing your practice for sale

Regardless of who will take over your practice, you need to prepare for its transition.

The most important aspect of selling your practice is knowing its finances and ensuring that they are in order. Any serious buyer will ask to examine your books, see how you are running the business, and assess its vitality and potential growth. Simply, a buyer will want to know where your revenue comes from and where it goes.

Your practice will be attractive to a buyer if it shows a stable or growing revenue base, an attractive payer mix, reasonable overhead, and personal income that is steady if not increasing. If your earning capacity is low or declining, you will need to explain why.

Timing is key

We strongly recommend beginning the process 3 to 5 years before your intended exit.

By starting early, up to 5 years in advance, you can maximize the likelihood that your practice will retain all or most of its value. Moreover, you can use the long lead time to thoroughly explore all available options and find a committed buyer.

Selling a practice can be a complicated affair, and many ObGyns do not have the requisite skills. So much of the success in selling depends on the specifics of the practice, the physician, and the market (the hospital and physician environment).

Identifying potential buyers

Other ObGyns. Recruiting an ObGyn to take over your practice seems to be the best option but can prove very difficult in today’s environment. Many younger clinicians are either joining large groups or becoming hospital employees.

Other physician groups. While working your way down your list of potential buyers, you should also be quietly, subtly, and tactfully assessing other practices, even your competitors, to see if any are candidates for merging with and/or acquiring yours and all your charts, records, and referring physicians.

Hospitals. In today’s health care environment, in which more than half of clinicians are becoming hospital employees, selling to your associated hospital may be a viable option.

Your practice is probably contributing millions of dollars in income to that hospital each year, and of course the hospital would like to maintain this revenue stream. You should consider talking to the hospital’s CEO or medical director.

Hospitals also know that, if you leave and the market cannot absorb the resulting increase in demand for care, patients may go elsewhere, to a competing hospital or outside the community. Rather than lose your market share, a hospital may consider the obvious solution: recruit a replacement ObGyn for your practice.

Your goal here is to negotiate an agreement in which your hospital will recruit a replacement ObGyn, provide financial support, and transition your practice to that ObGyn over a specified period.

The hospital could acquire your practice and either employ you during the transition or provide recruiting support and an income guarantee to help your practice pay the new physician’s salary. Whether to sell or remain independent is often driven by the needs and desires of the recruit. As the vast majority of clinicians coming out of training are seeking employment, in most cases the agreement will require a sale.

Selling to a hospital a few years before your retirement can be a plus. You might find employment a welcome respite from the daunting responsibility of managing your own practice. Life can become much less stressful as you introduce and transition your patients to the new ObGyn. You will be working less, taking fewer calls, and maintaining or even increasing your income, all without the burden of managing the practice.

Read about determining your practice’s value

Putting a monetary value on your practice

After deciding to sell your practice, you need to determine its value. Buying a practice may be the largest financial transaction a young ObGyn will ever make. For a retiring physician, valuation of a practice may reflect a career’s worth of “sweat equity.”

What is your practice worth?

All ObGyns believe their practice is worth far more than any young ObGyn or hospital is willing to pay for it. After all, you have spent a medical lifetime creating, building, and nurturing your practice. You have cared for several thousand patients, who have been loyal and may want to stay with the practice under its new ObGyn. So, how does a retiring physician put a value on his or her practice and then “cast the net” to the marketplace? How do you find a buyer who will pay the asking price and then help the practice make the transition from seller to buyer and continue to serve their patients?

The buyer’s perspective on value. In a pure sense, the value of any asset is what a potential buyer is willing to pay. From a value standpoint, the price that potential buyers are willing to pay varies by the specifics of the situation, regardless of what a valuation or practice appraisal might indicate.

For example, once your plan to retire becomes known, why would a young ObGyn agree to pay X dollars for all your medical records? After all, the potential buyer knows that your existing patients and your referral base will need to seek care from another ObGyn after you leave, and they will likely stay with the practice if they feel they will be treated well by the new clinician.

A hospital may take a similar tack but more often will be willing to pay fair market value for your practice. Hospitals, however, cannot legally pay more than fair market value as determined by an independent appraiser.

Related article:
Four pillars of a successful practice: 1. Keep your current patients happy

Valuation methods

The valuation of any business generally is approached in terms of market, assets, and income.

The market approach usually is taken only with regard to office real estate. Given the lack of reliable and comparable sales information, this approach is seldom used in the valuation of medical practices. If you own your office real estate, a real estate appraiser will establish its fair market value.

In the assets approach, the individual assets of a medical practice are valued on the basis of their current market values. These assets are either tangible or intangible.

Tangible assets can be seen and touched. Furniture, equipment, and office real estate are examples.

The fair market value of used furniture and equipment is most often determined by replacement cost. The value of these items is limited. Usually it starts at 50% of the cost of buying new furniture or equipment of the same utility. From there, the value is lowered on the basis of the age and condition of the items.

Often, the market value of major ObGyn office equipment, such as a DXA (dual-energy x-ray absorptiometry) scanner, is based on similar items for sale or recently sold in the used secondary equipment market.

Tangible assets may include accounts receivable (A/R). A/R represents uncollected payment for work performed. Most buyers want to avoid paying for A/R and assuming the risk of collections. Generally, you should expect to retain your A/R and pay a small or nominal fee to have the buyer handle the collections after you have retired.

Intangible assets are not physical. Examples include the physician’s name, phone number, reputation, referral base, trained staff, and medical records—in other words, what gets patients to keep coming back. Most physicians value these goodwill or “blue-sky” assets highly. Today, unfortunately, most sellers are unable to reap any financial benefit from their intangible assets.

The income approach is based on the premise that the value of any business is in the income it generates for its owner. In simple terms, value in the income approach is a multiple of the cash the business generates after expenses.

Read important keys to transitioning the practice

Transitioning the practice: Role of the seller and the buyer

First and very important is the contract agreement regarding the overlap period, when both the exiting ObGyn and the new ObGyn are at the practice. We suggest making the overlap a minimum of 6 months and a maximum of 1 year. During this period, the exiting physician can introduce the incoming physician to the patients. A face-to-face introduction can amount to an endorsement, which can ease a patient’s mind and help her decide to take on the new ObGyn and philosophy rather than search elsewhere for obstetric and gynecologic care. The new ObGyn also can use the overlap period to become familiar and comfortable with the staff and learn the process for physician and staff management of case flow, from scheduling and examination to insurance and patient follow-up.

We suggest that the exiting ObGyn send a farewell/welcome letter to patients and referring physicians. The letter should state the exiting ObGyn’s intention to leave (or retire from) the practice and should introduce the ObGyn who will be taking over.

The exiting ObGyn should also take the new ObGyn to meet the physicians who have been providing referrals over the years. We suggest visiting each referring physician’s office to make the introduction. Another good way to introduce a new ObGyn to referring physicians and other professionals—endocrinologists, cardiologists, nurses, pharmaceutical representatives—is to host an open house at your practice. Invite the staff members of the referring physicians as well, since they can be invaluable in making referrals.

We recommend that the exiting ObGyn spend the money to update all the practice’s stationery, brochures, and print materials and ensure they look professional. Note that it is not acceptable to place the new ObGyn’s name under the exiting ObGyn’s name. If the practice has a website, introduce the new physician there and make any necessary updates regarding office hours and accepted insurance plans.

If the exiting ObGyn’s practice lacks a robust Internet and social media presence, the new ObGyn should establish one. We recommend setting up an interactive website that patients can use to make appointments and pay bills. The website should have an email component that can be used to ask questions, raise concerns, and get answers. We also recommend opening Facebook, YouTube, and Twitter accounts for the practice and being active on these social media.

In our experience, smoothly transitioning practices can achieve patient retention rates as high as 90% to 95%. For practices without a plan, however, these rates may be as low as 50%, or worse. Therefore, work out a plan in advance, and include the steps described here, so that on arrival the new ObGyn can hit the ground running.

Acquiring a successful medical practice is doable and offers many advantages, such as autonomy and the ability to make business decisions affecting the practice. Despite all the changes happening in health care, we still think this is the best way to go.

Related article:
Four pillars of a successful practice: 4. Motivate your staff

Bottom line

Selling an ObGyn practice can be a daunting process. However, deciding to sell your practice, performing the valuation, and ensuring a smooth transition are part and parcel of making the transfer a success, equitable for both the buyer and the seller.

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

BOSTON—Between 30% and 40% of patients diagnosed with intractable epilepsy do not have epilepsy, according to an overview presented at the 69th Annual Meeting of the American Academy of Neurology. A combination of overreading and overemphasizing EEGs can contribute to misdiagnosis, said Selim R. Benbadis, MD, Professor of Neurology and Director of the Comprehensive Epilepsy Program at the University of South Florida in Tampa.

Neurologists overread EEGs “because of the perception that there is less risk in overdiagnosing epilepsy, as opposed to underdiagnosing [the disease], and that is not correct,” said Dr. Benbadis.

The consequences of an epilepsy misdiagnosis can be serious. Patients can lose driving privileges, which may limit their employment opportunities. Epilepsy also is associated with a stigma that can be difficult to dispel, said Dr. Benbadis. In addition, patients misdiagnosed with epilepsy can have side effects from seizure medications.

Why Are EEGs Overread?

Two of the major reasons for misinterpration of EEGs are lack of training and inexperience, said Dr. Benbadis. Currently, it is not mandatory to learn how to read an EEG during neurology residency. Many neurology programs do require EEG training, but many do not. “If you are not experienced in looking at [an EEG], you will overread and think that everything is abnormal,” said Dr. Benbadis. Many normal variants and artifacts can look like epileptiform discharges to neurologists who are inexperienced in reading EEG.

Commonly overread EEG patterns include normal variants such as wicket rhythms, nonspecific temporal fluctuations, and rhythmic midtemporal theta of drowsiness. In addition, one study found that most patients were misdiagnosed with epilepsy because of overread EEGs; nonspecific fluctuations in the temporal region were misread as sharp waves.

The idea that “phase reversals” represent EEG abnormalities is a misconception, said Dr. Benbadis. A phase reversal, which identifies the location of maximum voltage, does not indicate abnormalities. Every normal waveform can have phase reversals, he said. A “history bias” can also lead to a misdiagnosis of epilepsy. For example, if a patient has a history of seizures or suspected seizures, a neurologist might be biased toward a diagnosis of epilepsy, and “look too hard” when reading the EEG, said Dr. Benbadis.

Steps to Improve EEG Interpretation

When deciding whether a discharge is epileptiform, neurologists should look for waves with an asymmetric contour that clearly stand out from the ongoing background of an EEG. About 98% of the time, with clear epileptiform discharges, neurologists can be sure that they indicate epilepsy without knowing the patient’s history, said Dr. Benbadis. Experts should develop consensus guidelines for EEG interpretation, and all neurology residents should be required to train in the EEG laboratory, said Dr. Benbadis. In addition, when there is doubt about whether an EEG was abnormal, “we must obtain the very EEG previously read as abnormal and redo the tracing or consult a colleague,” he added. Patients who have been diagnosed with epilepsy due to an abnormal EEG are encouraged to get a second opinion from an epilepsy or EEG specialist.

—Erica Tricarico

Suggested Reading

Benbadis SR. “Just like EKGs!” Should EEGs undergo a confirmatory interpretation by a clinical neurophysiologist? Neurology. 2013; 80(1 Suppl 1):S47-S51.

BOSTON—Between 30% and 40% of patients diagnosed with intractable epilepsy do not have epilepsy, according to an overview presented at the 69th Annual Meeting of the American Academy of Neurology. A combination of overreading and overemphasizing EEGs can contribute to misdiagnosis, said Selim R. Benbadis, MD, Professor of Neurology and Director of the Comprehensive Epilepsy Program at the University of South Florida in Tampa.

Neurologists overread EEGs “because of the perception that there is less risk in overdiagnosing epilepsy, as opposed to underdiagnosing [the disease], and that is not correct,” said Dr. Benbadis.

The consequences of an epilepsy misdiagnosis can be serious. Patients can lose driving privileges, which may limit their employment opportunities. Epilepsy also is associated with a stigma that can be difficult to dispel, said Dr. Benbadis. In addition, patients misdiagnosed with epilepsy can have side effects from seizure medications.

Why Are EEGs Overread?

Two of the major reasons for misinterpration of EEGs are lack of training and inexperience, said Dr. Benbadis. Currently, it is not mandatory to learn how to read an EEG during neurology residency. Many neurology programs do require EEG training, but many do not. “If you are not experienced in looking at [an EEG], you will overread and think that everything is abnormal,” said Dr. Benbadis. Many normal variants and artifacts can look like epileptiform discharges to neurologists who are inexperienced in reading EEG.

Commonly overread EEG patterns include normal variants such as wicket rhythms, nonspecific temporal fluctuations, and rhythmic midtemporal theta of drowsiness. In addition, one study found that most patients were misdiagnosed with epilepsy because of overread EEGs; nonspecific fluctuations in the temporal region were misread as sharp waves.

The idea that “phase reversals” represent EEG abnormalities is a misconception, said Dr. Benbadis. A phase reversal, which identifies the location of maximum voltage, does not indicate abnormalities. Every normal waveform can have phase reversals, he said. A “history bias” can also lead to a misdiagnosis of epilepsy. For example, if a patient has a history of seizures or suspected seizures, a neurologist might be biased toward a diagnosis of epilepsy, and “look too hard” when reading the EEG, said Dr. Benbadis.

Steps to Improve EEG Interpretation

When deciding whether a discharge is epileptiform, neurologists should look for waves with an asymmetric contour that clearly stand out from the ongoing background of an EEG. About 98% of the time, with clear epileptiform discharges, neurologists can be sure that they indicate epilepsy without knowing the patient’s history, said Dr. Benbadis. Experts should develop consensus guidelines for EEG interpretation, and all neurology residents should be required to train in the EEG laboratory, said Dr. Benbadis. In addition, when there is doubt about whether an EEG was abnormal, “we must obtain the very EEG previously read as abnormal and redo the tracing or consult a colleague,” he added. Patients who have been diagnosed with epilepsy due to an abnormal EEG are encouraged to get a second opinion from an epilepsy or EEG specialist.

—Erica Tricarico

Suggested Reading

Benbadis SR. “Just like EKGs!” Should EEGs undergo a confirmatory interpretation by a clinical neurophysiologist? Neurology. 2013; 80(1 Suppl 1):S47-S51.

BOSTON—Between 30% and 40% of patients diagnosed with intractable epilepsy do not have epilepsy, according to an overview presented at the 69th Annual Meeting of the American Academy of Neurology. A combination of overreading and overemphasizing EEGs can contribute to misdiagnosis, said Selim R. Benbadis, MD, Professor of Neurology and Director of the Comprehensive Epilepsy Program at the University of South Florida in Tampa.

Neurologists overread EEGs “because of the perception that there is less risk in overdiagnosing epilepsy, as opposed to underdiagnosing [the disease], and that is not correct,” said Dr. Benbadis.

The consequences of an epilepsy misdiagnosis can be serious. Patients can lose driving privileges, which may limit their employment opportunities. Epilepsy also is associated with a stigma that can be difficult to dispel, said Dr. Benbadis. In addition, patients misdiagnosed with epilepsy can have side effects from seizure medications.

Why Are EEGs Overread?

Two of the major reasons for misinterpration of EEGs are lack of training and inexperience, said Dr. Benbadis. Currently, it is not mandatory to learn how to read an EEG during neurology residency. Many neurology programs do require EEG training, but many do not. “If you are not experienced in looking at [an EEG], you will overread and think that everything is abnormal,” said Dr. Benbadis. Many normal variants and artifacts can look like epileptiform discharges to neurologists who are inexperienced in reading EEG.

Commonly overread EEG patterns include normal variants such as wicket rhythms, nonspecific temporal fluctuations, and rhythmic midtemporal theta of drowsiness. In addition, one study found that most patients were misdiagnosed with epilepsy because of overread EEGs; nonspecific fluctuations in the temporal region were misread as sharp waves.

The idea that “phase reversals” represent EEG abnormalities is a misconception, said Dr. Benbadis. A phase reversal, which identifies the location of maximum voltage, does not indicate abnormalities. Every normal waveform can have phase reversals, he said. A “history bias” can also lead to a misdiagnosis of epilepsy. For example, if a patient has a history of seizures or suspected seizures, a neurologist might be biased toward a diagnosis of epilepsy, and “look too hard” when reading the EEG, said Dr. Benbadis.

Steps to Improve EEG Interpretation

When deciding whether a discharge is epileptiform, neurologists should look for waves with an asymmetric contour that clearly stand out from the ongoing background of an EEG. About 98% of the time, with clear epileptiform discharges, neurologists can be sure that they indicate epilepsy without knowing the patient’s history, said Dr. Benbadis. Experts should develop consensus guidelines for EEG interpretation, and all neurology residents should be required to train in the EEG laboratory, said Dr. Benbadis. In addition, when there is doubt about whether an EEG was abnormal, “we must obtain the very EEG previously read as abnormal and redo the tracing or consult a colleague,” he added. Patients who have been diagnosed with epilepsy due to an abnormal EEG are encouraged to get a second opinion from an epilepsy or EEG specialist.

—Erica Tricarico

Suggested Reading

Benbadis SR. “Just like EKGs!” Should EEGs undergo a confirmatory interpretation by a clinical neurophysiologist? Neurology. 2013; 80(1 Suppl 1):S47-S51.

During the past 45 years, the cesarean delivery (CD) rate in the United States has increased from 5.5% in 1970 to 33% from 2009 to 2013, followed by a small decrease to 32% in 2014 and 2015.¹ Many clinical problems cause clinicians and patients to decide that CD is an optimal birth route, including: abnormal labor progress, abnormal or indeterminate fetal heart rate pattern, breech presentation, multiple gestation, macrosomia, placental and cord abnormalities, preeclampsia, prior uterine surgery, and prior CD.² Recent secular trends that contribute to the current rate of CD include an adversarial liability environment,^3,4 increasing rates of maternal obesity,⁵ and widespread use of continuous fetal-heart monitoring during labor.⁶

Wide variation in CD rate has been reported among countries, states, and hospitals. The variation is due, in part, to different perspectives about balancing the harms and benefits of vaginal delivery versus CD. In Europe, in 2010 the CD rates in Sweden and Italy were 17.1% and 38%, respectively.⁷ In 2010, among the states, Alaska had the lowest rate of CD at 22% and Kentucky had the highest rate at 40%.⁸ In 2015, the highest rate was 38%, in Mississippi (FIGURE).⁹ In 2014, among Massachusetts hospitals with more than 2,500 births, the CD rate ranged from a low of 22% to a high of 37%.¹⁰

Clinicians, patients, policy experts, and the media are perplexed and troubled by the “high” US CD rate and the major variation in rate among countries, states, and hospitals. Labor management practices likely influence the rate of CD and diverse approaches to labor management likely account for the wide variation in CD rates.

A nationwide effort to standardize and continuously improve labor management might result in a decrease in the CD rate. Building on this opportunity, the American College of Obstetricians and Gynecologists (ACOG) and the Society of Maternal-Fetal Medicine (SMFM) have jointly recommended new labor management guidelines that may reduce the primary CD rate.⁸

The ACOG/SMFM guidelines encourage obstetricians to extend the time for labor progress in both the 1st and 2nd stages prior to recommending a CD.⁸ These new guidelines emphasize that for a modern obstetrician, patience is a virtue. There are 2 important caveats to this statement: to safely extend the length of time of labor requires both (1) a reassuring fetal heart rate tracing and (2) stable maternal health. If the fetus demonstrates a persistent worrisome Category II or a Category IIIheart-rate tracing, decisive intervention is necessary and permitting an extended labor would not be optimal. Similarly, if the mother has rapidly worsening preeclampsia it may not be wise to extend an induction of labor (IOL) over many days.

There are risks with extending the length of labor. An extended duration of the 1st stage of labor is associated with an increased rate of maternal chorioamnionitis and shoulder dystocia at birth.¹¹ An extended duration of the 2nd stage of labor is associated with an increase in the rate of maternal chorioamnionitis, anal sphincter injury, uterine atony, and neonatal admission to an intensive care unit.¹² Clinicians who adopt practices that permit an extended length of labor must weigh the benefits of avoiding a CD against these maternal and fetal complications.

Active phase redefined

Central to the ACOG/SMFM guidelines is a new definition of the active phase of labor. The research of Dr. Emmanuel Friedman indicated that at approximately 4 cm of cervical dilation many women in labor transition from the latent phase, a time of slow change in cervical dilation, to the active phase, a time of more rapid change in cervical dilation.^13,14 However, more recent research indicates that the transition between the latent and active phase is difficult to precisely define, but more often occurs at about 6 cm of cervical dilation and not 4 cm of dilation.¹⁵ Adopting these new norms means that laboring women will spend much more time in the latent phase, a phase of labor in which patience is a virtue.

The ACOG/SMFM guidelines

Main takeaways from the ACOG/SMFM guidelines are summarized below. Interventions that address common obstetric issues and labor abnormalities are outlined below.

Do not perform CD for a prolonged latent phase of labor, defined as regular contractions of >20 hours duration in nulliparous women and >14 hours duration in multiparous women. Patience with a prolonged latent phase will be rewarded by the majority of women entering the active phase of labor. Alternatively, if appropriate, cervical ripening followed by oxytocin IOL and amniotomy will help the patient with a prolonged latent phase to enter the active phase of labor.¹⁶

For women with an unfavorable cervix as assessed by the Bishop score, cervical ripening should be performed prior to IOL. Use of cervical ripening prior to IOL increases the chance of achieving vaginal delivery within 24 hours and may result in a modest decrease in the rate of CD.^17,18

Related article:
Should oxytocin and a Foley catheter be used concurrently for cervical ripening in induction of labor?
 

Failed IOL in the latent phase should only be diagnosed following 12 to 18 hours of both ruptured membranes and adequate contractions stimulated with oxytocin. The key ingredients for the successful management of the latent phase of labor are patience, oxytocin, and amniotomy.¹⁶

CD for the indication of active phase arrest requires cervical dilation ≥6 cm with ruptured membranes and no change in cervical dilation for ≥4 hours of adequate uterine activity. In the past, most obstetricians defined active phase arrest, a potential indication for CD, as the absence of cervical change for 2 or more hours in the presence of adequate uterine contractions and cervical dilation of at least 4 cm. Given the new definition of active phase arrest, slow but progressive progress in the 1st stage of labor is not an indication for CD.^11,19

“A specific absolute maximum length of time spent in the 2nd stage beyond which all women should be offered an operative delivery has not been identified.”⁸ Diagnosis of arrest of labor in the 2nd stage may be considered after at least 2 hours of pushing in multiparous women and 3 hours of pushing in nulliparous women, especially if no fetal descent is occurring. The guidelines also state “longer durations may be appropriate on an individualized basis (eg, with use of epidural analgesia or with fetal malposition)” as long as fetal descent is observed.

Patience is a virtue, especially in the management of the 2nd stage of labor. Extending the 2nd stage up to 4 hours appears to be reasonably safe if the fetal status is reassuring and the mother is physiologically stable. In a study from San Francisco of 42,268 births with normal newborn outcomes, the 95th percentile for the length of the 2nd stage of labor for nulliparous women was 3.3 hours without an epidural and 5.6 hours with an epidural.²⁰

In a study of 53,285 births, longer duration of pushing was associated with a small increase in the rate of neonatal adverse outcomes. In nulliparous women the rate of adverse neonatal outcomes increased from 1.3% with less than 60 minutes of pushing to 2.4% with greater than 240 minutes of pushing. Remarkably, even after 4 hours of pushing, 78% of nulliparous women who continued to push had a vaginal delivery.²¹ In this study, among nulliparous women the rate of anal sphincter injury increased from 5% with less than 60 minutes of pushing to 16% with greater than 240 minutes of pushing, and the rate of postpartum hemorrhage increased from 1% with less than 60 minutes of pushing to 3.3% with greater than 240 minutes of pushing.

I am not enthusiastic about patiently watching a labor extend into the 5th hour of the 2nd stage, especially if the fetus is at +2 station or lower. In a nulliparous woman, after 4 hours of managing the 2nd stage of labor, my patience is exhausted and I am inclined to identify a clear plan for delivery, either by enhanced labor coaching, operative vaginal delivery, or CD.

Operative vaginal delivery in the 2nd stage of labor is an acceptable alternative to CD. The rate of operative vaginal delivery in the United States has declined over the past 2 decades (TABLE). In Sweden in 2010 the operative vaginal delivery rate was 7.6% with a CD rate of 17.1%.⁷ In the United States in 2010 the operative delivery rate was 3.6%, and the CD rate was 33%.¹ A renewed focus on operative vaginal delivery with ongoing training and team simulation for the procedure would increase our use of operative delivery and decrease the overall rate of CD.

Related article:
STOP using instruments to assist with delivery of the head at cesarean
 

Encourage the detection of persistent fetal occiput posterior position by physical examination and/or ultrasound and consider manual rotation of the fetal occiput from the posterior to anterior position in the 2nd stage. Persistent occiput posterior is the most common fetal malposition.²² This malposition is associated with an increased rate of CD.²³ There are few randomized trials of manual rotation of the fetal occiput from posterior to anterior position in the 2nd stage of labor, and the evidence is insufficient to determine the efficacy of manual rotation.²⁴ Small nonrandomized studies report that manual rotation of the occiput from posterior to anterior position may reduce the CD rate.^25–27

For persistent 2nd stage fetal occiput posterior position in a woman with an adequate pelvis, where manual rotation was not successful and the fetus is at +2 station or below, operative vaginal delivery is an option. “Vacuum or forceps?” and “If forceps, to rotate or not to rotate?” those are the clinical questions. Forceps delivery is more likely to be successfulthan vacuum delivery.²⁸ Direct forceps delivery of the occiput posterior fetus is associated with more anal sphincter injuries than forceps delivery after successful rotation, but few clinicians regularly perform rotational forceps.²⁹ In a study of 2,351 women in the 2nd stage of labor with the fetus at +2 station or below, compared with either forceps or vacuum delivery, CD was associated with more maternal infections and fewer perineal lacerations. Neonatal composite morbidity was not significantly different among the 3 routes of operative delivery.³⁰

Amnioinfusion for repetitive variable decelerations of the fetal heart rate may reduce the risk of CD for an indeterminate fetal heart-rate pattern.³¹

IOL in a well-dated pregnancy at 41 weeks will reduce the risk of CD. In a large clinical trial, 3,407 women at 41 weeks of gestation were randomly assigned to IOL or expectant management. The rate of CD was significantly lower in the women assigned to IOL compared with expectant management (21% vs 25%, respectively; P = .03).³² The rate of neonatal morbidity was similar in the 2 groups.

Women with twin gestations and the first twin in a cephalic presentation may elect vaginal delivery. In a large clinical trial, 1,398 women with a twin gestation and the first twin in a cephalic presentation were randomly assigned to planned vaginal delivery (with cesarean only if necessary) or planned CD.³³ The rate of CD was 44% and 91% for the women in the planned-vaginal and planned-cesarean groups, respectively. There was no significant difference in composite fetal or neonatal death or serious morbidity. The authors concluded that, for twin pregnancy with the presenting twin in the cephalic presentation, there were no demonstrated benefits of planned CD.

Develop maternity care systems that encourage the use of trial of labor after cesarean (TOLAC). The ACOG/SMFM guidelines focus on interventions to reduce the rate of primary CD and do not address the role of TOLAC in reducing CD rates. There are little data from clinical trials to assess the benefits and harms from TOLAC versus scheduled repeat CD.³⁴ However, our experience with TOLAC in the 1990s strongly suggests that encouraging TOLAC will decrease the rate of CD. In 1996 the US rate of vaginal birth after cesarean (VBAC) peaked at 28%, and the rate of CD achieved a recent historic nadir of 21%. Growing concerns that TOLAC occasionally results in fetal harm was followed by a decrease in the VBAC rate to 12% in 2015.¹ A recent study of obstetric practices in countries with high and low VBAC rates concluded that patient and clinician commitment and comfort with prioritizing TOLAC over scheduled repeat CD greatly influenced the VBAC rate.³⁵

Related article:
Should lower uterine segment thickness measurement be included in the TOLAC decision-making process?

Labor management is an art

During labor obstetricians must balance the unique needs of mother and fetus, which requires great clinical skill and patience. Evolving concepts of normal labor progress necessitate that we change our expectations concerning the acceptable rate of progress in the 1st and 2nd stage of labor. Consistent application of these new labor guidelines may help to reduce the rate of CD.

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

During the past 45 years, the cesarean delivery (CD) rate in the United States has increased from 5.5% in 1970 to 33% from 2009 to 2013, followed by a small decrease to 32% in 2014 and 2015.¹ Many clinical problems cause clinicians and patients to decide that CD is an optimal birth route, including: abnormal labor progress, abnormal or indeterminate fetal heart rate pattern, breech presentation, multiple gestation, macrosomia, placental and cord abnormalities, preeclampsia, prior uterine surgery, and prior CD.² Recent secular trends that contribute to the current rate of CD include an adversarial liability environment,^3,4 increasing rates of maternal obesity,⁵ and widespread use of continuous fetal-heart monitoring during labor.⁶

Wide variation in CD rate has been reported among countries, states, and hospitals. The variation is due, in part, to different perspectives about balancing the harms and benefits of vaginal delivery versus CD. In Europe, in 2010 the CD rates in Sweden and Italy were 17.1% and 38%, respectively.⁷ In 2010, among the states, Alaska had the lowest rate of CD at 22% and Kentucky had the highest rate at 40%.⁸ In 2015, the highest rate was 38%, in Mississippi (FIGURE).⁹ In 2014, among Massachusetts hospitals with more than 2,500 births, the CD rate ranged from a low of 22% to a high of 37%.¹⁰

Clinicians, patients, policy experts, and the media are perplexed and troubled by the “high” US CD rate and the major variation in rate among countries, states, and hospitals. Labor management practices likely influence the rate of CD and diverse approaches to labor management likely account for the wide variation in CD rates.

A nationwide effort to standardize and continuously improve labor management might result in a decrease in the CD rate. Building on this opportunity, the American College of Obstetricians and Gynecologists (ACOG) and the Society of Maternal-Fetal Medicine (SMFM) have jointly recommended new labor management guidelines that may reduce the primary CD rate.⁸

The ACOG/SMFM guidelines encourage obstetricians to extend the time for labor progress in both the 1st and 2nd stages prior to recommending a CD.⁸ These new guidelines emphasize that for a modern obstetrician, patience is a virtue. There are 2 important caveats to this statement: to safely extend the length of time of labor requires both (1) a reassuring fetal heart rate tracing and (2) stable maternal health. If the fetus demonstrates a persistent worrisome Category II or a Category IIIheart-rate tracing, decisive intervention is necessary and permitting an extended labor would not be optimal. Similarly, if the mother has rapidly worsening preeclampsia it may not be wise to extend an induction of labor (IOL) over many days.

There are risks with extending the length of labor. An extended duration of the 1st stage of labor is associated with an increased rate of maternal chorioamnionitis and shoulder dystocia at birth.¹¹ An extended duration of the 2nd stage of labor is associated with an increase in the rate of maternal chorioamnionitis, anal sphincter injury, uterine atony, and neonatal admission to an intensive care unit.¹² Clinicians who adopt practices that permit an extended length of labor must weigh the benefits of avoiding a CD against these maternal and fetal complications.

Active phase redefined

Central to the ACOG/SMFM guidelines is a new definition of the active phase of labor. The research of Dr. Emmanuel Friedman indicated that at approximately 4 cm of cervical dilation many women in labor transition from the latent phase, a time of slow change in cervical dilation, to the active phase, a time of more rapid change in cervical dilation.^13,14 However, more recent research indicates that the transition between the latent and active phase is difficult to precisely define, but more often occurs at about 6 cm of cervical dilation and not 4 cm of dilation.¹⁵ Adopting these new norms means that laboring women will spend much more time in the latent phase, a phase of labor in which patience is a virtue.

The ACOG/SMFM guidelines

Main takeaways from the ACOG/SMFM guidelines are summarized below. Interventions that address common obstetric issues and labor abnormalities are outlined below.

Do not perform CD for a prolonged latent phase of labor, defined as regular contractions of >20 hours duration in nulliparous women and >14 hours duration in multiparous women. Patience with a prolonged latent phase will be rewarded by the majority of women entering the active phase of labor. Alternatively, if appropriate, cervical ripening followed by oxytocin IOL and amniotomy will help the patient with a prolonged latent phase to enter the active phase of labor.¹⁶

For women with an unfavorable cervix as assessed by the Bishop score, cervical ripening should be performed prior to IOL. Use of cervical ripening prior to IOL increases the chance of achieving vaginal delivery within 24 hours and may result in a modest decrease in the rate of CD.^17,18

Related article:
Should oxytocin and a Foley catheter be used concurrently for cervical ripening in induction of labor?
 

Failed IOL in the latent phase should only be diagnosed following 12 to 18 hours of both ruptured membranes and adequate contractions stimulated with oxytocin. The key ingredients for the successful management of the latent phase of labor are patience, oxytocin, and amniotomy.¹⁶

CD for the indication of active phase arrest requires cervical dilation ≥6 cm with ruptured membranes and no change in cervical dilation for ≥4 hours of adequate uterine activity. In the past, most obstetricians defined active phase arrest, a potential indication for CD, as the absence of cervical change for 2 or more hours in the presence of adequate uterine contractions and cervical dilation of at least 4 cm. Given the new definition of active phase arrest, slow but progressive progress in the 1st stage of labor is not an indication for CD.^11,19

“A specific absolute maximum length of time spent in the 2nd stage beyond which all women should be offered an operative delivery has not been identified.”⁸ Diagnosis of arrest of labor in the 2nd stage may be considered after at least 2 hours of pushing in multiparous women and 3 hours of pushing in nulliparous women, especially if no fetal descent is occurring. The guidelines also state “longer durations may be appropriate on an individualized basis (eg, with use of epidural analgesia or with fetal malposition)” as long as fetal descent is observed.

Patience is a virtue, especially in the management of the 2nd stage of labor. Extending the 2nd stage up to 4 hours appears to be reasonably safe if the fetal status is reassuring and the mother is physiologically stable. In a study from San Francisco of 42,268 births with normal newborn outcomes, the 95th percentile for the length of the 2nd stage of labor for nulliparous women was 3.3 hours without an epidural and 5.6 hours with an epidural.²⁰

In a study of 53,285 births, longer duration of pushing was associated with a small increase in the rate of neonatal adverse outcomes. In nulliparous women the rate of adverse neonatal outcomes increased from 1.3% with less than 60 minutes of pushing to 2.4% with greater than 240 minutes of pushing. Remarkably, even after 4 hours of pushing, 78% of nulliparous women who continued to push had a vaginal delivery.²¹ In this study, among nulliparous women the rate of anal sphincter injury increased from 5% with less than 60 minutes of pushing to 16% with greater than 240 minutes of pushing, and the rate of postpartum hemorrhage increased from 1% with less than 60 minutes of pushing to 3.3% with greater than 240 minutes of pushing.

I am not enthusiastic about patiently watching a labor extend into the 5th hour of the 2nd stage, especially if the fetus is at +2 station or lower. In a nulliparous woman, after 4 hours of managing the 2nd stage of labor, my patience is exhausted and I am inclined to identify a clear plan for delivery, either by enhanced labor coaching, operative vaginal delivery, or CD.

Operative vaginal delivery in the 2nd stage of labor is an acceptable alternative to CD. The rate of operative vaginal delivery in the United States has declined over the past 2 decades (TABLE). In Sweden in 2010 the operative vaginal delivery rate was 7.6% with a CD rate of 17.1%.⁷ In the United States in 2010 the operative delivery rate was 3.6%, and the CD rate was 33%.¹ A renewed focus on operative vaginal delivery with ongoing training and team simulation for the procedure would increase our use of operative delivery and decrease the overall rate of CD.

Related article:
STOP using instruments to assist with delivery of the head at cesarean
 

Encourage the detection of persistent fetal occiput posterior position by physical examination and/or ultrasound and consider manual rotation of the fetal occiput from the posterior to anterior position in the 2nd stage. Persistent occiput posterior is the most common fetal malposition.²² This malposition is associated with an increased rate of CD.²³ There are few randomized trials of manual rotation of the fetal occiput from posterior to anterior position in the 2nd stage of labor, and the evidence is insufficient to determine the efficacy of manual rotation.²⁴ Small nonrandomized studies report that manual rotation of the occiput from posterior to anterior position may reduce the CD rate.^25–27

For persistent 2nd stage fetal occiput posterior position in a woman with an adequate pelvis, where manual rotation was not successful and the fetus is at +2 station or below, operative vaginal delivery is an option. “Vacuum or forceps?” and “If forceps, to rotate or not to rotate?” those are the clinical questions. Forceps delivery is more likely to be successfulthan vacuum delivery.²⁸ Direct forceps delivery of the occiput posterior fetus is associated with more anal sphincter injuries than forceps delivery after successful rotation, but few clinicians regularly perform rotational forceps.²⁹ In a study of 2,351 women in the 2nd stage of labor with the fetus at +2 station or below, compared with either forceps or vacuum delivery, CD was associated with more maternal infections and fewer perineal lacerations. Neonatal composite morbidity was not significantly different among the 3 routes of operative delivery.³⁰

Amnioinfusion for repetitive variable decelerations of the fetal heart rate may reduce the risk of CD for an indeterminate fetal heart-rate pattern.³¹

IOL in a well-dated pregnancy at 41 weeks will reduce the risk of CD. In a large clinical trial, 3,407 women at 41 weeks of gestation were randomly assigned to IOL or expectant management. The rate of CD was significantly lower in the women assigned to IOL compared with expectant management (21% vs 25%, respectively; P = .03).³² The rate of neonatal morbidity was similar in the 2 groups.

Women with twin gestations and the first twin in a cephalic presentation may elect vaginal delivery. In a large clinical trial, 1,398 women with a twin gestation and the first twin in a cephalic presentation were randomly assigned to planned vaginal delivery (with cesarean only if necessary) or planned CD.³³ The rate of CD was 44% and 91% for the women in the planned-vaginal and planned-cesarean groups, respectively. There was no significant difference in composite fetal or neonatal death or serious morbidity. The authors concluded that, for twin pregnancy with the presenting twin in the cephalic presentation, there were no demonstrated benefits of planned CD.

Develop maternity care systems that encourage the use of trial of labor after cesarean (TOLAC). The ACOG/SMFM guidelines focus on interventions to reduce the rate of primary CD and do not address the role of TOLAC in reducing CD rates. There are little data from clinical trials to assess the benefits and harms from TOLAC versus scheduled repeat CD.³⁴ However, our experience with TOLAC in the 1990s strongly suggests that encouraging TOLAC will decrease the rate of CD. In 1996 the US rate of vaginal birth after cesarean (VBAC) peaked at 28%, and the rate of CD achieved a recent historic nadir of 21%. Growing concerns that TOLAC occasionally results in fetal harm was followed by a decrease in the VBAC rate to 12% in 2015.¹ A recent study of obstetric practices in countries with high and low VBAC rates concluded that patient and clinician commitment and comfort with prioritizing TOLAC over scheduled repeat CD greatly influenced the VBAC rate.³⁵

Related article:
Should lower uterine segment thickness measurement be included in the TOLAC decision-making process?

Labor management is an art

During labor obstetricians must balance the unique needs of mother and fetus, which requires great clinical skill and patience. Evolving concepts of normal labor progress necessitate that we change our expectations concerning the acceptable rate of progress in the 1st and 2nd stage of labor. Consistent application of these new labor guidelines may help to reduce the rate of CD.

Share your thoughts! Send your Letter to the Editor to [email protected]. Please include your name and the city and state in which you practice.

During the past 45 years, the cesarean delivery (CD) rate in the United States has increased from 5.5% in 1970 to 33% from 2009 to 2013, followed by a small decrease to 32% in 2014 and 2015.¹ Many clinical problems cause clinicians and patients to decide that CD is an optimal birth route, including: abnormal labor progress, abnormal or indeterminate fetal heart rate pattern, breech presentation, multiple gestation, macrosomia, placental and cord abnormalities, preeclampsia, prior uterine surgery, and prior CD.² Recent secular trends that contribute to the current rate of CD include an adversarial liability environment,^3,4 increasing rates of maternal obesity,⁵ and widespread use of continuous fetal-heart monitoring during labor.⁶

Wide variation in CD rate has been reported among countries, states, and hospitals. The variation is due, in part, to different perspectives about balancing the harms and benefits of vaginal delivery versus CD. In Europe, in 2010 the CD rates in Sweden and Italy were 17.1% and 38%, respectively.⁷ In 2010, among the states, Alaska had the lowest rate of CD at 22% and Kentucky had the highest rate at 40%.⁸ In 2015, the highest rate was 38%, in Mississippi (FIGURE).⁹ In 2014, among Massachusetts hospitals with more than 2,500 births, the CD rate ranged from a low of 22% to a high of 37%.¹⁰

Clinicians, patients, policy experts, and the media are perplexed and troubled by the “high” US CD rate and the major variation in rate among countries, states, and hospitals. Labor management practices likely influence the rate of CD and diverse approaches to labor management likely account for the wide variation in CD rates.

A nationwide effort to standardize and continuously improve labor management might result in a decrease in the CD rate. Building on this opportunity, the American College of Obstetricians and Gynecologists (ACOG) and the Society of Maternal-Fetal Medicine (SMFM) have jointly recommended new labor management guidelines that may reduce the primary CD rate.⁸

The ACOG/SMFM guidelines encourage obstetricians to extend the time for labor progress in both the 1st and 2nd stages prior to recommending a CD.⁸ These new guidelines emphasize that for a modern obstetrician, patience is a virtue. There are 2 important caveats to this statement: to safely extend the length of time of labor requires both (1) a reassuring fetal heart rate tracing and (2) stable maternal health. If the fetus demonstrates a persistent worrisome Category II or a Category IIIheart-rate tracing, decisive intervention is necessary and permitting an extended labor would not be optimal. Similarly, if the mother has rapidly worsening preeclampsia it may not be wise to extend an induction of labor (IOL) over many days.

There are risks with extending the length of labor. An extended duration of the 1st stage of labor is associated with an increased rate of maternal chorioamnionitis and shoulder dystocia at birth.¹¹ An extended duration of the 2nd stage of labor is associated with an increase in the rate of maternal chorioamnionitis, anal sphincter injury, uterine atony, and neonatal admission to an intensive care unit.¹² Clinicians who adopt practices that permit an extended length of labor must weigh the benefits of avoiding a CD against these maternal and fetal complications.

Active phase redefined

Central to the ACOG/SMFM guidelines is a new definition of the active phase of labor. The research of Dr. Emmanuel Friedman indicated that at approximately 4 cm of cervical dilation many women in labor transition from the latent phase, a time of slow change in cervical dilation, to the active phase, a time of more rapid change in cervical dilation.^13,14 However, more recent research indicates that the transition between the latent and active phase is difficult to precisely define, but more often occurs at about 6 cm of cervical dilation and not 4 cm of dilation.¹⁵ Adopting these new norms means that laboring women will spend much more time in the latent phase, a phase of labor in which patience is a virtue.

The ACOG/SMFM guidelines

Main takeaways from the ACOG/SMFM guidelines are summarized below. Interventions that address common obstetric issues and labor abnormalities are outlined below.

Do not perform CD for a prolonged latent phase of labor, defined as regular contractions of >20 hours duration in nulliparous women and >14 hours duration in multiparous women. Patience with a prolonged latent phase will be rewarded by the majority of women entering the active phase of labor. Alternatively, if appropriate, cervical ripening followed by oxytocin IOL and amniotomy will help the patient with a prolonged latent phase to enter the active phase of labor.¹⁶

For women with an unfavorable cervix as assessed by the Bishop score, cervical ripening should be performed prior to IOL. Use of cervical ripening prior to IOL increases the chance of achieving vaginal delivery within 24 hours and may result in a modest decrease in the rate of CD.^17,18

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Failed IOL in the latent phase should only be diagnosed following 12 to 18 hours of both ruptured membranes and adequate contractions stimulated with oxytocin. The key ingredients for the successful management of the latent phase of labor are patience, oxytocin, and amniotomy.¹⁶

CD for the indication of active phase arrest requires cervical dilation ≥6 cm with ruptured membranes and no change in cervical dilation for ≥4 hours of adequate uterine activity. In the past, most obstetricians defined active phase arrest, a potential indication for CD, as the absence of cervical change for 2 or more hours in the presence of adequate uterine contractions and cervical dilation of at least 4 cm. Given the new definition of active phase arrest, slow but progressive progress in the 1st stage of labor is not an indication for CD.^11,19

“A specific absolute maximum length of time spent in the 2nd stage beyond which all women should be offered an operative delivery has not been identified.”⁸ Diagnosis of arrest of labor in the 2nd stage may be considered after at least 2 hours of pushing in multiparous women and 3 hours of pushing in nulliparous women, especially if no fetal descent is occurring. The guidelines also state “longer durations may be appropriate on an individualized basis (eg, with use of epidural analgesia or with fetal malposition)” as long as fetal descent is observed.

Patience is a virtue, especially in the management of the 2nd stage of labor. Extending the 2nd stage up to 4 hours appears to be reasonably safe if the fetal status is reassuring and the mother is physiologically stable. In a study from San Francisco of 42,268 births with normal newborn outcomes, the 95th percentile for the length of the 2nd stage of labor for nulliparous women was 3.3 hours without an epidural and 5.6 hours with an epidural.²⁰

In a study of 53,285 births, longer duration of pushing was associated with a small increase in the rate of neonatal adverse outcomes. In nulliparous women the rate of adverse neonatal outcomes increased from 1.3% with less than 60 minutes of pushing to 2.4% with greater than 240 minutes of pushing. Remarkably, even after 4 hours of pushing, 78% of nulliparous women who continued to push had a vaginal delivery.²¹ In this study, among nulliparous women the rate of anal sphincter injury increased from 5% with less than 60 minutes of pushing to 16% with greater than 240 minutes of pushing, and the rate of postpartum hemorrhage increased from 1% with less than 60 minutes of pushing to 3.3% with greater than 240 minutes of pushing.

I am not enthusiastic about patiently watching a labor extend into the 5th hour of the 2nd stage, especially if the fetus is at +2 station or lower. In a nulliparous woman, after 4 hours of managing the 2nd stage of labor, my patience is exhausted and I am inclined to identify a clear plan for delivery, either by enhanced labor coaching, operative vaginal delivery, or CD.

Operative vaginal delivery in the 2nd stage of labor is an acceptable alternative to CD. The rate of operative vaginal delivery in the United States has declined over the past 2 decades (TABLE). In Sweden in 2010 the operative vaginal delivery rate was 7.6% with a CD rate of 17.1%.⁷ In the United States in 2010 the operative delivery rate was 3.6%, and the CD rate was 33%.¹ A renewed focus on operative vaginal delivery with ongoing training and team simulation for the procedure would increase our use of operative delivery and decrease the overall rate of CD.

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Encourage the detection of persistent fetal occiput posterior position by physical examination and/or ultrasound and consider manual rotation of the fetal occiput from the posterior to anterior position in the 2nd stage. Persistent occiput posterior is the most common fetal malposition.²² This malposition is associated with an increased rate of CD.²³ There are few randomized trials of manual rotation of the fetal occiput from posterior to anterior position in the 2nd stage of labor, and the evidence is insufficient to determine the efficacy of manual rotation.²⁴ Small nonrandomized studies report that manual rotation of the occiput from posterior to anterior position may reduce the CD rate.^25–27

For persistent 2nd stage fetal occiput posterior position in a woman with an adequate pelvis, where manual rotation was not successful and the fetus is at +2 station or below, operative vaginal delivery is an option. “Vacuum or forceps?” and “If forceps, to rotate or not to rotate?” those are the clinical questions. Forceps delivery is more likely to be successfulthan vacuum delivery.²⁸ Direct forceps delivery of the occiput posterior fetus is associated with more anal sphincter injuries than forceps delivery after successful rotation, but few clinicians regularly perform rotational forceps.²⁹ In a study of 2,351 women in the 2nd stage of labor with the fetus at +2 station or below, compared with either forceps or vacuum delivery, CD was associated with more maternal infections and fewer perineal lacerations. Neonatal composite morbidity was not significantly different among the 3 routes of operative delivery.³⁰

Amnioinfusion for repetitive variable decelerations of the fetal heart rate may reduce the risk of CD for an indeterminate fetal heart-rate pattern.³¹

IOL in a well-dated pregnancy at 41 weeks will reduce the risk of CD. In a large clinical trial, 3,407 women at 41 weeks of gestation were randomly assigned to IOL or expectant management. The rate of CD was significantly lower in the women assigned to IOL compared with expectant management (21% vs 25%, respectively; P = .03).³² The rate of neonatal morbidity was similar in the 2 groups.

Women with twin gestations and the first twin in a cephalic presentation may elect vaginal delivery. In a large clinical trial, 1,398 women with a twin gestation and the first twin in a cephalic presentation were randomly assigned to planned vaginal delivery (with cesarean only if necessary) or planned CD.³³ The rate of CD was 44% and 91% for the women in the planned-vaginal and planned-cesarean groups, respectively. There was no significant difference in composite fetal or neonatal death or serious morbidity. The authors concluded that, for twin pregnancy with the presenting twin in the cephalic presentation, there were no demonstrated benefits of planned CD.

Develop maternity care systems that encourage the use of trial of labor after cesarean (TOLAC). The ACOG/SMFM guidelines focus on interventions to reduce the rate of primary CD and do not address the role of TOLAC in reducing CD rates. There are little data from clinical trials to assess the benefits and harms from TOLAC versus scheduled repeat CD.³⁴ However, our experience with TOLAC in the 1990s strongly suggests that encouraging TOLAC will decrease the rate of CD. In 1996 the US rate of vaginal birth after cesarean (VBAC) peaked at 28%, and the rate of CD achieved a recent historic nadir of 21%. Growing concerns that TOLAC occasionally results in fetal harm was followed by a decrease in the VBAC rate to 12% in 2015.¹ A recent study of obstetric practices in countries with high and low VBAC rates concluded that patient and clinician commitment and comfort with prioritizing TOLAC over scheduled repeat CD greatly influenced the VBAC rate.³⁵

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Labor management is an art

During labor obstetricians must balance the unique needs of mother and fetus, which requires great clinical skill and patience. Evolving concepts of normal labor progress necessitate that we change our expectations concerning the acceptable rate of progress in the 1st and 2nd stage of labor. Consistent application of these new labor guidelines may help to reduce the rate of CD.

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