There is only one annual meeting dedicated to hospitalists, designed by hospitalists, and focusing purely on issues important to hospitalists. But even that isn’t enough to make sure more hospitalists show up every year.

That’s because a yearly conference can’t just be a rehash of the last one.
 

A valuable conference, certainly one worth spending the bulk of a continuing medical budget on, offers something new every year. Or, to look at the schedule for HM17, a lot of new every year.

Meeting evolving needs

New offerings aren’t limited to the main conference schedule. The 2017 roster of pre-courses includes one titled, “Bugs, Drugs and You: Infectious Diseases ‘Boot Camp’ for Hospitalists.” This daylong session hasn’t been held since 2013, and copresenter Jennifer Hanrahan, DO, associate professor of medicine at Case Western Reserve University in Cleveland, says the timing is good.

“I don’t know that the percentage of people hospitalized for infection has increased,” she said. “Because we are doing things more quickly than we did in the past, length of stays are shorter and there is a lot of pressure to get patients out of the hospital. There is a lot of consultation with Infectious Disease.”

Dr. Hanrahan, who also serves as medical director of infection prevention at Cleveland’s MetroHealth Medical Center, says that with so many patients hospitalized for infections, the value of updating one’s knowledge every few years is critical.

“I’ve been an infectious disease physician for 18 years and I’m also a hospitalist,” she said. “The types of questions I get vary a great deal depending on the experience of the hospitalist. My hope would be that we would be able to provide a basic level of understanding so that people would be more confident in approaching these problems.”

Another new feature this year is offer some of the most popular sessions at multiple times. In years past, popular sessions – such as “Update and Pearls in Infectious Diseases” and “Non–Evidence-Based Medicine: Things We Do for No Reason” – are standing room only events with attendees sitting on floors or gathered to eavesdrop from doorways.

“That says something about the content that’s being delivered, but that’s not very comfortable for folks who want to sit through a session,” Dr. Feldman said. “We’ve decided to add repeat sessions of popular presentations. We want everyone to be comfortable while they’re learning the important clinical content that’s being delivered at these sessions.”

The 2017 focus on healthcare policy is also new. Educational sessions on the policy landscape will be formally buttressed by plenary presentations from Patrick Conway, MD, MSc, MHM, deputy administrator for Innovation and Quality at the Centers for Medicare & Medicaid Services and director of the Center for Medicare and Medicaid Innovation, and Karen DeSalvo, MD, MPH, MSc, a former acting assistant secretary for health at the U.S. Department of Health and Human Services and national coordinator for health information technology.

“There’s a thirst for (policy news) among members of the Society of Hospital Medicine,” Dr. Feldman said. “It is easy to get lost in the day-to-day work that we do, but I think most of us really enjoy hearing about the bigger picture, especially when the bigger picture is in flux.”

“Right now, this is critical,” added Dr. Finn. “Health insurance coverage has a huge impact on hospitals. I think all practicing hospitalists will need to engage with the hospital C-suite if insurance and coverage changes. Since we are hospital based, we are directly tied to anything that the federal government does in terms of health care changes. It’s important for hospitalists to be knowledgeable about health policy.”

One major highlight of the meeting calendar – less new and more historically under-appreciated, in Dr. Feldman’s view – should be the 18 workshop presentations, which are essentially 90-minute dissertations, whittled down from roughly 150 submissions.

“These are the best submissions that we received,” Dr. Feldman said. “We worked hard to make sure that the workshops encompass the breadth and depth of hospital medicine. It is not just one area that’s covered in every workshop. We’ll have workshops ranging from clinical reasoning and communication with patients, to quality improvement issues and high value care discussions, as well as a case-based approach to inpatient dermatology.”

While annual meetings’ new offerings are always an important draw, Dr. Feldman says that the annual “standbys,” such as practice management and pediatrics, are necessary to keep attendees up to date on best practices in changing times.

“It’s pretty self-evident that if we’re going to be an important specialty, we need to serve those who are caring for patients day in and day out, as well as folks who are researching how we can do it better,” he said. “Then we must make sure that data is disseminated to all of us who are taking care of patients. That’s one of the really important parts of this meeting: dissemination of the important work.”

There is only one annual meeting dedicated to hospitalists, designed by hospitalists, and focusing purely on issues important to hospitalists. But even that isn’t enough to make sure more hospitalists show up every year.

That’s because a yearly conference can’t just be a rehash of the last one.
 

A valuable conference, certainly one worth spending the bulk of a continuing medical budget on, offers something new every year. Or, to look at the schedule for HM17, a lot of new every year.

Meeting evolving needs

New offerings aren’t limited to the main conference schedule. The 2017 roster of pre-courses includes one titled, “Bugs, Drugs and You: Infectious Diseases ‘Boot Camp’ for Hospitalists.” This daylong session hasn’t been held since 2013, and copresenter Jennifer Hanrahan, DO, associate professor of medicine at Case Western Reserve University in Cleveland, says the timing is good.

“I don’t know that the percentage of people hospitalized for infection has increased,” she said. “Because we are doing things more quickly than we did in the past, length of stays are shorter and there is a lot of pressure to get patients out of the hospital. There is a lot of consultation with Infectious Disease.”

Dr. Hanrahan, who also serves as medical director of infection prevention at Cleveland’s MetroHealth Medical Center, says that with so many patients hospitalized for infections, the value of updating one’s knowledge every few years is critical.

“I’ve been an infectious disease physician for 18 years and I’m also a hospitalist,” she said. “The types of questions I get vary a great deal depending on the experience of the hospitalist. My hope would be that we would be able to provide a basic level of understanding so that people would be more confident in approaching these problems.”

Another new feature this year is offer some of the most popular sessions at multiple times. In years past, popular sessions – such as “Update and Pearls in Infectious Diseases” and “Non–Evidence-Based Medicine: Things We Do for No Reason” – are standing room only events with attendees sitting on floors or gathered to eavesdrop from doorways.

“That says something about the content that’s being delivered, but that’s not very comfortable for folks who want to sit through a session,” Dr. Feldman said. “We’ve decided to add repeat sessions of popular presentations. We want everyone to be comfortable while they’re learning the important clinical content that’s being delivered at these sessions.”

The 2017 focus on healthcare policy is also new. Educational sessions on the policy landscape will be formally buttressed by plenary presentations from Patrick Conway, MD, MSc, MHM, deputy administrator for Innovation and Quality at the Centers for Medicare & Medicaid Services and director of the Center for Medicare and Medicaid Innovation, and Karen DeSalvo, MD, MPH, MSc, a former acting assistant secretary for health at the U.S. Department of Health and Human Services and national coordinator for health information technology.

“There’s a thirst for (policy news) among members of the Society of Hospital Medicine,” Dr. Feldman said. “It is easy to get lost in the day-to-day work that we do, but I think most of us really enjoy hearing about the bigger picture, especially when the bigger picture is in flux.”

“Right now, this is critical,” added Dr. Finn. “Health insurance coverage has a huge impact on hospitals. I think all practicing hospitalists will need to engage with the hospital C-suite if insurance and coverage changes. Since we are hospital based, we are directly tied to anything that the federal government does in terms of health care changes. It’s important for hospitalists to be knowledgeable about health policy.”

One major highlight of the meeting calendar – less new and more historically under-appreciated, in Dr. Feldman’s view – should be the 18 workshop presentations, which are essentially 90-minute dissertations, whittled down from roughly 150 submissions.

“These are the best submissions that we received,” Dr. Feldman said. “We worked hard to make sure that the workshops encompass the breadth and depth of hospital medicine. It is not just one area that’s covered in every workshop. We’ll have workshops ranging from clinical reasoning and communication with patients, to quality improvement issues and high value care discussions, as well as a case-based approach to inpatient dermatology.”

While annual meetings’ new offerings are always an important draw, Dr. Feldman says that the annual “standbys,” such as practice management and pediatrics, are necessary to keep attendees up to date on best practices in changing times.

“It’s pretty self-evident that if we’re going to be an important specialty, we need to serve those who are caring for patients day in and day out, as well as folks who are researching how we can do it better,” he said. “Then we must make sure that data is disseminated to all of us who are taking care of patients. That’s one of the really important parts of this meeting: dissemination of the important work.”

There is only one annual meeting dedicated to hospitalists, designed by hospitalists, and focusing purely on issues important to hospitalists. But even that isn’t enough to make sure more hospitalists show up every year.

That’s because a yearly conference can’t just be a rehash of the last one.
 

A valuable conference, certainly one worth spending the bulk of a continuing medical budget on, offers something new every year. Or, to look at the schedule for HM17, a lot of new every year.

Meeting evolving needs

New offerings aren’t limited to the main conference schedule. The 2017 roster of pre-courses includes one titled, “Bugs, Drugs and You: Infectious Diseases ‘Boot Camp’ for Hospitalists.” This daylong session hasn’t been held since 2013, and copresenter Jennifer Hanrahan, DO, associate professor of medicine at Case Western Reserve University in Cleveland, says the timing is good.

“I don’t know that the percentage of people hospitalized for infection has increased,” she said. “Because we are doing things more quickly than we did in the past, length of stays are shorter and there is a lot of pressure to get patients out of the hospital. There is a lot of consultation with Infectious Disease.”

Dr. Hanrahan, who also serves as medical director of infection prevention at Cleveland’s MetroHealth Medical Center, says that with so many patients hospitalized for infections, the value of updating one’s knowledge every few years is critical.

“I’ve been an infectious disease physician for 18 years and I’m also a hospitalist,” she said. “The types of questions I get vary a great deal depending on the experience of the hospitalist. My hope would be that we would be able to provide a basic level of understanding so that people would be more confident in approaching these problems.”

Another new feature this year is offer some of the most popular sessions at multiple times. In years past, popular sessions – such as “Update and Pearls in Infectious Diseases” and “Non–Evidence-Based Medicine: Things We Do for No Reason” – are standing room only events with attendees sitting on floors or gathered to eavesdrop from doorways.

“That says something about the content that’s being delivered, but that’s not very comfortable for folks who want to sit through a session,” Dr. Feldman said. “We’ve decided to add repeat sessions of popular presentations. We want everyone to be comfortable while they’re learning the important clinical content that’s being delivered at these sessions.”

The 2017 focus on healthcare policy is also new. Educational sessions on the policy landscape will be formally buttressed by plenary presentations from Patrick Conway, MD, MSc, MHM, deputy administrator for Innovation and Quality at the Centers for Medicare & Medicaid Services and director of the Center for Medicare and Medicaid Innovation, and Karen DeSalvo, MD, MPH, MSc, a former acting assistant secretary for health at the U.S. Department of Health and Human Services and national coordinator for health information technology.

“There’s a thirst for (policy news) among members of the Society of Hospital Medicine,” Dr. Feldman said. “It is easy to get lost in the day-to-day work that we do, but I think most of us really enjoy hearing about the bigger picture, especially when the bigger picture is in flux.”

“Right now, this is critical,” added Dr. Finn. “Health insurance coverage has a huge impact on hospitals. I think all practicing hospitalists will need to engage with the hospital C-suite if insurance and coverage changes. Since we are hospital based, we are directly tied to anything that the federal government does in terms of health care changes. It’s important for hospitalists to be knowledgeable about health policy.”

One major highlight of the meeting calendar – less new and more historically under-appreciated, in Dr. Feldman’s view – should be the 18 workshop presentations, which are essentially 90-minute dissertations, whittled down from roughly 150 submissions.

“These are the best submissions that we received,” Dr. Feldman said. “We worked hard to make sure that the workshops encompass the breadth and depth of hospital medicine. It is not just one area that’s covered in every workshop. We’ll have workshops ranging from clinical reasoning and communication with patients, to quality improvement issues and high value care discussions, as well as a case-based approach to inpatient dermatology.”

While annual meetings’ new offerings are always an important draw, Dr. Feldman says that the annual “standbys,” such as practice management and pediatrics, are necessary to keep attendees up to date on best practices in changing times.

“It’s pretty self-evident that if we’re going to be an important specialty, we need to serve those who are caring for patients day in and day out, as well as folks who are researching how we can do it better,” he said. “Then we must make sure that data is disseminated to all of us who are taking care of patients. That’s one of the really important parts of this meeting: dissemination of the important work.”

More work needs to be done to address the shortage of psychiatrists, including improvements in training and models of health care delivery, according to a new report from the National Council for Behavioral Health’s Medical Director Institute.

In framing the problem, Joseph Parks, MD, a psychiatrist who serves as medical director of the National Council, said during a March 28 teleconference to introduce the report that “55% of the counties in the United States have no psychiatrist in them” and “77% of the counties report a severe shortage.” He noted that the number of psychiatrists available declined by 10% between 2003 and 2013 and that the average age of practicing psychiatrists is in the mid-50s. In other medical specialties, the average age is in the mid-40s, he said.

“This has resulted in people having long wait times and being unable to get psychiatric services,” Dr. Parks said. Those factors are leading patients to pursue psychiatric care in alternative places, such as in primary care physician practices and emergency departments.

In emergency departments, the average wait for dispositions for some psychiatric patients can reach 23 hours, the report says. And more people are going to EDs for care.

“There has been a 42% increase in patients going to the emergency rooms for psychiatric services in the past 3 years,” Dr. Parks said. “But most of them aren’t staffed with psychiatrists. So people end up stuck in the emergency rooms for hours – two to three times as long as they spend for general medical conditions.”

The report looks at causes, and makes actionable recommendations for payers and providers. It also makes recommendations about the infrastructure needed to train future psychiatrists.

A key part of the problem is the increased demand, which is partly attributable to the expansion of health care coverage through the Affordable Care Act’s Medicaid expansion provisions as well as the normalization of views on behavioral health.

“People want psychiatric services,” said Dr. Parks, who has practiced medicine and worked as a policy maker in Missouri. “They know treatment works. It’s less stigmatized than it used to be, so people are more willing to accept and seek treatment.”

Among the trends cited by the report is a shortage of new psychiatrists coming out of medical schools.

“There are problems with not enough training capacity,” he said. “We’ve had increases in federal support for increased training capacity for ob.gyns. and primary care, but we’ve not had that same increase and support, and there are [fewer] supports for training of psychiatrists and fewer slots.”

Burnout is another problem facing psychiatrists.

“Psychiatrists who are practicing are in many cases forced to do it at lower than usual reimbursements [and] are having short visits,” Dr. Parks said. “They are rushed ... they don’t get the same supports that other physicians get. They don’t get the same ancillary staff to assist them in caring for the patients.”

Elaborating on the issues surrounding reimbursement, Dr. Parks noted that 40% of psychiatrists work on a cash-only basis, and 75% of behavioral health organizations lose money on fees collected for psychiatric services.

An ongoing workforce concern, especially in light of changes to the H-1B program, is that 50% of new trainees are foreign medical graduates.

“Luckily, there is a broad range of solutions, and there is something for all the major players to do here,” Dr. Parks said, noting that the report highlights many of these solutions.

“We need to change the care delivery system so it’s not the psychiatrist seeing everybody continuously,” he said. “Psychiatrists need to be used more as expert consultants. People need to be identified using data analytics as opposed to waiting for the patient to complain. And they need to be working more in teams, so they are doing the essential things that only a psychiatrist can do.”

Dr. Parks added that psychiatrists need to delegate “other parts of care and follow-up for people who are stable or for services that can be done by other professionals, such as psychiatric nurses or perhaps physician assistants. “We need an increase not only in more training capacity for psychiatrists but [also in] more alternative providers.”

Patrick Runnels, MD, a psychiatrist who cochairs the Medical Director Institute, highlighted several of the training issues.

“[W]e determined that psychiatrists also bear responsibility for improving this workforce crisis,” Dr. Runnels said during the call. “That starts with making our training consistent with the emerging needs and models of care that are attractive to potential trainees.”

And getting more clinicians into areas of high need, like psychiatry, starts at the medical school level.

“We were able to determine [that] in medical school, medical students were more likely to be recruited into psychiatry based on two characteristics – that the medical school had a strong reputation within their psychiatry department, particularly a strong rotation that was well rated by medical students in psychiatry, and that the length of the rotation was longer,” he said. “When those two things are put together, more students choose to go into psychiatry residency.”

In addition, more exposure is needed to aspects of practice that fit with the way in which medical care is being delivered, including better training in team-based collaborative care and medication-assisted treatment for substance use disorders.

“We also think that residents need to be placed in a range of settings, some settings in which they don’t get very much placement right now, including federally qualified health centers, patient-centered medical homes, [and] experience with telepsychiatry,” said Dr. Runnels, who also serves as medical director of the Centers for Families and Children in Cleveland.

“On top of that, we need our psychiatry residents to graduate with skills in health care data analysis, particularly at the population level,” Dr. Runnels continued. “We need our residents to understand the impact of the treatments that we have on entire populations and how to best allocate resources to deal with the whole population. Those things are hugely important.”

The National Council, based in Washington, is made up of 2,900 member organizations across the country that serve 10 million adults, children, and families who are living with mental health and substance use disorders.

[email protected]

More work needs to be done to address the shortage of psychiatrists, including improvements in training and models of health care delivery, according to a new report from the National Council for Behavioral Health’s Medical Director Institute.

In framing the problem, Joseph Parks, MD, a psychiatrist who serves as medical director of the National Council, said during a March 28 teleconference to introduce the report that “55% of the counties in the United States have no psychiatrist in them” and “77% of the counties report a severe shortage.” He noted that the number of psychiatrists available declined by 10% between 2003 and 2013 and that the average age of practicing psychiatrists is in the mid-50s. In other medical specialties, the average age is in the mid-40s, he said.

“This has resulted in people having long wait times and being unable to get psychiatric services,” Dr. Parks said. Those factors are leading patients to pursue psychiatric care in alternative places, such as in primary care physician practices and emergency departments.

In emergency departments, the average wait for dispositions for some psychiatric patients can reach 23 hours, the report says. And more people are going to EDs for care.

“There has been a 42% increase in patients going to the emergency rooms for psychiatric services in the past 3 years,” Dr. Parks said. “But most of them aren’t staffed with psychiatrists. So people end up stuck in the emergency rooms for hours – two to three times as long as they spend for general medical conditions.”

The report looks at causes, and makes actionable recommendations for payers and providers. It also makes recommendations about the infrastructure needed to train future psychiatrists.

A key part of the problem is the increased demand, which is partly attributable to the expansion of health care coverage through the Affordable Care Act’s Medicaid expansion provisions as well as the normalization of views on behavioral health.

“People want psychiatric services,” said Dr. Parks, who has practiced medicine and worked as a policy maker in Missouri. “They know treatment works. It’s less stigmatized than it used to be, so people are more willing to accept and seek treatment.”

Among the trends cited by the report is a shortage of new psychiatrists coming out of medical schools.

“There are problems with not enough training capacity,” he said. “We’ve had increases in federal support for increased training capacity for ob.gyns. and primary care, but we’ve not had that same increase and support, and there are [fewer] supports for training of psychiatrists and fewer slots.”

Burnout is another problem facing psychiatrists.

“Psychiatrists who are practicing are in many cases forced to do it at lower than usual reimbursements [and] are having short visits,” Dr. Parks said. “They are rushed ... they don’t get the same supports that other physicians get. They don’t get the same ancillary staff to assist them in caring for the patients.”

Elaborating on the issues surrounding reimbursement, Dr. Parks noted that 40% of psychiatrists work on a cash-only basis, and 75% of behavioral health organizations lose money on fees collected for psychiatric services.

An ongoing workforce concern, especially in light of changes to the H-1B program, is that 50% of new trainees are foreign medical graduates.

“Luckily, there is a broad range of solutions, and there is something for all the major players to do here,” Dr. Parks said, noting that the report highlights many of these solutions.

“We need to change the care delivery system so it’s not the psychiatrist seeing everybody continuously,” he said. “Psychiatrists need to be used more as expert consultants. People need to be identified using data analytics as opposed to waiting for the patient to complain. And they need to be working more in teams, so they are doing the essential things that only a psychiatrist can do.”

Dr. Parks added that psychiatrists need to delegate “other parts of care and follow-up for people who are stable or for services that can be done by other professionals, such as psychiatric nurses or perhaps physician assistants. “We need an increase not only in more training capacity for psychiatrists but [also in] more alternative providers.”

Patrick Runnels, MD, a psychiatrist who cochairs the Medical Director Institute, highlighted several of the training issues.

“[W]e determined that psychiatrists also bear responsibility for improving this workforce crisis,” Dr. Runnels said during the call. “That starts with making our training consistent with the emerging needs and models of care that are attractive to potential trainees.”

And getting more clinicians into areas of high need, like psychiatry, starts at the medical school level.

“We were able to determine [that] in medical school, medical students were more likely to be recruited into psychiatry based on two characteristics – that the medical school had a strong reputation within their psychiatry department, particularly a strong rotation that was well rated by medical students in psychiatry, and that the length of the rotation was longer,” he said. “When those two things are put together, more students choose to go into psychiatry residency.”

In addition, more exposure is needed to aspects of practice that fit with the way in which medical care is being delivered, including better training in team-based collaborative care and medication-assisted treatment for substance use disorders.

“We also think that residents need to be placed in a range of settings, some settings in which they don’t get very much placement right now, including federally qualified health centers, patient-centered medical homes, [and] experience with telepsychiatry,” said Dr. Runnels, who also serves as medical director of the Centers for Families and Children in Cleveland.

“On top of that, we need our psychiatry residents to graduate with skills in health care data analysis, particularly at the population level,” Dr. Runnels continued. “We need our residents to understand the impact of the treatments that we have on entire populations and how to best allocate resources to deal with the whole population. Those things are hugely important.”

The National Council, based in Washington, is made up of 2,900 member organizations across the country that serve 10 million adults, children, and families who are living with mental health and substance use disorders.

[email protected]

More work needs to be done to address the shortage of psychiatrists, including improvements in training and models of health care delivery, according to a new report from the National Council for Behavioral Health’s Medical Director Institute.

In framing the problem, Joseph Parks, MD, a psychiatrist who serves as medical director of the National Council, said during a March 28 teleconference to introduce the report that “55% of the counties in the United States have no psychiatrist in them” and “77% of the counties report a severe shortage.” He noted that the number of psychiatrists available declined by 10% between 2003 and 2013 and that the average age of practicing psychiatrists is in the mid-50s. In other medical specialties, the average age is in the mid-40s, he said.

“This has resulted in people having long wait times and being unable to get psychiatric services,” Dr. Parks said. Those factors are leading patients to pursue psychiatric care in alternative places, such as in primary care physician practices and emergency departments.

In emergency departments, the average wait for dispositions for some psychiatric patients can reach 23 hours, the report says. And more people are going to EDs for care.

“There has been a 42% increase in patients going to the emergency rooms for psychiatric services in the past 3 years,” Dr. Parks said. “But most of them aren’t staffed with psychiatrists. So people end up stuck in the emergency rooms for hours – two to three times as long as they spend for general medical conditions.”

The report looks at causes, and makes actionable recommendations for payers and providers. It also makes recommendations about the infrastructure needed to train future psychiatrists.

A key part of the problem is the increased demand, which is partly attributable to the expansion of health care coverage through the Affordable Care Act’s Medicaid expansion provisions as well as the normalization of views on behavioral health.

“People want psychiatric services,” said Dr. Parks, who has practiced medicine and worked as a policy maker in Missouri. “They know treatment works. It’s less stigmatized than it used to be, so people are more willing to accept and seek treatment.”

Among the trends cited by the report is a shortage of new psychiatrists coming out of medical schools.

“There are problems with not enough training capacity,” he said. “We’ve had increases in federal support for increased training capacity for ob.gyns. and primary care, but we’ve not had that same increase and support, and there are [fewer] supports for training of psychiatrists and fewer slots.”

Burnout is another problem facing psychiatrists.

“Psychiatrists who are practicing are in many cases forced to do it at lower than usual reimbursements [and] are having short visits,” Dr. Parks said. “They are rushed ... they don’t get the same supports that other physicians get. They don’t get the same ancillary staff to assist them in caring for the patients.”

Elaborating on the issues surrounding reimbursement, Dr. Parks noted that 40% of psychiatrists work on a cash-only basis, and 75% of behavioral health organizations lose money on fees collected for psychiatric services.

An ongoing workforce concern, especially in light of changes to the H-1B program, is that 50% of new trainees are foreign medical graduates.

“Luckily, there is a broad range of solutions, and there is something for all the major players to do here,” Dr. Parks said, noting that the report highlights many of these solutions.

“We need to change the care delivery system so it’s not the psychiatrist seeing everybody continuously,” he said. “Psychiatrists need to be used more as expert consultants. People need to be identified using data analytics as opposed to waiting for the patient to complain. And they need to be working more in teams, so they are doing the essential things that only a psychiatrist can do.”

Dr. Parks added that psychiatrists need to delegate “other parts of care and follow-up for people who are stable or for services that can be done by other professionals, such as psychiatric nurses or perhaps physician assistants. “We need an increase not only in more training capacity for psychiatrists but [also in] more alternative providers.”

Patrick Runnels, MD, a psychiatrist who cochairs the Medical Director Institute, highlighted several of the training issues.

“[W]e determined that psychiatrists also bear responsibility for improving this workforce crisis,” Dr. Runnels said during the call. “That starts with making our training consistent with the emerging needs and models of care that are attractive to potential trainees.”

And getting more clinicians into areas of high need, like psychiatry, starts at the medical school level.

“We were able to determine [that] in medical school, medical students were more likely to be recruited into psychiatry based on two characteristics – that the medical school had a strong reputation within their psychiatry department, particularly a strong rotation that was well rated by medical students in psychiatry, and that the length of the rotation was longer,” he said. “When those two things are put together, more students choose to go into psychiatry residency.”

In addition, more exposure is needed to aspects of practice that fit with the way in which medical care is being delivered, including better training in team-based collaborative care and medication-assisted treatment for substance use disorders.

“We also think that residents need to be placed in a range of settings, some settings in which they don’t get very much placement right now, including federally qualified health centers, patient-centered medical homes, [and] experience with telepsychiatry,” said Dr. Runnels, who also serves as medical director of the Centers for Families and Children in Cleveland.

“On top of that, we need our psychiatry residents to graduate with skills in health care data analysis, particularly at the population level,” Dr. Runnels continued. “We need our residents to understand the impact of the treatments that we have on entire populations and how to best allocate resources to deal with the whole population. Those things are hugely important.”

The National Council, based in Washington, is made up of 2,900 member organizations across the country that serve 10 million adults, children, and families who are living with mental health and substance use disorders.

[email protected]

For more than 2 decades, U.S. health systems have drawn on hospitalists’ expertise to lower length of stay and enhance safety for general medical patients. Many hospital medicine groups have extended this successful practice model across a growing list of services, stretching the role of generalists as far as it can go. While a diverse scope of practice excites some hospitalists, others find career satisfaction with a specific patient population. Some even balk at rotating through all of the possible primary and comanagement services staffed by their group. A growing number of job opportunities have emerged for individuals who are drawn to a specialized patient population but either remain generalist at heart or don’t want to complete a fellowship.

The latest State of Hospital Medicine (SoHM) report provides new insight into this trend, which brings our unique talents to subspecialty populations.

We look forward to tracking this area with subsequent surveys. Already, national meetings are developing for specialty hospitalists (for example, in oncology), and we see opportunities for specialty hospitalists to network through the Society of Hospital Medicine annual meeting and HMX online. My prediction is for growth in the number of groups reporting the employment of specialty hospitalists, but only time will tell. Hospital medicine group leaders should consider both participating in the next SOHM survey and digging into the details of the current report as ways to advance the best practices for developing specialty hospitalist positions.
 

Dr. White is associate professor of medicine at the University of Washington, Seattle, and a member of SHM’s Practice Analysis Committee.

For more than 2 decades, U.S. health systems have drawn on hospitalists’ expertise to lower length of stay and enhance safety for general medical patients. Many hospital medicine groups have extended this successful practice model across a growing list of services, stretching the role of generalists as far as it can go. While a diverse scope of practice excites some hospitalists, others find career satisfaction with a specific patient population. Some even balk at rotating through all of the possible primary and comanagement services staffed by their group. A growing number of job opportunities have emerged for individuals who are drawn to a specialized patient population but either remain generalist at heart or don’t want to complete a fellowship.

The latest State of Hospital Medicine (SoHM) report provides new insight into this trend, which brings our unique talents to subspecialty populations.

We look forward to tracking this area with subsequent surveys. Already, national meetings are developing for specialty hospitalists (for example, in oncology), and we see opportunities for specialty hospitalists to network through the Society of Hospital Medicine annual meeting and HMX online. My prediction is for growth in the number of groups reporting the employment of specialty hospitalists, but only time will tell. Hospital medicine group leaders should consider both participating in the next SOHM survey and digging into the details of the current report as ways to advance the best practices for developing specialty hospitalist positions.
 

Dr. White is associate professor of medicine at the University of Washington, Seattle, and a member of SHM’s Practice Analysis Committee.

For more than 2 decades, U.S. health systems have drawn on hospitalists’ expertise to lower length of stay and enhance safety for general medical patients. Many hospital medicine groups have extended this successful practice model across a growing list of services, stretching the role of generalists as far as it can go. While a diverse scope of practice excites some hospitalists, others find career satisfaction with a specific patient population. Some even balk at rotating through all of the possible primary and comanagement services staffed by their group. A growing number of job opportunities have emerged for individuals who are drawn to a specialized patient population but either remain generalist at heart or don’t want to complete a fellowship.

The latest State of Hospital Medicine (SoHM) report provides new insight into this trend, which brings our unique talents to subspecialty populations.

We look forward to tracking this area with subsequent surveys. Already, national meetings are developing for specialty hospitalists (for example, in oncology), and we see opportunities for specialty hospitalists to network through the Society of Hospital Medicine annual meeting and HMX online. My prediction is for growth in the number of groups reporting the employment of specialty hospitalists, but only time will tell. Hospital medicine group leaders should consider both participating in the next SOHM survey and digging into the details of the current report as ways to advance the best practices for developing specialty hospitalist positions.
 

Dr. White is associate professor of medicine at the University of Washington, Seattle, and a member of SHM’s Practice Analysis Committee.

LAS VEGAS — Not to sound like a Sin City come on, but pick a course, any course.

No, seriously.

Hospitalists and other attendees at the Hospitalist Medicine 2017 meeting next month will do well to figure out what sessions they want to attend before arriving at the Mandalay Bay Resort and Casino. This 4-day Super Bowl of hospital medicine prides itself on offering more than any attendee can find time for. This year is no exception, as the annual meeting has added five new educational tracks: High-Value Care, Clinical Updates, Health Policy, Diagnostic Reasoning, and Medical Education.

The committee does its job to fill the meeting with best-in-class educational sessions. Here are some of the group’s recommendations for this year’s meeting:

1. “The Hospitalist’s Role in the Opioid Epidemic” – Tuesday, May 2; 1:35 p.m.–2:35 p.m.

2. “Opioids for Acute Pain Management in the Seriously Ill – How to Safely Prescribe” – Wednesday, May 3; 2:50 p.m.–3:30 p.m.

3. “Non-opiate Pain Management for the Hospitalist” – Wednesday, May 3; 4:20 p.m.–5:00 p.m.

Elizabeth Cook, MD, medical director of the hospitalist division of Medical Associates of Central Virginia in Lynchburg, said, “The historical emphasis on pain control has helped contributed to the current epidemic of opioid abuse, overdoses, and deaths. Hospitalists have a need to use these medications for care of the hospitalized patient but have an important part to play in leading the way to appropriate use and patient education regarding the dangers of these medications. These sessions will provide hospitalists with some tools to use in beginning to effect a shift in pain management strategies and responsible use of narcotic pain medications.”

Miguel Angel Villagra, MD, FACP, FHM, hospitalist department program medical director at White River Medical Center in Batesville, Ark., said, “As primary front-line providers in the acute care setting, we face the everyday struggles in the management of chronic opioid users. Acquiring some general guidelines can help us tailor our approach within an ethical focus to improve the care of this population.”

Sarah Stella, MD, an academic hospitalist at Denver Health, said, “This is a crucial and timely topic. Hospitalists have had a hand in perpetuating the opioid epidemic and can play an important role in helping to end it. In this regard, there are many opportunities to do good, such as judicious prescribing and tapering medications for acute pain, starting eligible patients on Suboxone [buprenorphine] in-house, and arranging substance abuse treatment follow-up.”

4. “Focus on POCUS - Introduction to Point-of-Care Ultrasound for Pediatric Hospitalists” – Tuesday, May 2; 10:35 a.m.–11:35 a.m.

Richard Quinn is a freelance writer in New Jersey.

LAS VEGAS — Not to sound like a Sin City come on, but pick a course, any course.

No, seriously.

Hospitalists and other attendees at the Hospitalist Medicine 2017 meeting next month will do well to figure out what sessions they want to attend before arriving at the Mandalay Bay Resort and Casino. This 4-day Super Bowl of hospital medicine prides itself on offering more than any attendee can find time for. This year is no exception, as the annual meeting has added five new educational tracks: High-Value Care, Clinical Updates, Health Policy, Diagnostic Reasoning, and Medical Education.

The committee does its job to fill the meeting with best-in-class educational sessions. Here are some of the group’s recommendations for this year’s meeting:

1. “The Hospitalist’s Role in the Opioid Epidemic” – Tuesday, May 2; 1:35 p.m.–2:35 p.m.

2. “Opioids for Acute Pain Management in the Seriously Ill – How to Safely Prescribe” – Wednesday, May 3; 2:50 p.m.–3:30 p.m.

3. “Non-opiate Pain Management for the Hospitalist” – Wednesday, May 3; 4:20 p.m.–5:00 p.m.

Elizabeth Cook, MD, medical director of the hospitalist division of Medical Associates of Central Virginia in Lynchburg, said, “The historical emphasis on pain control has helped contributed to the current epidemic of opioid abuse, overdoses, and deaths. Hospitalists have a need to use these medications for care of the hospitalized patient but have an important part to play in leading the way to appropriate use and patient education regarding the dangers of these medications. These sessions will provide hospitalists with some tools to use in beginning to effect a shift in pain management strategies and responsible use of narcotic pain medications.”

Miguel Angel Villagra, MD, FACP, FHM, hospitalist department program medical director at White River Medical Center in Batesville, Ark., said, “As primary front-line providers in the acute care setting, we face the everyday struggles in the management of chronic opioid users. Acquiring some general guidelines can help us tailor our approach within an ethical focus to improve the care of this population.”

Sarah Stella, MD, an academic hospitalist at Denver Health, said, “This is a crucial and timely topic. Hospitalists have had a hand in perpetuating the opioid epidemic and can play an important role in helping to end it. In this regard, there are many opportunities to do good, such as judicious prescribing and tapering medications for acute pain, starting eligible patients on Suboxone [buprenorphine] in-house, and arranging substance abuse treatment follow-up.”

4. “Focus on POCUS - Introduction to Point-of-Care Ultrasound for Pediatric Hospitalists” – Tuesday, May 2; 10:35 a.m.–11:35 a.m.

Richard Quinn is a freelance writer in New Jersey.

LAS VEGAS — Not to sound like a Sin City come on, but pick a course, any course.

No, seriously.

Hospitalists and other attendees at the Hospitalist Medicine 2017 meeting next month will do well to figure out what sessions they want to attend before arriving at the Mandalay Bay Resort and Casino. This 4-day Super Bowl of hospital medicine prides itself on offering more than any attendee can find time for. This year is no exception, as the annual meeting has added five new educational tracks: High-Value Care, Clinical Updates, Health Policy, Diagnostic Reasoning, and Medical Education.

The committee does its job to fill the meeting with best-in-class educational sessions. Here are some of the group’s recommendations for this year’s meeting:

1. “The Hospitalist’s Role in the Opioid Epidemic” – Tuesday, May 2; 1:35 p.m.–2:35 p.m.

2. “Opioids for Acute Pain Management in the Seriously Ill – How to Safely Prescribe” – Wednesday, May 3; 2:50 p.m.–3:30 p.m.

3. “Non-opiate Pain Management for the Hospitalist” – Wednesday, May 3; 4:20 p.m.–5:00 p.m.

Elizabeth Cook, MD, medical director of the hospitalist division of Medical Associates of Central Virginia in Lynchburg, said, “The historical emphasis on pain control has helped contributed to the current epidemic of opioid abuse, overdoses, and deaths. Hospitalists have a need to use these medications for care of the hospitalized patient but have an important part to play in leading the way to appropriate use and patient education regarding the dangers of these medications. These sessions will provide hospitalists with some tools to use in beginning to effect a shift in pain management strategies and responsible use of narcotic pain medications.”

Miguel Angel Villagra, MD, FACP, FHM, hospitalist department program medical director at White River Medical Center in Batesville, Ark., said, “As primary front-line providers in the acute care setting, we face the everyday struggles in the management of chronic opioid users. Acquiring some general guidelines can help us tailor our approach within an ethical focus to improve the care of this population.”

Sarah Stella, MD, an academic hospitalist at Denver Health, said, “This is a crucial and timely topic. Hospitalists have had a hand in perpetuating the opioid epidemic and can play an important role in helping to end it. In this regard, there are many opportunities to do good, such as judicious prescribing and tapering medications for acute pain, starting eligible patients on Suboxone [buprenorphine] in-house, and arranging substance abuse treatment follow-up.”

4. “Focus on POCUS - Introduction to Point-of-Care Ultrasound for Pediatric Hospitalists” – Tuesday, May 2; 10:35 a.m.–11:35 a.m.

Richard Quinn is a freelance writer in New Jersey.

Vineet Arora, MD, understands the unique value of being named one of this year’s three Masters in Hospital Medicine. It’s an honor bestowed for hospitalists, by hospitalists.

“I take a lot of pride in an honor determined by peers,” said Dr. Arora, an academic hospitalist at University of Chicago Medicine. “While peers are often the biggest support you receive in your professional career, because they are in the trenches with you, they can also be your best critics. That is especially true of the type of work that I do, which relies on the buy-in of frontline clinicians – including hospitalists and trainees – to achieve better patient care and education.”

Vineet Arora, MD, understands the unique value of being named one of this year’s three Masters in Hospital Medicine. It’s an honor bestowed for hospitalists, by hospitalists.

“I take a lot of pride in an honor determined by peers,” said Dr. Arora, an academic hospitalist at University of Chicago Medicine. “While peers are often the biggest support you receive in your professional career, because they are in the trenches with you, they can also be your best critics. That is especially true of the type of work that I do, which relies on the buy-in of frontline clinicians – including hospitalists and trainees – to achieve better patient care and education.”

Vineet Arora, MD, understands the unique value of being named one of this year’s three Masters in Hospital Medicine. It’s an honor bestowed for hospitalists, by hospitalists.

“I take a lot of pride in an honor determined by peers,” said Dr. Arora, an academic hospitalist at University of Chicago Medicine. “While peers are often the biggest support you receive in your professional career, because they are in the trenches with you, they can also be your best critics. That is especially true of the type of work that I do, which relies on the buy-in of frontline clinicians – including hospitalists and trainees – to achieve better patient care and education.”

Occult cancers accounted for one in about every 12 major gastrointestinal bleeding events among patients taking warfarin or dabigatran for atrial fibrillation, according to a retrospective analysis of data from a randomized prospective trial reported in the May issue of Clinical Gastroenterology and Hepatology (2017. doi: org/10.1016/j.cgh.2016.10.011).

These bleeding events caused similarly significant morbidity among patients taking either drug, Kathryn F. Flack, MD, of Icahn School of Medicine at Mount Sinai in New York and her associates wrote. “Patients bleeding from cancer required a mean of approximately 10 nights in the hospital, and approximately one-fourth required intensive care, but 0 of 44 died as a direct result of the bleeding,” the researchers reported. They hoped the specific dabigatran reversal agent, idarucizumab (Praxbind), will improve bleeding outcomes in patients receiving dabigatran.

Source: American Gastroenterological Association

Major gastrointestinal bleeding (MGIB) is the first sign of occult malignancy in certain patients receiving anticoagulation therapy. Starting an anticoagulant is a type of “stress test” that can reveal an occult cancer, the researchers said. Although dabigatran etexilate (Pradaxa) is generally safe and effective, a twice-daily, 150-mg dose of this direct oral anticoagulant slightly increased MGIB, compared with a lower dose in the international, multicenter RE-LY (Randomized Evaluation of Long Term Anticoagulant Therapy) trial (N Engl J Med. 2009;361:1139-51). Furthermore, unlike warfarin, dabigatran therapy places active anticoagulant within the luminal gastrointestinal tract, which “might promote bleeding from friable gastrointestinal cancers,” the investigators noted. To explore this possibility, they evaluated 546 unique MGIB events among RE-LY patients.

Medical chart reviews identified 44 (8.1%) MGIB events resulting from occult gastrointestinal cancers. Cancer accounted for similar proportions of MGIB among warfarin and dabigatran recipients (8.5% and 6.8%; P = .6). Nearly all cancers were colorectal or gastric, except for one case each of ampullary cancer, renal cell carcinoma, and melanoma that had metastasized to the luminal gastrointestinal tract. Colorectal cancer accounted for 80% of cancer-related MGIB overall, including 88% in the dabigatran group and 50% in the warfarin group (P = .02). Conversely, warfarin recipients had more MGIB associated with gastric cancer (50%) than did dabigatran recipients (2.9%; P = .001).

Short-term outcomes of MGIB associated with cancer did not vary by anticoagulant, the investigators said. There were no deaths, but two (4.5%) MGIB events required emergency endoscopic treatment, one (2.3%) required emergency surgery, and 33 (75%) required at least one red blood cell transfusion. Compared with patients whose MGIB was unrelated to cancer, those with cancer were more likely to bleed for more than 7 days (27.3% vs. 63.6%; P less than .001). Patients with occult cancer also developed MGIB sooner after starting anticoagulation (223 vs. 343 days; P = .003), but time to bleeding did not significantly vary by type of anticoagulant.

“Most prior studies on cancer bleeding have been case reports and case series in patients receiving warfarin,” the investigators wrote. “Our study is relevant because of the increasing prevalence of atrial fibrillation and anticoagulation in the aging global population, the increasing prescription of direct oral anticoagulants, and the morbidity, mortality, and complex decision making associated with MGIB and especially cancer-related MGIB in patients receiving anticoagulation therapy.”

The RE-LY trial was sponsored by Boehringer Ingelheim . Dr. Flack reported no conflicts of interest. Senior author James Aisenberg, MD, disclosed advisory board and consulting relationships with Boehringer Ingelheim and Portola Pharmaceuticals. Five other coinvestigators disclosed ties to several pharmaceutical companies, and two coinvestigators reported employment with Boehringer Ingelheim. The other coinvestigators had no conflicts.

Occult cancers accounted for one in about every 12 major gastrointestinal bleeding events among patients taking warfarin or dabigatran for atrial fibrillation, according to a retrospective analysis of data from a randomized prospective trial reported in the May issue of Clinical Gastroenterology and Hepatology (2017. doi: org/10.1016/j.cgh.2016.10.011).

These bleeding events caused similarly significant morbidity among patients taking either drug, Kathryn F. Flack, MD, of Icahn School of Medicine at Mount Sinai in New York and her associates wrote. “Patients bleeding from cancer required a mean of approximately 10 nights in the hospital, and approximately one-fourth required intensive care, but 0 of 44 died as a direct result of the bleeding,” the researchers reported. They hoped the specific dabigatran reversal agent, idarucizumab (Praxbind), will improve bleeding outcomes in patients receiving dabigatran.

Source: American Gastroenterological Association

Major gastrointestinal bleeding (MGIB) is the first sign of occult malignancy in certain patients receiving anticoagulation therapy. Starting an anticoagulant is a type of “stress test” that can reveal an occult cancer, the researchers said. Although dabigatran etexilate (Pradaxa) is generally safe and effective, a twice-daily, 150-mg dose of this direct oral anticoagulant slightly increased MGIB, compared with a lower dose in the international, multicenter RE-LY (Randomized Evaluation of Long Term Anticoagulant Therapy) trial (N Engl J Med. 2009;361:1139-51). Furthermore, unlike warfarin, dabigatran therapy places active anticoagulant within the luminal gastrointestinal tract, which “might promote bleeding from friable gastrointestinal cancers,” the investigators noted. To explore this possibility, they evaluated 546 unique MGIB events among RE-LY patients.

Medical chart reviews identified 44 (8.1%) MGIB events resulting from occult gastrointestinal cancers. Cancer accounted for similar proportions of MGIB among warfarin and dabigatran recipients (8.5% and 6.8%; P = .6). Nearly all cancers were colorectal or gastric, except for one case each of ampullary cancer, renal cell carcinoma, and melanoma that had metastasized to the luminal gastrointestinal tract. Colorectal cancer accounted for 80% of cancer-related MGIB overall, including 88% in the dabigatran group and 50% in the warfarin group (P = .02). Conversely, warfarin recipients had more MGIB associated with gastric cancer (50%) than did dabigatran recipients (2.9%; P = .001).

Short-term outcomes of MGIB associated with cancer did not vary by anticoagulant, the investigators said. There were no deaths, but two (4.5%) MGIB events required emergency endoscopic treatment, one (2.3%) required emergency surgery, and 33 (75%) required at least one red blood cell transfusion. Compared with patients whose MGIB was unrelated to cancer, those with cancer were more likely to bleed for more than 7 days (27.3% vs. 63.6%; P less than .001). Patients with occult cancer also developed MGIB sooner after starting anticoagulation (223 vs. 343 days; P = .003), but time to bleeding did not significantly vary by type of anticoagulant.

“Most prior studies on cancer bleeding have been case reports and case series in patients receiving warfarin,” the investigators wrote. “Our study is relevant because of the increasing prevalence of atrial fibrillation and anticoagulation in the aging global population, the increasing prescription of direct oral anticoagulants, and the morbidity, mortality, and complex decision making associated with MGIB and especially cancer-related MGIB in patients receiving anticoagulation therapy.”

The RE-LY trial was sponsored by Boehringer Ingelheim . Dr. Flack reported no conflicts of interest. Senior author James Aisenberg, MD, disclosed advisory board and consulting relationships with Boehringer Ingelheim and Portola Pharmaceuticals. Five other coinvestigators disclosed ties to several pharmaceutical companies, and two coinvestigators reported employment with Boehringer Ingelheim. The other coinvestigators had no conflicts.

Occult cancers accounted for one in about every 12 major gastrointestinal bleeding events among patients taking warfarin or dabigatran for atrial fibrillation, according to a retrospective analysis of data from a randomized prospective trial reported in the May issue of Clinical Gastroenterology and Hepatology (2017. doi: org/10.1016/j.cgh.2016.10.011).

These bleeding events caused similarly significant morbidity among patients taking either drug, Kathryn F. Flack, MD, of Icahn School of Medicine at Mount Sinai in New York and her associates wrote. “Patients bleeding from cancer required a mean of approximately 10 nights in the hospital, and approximately one-fourth required intensive care, but 0 of 44 died as a direct result of the bleeding,” the researchers reported. They hoped the specific dabigatran reversal agent, idarucizumab (Praxbind), will improve bleeding outcomes in patients receiving dabigatran.

Source: American Gastroenterological Association

Major gastrointestinal bleeding (MGIB) is the first sign of occult malignancy in certain patients receiving anticoagulation therapy. Starting an anticoagulant is a type of “stress test” that can reveal an occult cancer, the researchers said. Although dabigatran etexilate (Pradaxa) is generally safe and effective, a twice-daily, 150-mg dose of this direct oral anticoagulant slightly increased MGIB, compared with a lower dose in the international, multicenter RE-LY (Randomized Evaluation of Long Term Anticoagulant Therapy) trial (N Engl J Med. 2009;361:1139-51). Furthermore, unlike warfarin, dabigatran therapy places active anticoagulant within the luminal gastrointestinal tract, which “might promote bleeding from friable gastrointestinal cancers,” the investigators noted. To explore this possibility, they evaluated 546 unique MGIB events among RE-LY patients.

Medical chart reviews identified 44 (8.1%) MGIB events resulting from occult gastrointestinal cancers. Cancer accounted for similar proportions of MGIB among warfarin and dabigatran recipients (8.5% and 6.8%; P = .6). Nearly all cancers were colorectal or gastric, except for one case each of ampullary cancer, renal cell carcinoma, and melanoma that had metastasized to the luminal gastrointestinal tract. Colorectal cancer accounted for 80% of cancer-related MGIB overall, including 88% in the dabigatran group and 50% in the warfarin group (P = .02). Conversely, warfarin recipients had more MGIB associated with gastric cancer (50%) than did dabigatran recipients (2.9%; P = .001).

Short-term outcomes of MGIB associated with cancer did not vary by anticoagulant, the investigators said. There were no deaths, but two (4.5%) MGIB events required emergency endoscopic treatment, one (2.3%) required emergency surgery, and 33 (75%) required at least one red blood cell transfusion. Compared with patients whose MGIB was unrelated to cancer, those with cancer were more likely to bleed for more than 7 days (27.3% vs. 63.6%; P less than .001). Patients with occult cancer also developed MGIB sooner after starting anticoagulation (223 vs. 343 days; P = .003), but time to bleeding did not significantly vary by type of anticoagulant.

“Most prior studies on cancer bleeding have been case reports and case series in patients receiving warfarin,” the investigators wrote. “Our study is relevant because of the increasing prevalence of atrial fibrillation and anticoagulation in the aging global population, the increasing prescription of direct oral anticoagulants, and the morbidity, mortality, and complex decision making associated with MGIB and especially cancer-related MGIB in patients receiving anticoagulation therapy.”

The RE-LY trial was sponsored by Boehringer Ingelheim . Dr. Flack reported no conflicts of interest. Senior author James Aisenberg, MD, disclosed advisory board and consulting relationships with Boehringer Ingelheim and Portola Pharmaceuticals. Five other coinvestigators disclosed ties to several pharmaceutical companies, and two coinvestigators reported employment with Boehringer Ingelheim. The other coinvestigators had no conflicts.

Robert Wachter, MD, MHM, has given the final plenary address at every SHM annual meeting since 2007. His talks are peppered with his one-of-a-kind take on the confluence of medicine, politics, and policy – and at least once he broke into an Elton John parody.

Where does that point of view come from? As the “dean” of hospital medicine says in his ever-popular Twitter bio, he is “what happens when a poli sci major becomes an academic physician.”

That’s a needed perspective this year, as the level of political upheaval in the United States ups the ante on the tumult the health care field has experienced over the past few years. Questions surrounding the implementation of the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) and the continued struggles experienced by clinicians using electronic health records (EHR) are among the topics to be addressed.

Robert Wachter, MD, MHM, has given the final plenary address at every SHM annual meeting since 2007. His talks are peppered with his one-of-a-kind take on the confluence of medicine, politics, and policy – and at least once he broke into an Elton John parody.

Where does that point of view come from? As the “dean” of hospital medicine says in his ever-popular Twitter bio, he is “what happens when a poli sci major becomes an academic physician.”

That’s a needed perspective this year, as the level of political upheaval in the United States ups the ante on the tumult the health care field has experienced over the past few years. Questions surrounding the implementation of the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) and the continued struggles experienced by clinicians using electronic health records (EHR) are among the topics to be addressed.

Robert Wachter, MD, MHM, has given the final plenary address at every SHM annual meeting since 2007. His talks are peppered with his one-of-a-kind take on the confluence of medicine, politics, and policy – and at least once he broke into an Elton John parody.

Where does that point of view come from? As the “dean” of hospital medicine says in his ever-popular Twitter bio, he is “what happens when a poli sci major becomes an academic physician.”

That’s a needed perspective this year, as the level of political upheaval in the United States ups the ante on the tumult the health care field has experienced over the past few years. Questions surrounding the implementation of the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) and the continued struggles experienced by clinicians using electronic health records (EHR) are among the topics to be addressed.

Ivan Misner once spent one week on Necker Island – the tony 74-acre island in the British Virgin Islands that is entirely owned by billionaire Sir Richard Branson – because he met a guy at a convention.

And Misner is really good at networking.

“I stayed in touch with the person, and when there was an opportunity, I got invited to this incredible ethics program on Necker where I had a chance to meet Sir Richard. It all comes from building relationships with people,” said Misner, founder and chairman of BNI (Business Network International), a 32-year-old global business networking platform based in Charlotte, N.C., that has led CNN to call him “the father of modern networking.”

Ivan Misner once spent one week on Necker Island – the tony 74-acre island in the British Virgin Islands that is entirely owned by billionaire Sir Richard Branson – because he met a guy at a convention.

And Misner is really good at networking.

“I stayed in touch with the person, and when there was an opportunity, I got invited to this incredible ethics program on Necker where I had a chance to meet Sir Richard. It all comes from building relationships with people,” said Misner, founder and chairman of BNI (Business Network International), a 32-year-old global business networking platform based in Charlotte, N.C., that has led CNN to call him “the father of modern networking.”

Ivan Misner once spent one week on Necker Island – the tony 74-acre island in the British Virgin Islands that is entirely owned by billionaire Sir Richard Branson – because he met a guy at a convention.

And Misner is really good at networking.

“I stayed in touch with the person, and when there was an opportunity, I got invited to this incredible ethics program on Necker where I had a chance to meet Sir Richard. It all comes from building relationships with people,” said Misner, founder and chairman of BNI (Business Network International), a 32-year-old global business networking platform based in Charlotte, N.C., that has led CNN to call him “the father of modern networking.”

Hospital-acquired venous thromboembolism (VTE) continues to be a critical quality challenge for U.S. hospitals,¹ and high-risk patients are often not adequately prophylaxed. Use of VTE prophylaxis (VTEP) varies as widely as 26% to 85% of patients in various studies, as does patient outcomes and care expenditures.^2-6 The 9th edition of the American College of Chest Physicians (CHEST) guidelines⁷ recommend the Padua Prediction Score (PPS) to select individual patients who may be at high risk for venous thromboembolism (VTE) and could benefit from thromboprophylaxis. Use of the manually calculated PPS to select patients for thromboprophylaxis has been shown to help decrease 30-day and 90-day mortality associated with VTE events after hospitalization to medical services.⁸ However, the PPS requires time-consuming manual calculation by a provider, who may be focused on more immediate aspects of patient care and several other risk scores competing for his attention, potentially decreasing its use.

Other risk scores that use only discrete scalar data, such as vital signs and lab results to predict early recognition of sepsis, have been successfully automated and implemented within electronic health records (EHRs).^9-11 Successful automation of scores requiring input of diagnoses, recent medical events, and current clinical status such as the PPS remains difficult.¹² Data representing these characteristics are more prone to error, and harder to translate clearly into a single data field than discrete elements like heart rate, potentially impacting validity of the calculated result.¹³ To improve usage of guideline based VTE risk assessment and decrease physician burden, we developed an algorithm called Automated Padua Prediction Score (APPS) that automatically calculates the PPS using only EHR data available within prior encounters and the first 4 hours of admission, a similar timeframe to when admitting providers would be entering orders. Our goal was to assess if an automatically calculated version of the PPS, a score that depends on criteria more complex than vital signs and labs, would accurately assess risk for hospital-acquired VTE when compared to traditional manual calculation of the Padua Prediction Score by a provider.

Site Description and Ethics

The study was conducted at University of California, San Francisco Medical Center, a 790-bed academic hospital; its Institutional Review Board approved the study and collection of data via chart review. Handling of patient information complied with the Health Insurance Portability and Accountability Act of 1996.

Patient Inclusion

Adult patients admitted to a medical or surgical service between July 1, 2012 and April 1, 2014 were included in the study if they were candidates for VTEP, defined as: length of stay (LOS) greater than 2 days, not on hospice care, not pregnant at admission, no present on admission VTE diagnosis, no known contraindications to prophylaxis (eg, gastrointestinal bleed), and were not receiving therapeutic doses of warfarin, low molecular weight heparins, heparin, or novel anticoagulants prior to admission.

Data Sources

Clinical variables were extracted from the EHR’s enterprise data warehouse (EDW) by SQL Server query (Microsoft, Redmond, Washington) and deposited in a secure database. Chart review was conducted by a trained researcher (Mr. Jacolbia) using the EHR and a standardized protocol. Findings were recorded using REDCap (REDCap Consortium, Vanderbilt University, Nashville, Tennessee). The specific ICD-9, procedure, and lab codes used to determine each criterion of APPS are available in the Appendix.

Creation of the Automated Padua Prediction Score (APPS)

We developed APPS from the original 11 criteria that comprise the Padua Prediction Score: active cancer, previous VTE (excluding superficial vein thrombosis), reduced mobility, known thrombophilic condition, recent (1 month or less) trauma and/or surgery, age 70 years or older, heart and/or respiratory failure, acute myocardial infarction and/or ischemic stroke, acute infection and/or rheumatologic disorder, body mass index (BMI) 30 or higher, and ongoing hormonal treatment.¹³ APPS has the same scoring methodology as PPS: criteria are weighted from 1 to 3 points and summed with a maximum score of 20, representing highest risk of VTE. To automate the score calculation from data routinely available in the EHR, APPS checks pre-selected structured data fields for specific values within laboratory results, orders, nursing flowsheets and claims. Claims data included all ICD-9 and procedure codes used for billing purposes. If any of the predetermined data elements are found, then the specific criterion is considered positive; otherwise, it is scored as negative. The creators of the PPS were consulted in the generation of these data queries to replicate the original standards for deeming a criterion positive. The automated calculation required no use of natural language processing.

Characterization of Study Population

We recorded patient demographics (age, race, gender, BMI), LOS, and rate of hospital-acquired VTE. These patients were separated into 2 cohorts determined by the VTE prophylaxis they received. The risk profile of patients who received pharmacologic prophylaxis was hypothesized to be inherently different from those who had not. To evaluate APPS within this heterogeneous cohort, patients were divided into 2 major categories: pharmacologic vs. no pharmacologic prophylaxis. If they had a completed order or medication administration record on the institution’s approved formulary for pharmacologic VTEP, they were considered to have received pharmacologic prophylaxis. If they had only a completed order for usage of mechanical prophylaxis (sequential compression devices) or no evidence of any form of VTEP, they were considered to have received no pharmacologic prophylaxis. Patients with evidence of both pharmacologic and mechanical were placed in the pharmacologic prophylaxis group. To ensure that automated designation of prophylaxis group was accurate, we reviewed 40 randomly chosen charts because prior researchers were able to achieve sensitivity and specificity greater than 90% with that sample size.¹⁴

The primary outcome of hospital-acquired VTE was defined as an ICD-9 code for VTE (specific codes are found in the Appendix) paired with a “present on admission = no” flag on that encounter’s hospital billing data, abstracted from the EDW. A previous study at this institution used the same methodology and found 212/226 (94%) of patients with a VTE ICD-9 code on claim had evidence of a hospital-acquired VTE event upon chart review.¹⁴ Chart review was also completed to ensure that the primary outcome of newly discovered hospital-acquired VTE was differentiated from chronic VTE or history of VTE. Theoretically, ICD-9 codes and other data elements treat chronic VTE, history of VTE, and hospital-acquired VTE as distinct diagnoses, but it was unclear if this was true in our dataset. For 75 randomly selected cases of presumed hospital-acquired VTE, charts were reviewed for evidence that confirmed newly found VTE during that encounter.

Validation of APPS through Comparison to Manual Calculation of the Original PPS

To compare our automated calculation to standard clinical practice, we manually calculated the PPS through chart review within the first 2 days of admission on 300 random patients, a subsample of the entire study cohort. The largest study we could find had manually calculated the PPS of 1,080 hospitalized patients with a mean PPS of 4.86 (standard deviation [SD], 2.26).¹⁵ One researcher (Mr. Jacolbia) accessed the EHR with all patient information available to physicians, including admission notes, orders, labs, flowsheets, past medical history, and all prior encounters to calculate and record the PPS. To limit potential score bias, 2 authors (Drs. Elias and Davies) assessed 30 randomly selected charts from the cohort of 300. The standardized chart review protocol mimicked a physician’s approach to determine if a patient met a criterion, such as concluding if he/she had active cancer by examining medication lists for chemotherapy, procedure notes for radiation, and recent diagnoses on problem lists. After the original PPS was manually calculated, APPS was automatically calculated for the same 300 patients. We intended to characterize similarities and differences between APPS and manual calculation prior to investigating APPS’ predictive capacity for the entire study population, because it would not be feasible to manually calculate the PPS for all 30,726 patients.

Statistical Analysis

For the 75 randomly selected cases of presumed hospital-acquired VTE, the number of cases was chosen by powering our analysis to find a difference in proportion of 20% with 90% power, α = 0.05 (two-sided). We conducted χ² tests on the entire study cohort to determine if there were significant differences in demographics, LOS, and incidence of hospital-acquired VTE by prophylaxis received. For both the pharmacologic and the no pharmacologic prophylaxis groups, we conducted 2-sample Student t tests to determine significant differences in demographics and LOS between patients who experienced a hospital-acquired VTE and those who did not.

For the comparison of our automated calculation to standard clinical practice, we manually calculated the PPS through chart review within the first 2 days of admission on a subsample of 300 random patients. We powered our analysis to detect a difference in mean PPS from 4.86 to 4.36, enough to alter the point value, with 90% power and α = 0.05 (two-sided) and found 300 patients to be comfortably above the required sample size. We compared APPS and manual calculation in the 300-patient cohort using: 2-sample Student t tests to compare mean scores, χ² tests to compare the frequency with which criteria were positive, and receiver operating characteristic (ROC) curves to determine capacity to predict a hospital-acquired VTE event. Pearson’s correlation was also completed to assess score agreement between APPS and manual calculation on a per-patient basis. After comparing automated calculation of APPS to manual chart review on the same 300 patients, we used APPS to calculate scores for the entire study cohort (n = 30,726). We calculated the mean of APPS by prophylaxis group and whether hospital-acquired VTE had occurred. We analyzed APPS’ ROC curve statistics by prophylaxis group to determine its overall predictive capacity in our study population. Lastly, we computed the time required to calculate APPS per patient. Statistical analyses were conducted using SPSS Statistics (IBM, Armonk, New York) and Python 2.7 (Python Software Foundation, Beaverton, Oregon); 95% confidence intervals (CI) and (SD) were reported when appropriate.

Among the 30,726 unique patients in our entire cohort (all patients admitted during the time period who met the study criteria), we found 6574 (21.4%) on pharmacologic (with or without mechanical) prophylaxis, 13,511 (44.0%) on mechanical only, and 10,641 (34.6%) on no prophylaxis. χ² tests found no significant differences in demographics, LOS, or incidence of hospital-acquired VTE between the patients who received mechanical prophylaxis only and those who received no prophylaxis (Table 1). Similarly, there were no differences in these characteristics in patients receiving pharmacologic prophylaxis with or without the addition of mechanical prophylaxis. Designation of prophylaxis group by manual chart review vs. our automated process was found to agree in categorization for 39/40 (97.5%) sampled encounters. When comparing the cohort that received pharmacologic prophylaxis against the cohort that did not, there were significant differences in racial distribution, sex, BMI, and average LOS as shown in Table 1. Those who received pharmacologic prophylaxis were found to be significantly older than those who did not (62.7 years versus 53.2 years, P < 0.001), more likely to be male (50.6% vs, 42.4%, P < 0.001), more likely to have hospital-acquired VTE (2.2% vs. 0.5%, P < 0.001), and to have a shorter LOS (7.1 days vs. 9.8, P < 0.001).

Within the cohort group receiving pharmacologic prophylaxis (n = 6574), hospital-acquired VTE occurred in patients who were significantly younger (58.2 years vs. 62.8 years, P = 0.003) with a greater LOS (23.8 days vs. 6.7, P < 0.001) than those without. Within the group receiving no pharmacologic prophylaxis (n = 24,152), hospital-acquired VTE occurred in patients who were significantly older (57.1 years vs. 53.2 years, P = 0.014) with more than twice the LOS (20.2 days vs. 9.7 days, P < 0.001) compared to those without. Sixty-six of 75 (88%) randomly selected patients in which new VTE was identified by the automated electronic query had this diagnosis confirmed during manual chart review.

As shown in Table 2, automated calculation on a subsample of 300 randomly selected patients using APPS had a mean of 5.5 (SD, 2.9) while manual calculation of the original PPS on the same patients had a mean of 5.1 (SD, 2.6). There was no significant difference in mean between manual calculation and APPS (P = 0.073). There were, however, significant differences in how often individual criteria were considered present. The largest contributors to the difference in scores between APPS and manual calculation were “prior VTE” (positive, 16% vs. 8.3%, respectively) and “reduced mobility” (positive, 74.3% vs. 66%, respectively) as shown in Table 2. In the subsample, there were a total of 6 (2.0%) hospital-acquired VTE events. APPS’ automated calculation had an AUC = 0.79 (CI, 0.63-0.95) that was significant (P = 0.016) with a cutoff value of 5. Chart review’s manual calculation of the PPS had an AUC = 0.76 (CI 0.61-0.91) that was also significant (P = 0.029).

Distribution of Patient Characteristics in Cohort

Automated calculation of APPS using EHR data from prior encounters and the first 4 hours of admission was predictive of in-hospital VTE. APPS performed as well as traditional manual score calculation of the PPS. It was able to do so with no physician input, significantly lessening the burden of calculation and potentially increasing frequency of data-driven VTE risk assessment.

While automated calculation of certain scores is becoming more common, risk calculators that require data beyond vital signs and lab results have lagged,^16-19 in part because of uncertainty about 2 issues. The first is whether EHR data accurately represent the current clinical picture. The second is if a machine-interpretable algorithm to determine a clinical status (eg, “active cancer”) would be similar to a doctor’s perception of that same concept. We attempted to better understand these 2 challenges through developing APPS. Concerning accuracy, EHR data correctly represent the clinical scenario: designations of VTEP and hospital-acquired VTE were accurate in approximately 90% of reviewed cases. Regarding the second concern, when comparing APPS to manual calculation, we found significant differences (P < 0.001) in how often 8 of the 11 criteria were positive, yet no significant difference in overall score and similar predictive capacity. Manual calculation appeared more likely to find data in the index encounter or in structured data. For example, “active cancer” may be documented only in a physician’s note, easily accounted for during a physician’s calculation but missed by APPS looking only for structured data. In contrast, automated calculation found historic criteria, such as “prior VTE” or “known thrombophilic condition,” positive more often. If the patient is being admitted for a problem unrelated to blood clots, the physician may have little time or interest to look through hundreds of EHR documents to discover a 2-year-old VTE. As patients’ records become larger and denser, more historic data can become buried and forgotten. While the 2 scores differ on individual criteria, they are similarly predictive and able to bifurcate the at-risk population to those who should and should not receive pharmacologic prophylaxis.

Disclosures

Dr. Auerbach was supported by NHLBI K24HL098372 during the period of this study. Dr. Khanna, who is an implementation scientist at the University of California San Francisco Center for Digital Health Innovation, is the principal inventor of CareWeb, and may benefit financially from its commercialization. The other authors report no financial conflicts of interest.

Hospital-acquired venous thromboembolism (VTE) continues to be a critical quality challenge for U.S. hospitals,¹ and high-risk patients are often not adequately prophylaxed. Use of VTE prophylaxis (VTEP) varies as widely as 26% to 85% of patients in various studies, as does patient outcomes and care expenditures.^2-6 The 9th edition of the American College of Chest Physicians (CHEST) guidelines⁷ recommend the Padua Prediction Score (PPS) to select individual patients who may be at high risk for venous thromboembolism (VTE) and could benefit from thromboprophylaxis. Use of the manually calculated PPS to select patients for thromboprophylaxis has been shown to help decrease 30-day and 90-day mortality associated with VTE events after hospitalization to medical services.⁸ However, the PPS requires time-consuming manual calculation by a provider, who may be focused on more immediate aspects of patient care and several other risk scores competing for his attention, potentially decreasing its use.

Other risk scores that use only discrete scalar data, such as vital signs and lab results to predict early recognition of sepsis, have been successfully automated and implemented within electronic health records (EHRs).^9-11 Successful automation of scores requiring input of diagnoses, recent medical events, and current clinical status such as the PPS remains difficult.¹² Data representing these characteristics are more prone to error, and harder to translate clearly into a single data field than discrete elements like heart rate, potentially impacting validity of the calculated result.¹³ To improve usage of guideline based VTE risk assessment and decrease physician burden, we developed an algorithm called Automated Padua Prediction Score (APPS) that automatically calculates the PPS using only EHR data available within prior encounters and the first 4 hours of admission, a similar timeframe to when admitting providers would be entering orders. Our goal was to assess if an automatically calculated version of the PPS, a score that depends on criteria more complex than vital signs and labs, would accurately assess risk for hospital-acquired VTE when compared to traditional manual calculation of the Padua Prediction Score by a provider.

Site Description and Ethics

The study was conducted at University of California, San Francisco Medical Center, a 790-bed academic hospital; its Institutional Review Board approved the study and collection of data via chart review. Handling of patient information complied with the Health Insurance Portability and Accountability Act of 1996.

Patient Inclusion

Adult patients admitted to a medical or surgical service between July 1, 2012 and April 1, 2014 were included in the study if they were candidates for VTEP, defined as: length of stay (LOS) greater than 2 days, not on hospice care, not pregnant at admission, no present on admission VTE diagnosis, no known contraindications to prophylaxis (eg, gastrointestinal bleed), and were not receiving therapeutic doses of warfarin, low molecular weight heparins, heparin, or novel anticoagulants prior to admission.

Data Sources

Clinical variables were extracted from the EHR’s enterprise data warehouse (EDW) by SQL Server query (Microsoft, Redmond, Washington) and deposited in a secure database. Chart review was conducted by a trained researcher (Mr. Jacolbia) using the EHR and a standardized protocol. Findings were recorded using REDCap (REDCap Consortium, Vanderbilt University, Nashville, Tennessee). The specific ICD-9, procedure, and lab codes used to determine each criterion of APPS are available in the Appendix.

Creation of the Automated Padua Prediction Score (APPS)

We developed APPS from the original 11 criteria that comprise the Padua Prediction Score: active cancer, previous VTE (excluding superficial vein thrombosis), reduced mobility, known thrombophilic condition, recent (1 month or less) trauma and/or surgery, age 70 years or older, heart and/or respiratory failure, acute myocardial infarction and/or ischemic stroke, acute infection and/or rheumatologic disorder, body mass index (BMI) 30 or higher, and ongoing hormonal treatment.¹³ APPS has the same scoring methodology as PPS: criteria are weighted from 1 to 3 points and summed with a maximum score of 20, representing highest risk of VTE. To automate the score calculation from data routinely available in the EHR, APPS checks pre-selected structured data fields for specific values within laboratory results, orders, nursing flowsheets and claims. Claims data included all ICD-9 and procedure codes used for billing purposes. If any of the predetermined data elements are found, then the specific criterion is considered positive; otherwise, it is scored as negative. The creators of the PPS were consulted in the generation of these data queries to replicate the original standards for deeming a criterion positive. The automated calculation required no use of natural language processing.

Characterization of Study Population

We recorded patient demographics (age, race, gender, BMI), LOS, and rate of hospital-acquired VTE. These patients were separated into 2 cohorts determined by the VTE prophylaxis they received. The risk profile of patients who received pharmacologic prophylaxis was hypothesized to be inherently different from those who had not. To evaluate APPS within this heterogeneous cohort, patients were divided into 2 major categories: pharmacologic vs. no pharmacologic prophylaxis. If they had a completed order or medication administration record on the institution’s approved formulary for pharmacologic VTEP, they were considered to have received pharmacologic prophylaxis. If they had only a completed order for usage of mechanical prophylaxis (sequential compression devices) or no evidence of any form of VTEP, they were considered to have received no pharmacologic prophylaxis. Patients with evidence of both pharmacologic and mechanical were placed in the pharmacologic prophylaxis group. To ensure that automated designation of prophylaxis group was accurate, we reviewed 40 randomly chosen charts because prior researchers were able to achieve sensitivity and specificity greater than 90% with that sample size.¹⁴

The primary outcome of hospital-acquired VTE was defined as an ICD-9 code for VTE (specific codes are found in the Appendix) paired with a “present on admission = no” flag on that encounter’s hospital billing data, abstracted from the EDW. A previous study at this institution used the same methodology and found 212/226 (94%) of patients with a VTE ICD-9 code on claim had evidence of a hospital-acquired VTE event upon chart review.¹⁴ Chart review was also completed to ensure that the primary outcome of newly discovered hospital-acquired VTE was differentiated from chronic VTE or history of VTE. Theoretically, ICD-9 codes and other data elements treat chronic VTE, history of VTE, and hospital-acquired VTE as distinct diagnoses, but it was unclear if this was true in our dataset. For 75 randomly selected cases of presumed hospital-acquired VTE, charts were reviewed for evidence that confirmed newly found VTE during that encounter.

Validation of APPS through Comparison to Manual Calculation of the Original PPS

To compare our automated calculation to standard clinical practice, we manually calculated the PPS through chart review within the first 2 days of admission on 300 random patients, a subsample of the entire study cohort. The largest study we could find had manually calculated the PPS of 1,080 hospitalized patients with a mean PPS of 4.86 (standard deviation [SD], 2.26).¹⁵ One researcher (Mr. Jacolbia) accessed the EHR with all patient information available to physicians, including admission notes, orders, labs, flowsheets, past medical history, and all prior encounters to calculate and record the PPS. To limit potential score bias, 2 authors (Drs. Elias and Davies) assessed 30 randomly selected charts from the cohort of 300. The standardized chart review protocol mimicked a physician’s approach to determine if a patient met a criterion, such as concluding if he/she had active cancer by examining medication lists for chemotherapy, procedure notes for radiation, and recent diagnoses on problem lists. After the original PPS was manually calculated, APPS was automatically calculated for the same 300 patients. We intended to characterize similarities and differences between APPS and manual calculation prior to investigating APPS’ predictive capacity for the entire study population, because it would not be feasible to manually calculate the PPS for all 30,726 patients.

Statistical Analysis

For the 75 randomly selected cases of presumed hospital-acquired VTE, the number of cases was chosen by powering our analysis to find a difference in proportion of 20% with 90% power, α = 0.05 (two-sided). We conducted χ² tests on the entire study cohort to determine if there were significant differences in demographics, LOS, and incidence of hospital-acquired VTE by prophylaxis received. For both the pharmacologic and the no pharmacologic prophylaxis groups, we conducted 2-sample Student t tests to determine significant differences in demographics and LOS between patients who experienced a hospital-acquired VTE and those who did not.

For the comparison of our automated calculation to standard clinical practice, we manually calculated the PPS through chart review within the first 2 days of admission on a subsample of 300 random patients. We powered our analysis to detect a difference in mean PPS from 4.86 to 4.36, enough to alter the point value, with 90% power and α = 0.05 (two-sided) and found 300 patients to be comfortably above the required sample size. We compared APPS and manual calculation in the 300-patient cohort using: 2-sample Student t tests to compare mean scores, χ² tests to compare the frequency with which criteria were positive, and receiver operating characteristic (ROC) curves to determine capacity to predict a hospital-acquired VTE event. Pearson’s correlation was also completed to assess score agreement between APPS and manual calculation on a per-patient basis. After comparing automated calculation of APPS to manual chart review on the same 300 patients, we used APPS to calculate scores for the entire study cohort (n = 30,726). We calculated the mean of APPS by prophylaxis group and whether hospital-acquired VTE had occurred. We analyzed APPS’ ROC curve statistics by prophylaxis group to determine its overall predictive capacity in our study population. Lastly, we computed the time required to calculate APPS per patient. Statistical analyses were conducted using SPSS Statistics (IBM, Armonk, New York) and Python 2.7 (Python Software Foundation, Beaverton, Oregon); 95% confidence intervals (CI) and (SD) were reported when appropriate.

Among the 30,726 unique patients in our entire cohort (all patients admitted during the time period who met the study criteria), we found 6574 (21.4%) on pharmacologic (with or without mechanical) prophylaxis, 13,511 (44.0%) on mechanical only, and 10,641 (34.6%) on no prophylaxis. χ² tests found no significant differences in demographics, LOS, or incidence of hospital-acquired VTE between the patients who received mechanical prophylaxis only and those who received no prophylaxis (Table 1). Similarly, there were no differences in these characteristics in patients receiving pharmacologic prophylaxis with or without the addition of mechanical prophylaxis. Designation of prophylaxis group by manual chart review vs. our automated process was found to agree in categorization for 39/40 (97.5%) sampled encounters. When comparing the cohort that received pharmacologic prophylaxis against the cohort that did not, there were significant differences in racial distribution, sex, BMI, and average LOS as shown in Table 1. Those who received pharmacologic prophylaxis were found to be significantly older than those who did not (62.7 years versus 53.2 years, P < 0.001), more likely to be male (50.6% vs, 42.4%, P < 0.001), more likely to have hospital-acquired VTE (2.2% vs. 0.5%, P < 0.001), and to have a shorter LOS (7.1 days vs. 9.8, P < 0.001).

Within the cohort group receiving pharmacologic prophylaxis (n = 6574), hospital-acquired VTE occurred in patients who were significantly younger (58.2 years vs. 62.8 years, P = 0.003) with a greater LOS (23.8 days vs. 6.7, P < 0.001) than those without. Within the group receiving no pharmacologic prophylaxis (n = 24,152), hospital-acquired VTE occurred in patients who were significantly older (57.1 years vs. 53.2 years, P = 0.014) with more than twice the LOS (20.2 days vs. 9.7 days, P < 0.001) compared to those without. Sixty-six of 75 (88%) randomly selected patients in which new VTE was identified by the automated electronic query had this diagnosis confirmed during manual chart review.

As shown in Table 2, automated calculation on a subsample of 300 randomly selected patients using APPS had a mean of 5.5 (SD, 2.9) while manual calculation of the original PPS on the same patients had a mean of 5.1 (SD, 2.6). There was no significant difference in mean between manual calculation and APPS (P = 0.073). There were, however, significant differences in how often individual criteria were considered present. The largest contributors to the difference in scores between APPS and manual calculation were “prior VTE” (positive, 16% vs. 8.3%, respectively) and “reduced mobility” (positive, 74.3% vs. 66%, respectively) as shown in Table 2. In the subsample, there were a total of 6 (2.0%) hospital-acquired VTE events. APPS’ automated calculation had an AUC = 0.79 (CI, 0.63-0.95) that was significant (P = 0.016) with a cutoff value of 5. Chart review’s manual calculation of the PPS had an AUC = 0.76 (CI 0.61-0.91) that was also significant (P = 0.029).

Distribution of Patient Characteristics in Cohort

Automated calculation of APPS using EHR data from prior encounters and the first 4 hours of admission was predictive of in-hospital VTE. APPS performed as well as traditional manual score calculation of the PPS. It was able to do so with no physician input, significantly lessening the burden of calculation and potentially increasing frequency of data-driven VTE risk assessment.

While automated calculation of certain scores is becoming more common, risk calculators that require data beyond vital signs and lab results have lagged,^16-19 in part because of uncertainty about 2 issues. The first is whether EHR data accurately represent the current clinical picture. The second is if a machine-interpretable algorithm to determine a clinical status (eg, “active cancer”) would be similar to a doctor’s perception of that same concept. We attempted to better understand these 2 challenges through developing APPS. Concerning accuracy, EHR data correctly represent the clinical scenario: designations of VTEP and hospital-acquired VTE were accurate in approximately 90% of reviewed cases. Regarding the second concern, when comparing APPS to manual calculation, we found significant differences (P < 0.001) in how often 8 of the 11 criteria were positive, yet no significant difference in overall score and similar predictive capacity. Manual calculation appeared more likely to find data in the index encounter or in structured data. For example, “active cancer” may be documented only in a physician’s note, easily accounted for during a physician’s calculation but missed by APPS looking only for structured data. In contrast, automated calculation found historic criteria, such as “prior VTE” or “known thrombophilic condition,” positive more often. If the patient is being admitted for a problem unrelated to blood clots, the physician may have little time or interest to look through hundreds of EHR documents to discover a 2-year-old VTE. As patients’ records become larger and denser, more historic data can become buried and forgotten. While the 2 scores differ on individual criteria, they are similarly predictive and able to bifurcate the at-risk population to those who should and should not receive pharmacologic prophylaxis.

Disclosures

Dr. Auerbach was supported by NHLBI K24HL098372 during the period of this study. Dr. Khanna, who is an implementation scientist at the University of California San Francisco Center for Digital Health Innovation, is the principal inventor of CareWeb, and may benefit financially from its commercialization. The other authors report no financial conflicts of interest.

Hospital-acquired venous thromboembolism (VTE) continues to be a critical quality challenge for U.S. hospitals,¹ and high-risk patients are often not adequately prophylaxed. Use of VTE prophylaxis (VTEP) varies as widely as 26% to 85% of patients in various studies, as does patient outcomes and care expenditures.^2-6 The 9th edition of the American College of Chest Physicians (CHEST) guidelines⁷ recommend the Padua Prediction Score (PPS) to select individual patients who may be at high risk for venous thromboembolism (VTE) and could benefit from thromboprophylaxis. Use of the manually calculated PPS to select patients for thromboprophylaxis has been shown to help decrease 30-day and 90-day mortality associated with VTE events after hospitalization to medical services.⁸ However, the PPS requires time-consuming manual calculation by a provider, who may be focused on more immediate aspects of patient care and several other risk scores competing for his attention, potentially decreasing its use.

Other risk scores that use only discrete scalar data, such as vital signs and lab results to predict early recognition of sepsis, have been successfully automated and implemented within electronic health records (EHRs).^9-11 Successful automation of scores requiring input of diagnoses, recent medical events, and current clinical status such as the PPS remains difficult.¹² Data representing these characteristics are more prone to error, and harder to translate clearly into a single data field than discrete elements like heart rate, potentially impacting validity of the calculated result.¹³ To improve usage of guideline based VTE risk assessment and decrease physician burden, we developed an algorithm called Automated Padua Prediction Score (APPS) that automatically calculates the PPS using only EHR data available within prior encounters and the first 4 hours of admission, a similar timeframe to when admitting providers would be entering orders. Our goal was to assess if an automatically calculated version of the PPS, a score that depends on criteria more complex than vital signs and labs, would accurately assess risk for hospital-acquired VTE when compared to traditional manual calculation of the Padua Prediction Score by a provider.

Site Description and Ethics

The study was conducted at University of California, San Francisco Medical Center, a 790-bed academic hospital; its Institutional Review Board approved the study and collection of data via chart review. Handling of patient information complied with the Health Insurance Portability and Accountability Act of 1996.

Patient Inclusion

Adult patients admitted to a medical or surgical service between July 1, 2012 and April 1, 2014 were included in the study if they were candidates for VTEP, defined as: length of stay (LOS) greater than 2 days, not on hospice care, not pregnant at admission, no present on admission VTE diagnosis, no known contraindications to prophylaxis (eg, gastrointestinal bleed), and were not receiving therapeutic doses of warfarin, low molecular weight heparins, heparin, or novel anticoagulants prior to admission.

Data Sources

Clinical variables were extracted from the EHR’s enterprise data warehouse (EDW) by SQL Server query (Microsoft, Redmond, Washington) and deposited in a secure database. Chart review was conducted by a trained researcher (Mr. Jacolbia) using the EHR and a standardized protocol. Findings were recorded using REDCap (REDCap Consortium, Vanderbilt University, Nashville, Tennessee). The specific ICD-9, procedure, and lab codes used to determine each criterion of APPS are available in the Appendix.

Creation of the Automated Padua Prediction Score (APPS)

We developed APPS from the original 11 criteria that comprise the Padua Prediction Score: active cancer, previous VTE (excluding superficial vein thrombosis), reduced mobility, known thrombophilic condition, recent (1 month or less) trauma and/or surgery, age 70 years or older, heart and/or respiratory failure, acute myocardial infarction and/or ischemic stroke, acute infection and/or rheumatologic disorder, body mass index (BMI) 30 or higher, and ongoing hormonal treatment.¹³ APPS has the same scoring methodology as PPS: criteria are weighted from 1 to 3 points and summed with a maximum score of 20, representing highest risk of VTE. To automate the score calculation from data routinely available in the EHR, APPS checks pre-selected structured data fields for specific values within laboratory results, orders, nursing flowsheets and claims. Claims data included all ICD-9 and procedure codes used for billing purposes. If any of the predetermined data elements are found, then the specific criterion is considered positive; otherwise, it is scored as negative. The creators of the PPS were consulted in the generation of these data queries to replicate the original standards for deeming a criterion positive. The automated calculation required no use of natural language processing.

Characterization of Study Population

We recorded patient demographics (age, race, gender, BMI), LOS, and rate of hospital-acquired VTE. These patients were separated into 2 cohorts determined by the VTE prophylaxis they received. The risk profile of patients who received pharmacologic prophylaxis was hypothesized to be inherently different from those who had not. To evaluate APPS within this heterogeneous cohort, patients were divided into 2 major categories: pharmacologic vs. no pharmacologic prophylaxis. If they had a completed order or medication administration record on the institution’s approved formulary for pharmacologic VTEP, they were considered to have received pharmacologic prophylaxis. If they had only a completed order for usage of mechanical prophylaxis (sequential compression devices) or no evidence of any form of VTEP, they were considered to have received no pharmacologic prophylaxis. Patients with evidence of both pharmacologic and mechanical were placed in the pharmacologic prophylaxis group. To ensure that automated designation of prophylaxis group was accurate, we reviewed 40 randomly chosen charts because prior researchers were able to achieve sensitivity and specificity greater than 90% with that sample size.¹⁴

The primary outcome of hospital-acquired VTE was defined as an ICD-9 code for VTE (specific codes are found in the Appendix) paired with a “present on admission = no” flag on that encounter’s hospital billing data, abstracted from the EDW. A previous study at this institution used the same methodology and found 212/226 (94%) of patients with a VTE ICD-9 code on claim had evidence of a hospital-acquired VTE event upon chart review.¹⁴ Chart review was also completed to ensure that the primary outcome of newly discovered hospital-acquired VTE was differentiated from chronic VTE or history of VTE. Theoretically, ICD-9 codes and other data elements treat chronic VTE, history of VTE, and hospital-acquired VTE as distinct diagnoses, but it was unclear if this was true in our dataset. For 75 randomly selected cases of presumed hospital-acquired VTE, charts were reviewed for evidence that confirmed newly found VTE during that encounter.

Validation of APPS through Comparison to Manual Calculation of the Original PPS

To compare our automated calculation to standard clinical practice, we manually calculated the PPS through chart review within the first 2 days of admission on 300 random patients, a subsample of the entire study cohort. The largest study we could find had manually calculated the PPS of 1,080 hospitalized patients with a mean PPS of 4.86 (standard deviation [SD], 2.26).¹⁵ One researcher (Mr. Jacolbia) accessed the EHR with all patient information available to physicians, including admission notes, orders, labs, flowsheets, past medical history, and all prior encounters to calculate and record the PPS. To limit potential score bias, 2 authors (Drs. Elias and Davies) assessed 30 randomly selected charts from the cohort of 300. The standardized chart review protocol mimicked a physician’s approach to determine if a patient met a criterion, such as concluding if he/she had active cancer by examining medication lists for chemotherapy, procedure notes for radiation, and recent diagnoses on problem lists. After the original PPS was manually calculated, APPS was automatically calculated for the same 300 patients. We intended to characterize similarities and differences between APPS and manual calculation prior to investigating APPS’ predictive capacity for the entire study population, because it would not be feasible to manually calculate the PPS for all 30,726 patients.

Statistical Analysis

For the 75 randomly selected cases of presumed hospital-acquired VTE, the number of cases was chosen by powering our analysis to find a difference in proportion of 20% with 90% power, α = 0.05 (two-sided). We conducted χ² tests on the entire study cohort to determine if there were significant differences in demographics, LOS, and incidence of hospital-acquired VTE by prophylaxis received. For both the pharmacologic and the no pharmacologic prophylaxis groups, we conducted 2-sample Student t tests to determine significant differences in demographics and LOS between patients who experienced a hospital-acquired VTE and those who did not.

For the comparison of our automated calculation to standard clinical practice, we manually calculated the PPS through chart review within the first 2 days of admission on a subsample of 300 random patients. We powered our analysis to detect a difference in mean PPS from 4.86 to 4.36, enough to alter the point value, with 90% power and α = 0.05 (two-sided) and found 300 patients to be comfortably above the required sample size. We compared APPS and manual calculation in the 300-patient cohort using: 2-sample Student t tests to compare mean scores, χ² tests to compare the frequency with which criteria were positive, and receiver operating characteristic (ROC) curves to determine capacity to predict a hospital-acquired VTE event. Pearson’s correlation was also completed to assess score agreement between APPS and manual calculation on a per-patient basis. After comparing automated calculation of APPS to manual chart review on the same 300 patients, we used APPS to calculate scores for the entire study cohort (n = 30,726). We calculated the mean of APPS by prophylaxis group and whether hospital-acquired VTE had occurred. We analyzed APPS’ ROC curve statistics by prophylaxis group to determine its overall predictive capacity in our study population. Lastly, we computed the time required to calculate APPS per patient. Statistical analyses were conducted using SPSS Statistics (IBM, Armonk, New York) and Python 2.7 (Python Software Foundation, Beaverton, Oregon); 95% confidence intervals (CI) and (SD) were reported when appropriate.

Among the 30,726 unique patients in our entire cohort (all patients admitted during the time period who met the study criteria), we found 6574 (21.4%) on pharmacologic (with or without mechanical) prophylaxis, 13,511 (44.0%) on mechanical only, and 10,641 (34.6%) on no prophylaxis. χ² tests found no significant differences in demographics, LOS, or incidence of hospital-acquired VTE between the patients who received mechanical prophylaxis only and those who received no prophylaxis (Table 1). Similarly, there were no differences in these characteristics in patients receiving pharmacologic prophylaxis with or without the addition of mechanical prophylaxis. Designation of prophylaxis group by manual chart review vs. our automated process was found to agree in categorization for 39/40 (97.5%) sampled encounters. When comparing the cohort that received pharmacologic prophylaxis against the cohort that did not, there were significant differences in racial distribution, sex, BMI, and average LOS as shown in Table 1. Those who received pharmacologic prophylaxis were found to be significantly older than those who did not (62.7 years versus 53.2 years, P < 0.001), more likely to be male (50.6% vs, 42.4%, P < 0.001), more likely to have hospital-acquired VTE (2.2% vs. 0.5%, P < 0.001), and to have a shorter LOS (7.1 days vs. 9.8, P < 0.001).

Within the cohort group receiving pharmacologic prophylaxis (n = 6574), hospital-acquired VTE occurred in patients who were significantly younger (58.2 years vs. 62.8 years, P = 0.003) with a greater LOS (23.8 days vs. 6.7, P < 0.001) than those without. Within the group receiving no pharmacologic prophylaxis (n = 24,152), hospital-acquired VTE occurred in patients who were significantly older (57.1 years vs. 53.2 years, P = 0.014) with more than twice the LOS (20.2 days vs. 9.7 days, P < 0.001) compared to those without. Sixty-six of 75 (88%) randomly selected patients in which new VTE was identified by the automated electronic query had this diagnosis confirmed during manual chart review.

As shown in Table 2, automated calculation on a subsample of 300 randomly selected patients using APPS had a mean of 5.5 (SD, 2.9) while manual calculation of the original PPS on the same patients had a mean of 5.1 (SD, 2.6). There was no significant difference in mean between manual calculation and APPS (P = 0.073). There were, however, significant differences in how often individual criteria were considered present. The largest contributors to the difference in scores between APPS and manual calculation were “prior VTE” (positive, 16% vs. 8.3%, respectively) and “reduced mobility” (positive, 74.3% vs. 66%, respectively) as shown in Table 2. In the subsample, there were a total of 6 (2.0%) hospital-acquired VTE events. APPS’ automated calculation had an AUC = 0.79 (CI, 0.63-0.95) that was significant (P = 0.016) with a cutoff value of 5. Chart review’s manual calculation of the PPS had an AUC = 0.76 (CI 0.61-0.91) that was also significant (P = 0.029).

Distribution of Patient Characteristics in Cohort

Automated calculation of APPS using EHR data from prior encounters and the first 4 hours of admission was predictive of in-hospital VTE. APPS performed as well as traditional manual score calculation of the PPS. It was able to do so with no physician input, significantly lessening the burden of calculation and potentially increasing frequency of data-driven VTE risk assessment.

While automated calculation of certain scores is becoming more common, risk calculators that require data beyond vital signs and lab results have lagged,^16-19 in part because of uncertainty about 2 issues. The first is whether EHR data accurately represent the current clinical picture. The second is if a machine-interpretable algorithm to determine a clinical status (eg, “active cancer”) would be similar to a doctor’s perception of that same concept. We attempted to better understand these 2 challenges through developing APPS. Concerning accuracy, EHR data correctly represent the clinical scenario: designations of VTEP and hospital-acquired VTE were accurate in approximately 90% of reviewed cases. Regarding the second concern, when comparing APPS to manual calculation, we found significant differences (P < 0.001) in how often 8 of the 11 criteria were positive, yet no significant difference in overall score and similar predictive capacity. Manual calculation appeared more likely to find data in the index encounter or in structured data. For example, “active cancer” may be documented only in a physician’s note, easily accounted for during a physician’s calculation but missed by APPS looking only for structured data. In contrast, automated calculation found historic criteria, such as “prior VTE” or “known thrombophilic condition,” positive more often. If the patient is being admitted for a problem unrelated to blood clots, the physician may have little time or interest to look through hundreds of EHR documents to discover a 2-year-old VTE. As patients’ records become larger and denser, more historic data can become buried and forgotten. While the 2 scores differ on individual criteria, they are similarly predictive and able to bifurcate the at-risk population to those who should and should not receive pharmacologic prophylaxis.

Disclosures

Dr. Auerbach was supported by NHLBI K24HL098372 during the period of this study. Dr. Khanna, who is an implementation scientist at the University of California San Francisco Center for Digital Health Innovation, is the principal inventor of CareWeb, and may benefit financially from its commercialization. The other authors report no financial conflicts of interest.

Approximately 20% of hospitalized Medicare beneficiaries in the U.S. are discharged to skilled nursing facilities (SNFs) for post-acute care,^1,2 and 23.5% of these patients are readmitted within 30 days.³ Because hospital readmissions are costly and associated with worse outcomes,^4,5 30-day readmission rates are considered a quality indicator,⁶ and there are financial penalties for hospitals with higher than expected rates.⁷ As a result, hospitals invest substantial resources in programs to reduce readmissions.^8-10 The SNFs represent an attractive target for readmission reduction efforts, since SNFs contribute a disproportionate share of readmissions.^3,4 Because SNF patients are in a monitored environment with high medication adherence, risk factors for readmission likely differ between patients discharged to SNFs and those sent home. For example, 1 study showed that among heart failure patients with cognitive impairment, those discharged to SNFs had lower readmissions during the first 20 days, likely due to better medication adherence.¹¹ Patients discharged to SNFs generally have more complex illnesses, lower functional status, and higher 1-year mortality than patients discharged to the community.^12,13 Despite this, SNF patients might have infrequent contact with physicians. Federal regulations require only that patients discharged to SNFs need to be seen within 30 days and then at least once every 30 days thereafter.¹⁴ According to the 2014 Office of Inspector General report, one-third of Medicare beneficiaries in SNFs experience adverse events from substandard treatment, inadequate resident monitoring and failure or delay of necessary care, most of which are thought to be preventable.¹⁵

To address this issue, the Cleveland Clinic developed a program called “Connected Care SNF,” in which hospital-employed physicians and advanced practice professionals visit patients in selected SNFs 4 to 5 times per week, for the purpose of reducing preventable readmissions. The aim of this study was to assess whether the program reduced 30-day readmissions, and to identify which patients benefited most from the program.

Setting and Intervention

The Cleveland Clinic main campus is a tertiary academic medical center with 1400 beds and approximately 50,000 admissions per year. In late 2012, the Cleveland Clinic implemented the Connected Care SNF program, wherein Cleveland Clinic physicians regularly visited patients who were discharged from the Cleveland Clinic main campus to 7 regional SNFs. Beginning in December 2012, these 7 high-volume referral SNFs that were not part of the Cleveland Clinic Health System (CCHS) agreed to participate in the program, which focused on reducing avoidable hospital readmissions and delivering quality care (Table 1). The Connected Care team, comprised of 2 geriatricians (1 of whom was also a palliative medicine specialist), 1 internist, 1 family physician, and 5 advanced practice professionals (nurse practitioners and physician assistants), provided medical services at the participating SNFs. These providers aimed to see patients 4 to 5 times per week, were available on site during working hours, and provided telephone coverage at nights and on weekends. All providers had access to hospital electronic medical records and could communicate with the discharging physician and with specialists familiar with the patient as needed. Prior to the admission, providers were informed about patient arrival and, at the time of admission to the SNF, providers reviewed medications and discussed goals of care with patients and their families. In the SNF, providers worked closely with staff members to deliver medications and timely treatment. They also met monthly with multidisciplinary teams for continuous quality improvement and to review outcomes. Patients at Connected Care SNFs who had their own physicians, including most long-stay and some short-stay residents, did not receive the Connected Care intervention. They constituted less than 10% of the patients discharged from Cleveland Clinic main campus.

Study Design and Population

We reviewed administrative and clinical data from a retrospective cohort of patients discharged to SNF from the Cleveland Clinic main campus from January 1, 2011 to December 31, 2014. We included all patients who were discharged to an SNF during the study period. Our main outcome measure was 30-day all-cause readmissions to any hospital in the Cleveland Clinic Health System (CCHS), including the main campus and 8 regional community hospitals. Study patients were followed until January 30, 2015 to capture 30-day readmissions. According to 2012 Medicare data, of CCHS patients who were readmitted within 30 days, 83% of pneumonia, 81% of major joint replacement, 72% of heart failure and 57% of acute myocardial infarction patients were readmitted to a CCHS facility. As the Cleveland Clinic main campus attracts cardiac patients from a 100+-mile radius, they may be more likely to seek care readmission near home and are not reflective of CCHS patients overall. Because we did not have access to readmissions data from non-CCHS hospitals, we excluded patients who were discharged to SNFs beyond a 25-mile radius from the main campus, where they may be more likely to utilize non-CCHS hospitals for acute hospitalization. We also excluded patients discharged to non-CCHS hospital-based SNFs, which may refer readmissions to their own hospital system. Because the Connected Care program began in December 2012, the years 2011-2012 served as the baseline period. The intervention was conducted at 7 SNFs. All other SNFs within the 25-mile radius were included as controls, except for 3 hospital-based SNFs that would be unlikely to admit patients to CCHS. We compared the change in all-cause 30-day readmission rates after implementation of Connected Care, using all patients discharged to SNFs within 25 miles to control for temporal changes in local readmission rates. Discharge to specific SNFs was determined solely by patient choice.

Data Collection

For each patient, we collected the following data that has been shown to be associated with readmissions:^16-18 demographics (age, race, sex, ZIP code), lab values on discharge (hemoglobin and sodium); hemodialysis status; medicine or surgical service; elective surgery or nonelective surgery; details of the index admission index (diagnosis-related group [DRG], Medicare severity-diagnosis-related groups [MS-DRG] weight, primary diagnosis code; principal procedure code; admission date; discharge date, length of stay, and post-acute care provider); and common comorbidities, as listed in Table 2. We also calculated each patient’s HOSPITAL^19,20 score. The HOSPITAL score was developed to predict risk of preventable 30-day readmissions,¹⁹ but it has also been validated to predict 30-day all-cause readmission rates for patients discharged to SNF.²¹ The model contains 7 elements (hemoglobin, oncology service, sodium, procedure, index type, admissions within the last year, length of stay) (supplemental Table).Patients with a high score (7 or higher) have a 41% chance of readmission, while those with a low score (4 or lower) have only a 15% chance. ²¹ We assessed all cause 30-day readmission status from CCHS administrative data. Observation patients and outpatient same-day surgeries were not considered to be admissions. For patients with multiple admissions, each admission was counted as a separate index hospitalization. Cleveland Clinic’s Institutional Review Board approved the study.

Statistical Analysis

For the 7 intervention SNFs, patient characteristics were summarized as means and standard deviations or frequencies and percentages for the periods of 2011-2012 and 2013-2014, respectively, and the 2 periods were compared using the Student t test or χ² test as appropriate.

Mixed-effects logistic regression models were used to model 30-day readmission rates. Since the intervention was implemented in the last quarter of 2012, we examined the difference in readmission rates before and after that time point. The model included the following fixed effects: SNF type (intervention or usual care), time points (quarters of 2011-2014), whether the time is pre- or postintervention (binary), and the 3-way interaction between SNF type, pre- or postintervention and time points, and patient characteristics. The model also contained a Gaussian random effect at the SNF level to account for possible correlations among the outcomes of patients from the same SNF. For each quarter, the mean adjusted readmission rates of 2 types of SNFs were calculated from the fitted mixed models and plotted over time. Furthermore, we compared the mean readmission rates of the 2 groups in the pre- and postintervention periods. Subgroup analyses were performed for medical and surgical patients, and for patients in the low, intermediate and high HOSPITAL score groups.

All analyses were performed using RStudio (Boston, Massachusetts). Statistical significance was established with 2-sided P values less than 0.05.

We identified 119 SNFs within a 25-mile radius of the hospital. Of these, 6 did not receive any referrals. Three non-CCHS hospital-based SNFs were excluded, leaving a total of 110 SNFs in the study sample: 7 intervention SNFs and 103 usual-care SNFs. Between January 2011 and December 2014, there were 23,408 SNF discharges from Cleveland Clinic main campus, including 13,544 who were discharged to study SNFs (Supplemental Figure). Of these, 3334 were discharged to 7 intervention SNFs and 10,210 were discharged to usual care SNFs. Characteristics of patients in both periods appear in Table 2. At baseline, patients in the intervention and control SNFs varied in a number of ways. Patients at intervention SNFs were older (75.6 vs. 70.2 years; P < 0.001), more likely to be African American (45.5% vs. 35.9%; P < 0.001), female (61% vs. 55.4%; P < 0.001) and to be insured by Medicare (85.2% vs. 71.4%; P < 0.001). Both groups had similar proportions of patients with high, intermediate, and low readmission risk as measured by HOSPITAL score. Compared to the 2011-2012 pre-intervention period, during the 2013-2014 intervention period, there were more surgeries (34.3% vs. 41.9%; P < 0.001), more elective surgeries (21.8% vs. 25.5%; P = 0.01), fewer medical patients (65.7% vs. 58.1%; P < 0.001), and an increase in comorbidities, including myocardial infarction, peripheral vascular disease, and liver disease (Table 2).

Table 3 shows adjusted 30-day readmissions rates, before and during the intervention period at the intervention and usual care SNFs. Compared to the pre-intervention period, 30-day all-cause adjusted readmission rates declined in the intervention SNFs (28.1% to 21.7%, P < 0.001), while it increased slightly at control sites (27.1% to 28.5%, P < 0.001). The Figure shows the adjusted 30-day readmission rates by quarter throughout the study period.

In this retrospective study of 4 years of discharges to 110 SNFs, we report on the impact of a Connected Care program, in which a physician visited patients on admission to the SNF and 4 to 5 times per week during their stay. Introduction of the program was followed by a 6.8% absolute reduction in all-cause 30-day readmission rates compared to usual care. The absolute reductions ranged from 4.6% for patients at low risk for readmission to 9.1% for patients at high risk, and medical patients benefited more than surgical patients.

Most studies of interventions to reduce hospital readmissions have focused on patients discharged to the community setting.^7-9 Interventions have centered on discharge planning, medication reconciliation, and close follow-up to assess for medication adherence and early signs of deterioration. Because patients in SNFs have their medications administered by staff and are under frequent surveillance, such interventions are unlikely to be helpful in this population. We found no studies that focus on short-stay or skilled patients discharged to SNF. Two studies have demonstrated that interventions can reduce hospitalization from nursing homes.^22,23 Neither study included readmissions. The Evercare model consisted of nurse practitioners providing active primary care services within the nursing home, as well as offering incentive payments to nursing homes for not hospitalizing patients.²² During a 2-year period, long term residents who enrolled in Evercare had an almost 50% reduction in incident hospitalizations compared to those who did not.²² INTERACT II was a quality improvement intervention that provided tools, education, and strategies to help identify and manage acute conditions proactively.²³ In 25 nursing homes employing INTERACT II, there was a 17% reduction in self-reported hospital admissions during the 6-month project, with higher rates of reduction among nursing homes rated as more engaged in the process.²³ Although nursing homes may serve some short-stay or skilled patients, they generally serve long-term populations, and studies have shown that short-stay patients are at higher risk for 30-day readmissions.²⁴

There are a number of reasons that short-term SNF patients are at higher risk for readmission. Although prior to admission, they were considered hospital level of care and received a physician visit daily, on transfer to the SNF, relatively little medical care is available. Current federal regulations regarding physician services at a SNF require the resident to be seen by a physician at least once every 30 days for the first 90 days after admission, and at least once every 60 days thereafter.²⁵

The Connected Care program physicians provided a smooth transition of care from hospital to SNF as well as frequent reassessment. Physicians were alerted prior to hospital discharge and performed an initial comprehensive visit generally on the day of admission to the SNF and always within 48 hours. The initial evaluation is important because miscommunication during the handoff from hospital to SNF may result in incorrect medication regimens or inaccurate assessments. By performing prompt medication reconciliation and periodic reassessments of a patient’s medical condition, the Connected Care providers recreate some of the essential elements of successful outpatient readmissions prevention programs.

They also worked together with each SNF’s interdisciplinary team to deliver quality care. There were monthly meetings at each participating Connected Care SNF. Physicians reviewed monthly 30-day readmissions and performed root-cause analysis. When they discovered challenges to timely medication and treatment delivery during daily rounds, they provided in-services to SNF nurses.

In addition, Connected Care providers discussed goals of care—something that is often overlooked on admission to a SNF. This is particularly important because patients with chronic illnesses who are discharged to SNF often have poor prognoses. For example, Medicare patients with heart failure who are discharged to SNFs have 1-year mortality in excess of 50%.¹³ By implementing a plan of care consistent with patient and family goals, inappropriate readmissions for terminal patients may be avoided.

Reducing readmissions is important for hospitals because under the Hospital Readmissions Reduction Program, hospitals now face substantial penalties for higher than expected readmissions rates. Hospitals involved in bundled payments or other total cost-of-care arrangements have additional incentive to avoid readmissions. Beginning in 2019, SNFs will also receive incentive payments based on their 30-day all-cause hospital readmissions as part of the Skilled Nursing Facility Value-Based Purchasing program.²⁵ The Connected Care model offers 1 means of achieving this goal through partnership between hospitals and SNFs.

Our study has several limitations. First, our study was observational in nature, so the observed reduction in readmissions could have been due to temporal trends unrelated to the intervention. However, no significant reduction was noted during the same time period in other area SNFs. There was also little change in the characteristics of patients admitted to the intervention SNFs. Importantly, the HOSPITAL score, which can predict 30-day readmission rates,²⁰ did not change throughout the study period. Second, the results reflect patients discharged from a single hospital and may not be generalizable to other geographic areas. However, because the program included 7 SNFs, we believe it could be reproduced in other settings. Third, our readmissions measure included only those patients who returned to a CCHS facility. Although we may have missed some readmissions to other hospital systems, such leakage is uncommon—more than 80% of CCHS patients are readmitted to CCHS facilities—and would be unlikely to differ across the short duration of the study. Finally, at the intervention SNFs, most long-stay and some short-stay residents did not receive the Connected Care intervention because they were cared for by their own physicians who did not participate in Connected Care. Had these patients’ readmissions been excluded from our results, the intervention might appear even more effective.

CONCLUSION

A Connected Care intervention reduced 30-day readmission rates among patients discharged to SNFs from a tertiary academic center. While all subgroups had substantial reductions in readmissions following the implementation of the intervention, patients who are at the highest risk of readmission benefited the most. Further study is necessary to know whether Connected Care can be reproduced in other health care systems and whether it reduces overall costs.

Acknowledgments

The authors would like to thank Michael Felver, MD, and teams for their clinical care of patients; Michael Felver, MD, William Zafirau, MD, Dan Blechschmid, MHA, and Kathy Brezine, and Seth Vilensky, MBA, for their administrative support; and Brad Souder, MPT, for assistance with data collection.

Disclosure

Nothing to report.

Approximately 20% of hospitalized Medicare beneficiaries in the U.S. are discharged to skilled nursing facilities (SNFs) for post-acute care,^1,2 and 23.5% of these patients are readmitted within 30 days.³ Because hospital readmissions are costly and associated with worse outcomes,^4,5 30-day readmission rates are considered a quality indicator,⁶ and there are financial penalties for hospitals with higher than expected rates.⁷ As a result, hospitals invest substantial resources in programs to reduce readmissions.^8-10 The SNFs represent an attractive target for readmission reduction efforts, since SNFs contribute a disproportionate share of readmissions.^3,4 Because SNF patients are in a monitored environment with high medication adherence, risk factors for readmission likely differ between patients discharged to SNFs and those sent home. For example, 1 study showed that among heart failure patients with cognitive impairment, those discharged to SNFs had lower readmissions during the first 20 days, likely due to better medication adherence.¹¹ Patients discharged to SNFs generally have more complex illnesses, lower functional status, and higher 1-year mortality than patients discharged to the community.^12,13 Despite this, SNF patients might have infrequent contact with physicians. Federal regulations require only that patients discharged to SNFs need to be seen within 30 days and then at least once every 30 days thereafter.¹⁴ According to the 2014 Office of Inspector General report, one-third of Medicare beneficiaries in SNFs experience adverse events from substandard treatment, inadequate resident monitoring and failure or delay of necessary care, most of which are thought to be preventable.¹⁵

To address this issue, the Cleveland Clinic developed a program called “Connected Care SNF,” in which hospital-employed physicians and advanced practice professionals visit patients in selected SNFs 4 to 5 times per week, for the purpose of reducing preventable readmissions. The aim of this study was to assess whether the program reduced 30-day readmissions, and to identify which patients benefited most from the program.

Setting and Intervention

The Cleveland Clinic main campus is a tertiary academic medical center with 1400 beds and approximately 50,000 admissions per year. In late 2012, the Cleveland Clinic implemented the Connected Care SNF program, wherein Cleveland Clinic physicians regularly visited patients who were discharged from the Cleveland Clinic main campus to 7 regional SNFs. Beginning in December 2012, these 7 high-volume referral SNFs that were not part of the Cleveland Clinic Health System (CCHS) agreed to participate in the program, which focused on reducing avoidable hospital readmissions and delivering quality care (Table 1). The Connected Care team, comprised of 2 geriatricians (1 of whom was also a palliative medicine specialist), 1 internist, 1 family physician, and 5 advanced practice professionals (nurse practitioners and physician assistants), provided medical services at the participating SNFs. These providers aimed to see patients 4 to 5 times per week, were available on site during working hours, and provided telephone coverage at nights and on weekends. All providers had access to hospital electronic medical records and could communicate with the discharging physician and with specialists familiar with the patient as needed. Prior to the admission, providers were informed about patient arrival and, at the time of admission to the SNF, providers reviewed medications and discussed goals of care with patients and their families. In the SNF, providers worked closely with staff members to deliver medications and timely treatment. They also met monthly with multidisciplinary teams for continuous quality improvement and to review outcomes. Patients at Connected Care SNFs who had their own physicians, including most long-stay and some short-stay residents, did not receive the Connected Care intervention. They constituted less than 10% of the patients discharged from Cleveland Clinic main campus.

Study Design and Population

We reviewed administrative and clinical data from a retrospective cohort of patients discharged to SNF from the Cleveland Clinic main campus from January 1, 2011 to December 31, 2014. We included all patients who were discharged to an SNF during the study period. Our main outcome measure was 30-day all-cause readmissions to any hospital in the Cleveland Clinic Health System (CCHS), including the main campus and 8 regional community hospitals. Study patients were followed until January 30, 2015 to capture 30-day readmissions. According to 2012 Medicare data, of CCHS patients who were readmitted within 30 days, 83% of pneumonia, 81% of major joint replacement, 72% of heart failure and 57% of acute myocardial infarction patients were readmitted to a CCHS facility. As the Cleveland Clinic main campus attracts cardiac patients from a 100+-mile radius, they may be more likely to seek care readmission near home and are not reflective of CCHS patients overall. Because we did not have access to readmissions data from non-CCHS hospitals, we excluded patients who were discharged to SNFs beyond a 25-mile radius from the main campus, where they may be more likely to utilize non-CCHS hospitals for acute hospitalization. We also excluded patients discharged to non-CCHS hospital-based SNFs, which may refer readmissions to their own hospital system. Because the Connected Care program began in December 2012, the years 2011-2012 served as the baseline period. The intervention was conducted at 7 SNFs. All other SNFs within the 25-mile radius were included as controls, except for 3 hospital-based SNFs that would be unlikely to admit patients to CCHS. We compared the change in all-cause 30-day readmission rates after implementation of Connected Care, using all patients discharged to SNFs within 25 miles to control for temporal changes in local readmission rates. Discharge to specific SNFs was determined solely by patient choice.

Data Collection

For each patient, we collected the following data that has been shown to be associated with readmissions:^16-18 demographics (age, race, sex, ZIP code), lab values on discharge (hemoglobin and sodium); hemodialysis status; medicine or surgical service; elective surgery or nonelective surgery; details of the index admission index (diagnosis-related group [DRG], Medicare severity-diagnosis-related groups [MS-DRG] weight, primary diagnosis code; principal procedure code; admission date; discharge date, length of stay, and post-acute care provider); and common comorbidities, as listed in Table 2. We also calculated each patient’s HOSPITAL^19,20 score. The HOSPITAL score was developed to predict risk of preventable 30-day readmissions,¹⁹ but it has also been validated to predict 30-day all-cause readmission rates for patients discharged to SNF.²¹ The model contains 7 elements (hemoglobin, oncology service, sodium, procedure, index type, admissions within the last year, length of stay) (supplemental Table).Patients with a high score (7 or higher) have a 41% chance of readmission, while those with a low score (4 or lower) have only a 15% chance. ²¹ We assessed all cause 30-day readmission status from CCHS administrative data. Observation patients and outpatient same-day surgeries were not considered to be admissions. For patients with multiple admissions, each admission was counted as a separate index hospitalization. Cleveland Clinic’s Institutional Review Board approved the study.

Statistical Analysis

For the 7 intervention SNFs, patient characteristics were summarized as means and standard deviations or frequencies and percentages for the periods of 2011-2012 and 2013-2014, respectively, and the 2 periods were compared using the Student t test or χ² test as appropriate.

Mixed-effects logistic regression models were used to model 30-day readmission rates. Since the intervention was implemented in the last quarter of 2012, we examined the difference in readmission rates before and after that time point. The model included the following fixed effects: SNF type (intervention or usual care), time points (quarters of 2011-2014), whether the time is pre- or postintervention (binary), and the 3-way interaction between SNF type, pre- or postintervention and time points, and patient characteristics. The model also contained a Gaussian random effect at the SNF level to account for possible correlations among the outcomes of patients from the same SNF. For each quarter, the mean adjusted readmission rates of 2 types of SNFs were calculated from the fitted mixed models and plotted over time. Furthermore, we compared the mean readmission rates of the 2 groups in the pre- and postintervention periods. Subgroup analyses were performed for medical and surgical patients, and for patients in the low, intermediate and high HOSPITAL score groups.

All analyses were performed using RStudio (Boston, Massachusetts). Statistical significance was established with 2-sided P values less than 0.05.

We identified 119 SNFs within a 25-mile radius of the hospital. Of these, 6 did not receive any referrals. Three non-CCHS hospital-based SNFs were excluded, leaving a total of 110 SNFs in the study sample: 7 intervention SNFs and 103 usual-care SNFs. Between January 2011 and December 2014, there were 23,408 SNF discharges from Cleveland Clinic main campus, including 13,544 who were discharged to study SNFs (Supplemental Figure). Of these, 3334 were discharged to 7 intervention SNFs and 10,210 were discharged to usual care SNFs. Characteristics of patients in both periods appear in Table 2. At baseline, patients in the intervention and control SNFs varied in a number of ways. Patients at intervention SNFs were older (75.6 vs. 70.2 years; P < 0.001), more likely to be African American (45.5% vs. 35.9%; P < 0.001), female (61% vs. 55.4%; P < 0.001) and to be insured by Medicare (85.2% vs. 71.4%; P < 0.001). Both groups had similar proportions of patients with high, intermediate, and low readmission risk as measured by HOSPITAL score. Compared to the 2011-2012 pre-intervention period, during the 2013-2014 intervention period, there were more surgeries (34.3% vs. 41.9%; P < 0.001), more elective surgeries (21.8% vs. 25.5%; P = 0.01), fewer medical patients (65.7% vs. 58.1%; P < 0.001), and an increase in comorbidities, including myocardial infarction, peripheral vascular disease, and liver disease (Table 2).

Table 3 shows adjusted 30-day readmissions rates, before and during the intervention period at the intervention and usual care SNFs. Compared to the pre-intervention period, 30-day all-cause adjusted readmission rates declined in the intervention SNFs (28.1% to 21.7%, P < 0.001), while it increased slightly at control sites (27.1% to 28.5%, P < 0.001). The Figure shows the adjusted 30-day readmission rates by quarter throughout the study period.

In this retrospective study of 4 years of discharges to 110 SNFs, we report on the impact of a Connected Care program, in which a physician visited patients on admission to the SNF and 4 to 5 times per week during their stay. Introduction of the program was followed by a 6.8% absolute reduction in all-cause 30-day readmission rates compared to usual care. The absolute reductions ranged from 4.6% for patients at low risk for readmission to 9.1% for patients at high risk, and medical patients benefited more than surgical patients.

Most studies of interventions to reduce hospital readmissions have focused on patients discharged to the community setting.^7-9 Interventions have centered on discharge planning, medication reconciliation, and close follow-up to assess for medication adherence and early signs of deterioration. Because patients in SNFs have their medications administered by staff and are under frequent surveillance, such interventions are unlikely to be helpful in this population. We found no studies that focus on short-stay or skilled patients discharged to SNF. Two studies have demonstrated that interventions can reduce hospitalization from nursing homes.^22,23 Neither study included readmissions. The Evercare model consisted of nurse practitioners providing active primary care services within the nursing home, as well as offering incentive payments to nursing homes for not hospitalizing patients.²² During a 2-year period, long term residents who enrolled in Evercare had an almost 50% reduction in incident hospitalizations compared to those who did not.²² INTERACT II was a quality improvement intervention that provided tools, education, and strategies to help identify and manage acute conditions proactively.²³ In 25 nursing homes employing INTERACT II, there was a 17% reduction in self-reported hospital admissions during the 6-month project, with higher rates of reduction among nursing homes rated as more engaged in the process.²³ Although nursing homes may serve some short-stay or skilled patients, they generally serve long-term populations, and studies have shown that short-stay patients are at higher risk for 30-day readmissions.²⁴

There are a number of reasons that short-term SNF patients are at higher risk for readmission. Although prior to admission, they were considered hospital level of care and received a physician visit daily, on transfer to the SNF, relatively little medical care is available. Current federal regulations regarding physician services at a SNF require the resident to be seen by a physician at least once every 30 days for the first 90 days after admission, and at least once every 60 days thereafter.²⁵

The Connected Care program physicians provided a smooth transition of care from hospital to SNF as well as frequent reassessment. Physicians were alerted prior to hospital discharge and performed an initial comprehensive visit generally on the day of admission to the SNF and always within 48 hours. The initial evaluation is important because miscommunication during the handoff from hospital to SNF may result in incorrect medication regimens or inaccurate assessments. By performing prompt medication reconciliation and periodic reassessments of a patient’s medical condition, the Connected Care providers recreate some of the essential elements of successful outpatient readmissions prevention programs.

They also worked together with each SNF’s interdisciplinary team to deliver quality care. There were monthly meetings at each participating Connected Care SNF. Physicians reviewed monthly 30-day readmissions and performed root-cause analysis. When they discovered challenges to timely medication and treatment delivery during daily rounds, they provided in-services to SNF nurses.

In addition, Connected Care providers discussed goals of care—something that is often overlooked on admission to a SNF. This is particularly important because patients with chronic illnesses who are discharged to SNF often have poor prognoses. For example, Medicare patients with heart failure who are discharged to SNFs have 1-year mortality in excess of 50%.¹³ By implementing a plan of care consistent with patient and family goals, inappropriate readmissions for terminal patients may be avoided.

Reducing readmissions is important for hospitals because under the Hospital Readmissions Reduction Program, hospitals now face substantial penalties for higher than expected readmissions rates. Hospitals involved in bundled payments or other total cost-of-care arrangements have additional incentive to avoid readmissions. Beginning in 2019, SNFs will also receive incentive payments based on their 30-day all-cause hospital readmissions as part of the Skilled Nursing Facility Value-Based Purchasing program.²⁵ The Connected Care model offers 1 means of achieving this goal through partnership between hospitals and SNFs.

Our study has several limitations. First, our study was observational in nature, so the observed reduction in readmissions could have been due to temporal trends unrelated to the intervention. However, no significant reduction was noted during the same time period in other area SNFs. There was also little change in the characteristics of patients admitted to the intervention SNFs. Importantly, the HOSPITAL score, which can predict 30-day readmission rates,²⁰ did not change throughout the study period. Second, the results reflect patients discharged from a single hospital and may not be generalizable to other geographic areas. However, because the program included 7 SNFs, we believe it could be reproduced in other settings. Third, our readmissions measure included only those patients who returned to a CCHS facility. Although we may have missed some readmissions to other hospital systems, such leakage is uncommon—more than 80% of CCHS patients are readmitted to CCHS facilities—and would be unlikely to differ across the short duration of the study. Finally, at the intervention SNFs, most long-stay and some short-stay residents did not receive the Connected Care intervention because they were cared for by their own physicians who did not participate in Connected Care. Had these patients’ readmissions been excluded from our results, the intervention might appear even more effective.

CONCLUSION

A Connected Care intervention reduced 30-day readmission rates among patients discharged to SNFs from a tertiary academic center. While all subgroups had substantial reductions in readmissions following the implementation of the intervention, patients who are at the highest risk of readmission benefited the most. Further study is necessary to know whether Connected Care can be reproduced in other health care systems and whether it reduces overall costs.

Acknowledgments

The authors would like to thank Michael Felver, MD, and teams for their clinical care of patients; Michael Felver, MD, William Zafirau, MD, Dan Blechschmid, MHA, and Kathy Brezine, and Seth Vilensky, MBA, for their administrative support; and Brad Souder, MPT, for assistance with data collection.

Disclosure

Nothing to report.

Approximately 20% of hospitalized Medicare beneficiaries in the U.S. are discharged to skilled nursing facilities (SNFs) for post-acute care,^1,2 and 23.5% of these patients are readmitted within 30 days.³ Because hospital readmissions are costly and associated with worse outcomes,^4,5 30-day readmission rates are considered a quality indicator,⁶ and there are financial penalties for hospitals with higher than expected rates.⁷ As a result, hospitals invest substantial resources in programs to reduce readmissions.^8-10 The SNFs represent an attractive target for readmission reduction efforts, since SNFs contribute a disproportionate share of readmissions.^3,4 Because SNF patients are in a monitored environment with high medication adherence, risk factors for readmission likely differ between patients discharged to SNFs and those sent home. For example, 1 study showed that among heart failure patients with cognitive impairment, those discharged to SNFs had lower readmissions during the first 20 days, likely due to better medication adherence.¹¹ Patients discharged to SNFs generally have more complex illnesses, lower functional status, and higher 1-year mortality than patients discharged to the community.^12,13 Despite this, SNF patients might have infrequent contact with physicians. Federal regulations require only that patients discharged to SNFs need to be seen within 30 days and then at least once every 30 days thereafter.¹⁴ According to the 2014 Office of Inspector General report, one-third of Medicare beneficiaries in SNFs experience adverse events from substandard treatment, inadequate resident monitoring and failure or delay of necessary care, most of which are thought to be preventable.¹⁵

To address this issue, the Cleveland Clinic developed a program called “Connected Care SNF,” in which hospital-employed physicians and advanced practice professionals visit patients in selected SNFs 4 to 5 times per week, for the purpose of reducing preventable readmissions. The aim of this study was to assess whether the program reduced 30-day readmissions, and to identify which patients benefited most from the program.

Setting and Intervention

The Cleveland Clinic main campus is a tertiary academic medical center with 1400 beds and approximately 50,000 admissions per year. In late 2012, the Cleveland Clinic implemented the Connected Care SNF program, wherein Cleveland Clinic physicians regularly visited patients who were discharged from the Cleveland Clinic main campus to 7 regional SNFs. Beginning in December 2012, these 7 high-volume referral SNFs that were not part of the Cleveland Clinic Health System (CCHS) agreed to participate in the program, which focused on reducing avoidable hospital readmissions and delivering quality care (Table 1). The Connected Care team, comprised of 2 geriatricians (1 of whom was also a palliative medicine specialist), 1 internist, 1 family physician, and 5 advanced practice professionals (nurse practitioners and physician assistants), provided medical services at the participating SNFs. These providers aimed to see patients 4 to 5 times per week, were available on site during working hours, and provided telephone coverage at nights and on weekends. All providers had access to hospital electronic medical records and could communicate with the discharging physician and with specialists familiar with the patient as needed. Prior to the admission, providers were informed about patient arrival and, at the time of admission to the SNF, providers reviewed medications and discussed goals of care with patients and their families. In the SNF, providers worked closely with staff members to deliver medications and timely treatment. They also met monthly with multidisciplinary teams for continuous quality improvement and to review outcomes. Patients at Connected Care SNFs who had their own physicians, including most long-stay and some short-stay residents, did not receive the Connected Care intervention. They constituted less than 10% of the patients discharged from Cleveland Clinic main campus.

Study Design and Population

We reviewed administrative and clinical data from a retrospective cohort of patients discharged to SNF from the Cleveland Clinic main campus from January 1, 2011 to December 31, 2014. We included all patients who were discharged to an SNF during the study period. Our main outcome measure was 30-day all-cause readmissions to any hospital in the Cleveland Clinic Health System (CCHS), including the main campus and 8 regional community hospitals. Study patients were followed until January 30, 2015 to capture 30-day readmissions. According to 2012 Medicare data, of CCHS patients who were readmitted within 30 days, 83% of pneumonia, 81% of major joint replacement, 72% of heart failure and 57% of acute myocardial infarction patients were readmitted to a CCHS facility. As the Cleveland Clinic main campus attracts cardiac patients from a 100+-mile radius, they may be more likely to seek care readmission near home and are not reflective of CCHS patients overall. Because we did not have access to readmissions data from non-CCHS hospitals, we excluded patients who were discharged to SNFs beyond a 25-mile radius from the main campus, where they may be more likely to utilize non-CCHS hospitals for acute hospitalization. We also excluded patients discharged to non-CCHS hospital-based SNFs, which may refer readmissions to their own hospital system. Because the Connected Care program began in December 2012, the years 2011-2012 served as the baseline period. The intervention was conducted at 7 SNFs. All other SNFs within the 25-mile radius were included as controls, except for 3 hospital-based SNFs that would be unlikely to admit patients to CCHS. We compared the change in all-cause 30-day readmission rates after implementation of Connected Care, using all patients discharged to SNFs within 25 miles to control for temporal changes in local readmission rates. Discharge to specific SNFs was determined solely by patient choice.

Data Collection

For each patient, we collected the following data that has been shown to be associated with readmissions:^16-18 demographics (age, race, sex, ZIP code), lab values on discharge (hemoglobin and sodium); hemodialysis status; medicine or surgical service; elective surgery or nonelective surgery; details of the index admission index (diagnosis-related group [DRG], Medicare severity-diagnosis-related groups [MS-DRG] weight, primary diagnosis code; principal procedure code; admission date; discharge date, length of stay, and post-acute care provider); and common comorbidities, as listed in Table 2. We also calculated each patient’s HOSPITAL^19,20 score. The HOSPITAL score was developed to predict risk of preventable 30-day readmissions,¹⁹ but it has also been validated to predict 30-day all-cause readmission rates for patients discharged to SNF.²¹ The model contains 7 elements (hemoglobin, oncology service, sodium, procedure, index type, admissions within the last year, length of stay) (supplemental Table).Patients with a high score (7 or higher) have a 41% chance of readmission, while those with a low score (4 or lower) have only a 15% chance. ²¹ We assessed all cause 30-day readmission status from CCHS administrative data. Observation patients and outpatient same-day surgeries were not considered to be admissions. For patients with multiple admissions, each admission was counted as a separate index hospitalization. Cleveland Clinic’s Institutional Review Board approved the study.

Statistical Analysis

For the 7 intervention SNFs, patient characteristics were summarized as means and standard deviations or frequencies and percentages for the periods of 2011-2012 and 2013-2014, respectively, and the 2 periods were compared using the Student t test or χ² test as appropriate.

Mixed-effects logistic regression models were used to model 30-day readmission rates. Since the intervention was implemented in the last quarter of 2012, we examined the difference in readmission rates before and after that time point. The model included the following fixed effects: SNF type (intervention or usual care), time points (quarters of 2011-2014), whether the time is pre- or postintervention (binary), and the 3-way interaction between SNF type, pre- or postintervention and time points, and patient characteristics. The model also contained a Gaussian random effect at the SNF level to account for possible correlations among the outcomes of patients from the same SNF. For each quarter, the mean adjusted readmission rates of 2 types of SNFs were calculated from the fitted mixed models and plotted over time. Furthermore, we compared the mean readmission rates of the 2 groups in the pre- and postintervention periods. Subgroup analyses were performed for medical and surgical patients, and for patients in the low, intermediate and high HOSPITAL score groups.

All analyses were performed using RStudio (Boston, Massachusetts). Statistical significance was established with 2-sided P values less than 0.05.

We identified 119 SNFs within a 25-mile radius of the hospital. Of these, 6 did not receive any referrals. Three non-CCHS hospital-based SNFs were excluded, leaving a total of 110 SNFs in the study sample: 7 intervention SNFs and 103 usual-care SNFs. Between January 2011 and December 2014, there were 23,408 SNF discharges from Cleveland Clinic main campus, including 13,544 who were discharged to study SNFs (Supplemental Figure). Of these, 3334 were discharged to 7 intervention SNFs and 10,210 were discharged to usual care SNFs. Characteristics of patients in both periods appear in Table 2. At baseline, patients in the intervention and control SNFs varied in a number of ways. Patients at intervention SNFs were older (75.6 vs. 70.2 years; P < 0.001), more likely to be African American (45.5% vs. 35.9%; P < 0.001), female (61% vs. 55.4%; P < 0.001) and to be insured by Medicare (85.2% vs. 71.4%; P < 0.001). Both groups had similar proportions of patients with high, intermediate, and low readmission risk as measured by HOSPITAL score. Compared to the 2011-2012 pre-intervention period, during the 2013-2014 intervention period, there were more surgeries (34.3% vs. 41.9%; P < 0.001), more elective surgeries (21.8% vs. 25.5%; P = 0.01), fewer medical patients (65.7% vs. 58.1%; P < 0.001), and an increase in comorbidities, including myocardial infarction, peripheral vascular disease, and liver disease (Table 2).

Table 3 shows adjusted 30-day readmissions rates, before and during the intervention period at the intervention and usual care SNFs. Compared to the pre-intervention period, 30-day all-cause adjusted readmission rates declined in the intervention SNFs (28.1% to 21.7%, P < 0.001), while it increased slightly at control sites (27.1% to 28.5%, P < 0.001). The Figure shows the adjusted 30-day readmission rates by quarter throughout the study period.

In this retrospective study of 4 years of discharges to 110 SNFs, we report on the impact of a Connected Care program, in which a physician visited patients on admission to the SNF and 4 to 5 times per week during their stay. Introduction of the program was followed by a 6.8% absolute reduction in all-cause 30-day readmission rates compared to usual care. The absolute reductions ranged from 4.6% for patients at low risk for readmission to 9.1% for patients at high risk, and medical patients benefited more than surgical patients.

Most studies of interventions to reduce hospital readmissions have focused on patients discharged to the community setting.^7-9 Interventions have centered on discharge planning, medication reconciliation, and close follow-up to assess for medication adherence and early signs of deterioration. Because patients in SNFs have their medications administered by staff and are under frequent surveillance, such interventions are unlikely to be helpful in this population. We found no studies that focus on short-stay or skilled patients discharged to SNF. Two studies have demonstrated that interventions can reduce hospitalization from nursing homes.^22,23 Neither study included readmissions. The Evercare model consisted of nurse practitioners providing active primary care services within the nursing home, as well as offering incentive payments to nursing homes for not hospitalizing patients.²² During a 2-year period, long term residents who enrolled in Evercare had an almost 50% reduction in incident hospitalizations compared to those who did not.²² INTERACT II was a quality improvement intervention that provided tools, education, and strategies to help identify and manage acute conditions proactively.²³ In 25 nursing homes employing INTERACT II, there was a 17% reduction in self-reported hospital admissions during the 6-month project, with higher rates of reduction among nursing homes rated as more engaged in the process.²³ Although nursing homes may serve some short-stay or skilled patients, they generally serve long-term populations, and studies have shown that short-stay patients are at higher risk for 30-day readmissions.²⁴

There are a number of reasons that short-term SNF patients are at higher risk for readmission. Although prior to admission, they were considered hospital level of care and received a physician visit daily, on transfer to the SNF, relatively little medical care is available. Current federal regulations regarding physician services at a SNF require the resident to be seen by a physician at least once every 30 days for the first 90 days after admission, and at least once every 60 days thereafter.²⁵

The Connected Care program physicians provided a smooth transition of care from hospital to SNF as well as frequent reassessment. Physicians were alerted prior to hospital discharge and performed an initial comprehensive visit generally on the day of admission to the SNF and always within 48 hours. The initial evaluation is important because miscommunication during the handoff from hospital to SNF may result in incorrect medication regimens or inaccurate assessments. By performing prompt medication reconciliation and periodic reassessments of a patient’s medical condition, the Connected Care providers recreate some of the essential elements of successful outpatient readmissions prevention programs.

They also worked together with each SNF’s interdisciplinary team to deliver quality care. There were monthly meetings at each participating Connected Care SNF. Physicians reviewed monthly 30-day readmissions and performed root-cause analysis. When they discovered challenges to timely medication and treatment delivery during daily rounds, they provided in-services to SNF nurses.

In addition, Connected Care providers discussed goals of care—something that is often overlooked on admission to a SNF. This is particularly important because patients with chronic illnesses who are discharged to SNF often have poor prognoses. For example, Medicare patients with heart failure who are discharged to SNFs have 1-year mortality in excess of 50%.¹³ By implementing a plan of care consistent with patient and family goals, inappropriate readmissions for terminal patients may be avoided.

Reducing readmissions is important for hospitals because under the Hospital Readmissions Reduction Program, hospitals now face substantial penalties for higher than expected readmissions rates. Hospitals involved in bundled payments or other total cost-of-care arrangements have additional incentive to avoid readmissions. Beginning in 2019, SNFs will also receive incentive payments based on their 30-day all-cause hospital readmissions as part of the Skilled Nursing Facility Value-Based Purchasing program.²⁵ The Connected Care model offers 1 means of achieving this goal through partnership between hospitals and SNFs.

Our study has several limitations. First, our study was observational in nature, so the observed reduction in readmissions could have been due to temporal trends unrelated to the intervention. However, no significant reduction was noted during the same time period in other area SNFs. There was also little change in the characteristics of patients admitted to the intervention SNFs. Importantly, the HOSPITAL score, which can predict 30-day readmission rates,²⁰ did not change throughout the study period. Second, the results reflect patients discharged from a single hospital and may not be generalizable to other geographic areas. However, because the program included 7 SNFs, we believe it could be reproduced in other settings. Third, our readmissions measure included only those patients who returned to a CCHS facility. Although we may have missed some readmissions to other hospital systems, such leakage is uncommon—more than 80% of CCHS patients are readmitted to CCHS facilities—and would be unlikely to differ across the short duration of the study. Finally, at the intervention SNFs, most long-stay and some short-stay residents did not receive the Connected Care intervention because they were cared for by their own physicians who did not participate in Connected Care. Had these patients’ readmissions been excluded from our results, the intervention might appear even more effective.

CONCLUSION

A Connected Care intervention reduced 30-day readmission rates among patients discharged to SNFs from a tertiary academic center. While all subgroups had substantial reductions in readmissions following the implementation of the intervention, patients who are at the highest risk of readmission benefited the most. Further study is necessary to know whether Connected Care can be reproduced in other health care systems and whether it reduces overall costs.

Acknowledgments

The authors would like to thank Michael Felver, MD, and teams for their clinical care of patients; Michael Felver, MD, William Zafirau, MD, Dan Blechschmid, MHA, and Kathy Brezine, and Seth Vilensky, MBA, for their administrative support; and Brad Souder, MPT, for assistance with data collection.

Disclosure

Nothing to report.