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Duty Hours Pose Ethical Dilemmas
As a surgical resident over two decades ago, I often cringed when a faculty member would say, " When I was a resident," and launch into a story about how difficult things were "in the old days." Sometimes I was reminded of jokes about our forebears having to walk 5 miles to school every day uphill -- both ways!
I’m sure my residents have a similar reaction when I talk about how tough we had it compared with now. However, today’s residents have a much more difficult road than I ever had in terms of the choices they must make because of duty-hour limits.
In prior decades, the excellent resident was the one who always knew what was going on with his or her patients, and who came in early and stayed as late as necessary to get everything done. When I was a junior resident, the chief resident role models we all emulated were those who worked the hardest (i.e., the longest hours). I often felt that the willingness to work hard more clearly defined who succeed than intelligence, efficiency, or technical abilities. However, today’s surgical residents are constantly being challenged to make ethical choices that were unheard of in years gone by.
Recently, a midlevel surgical resident who I respect very much related the following case to me. At about 5 a.m. during his night of call he had admitted a 79-year-old man in septic shock with an acute abdomen to the surgical intensive care unit. The patient had required significant fluid resuscitation prior to safely going to the operating room for an exploratory laparotomy. The surgery was just beginning at 7 a.m. when the the attending asked the resident when his shift ended. The resident said he wanted to stay to do the case, but was conflicted by the fact that doing so would mean exceeding his work hour limit for the week. In addition, the program director had recently sent residents a notice saying that it was unacceptable to ignore the work hour limits. The resident felt that in order to stay to do the case, he would be required to lie on his work hour log.
This challenge of weighing what might be good for the individual resident against the potential harm to the program for work hour violations is a new ethical tension. The need to choose between continuity of care (which might be good for a patient and aid the resident’s education) and the requirement to sign out to other residents to maintain the accreditation of the residency program is a conflict that didn’t exist in previous decades.
It is unclear what the ideal role model should be for a surgical resident today. Simply spending more time taking care of patients than anyone else can no longer be considered as the optimal position for a surgical resident. However, many surgical faculty have not altered their concept of the ideal resident to meet the necessary time constraints that are required of residents. As a result, residents are often held to unreasonable standards based on prior concepts of how "great" residents used to act.
Today, surgical faculty and surgical residents must seek to define the new ideal role model for a surgical resident. This person should not have any less commitment to patients, but must have high levels of efficiency to complete the work within the allotted time. Even more important, a resident who cannot provide continuity of care must communicate well enough with other residents to ensure that high levels of surgical care will be possible throughout a patient’s hospitalization.
Surgical faculty must understand these new ethical challenges to help residents succeed and to formulate a new concept of what the ideal surgical resident role model looks like in the 21st century.
Dr. Peter Angelos is an ACS Fellow, chief of endocrine surgery, and associate director of the MacLean Center for Clinical Medical Ethics, University of Chicago.
As a surgical resident over two decades ago, I often cringed when a faculty member would say, " When I was a resident," and launch into a story about how difficult things were "in the old days." Sometimes I was reminded of jokes about our forebears having to walk 5 miles to school every day uphill -- both ways!
I’m sure my residents have a similar reaction when I talk about how tough we had it compared with now. However, today’s residents have a much more difficult road than I ever had in terms of the choices they must make because of duty-hour limits.
In prior decades, the excellent resident was the one who always knew what was going on with his or her patients, and who came in early and stayed as late as necessary to get everything done. When I was a junior resident, the chief resident role models we all emulated were those who worked the hardest (i.e., the longest hours). I often felt that the willingness to work hard more clearly defined who succeed than intelligence, efficiency, or technical abilities. However, today’s surgical residents are constantly being challenged to make ethical choices that were unheard of in years gone by.
Recently, a midlevel surgical resident who I respect very much related the following case to me. At about 5 a.m. during his night of call he had admitted a 79-year-old man in septic shock with an acute abdomen to the surgical intensive care unit. The patient had required significant fluid resuscitation prior to safely going to the operating room for an exploratory laparotomy. The surgery was just beginning at 7 a.m. when the the attending asked the resident when his shift ended. The resident said he wanted to stay to do the case, but was conflicted by the fact that doing so would mean exceeding his work hour limit for the week. In addition, the program director had recently sent residents a notice saying that it was unacceptable to ignore the work hour limits. The resident felt that in order to stay to do the case, he would be required to lie on his work hour log.
This challenge of weighing what might be good for the individual resident against the potential harm to the program for work hour violations is a new ethical tension. The need to choose between continuity of care (which might be good for a patient and aid the resident’s education) and the requirement to sign out to other residents to maintain the accreditation of the residency program is a conflict that didn’t exist in previous decades.
It is unclear what the ideal role model should be for a surgical resident today. Simply spending more time taking care of patients than anyone else can no longer be considered as the optimal position for a surgical resident. However, many surgical faculty have not altered their concept of the ideal resident to meet the necessary time constraints that are required of residents. As a result, residents are often held to unreasonable standards based on prior concepts of how "great" residents used to act.
Today, surgical faculty and surgical residents must seek to define the new ideal role model for a surgical resident. This person should not have any less commitment to patients, but must have high levels of efficiency to complete the work within the allotted time. Even more important, a resident who cannot provide continuity of care must communicate well enough with other residents to ensure that high levels of surgical care will be possible throughout a patient’s hospitalization.
Surgical faculty must understand these new ethical challenges to help residents succeed and to formulate a new concept of what the ideal surgical resident role model looks like in the 21st century.
Dr. Peter Angelos is an ACS Fellow, chief of endocrine surgery, and associate director of the MacLean Center for Clinical Medical Ethics, University of Chicago.
As a surgical resident over two decades ago, I often cringed when a faculty member would say, " When I was a resident," and launch into a story about how difficult things were "in the old days." Sometimes I was reminded of jokes about our forebears having to walk 5 miles to school every day uphill -- both ways!
I’m sure my residents have a similar reaction when I talk about how tough we had it compared with now. However, today’s residents have a much more difficult road than I ever had in terms of the choices they must make because of duty-hour limits.
In prior decades, the excellent resident was the one who always knew what was going on with his or her patients, and who came in early and stayed as late as necessary to get everything done. When I was a junior resident, the chief resident role models we all emulated were those who worked the hardest (i.e., the longest hours). I often felt that the willingness to work hard more clearly defined who succeed than intelligence, efficiency, or technical abilities. However, today’s surgical residents are constantly being challenged to make ethical choices that were unheard of in years gone by.
Recently, a midlevel surgical resident who I respect very much related the following case to me. At about 5 a.m. during his night of call he had admitted a 79-year-old man in septic shock with an acute abdomen to the surgical intensive care unit. The patient had required significant fluid resuscitation prior to safely going to the operating room for an exploratory laparotomy. The surgery was just beginning at 7 a.m. when the the attending asked the resident when his shift ended. The resident said he wanted to stay to do the case, but was conflicted by the fact that doing so would mean exceeding his work hour limit for the week. In addition, the program director had recently sent residents a notice saying that it was unacceptable to ignore the work hour limits. The resident felt that in order to stay to do the case, he would be required to lie on his work hour log.
This challenge of weighing what might be good for the individual resident against the potential harm to the program for work hour violations is a new ethical tension. The need to choose between continuity of care (which might be good for a patient and aid the resident’s education) and the requirement to sign out to other residents to maintain the accreditation of the residency program is a conflict that didn’t exist in previous decades.
It is unclear what the ideal role model should be for a surgical resident today. Simply spending more time taking care of patients than anyone else can no longer be considered as the optimal position for a surgical resident. However, many surgical faculty have not altered their concept of the ideal resident to meet the necessary time constraints that are required of residents. As a result, residents are often held to unreasonable standards based on prior concepts of how "great" residents used to act.
Today, surgical faculty and surgical residents must seek to define the new ideal role model for a surgical resident. This person should not have any less commitment to patients, but must have high levels of efficiency to complete the work within the allotted time. Even more important, a resident who cannot provide continuity of care must communicate well enough with other residents to ensure that high levels of surgical care will be possible throughout a patient’s hospitalization.
Surgical faculty must understand these new ethical challenges to help residents succeed and to formulate a new concept of what the ideal surgical resident role model looks like in the 21st century.
Dr. Peter Angelos is an ACS Fellow, chief of endocrine surgery, and associate director of the MacLean Center for Clinical Medical Ethics, University of Chicago.
How 'Innovation' Informs Consent
The common scenario for the consent process is that when a patient has a medical condition requiring surgery, the surgeon explains the surgical risks. The patient might not want to have the operation, but once it is clear that the benefits outweigh the risks, the patient consents to the operation. This dynamic of the surgeon explaining what the patient needs and the patient considering that advice before agreeing to the operation is well accepted and firmly grounded on the principle of respect for patient autonomy. Unfortunately, in the current era of Internet marketing and the public’s fascination with the latest innovative technology, the traditional consent process is often dramatically altered.
Recently, a patient of mine requested a surgical procedure not commonly performed in the United States. The patient stated that she had read all about it on the Internet. When I asked what she had learned, my patient described the new surgical approach as an "innovative technique" that made use of the "latest technology" to allow the surgery to be performed in a "minimally invasive" fashion. When I pointed out that this approach was new and therefore less was known about it, she assured me that she likes to take on new challenges and she would be happy to be a pioneering patient for this innovative procedure.
In some ways, this scenario may sound ideal. A patient has actively explored the options to treat her disease and has found something appealing. Certainly education is good, and what could be better than a motivated patient taking an active role in her own health care decision making? Nevertheless, something made me uncomfortable. Perhaps it was the fact that the patient had misinterpreted the information that she had read and was requesting a procedure for which there was very little published safety data. Perhaps her enthusiasm for the operation took me by surprise. As I presented the risks, she seemed to disregard each and every one, having decided that this operation was "the best one" for her. Our traditional roles in the consent process had changed: The patient was pushing for an operation while the surgeon was wary of the unknown risks.
If a patient is already sold on a new procedure even before seeing a surgeon, then the surgeon has the ethical responsibility to ensure that risks are adequately considered. In this setting, the traditional emphasis on respecting the autonomous choices of patients may need to be tempered by the principle of nonmaleficence – that is, the physician’s responsibility to avoid bringing harm to the patient. I would never endorse a shift back to the old days of paternalism, when doctors made decisions for patients. However, we may be seeing the emergence of an era in which surgeons must exercise their professionalism by not offering patients everything that they might want.
The best surgeons are not always those who offer the latest high-tech, innovative procedures, but rather those who carefully explain why such an operation may not be appropriate. Surgeons have always had the ethical responsibility to apply their surgical skills to benefit their patients, and now they have the added challenge of trying to convince their patients that, despite marketing claims, a new operation may not be improved.
In trying to talk patients out of operations that they want, surgeons must ignore self-interest and, as objectively as possible, make thoughtful recommendations. As the public’s fascination with the "new," "high tech," and "innovative" procedures grows, the challenge will be to ignore the hype and make recommendations based on what is truly known about safety and efficacy.
Dr. Peter Angelos is an ACS Fellow, the Linda Kohler Anderson Professor of Surgery and Surgical Ethics, chief of endocrine surgery, and associate director, MacLean Center for Clinical Medical Ethics, University of Chicago.
The common scenario for the consent process is that when a patient has a medical condition requiring surgery, the surgeon explains the surgical risks. The patient might not want to have the operation, but once it is clear that the benefits outweigh the risks, the patient consents to the operation. This dynamic of the surgeon explaining what the patient needs and the patient considering that advice before agreeing to the operation is well accepted and firmly grounded on the principle of respect for patient autonomy. Unfortunately, in the current era of Internet marketing and the public’s fascination with the latest innovative technology, the traditional consent process is often dramatically altered.
Recently, a patient of mine requested a surgical procedure not commonly performed in the United States. The patient stated that she had read all about it on the Internet. When I asked what she had learned, my patient described the new surgical approach as an "innovative technique" that made use of the "latest technology" to allow the surgery to be performed in a "minimally invasive" fashion. When I pointed out that this approach was new and therefore less was known about it, she assured me that she likes to take on new challenges and she would be happy to be a pioneering patient for this innovative procedure.
In some ways, this scenario may sound ideal. A patient has actively explored the options to treat her disease and has found something appealing. Certainly education is good, and what could be better than a motivated patient taking an active role in her own health care decision making? Nevertheless, something made me uncomfortable. Perhaps it was the fact that the patient had misinterpreted the information that she had read and was requesting a procedure for which there was very little published safety data. Perhaps her enthusiasm for the operation took me by surprise. As I presented the risks, she seemed to disregard each and every one, having decided that this operation was "the best one" for her. Our traditional roles in the consent process had changed: The patient was pushing for an operation while the surgeon was wary of the unknown risks.
If a patient is already sold on a new procedure even before seeing a surgeon, then the surgeon has the ethical responsibility to ensure that risks are adequately considered. In this setting, the traditional emphasis on respecting the autonomous choices of patients may need to be tempered by the principle of nonmaleficence – that is, the physician’s responsibility to avoid bringing harm to the patient. I would never endorse a shift back to the old days of paternalism, when doctors made decisions for patients. However, we may be seeing the emergence of an era in which surgeons must exercise their professionalism by not offering patients everything that they might want.
The best surgeons are not always those who offer the latest high-tech, innovative procedures, but rather those who carefully explain why such an operation may not be appropriate. Surgeons have always had the ethical responsibility to apply their surgical skills to benefit their patients, and now they have the added challenge of trying to convince their patients that, despite marketing claims, a new operation may not be improved.
In trying to talk patients out of operations that they want, surgeons must ignore self-interest and, as objectively as possible, make thoughtful recommendations. As the public’s fascination with the "new," "high tech," and "innovative" procedures grows, the challenge will be to ignore the hype and make recommendations based on what is truly known about safety and efficacy.
Dr. Peter Angelos is an ACS Fellow, the Linda Kohler Anderson Professor of Surgery and Surgical Ethics, chief of endocrine surgery, and associate director, MacLean Center for Clinical Medical Ethics, University of Chicago.
The common scenario for the consent process is that when a patient has a medical condition requiring surgery, the surgeon explains the surgical risks. The patient might not want to have the operation, but once it is clear that the benefits outweigh the risks, the patient consents to the operation. This dynamic of the surgeon explaining what the patient needs and the patient considering that advice before agreeing to the operation is well accepted and firmly grounded on the principle of respect for patient autonomy. Unfortunately, in the current era of Internet marketing and the public’s fascination with the latest innovative technology, the traditional consent process is often dramatically altered.
Recently, a patient of mine requested a surgical procedure not commonly performed in the United States. The patient stated that she had read all about it on the Internet. When I asked what she had learned, my patient described the new surgical approach as an "innovative technique" that made use of the "latest technology" to allow the surgery to be performed in a "minimally invasive" fashion. When I pointed out that this approach was new and therefore less was known about it, she assured me that she likes to take on new challenges and she would be happy to be a pioneering patient for this innovative procedure.
In some ways, this scenario may sound ideal. A patient has actively explored the options to treat her disease and has found something appealing. Certainly education is good, and what could be better than a motivated patient taking an active role in her own health care decision making? Nevertheless, something made me uncomfortable. Perhaps it was the fact that the patient had misinterpreted the information that she had read and was requesting a procedure for which there was very little published safety data. Perhaps her enthusiasm for the operation took me by surprise. As I presented the risks, she seemed to disregard each and every one, having decided that this operation was "the best one" for her. Our traditional roles in the consent process had changed: The patient was pushing for an operation while the surgeon was wary of the unknown risks.
If a patient is already sold on a new procedure even before seeing a surgeon, then the surgeon has the ethical responsibility to ensure that risks are adequately considered. In this setting, the traditional emphasis on respecting the autonomous choices of patients may need to be tempered by the principle of nonmaleficence – that is, the physician’s responsibility to avoid bringing harm to the patient. I would never endorse a shift back to the old days of paternalism, when doctors made decisions for patients. However, we may be seeing the emergence of an era in which surgeons must exercise their professionalism by not offering patients everything that they might want.
The best surgeons are not always those who offer the latest high-tech, innovative procedures, but rather those who carefully explain why such an operation may not be appropriate. Surgeons have always had the ethical responsibility to apply their surgical skills to benefit their patients, and now they have the added challenge of trying to convince their patients that, despite marketing claims, a new operation may not be improved.
In trying to talk patients out of operations that they want, surgeons must ignore self-interest and, as objectively as possible, make thoughtful recommendations. As the public’s fascination with the "new," "high tech," and "innovative" procedures grows, the challenge will be to ignore the hype and make recommendations based on what is truly known about safety and efficacy.
Dr. Peter Angelos is an ACS Fellow, the Linda Kohler Anderson Professor of Surgery and Surgical Ethics, chief of endocrine surgery, and associate director, MacLean Center for Clinical Medical Ethics, University of Chicago.
The Double-Edged Sword of Doctor Speak
Imagine if you will that you’re in the throes of labor (there is a point to this exercise in unplanned parenthood, so bear with me).
Between contractions, there’s a nattering in your ear about the use of local anesthesia prior to the epidural that friends swear will allow you to actually consider doing this again.
The injection is announced by someone saying either, "We are going to give you a local anesthetic that will numb the area so that you will be comfortable during the procedure" or "You are going to feel a big bee sting; this is the worst part of the procedure."
Not surprising, the perceived pain was found to be significantly greater after the latter statement.
German investigators highlight this experiment as part of a detailed and fascinating look at the nocebo phenomenon, or the opposite of the placebo phenomenon, in medicine.
The topic has apparently been given the short shrift by scientists and clinicians. A recent PubMed search by the Germans revealed roughly 2,200 studies penned on the placebo effect, but only 151 publications on the nocebo effect, with the vast majority of these being editorials, commentaries, and reviews, rather than empirical studies.
Dr. Winfried Häuser of the Klinikum Saarbrücken and his associates, nail the crux of the issue with a quote from cardiologist and Nobel laureate Dr. Bernard Lown that "Words are the most powerful tool a doctor possesses, but words, like a two-edged sword, can maim as well as heal."
The article touches on the neurobiological mechanisms of the nocebo effect, which like those for the placebo effect, center around conditioning and reaction to expectations – albeit in this case negative expectations.
There is a discussion about who might be at risk of nocebo responses (yes, ladies he’s speaking to us), and an amusing array of clinical studies illustrating the nocebo effect.
There’s a randomized controlled trial (RCT) of finasteride in benign prostate hyperplasia, in which sexual dysfunction was reported by 44% of patients informed of this possible side effect, compared with only 15% of those not informed.
Similarly, there’s another RCT of the beta-blocker atenolol in coronary heart disease. Rates of sexual dysfunction jumped from 3% of patients not told of the drug or side effect to 31% of those treated to complete details about both the drug and the possible sexual dysfunction
Where the review really hits its stride, however, is in the discussion of ethical problems that arise in everyday clinical practice where the nocebo phenomenon may be triggered by verbal and non-verbal communications by physicians and nurses.
The authors note that physicians are obliged to inform patients about the possible adverse events of a proposed treatment so they can make an informed decision, but also have a duty to minimize the risks of a medical intervention, including those induced by the patient briefing.
Strategies are offered to reduce this dilemma with the most obvious being patient education and communications training for medical staff.
Clinicians are also advised to focus on the proportion of patients who tolerate a procedure or drug rather than the proportion experiencing adverse events.
The most controversial suggestion is the concept of "permitted non-information." Patients agree not to receive information on mild and/or transient side effects, but must be briefed about severe and/or irreversible side effects. To respect their autonomy and preferences, patients could pick and chose what side effects they want to briefed on (or forego) from a list of categories of possible side effects for a drug or procedure.
When the German Medical Association gets round to updating its 1990 recommendations on patient briefing, the authors say there needs to be discussion on "whether it is legitimate to express a right of the patient not to know about complications and side effects of medical procedures and whether this must be respected by the physician."
There should also be debate on whether some patients might be left confused or uncertain by their inability to follow the comprehensive adverse event information found on package inserts or consent forms.
Such a strategy could be problematic in the United States, where nearly half of all adults (90 million people) have difficulty understanding and acting upon health information, according to the Institute of Medicine report "Health Literacy: A Prescription to End Confusion."
Throw in the wracking pain of childbirth, the instability of bipolarity, or the confusion of Parkinson’s, and you’ve just made the lawyers of America incandescently happy.
Dr. Häuser reports reimbursement for training and travel costs from Eli Lilly and the Falk Foundation, and lecture fees from Lilly, the Falk Foundation and Janssen-Cilag. A co-author reports research funds from Sorin, Italy.
Imagine if you will that you’re in the throes of labor (there is a point to this exercise in unplanned parenthood, so bear with me).
Between contractions, there’s a nattering in your ear about the use of local anesthesia prior to the epidural that friends swear will allow you to actually consider doing this again.
The injection is announced by someone saying either, "We are going to give you a local anesthetic that will numb the area so that you will be comfortable during the procedure" or "You are going to feel a big bee sting; this is the worst part of the procedure."
Not surprising, the perceived pain was found to be significantly greater after the latter statement.
German investigators highlight this experiment as part of a detailed and fascinating look at the nocebo phenomenon, or the opposite of the placebo phenomenon, in medicine.
The topic has apparently been given the short shrift by scientists and clinicians. A recent PubMed search by the Germans revealed roughly 2,200 studies penned on the placebo effect, but only 151 publications on the nocebo effect, with the vast majority of these being editorials, commentaries, and reviews, rather than empirical studies.
Dr. Winfried Häuser of the Klinikum Saarbrücken and his associates, nail the crux of the issue with a quote from cardiologist and Nobel laureate Dr. Bernard Lown that "Words are the most powerful tool a doctor possesses, but words, like a two-edged sword, can maim as well as heal."
The article touches on the neurobiological mechanisms of the nocebo effect, which like those for the placebo effect, center around conditioning and reaction to expectations – albeit in this case negative expectations.
There is a discussion about who might be at risk of nocebo responses (yes, ladies he’s speaking to us), and an amusing array of clinical studies illustrating the nocebo effect.
There’s a randomized controlled trial (RCT) of finasteride in benign prostate hyperplasia, in which sexual dysfunction was reported by 44% of patients informed of this possible side effect, compared with only 15% of those not informed.
Similarly, there’s another RCT of the beta-blocker atenolol in coronary heart disease. Rates of sexual dysfunction jumped from 3% of patients not told of the drug or side effect to 31% of those treated to complete details about both the drug and the possible sexual dysfunction
Where the review really hits its stride, however, is in the discussion of ethical problems that arise in everyday clinical practice where the nocebo phenomenon may be triggered by verbal and non-verbal communications by physicians and nurses.
The authors note that physicians are obliged to inform patients about the possible adverse events of a proposed treatment so they can make an informed decision, but also have a duty to minimize the risks of a medical intervention, including those induced by the patient briefing.
Strategies are offered to reduce this dilemma with the most obvious being patient education and communications training for medical staff.
Clinicians are also advised to focus on the proportion of patients who tolerate a procedure or drug rather than the proportion experiencing adverse events.
The most controversial suggestion is the concept of "permitted non-information." Patients agree not to receive information on mild and/or transient side effects, but must be briefed about severe and/or irreversible side effects. To respect their autonomy and preferences, patients could pick and chose what side effects they want to briefed on (or forego) from a list of categories of possible side effects for a drug or procedure.
When the German Medical Association gets round to updating its 1990 recommendations on patient briefing, the authors say there needs to be discussion on "whether it is legitimate to express a right of the patient not to know about complications and side effects of medical procedures and whether this must be respected by the physician."
There should also be debate on whether some patients might be left confused or uncertain by their inability to follow the comprehensive adverse event information found on package inserts or consent forms.
Such a strategy could be problematic in the United States, where nearly half of all adults (90 million people) have difficulty understanding and acting upon health information, according to the Institute of Medicine report "Health Literacy: A Prescription to End Confusion."
Throw in the wracking pain of childbirth, the instability of bipolarity, or the confusion of Parkinson’s, and you’ve just made the lawyers of America incandescently happy.
Dr. Häuser reports reimbursement for training and travel costs from Eli Lilly and the Falk Foundation, and lecture fees from Lilly, the Falk Foundation and Janssen-Cilag. A co-author reports research funds from Sorin, Italy.
Imagine if you will that you’re in the throes of labor (there is a point to this exercise in unplanned parenthood, so bear with me).
Between contractions, there’s a nattering in your ear about the use of local anesthesia prior to the epidural that friends swear will allow you to actually consider doing this again.
The injection is announced by someone saying either, "We are going to give you a local anesthetic that will numb the area so that you will be comfortable during the procedure" or "You are going to feel a big bee sting; this is the worst part of the procedure."
Not surprising, the perceived pain was found to be significantly greater after the latter statement.
German investigators highlight this experiment as part of a detailed and fascinating look at the nocebo phenomenon, or the opposite of the placebo phenomenon, in medicine.
The topic has apparently been given the short shrift by scientists and clinicians. A recent PubMed search by the Germans revealed roughly 2,200 studies penned on the placebo effect, but only 151 publications on the nocebo effect, with the vast majority of these being editorials, commentaries, and reviews, rather than empirical studies.
Dr. Winfried Häuser of the Klinikum Saarbrücken and his associates, nail the crux of the issue with a quote from cardiologist and Nobel laureate Dr. Bernard Lown that "Words are the most powerful tool a doctor possesses, but words, like a two-edged sword, can maim as well as heal."
The article touches on the neurobiological mechanisms of the nocebo effect, which like those for the placebo effect, center around conditioning and reaction to expectations – albeit in this case negative expectations.
There is a discussion about who might be at risk of nocebo responses (yes, ladies he’s speaking to us), and an amusing array of clinical studies illustrating the nocebo effect.
There’s a randomized controlled trial (RCT) of finasteride in benign prostate hyperplasia, in which sexual dysfunction was reported by 44% of patients informed of this possible side effect, compared with only 15% of those not informed.
Similarly, there’s another RCT of the beta-blocker atenolol in coronary heart disease. Rates of sexual dysfunction jumped from 3% of patients not told of the drug or side effect to 31% of those treated to complete details about both the drug and the possible sexual dysfunction
Where the review really hits its stride, however, is in the discussion of ethical problems that arise in everyday clinical practice where the nocebo phenomenon may be triggered by verbal and non-verbal communications by physicians and nurses.
The authors note that physicians are obliged to inform patients about the possible adverse events of a proposed treatment so they can make an informed decision, but also have a duty to minimize the risks of a medical intervention, including those induced by the patient briefing.
Strategies are offered to reduce this dilemma with the most obvious being patient education and communications training for medical staff.
Clinicians are also advised to focus on the proportion of patients who tolerate a procedure or drug rather than the proportion experiencing adverse events.
The most controversial suggestion is the concept of "permitted non-information." Patients agree not to receive information on mild and/or transient side effects, but must be briefed about severe and/or irreversible side effects. To respect their autonomy and preferences, patients could pick and chose what side effects they want to briefed on (or forego) from a list of categories of possible side effects for a drug or procedure.
When the German Medical Association gets round to updating its 1990 recommendations on patient briefing, the authors say there needs to be discussion on "whether it is legitimate to express a right of the patient not to know about complications and side effects of medical procedures and whether this must be respected by the physician."
There should also be debate on whether some patients might be left confused or uncertain by their inability to follow the comprehensive adverse event information found on package inserts or consent forms.
Such a strategy could be problematic in the United States, where nearly half of all adults (90 million people) have difficulty understanding and acting upon health information, according to the Institute of Medicine report "Health Literacy: A Prescription to End Confusion."
Throw in the wracking pain of childbirth, the instability of bipolarity, or the confusion of Parkinson’s, and you’ve just made the lawyers of America incandescently happy.
Dr. Häuser reports reimbursement for training and travel costs from Eli Lilly and the Falk Foundation, and lecture fees from Lilly, the Falk Foundation and Janssen-Cilag. A co-author reports research funds from Sorin, Italy.
Top Five Targets for Primary Care
Even by conservative predictions, patient quality of care will improve significantly under Accountable Care Organizations, while saving Medicare millions of dollars. And, by some estimates, primary care incomes will double.
Why is that the case?
ACOs are designed to motivate providers to follow evidence-based practices in the management of patient populations. Total expenditures for that population are tracked and, if there are savings relative to an unmanaged population, providers typically will receive about half of the savings.
Of all the possible ACO initiatives that could deliver value, five represent the highest-impact targets that are expected to deliver the biggest and earliest bang for the buck. Primary care will likely thrive under ACOs because all five targets are in the specialty’s "sweet spot."
• Prevention and Wellness – This is the clearest example of health care’s shift from payment for volume under fee for service, to payment for value under accountable care. Of course, you’ve always seen the cost-saving impact of making and keeping people healthy; the sicker a patient becomes, the more money providers make treating sometimes quite avoidable issues. Now, with a shift toward managing the total costs for a patient population, successful prevention and wellness will be tied to powerful economic rewards. Primary care physicians will now be paid to spend that extra time with patients, to do more follow-up, to build a medical home, and to influence healthy lifestyles.
• Chronic Disease Management – Chronic disease now represents some 75% of all health care spending, and much of it is preventable. For Medicare, it is an even greater percentage. According to a recent report by Forbes Insights, in 2005, an average patient with one chronic disease cost $7,000 annually $15,000 with two diseases, and $32,000 with three. Chronic diseases are complex, harder to reverse, and involve more specialists, but primary care-driven care coordination is still key.
• Reduced Hospitalizations (ER Avoidance) – It is important to make clear that this refers only to avoidable hospitalizations. Lifestyle-related chronic diseases drive many avoidable admissions; lack of prevention or coordination of care drives others. Primary care can reduce hospitalizations through a sound emergency department diversion policy for non-emergencies. Establishing a physician-patient relationship will help the patient avoid using the ED as a default primary care office.
• Care Transitions –A fundamental premise behind the medical home concept is that it helps coordinate care by helping patients navigate through the system that heretofore consisted of fragmented segments. Care transitioning is not the sole province of primary care medicine, but the medical home’s ability to help transition patients and coordinate their care will be a significant factor in ACO success.
• Multispecialty Care Coordination of Complex Patients – These are the patients who consume a hugely disproportionate share of health care dollars. Early ACO activity suggests that if the ACO has a medical home component, it serves as the organizational hub for care coordination for complex patients, with enhanced administrative support by the ACO’s informatics center and an increased role of select specialists. The patient is assigned to a coordinating physician who ensures that there is an appropriate care plan. Pharmacy, specialists, home health, physical therapy, and case management services are all coordinated for the complex patient pursuant to the plan.
These five targets are the proverbial "low-hanging fruit" for ACOs. Primary care has the opportunity, and oftentimes the necessity, for significant involvement in all of them. It is no wonder that primary care physicians are essential for ACO success. ACO compensation, say through shared savings, is designed to incentivize and reward those who follow best practices and who generate the savings. Thus, primary care should experience not only deep professional rewards from having the tools and teammates to positively impact so many patients, but also significant financial rewards. A physician approached by an ACO can evaluate its likelihood of sustainability and its appreciation of the role of primary care, by comparing its initiatives against the top five ACO targets described above.
Mr. Bobbitt is a senior partner and head of the Health Law Group at the Smith Anderson law firm in Raleigh, N.C. He has many years’ experience assisting physicians form integrated delivery systems. He has spoken and written nationally to primary care physicians on the strategies and practicalities of forming or joining ACOs. This article is meant to be educational and does not constitute legal advice. Contact him at [email protected].
Even by conservative predictions, patient quality of care will improve significantly under Accountable Care Organizations, while saving Medicare millions of dollars. And, by some estimates, primary care incomes will double.
Why is that the case?
ACOs are designed to motivate providers to follow evidence-based practices in the management of patient populations. Total expenditures for that population are tracked and, if there are savings relative to an unmanaged population, providers typically will receive about half of the savings.
Of all the possible ACO initiatives that could deliver value, five represent the highest-impact targets that are expected to deliver the biggest and earliest bang for the buck. Primary care will likely thrive under ACOs because all five targets are in the specialty’s "sweet spot."
• Prevention and Wellness – This is the clearest example of health care’s shift from payment for volume under fee for service, to payment for value under accountable care. Of course, you’ve always seen the cost-saving impact of making and keeping people healthy; the sicker a patient becomes, the more money providers make treating sometimes quite avoidable issues. Now, with a shift toward managing the total costs for a patient population, successful prevention and wellness will be tied to powerful economic rewards. Primary care physicians will now be paid to spend that extra time with patients, to do more follow-up, to build a medical home, and to influence healthy lifestyles.
• Chronic Disease Management – Chronic disease now represents some 75% of all health care spending, and much of it is preventable. For Medicare, it is an even greater percentage. According to a recent report by Forbes Insights, in 2005, an average patient with one chronic disease cost $7,000 annually $15,000 with two diseases, and $32,000 with three. Chronic diseases are complex, harder to reverse, and involve more specialists, but primary care-driven care coordination is still key.
• Reduced Hospitalizations (ER Avoidance) – It is important to make clear that this refers only to avoidable hospitalizations. Lifestyle-related chronic diseases drive many avoidable admissions; lack of prevention or coordination of care drives others. Primary care can reduce hospitalizations through a sound emergency department diversion policy for non-emergencies. Establishing a physician-patient relationship will help the patient avoid using the ED as a default primary care office.
• Care Transitions –A fundamental premise behind the medical home concept is that it helps coordinate care by helping patients navigate through the system that heretofore consisted of fragmented segments. Care transitioning is not the sole province of primary care medicine, but the medical home’s ability to help transition patients and coordinate their care will be a significant factor in ACO success.
• Multispecialty Care Coordination of Complex Patients – These are the patients who consume a hugely disproportionate share of health care dollars. Early ACO activity suggests that if the ACO has a medical home component, it serves as the organizational hub for care coordination for complex patients, with enhanced administrative support by the ACO’s informatics center and an increased role of select specialists. The patient is assigned to a coordinating physician who ensures that there is an appropriate care plan. Pharmacy, specialists, home health, physical therapy, and case management services are all coordinated for the complex patient pursuant to the plan.
These five targets are the proverbial "low-hanging fruit" for ACOs. Primary care has the opportunity, and oftentimes the necessity, for significant involvement in all of them. It is no wonder that primary care physicians are essential for ACO success. ACO compensation, say through shared savings, is designed to incentivize and reward those who follow best practices and who generate the savings. Thus, primary care should experience not only deep professional rewards from having the tools and teammates to positively impact so many patients, but also significant financial rewards. A physician approached by an ACO can evaluate its likelihood of sustainability and its appreciation of the role of primary care, by comparing its initiatives against the top five ACO targets described above.
Mr. Bobbitt is a senior partner and head of the Health Law Group at the Smith Anderson law firm in Raleigh, N.C. He has many years’ experience assisting physicians form integrated delivery systems. He has spoken and written nationally to primary care physicians on the strategies and practicalities of forming or joining ACOs. This article is meant to be educational and does not constitute legal advice. Contact him at [email protected].
Even by conservative predictions, patient quality of care will improve significantly under Accountable Care Organizations, while saving Medicare millions of dollars. And, by some estimates, primary care incomes will double.
Why is that the case?
ACOs are designed to motivate providers to follow evidence-based practices in the management of patient populations. Total expenditures for that population are tracked and, if there are savings relative to an unmanaged population, providers typically will receive about half of the savings.
Of all the possible ACO initiatives that could deliver value, five represent the highest-impact targets that are expected to deliver the biggest and earliest bang for the buck. Primary care will likely thrive under ACOs because all five targets are in the specialty’s "sweet spot."
• Prevention and Wellness – This is the clearest example of health care’s shift from payment for volume under fee for service, to payment for value under accountable care. Of course, you’ve always seen the cost-saving impact of making and keeping people healthy; the sicker a patient becomes, the more money providers make treating sometimes quite avoidable issues. Now, with a shift toward managing the total costs for a patient population, successful prevention and wellness will be tied to powerful economic rewards. Primary care physicians will now be paid to spend that extra time with patients, to do more follow-up, to build a medical home, and to influence healthy lifestyles.
• Chronic Disease Management – Chronic disease now represents some 75% of all health care spending, and much of it is preventable. For Medicare, it is an even greater percentage. According to a recent report by Forbes Insights, in 2005, an average patient with one chronic disease cost $7,000 annually $15,000 with two diseases, and $32,000 with three. Chronic diseases are complex, harder to reverse, and involve more specialists, but primary care-driven care coordination is still key.
• Reduced Hospitalizations (ER Avoidance) – It is important to make clear that this refers only to avoidable hospitalizations. Lifestyle-related chronic diseases drive many avoidable admissions; lack of prevention or coordination of care drives others. Primary care can reduce hospitalizations through a sound emergency department diversion policy for non-emergencies. Establishing a physician-patient relationship will help the patient avoid using the ED as a default primary care office.
• Care Transitions –A fundamental premise behind the medical home concept is that it helps coordinate care by helping patients navigate through the system that heretofore consisted of fragmented segments. Care transitioning is not the sole province of primary care medicine, but the medical home’s ability to help transition patients and coordinate their care will be a significant factor in ACO success.
• Multispecialty Care Coordination of Complex Patients – These are the patients who consume a hugely disproportionate share of health care dollars. Early ACO activity suggests that if the ACO has a medical home component, it serves as the organizational hub for care coordination for complex patients, with enhanced administrative support by the ACO’s informatics center and an increased role of select specialists. The patient is assigned to a coordinating physician who ensures that there is an appropriate care plan. Pharmacy, specialists, home health, physical therapy, and case management services are all coordinated for the complex patient pursuant to the plan.
These five targets are the proverbial "low-hanging fruit" for ACOs. Primary care has the opportunity, and oftentimes the necessity, for significant involvement in all of them. It is no wonder that primary care physicians are essential for ACO success. ACO compensation, say through shared savings, is designed to incentivize and reward those who follow best practices and who generate the savings. Thus, primary care should experience not only deep professional rewards from having the tools and teammates to positively impact so many patients, but also significant financial rewards. A physician approached by an ACO can evaluate its likelihood of sustainability and its appreciation of the role of primary care, by comparing its initiatives against the top five ACO targets described above.
Mr. Bobbitt is a senior partner and head of the Health Law Group at the Smith Anderson law firm in Raleigh, N.C. He has many years’ experience assisting physicians form integrated delivery systems. He has spoken and written nationally to primary care physicians on the strategies and practicalities of forming or joining ACOs. This article is meant to be educational and does not constitute legal advice. Contact him at [email protected].
Southern California Hospitals Using BOOST Model Report Readmission Rate Reductions
Seven Southern California hospitals participating in the yearlong Readmissions Reduction Collaborative, modeled after Project BOOST and sponsored by SHM and the Hospital Association of Southern California (HASC), reported on their experience at a June meeting in Montebello, Calif. Quality teams from four of the seven hospitals demonstrated reductions in readmission rates ranging from 24% to 55%. The other three hospitals are still implementing quality processes and are just now starting to see measurable results.
Several of the participating hospitals do not employ traditional hospitalist services. However, all seven benefit from mentoring by Project BOOST experts and have adopted a number of its approaches and techniques: 72-hour follow-up calls to discharged patients, the use of discharge advocates, medication reconciliation at time of discharge, enhanced discharge planning, and BOOST’s “8Ps” patient risk stratification tool. Another popular approach in use is the “teachback” communication technique, in which patients are asked to repeat in their own words what they understand the professional has told them about their condition and self-care.
One reason many Southern California hospitals do not have a strong hospitalist presence is the widespread prevalence of independent practice associations (IPAs), which often designate members of their medical groups to fill the hospitalist role for patients at a given hospital, says Z. Joseph Wanski, MD, FA
At Harbor UCLA Medical Center in Torrance, a major safety-net facility for Los Angeles County, the readmissions team initially focused on heart failure patients and was able to demonstrate a 5.5% decrease in readmissions for all heart failure patients at a time when readmissions for the hospital as a whole remained the same. The team built relationships with outside partners, including a nearby adult daycare center, home health agencies, and a care-transitions coach while emphasizing early identification of patients for referral to a heart failure disease management registry. The readmissions team also was instrumental in developing the Cardiovascular Open Access Rapid Evaluation (CORE) service, an observation unit for heart failure patients aimed at allieviating ED overcrowding.
“Hospitalists have been very cooperative with our project,” reports Adriana Quintero, MSW, the full-time Project BOOST facilitator at Valley Presbyterian Hospital in Van Nuys. “They see a lot of our patients in their offices.”
Three Valley Presbyterian physicians who work part-time as hospitalists and maintain office practices have agreed to carve out time to see patients who are going home without scheduled appointments with their primary-care physicians (PCPs) within seven days of discharge.
“We find that many of our discharged patients do not call their primary-care physicians for post-discharge appointments,” says Quintero, adding that such patients often decline the hospital team’s offers for help. The readmissions team at Valley Presbyterian is redesigning its clinical multidisciplinary rounds using a rounding script focusing more on discharge planning in rounding.
Larry Beresford is a freelance writer in Oakland, Calif.
Seven Southern California hospitals participating in the yearlong Readmissions Reduction Collaborative, modeled after Project BOOST and sponsored by SHM and the Hospital Association of Southern California (HASC), reported on their experience at a June meeting in Montebello, Calif. Quality teams from four of the seven hospitals demonstrated reductions in readmission rates ranging from 24% to 55%. The other three hospitals are still implementing quality processes and are just now starting to see measurable results.
Several of the participating hospitals do not employ traditional hospitalist services. However, all seven benefit from mentoring by Project BOOST experts and have adopted a number of its approaches and techniques: 72-hour follow-up calls to discharged patients, the use of discharge advocates, medication reconciliation at time of discharge, enhanced discharge planning, and BOOST’s “8Ps” patient risk stratification tool. Another popular approach in use is the “teachback” communication technique, in which patients are asked to repeat in their own words what they understand the professional has told them about their condition and self-care.
One reason many Southern California hospitals do not have a strong hospitalist presence is the widespread prevalence of independent practice associations (IPAs), which often designate members of their medical groups to fill the hospitalist role for patients at a given hospital, says Z. Joseph Wanski, MD, FA
At Harbor UCLA Medical Center in Torrance, a major safety-net facility for Los Angeles County, the readmissions team initially focused on heart failure patients and was able to demonstrate a 5.5% decrease in readmissions for all heart failure patients at a time when readmissions for the hospital as a whole remained the same. The team built relationships with outside partners, including a nearby adult daycare center, home health agencies, and a care-transitions coach while emphasizing early identification of patients for referral to a heart failure disease management registry. The readmissions team also was instrumental in developing the Cardiovascular Open Access Rapid Evaluation (CORE) service, an observation unit for heart failure patients aimed at allieviating ED overcrowding.
“Hospitalists have been very cooperative with our project,” reports Adriana Quintero, MSW, the full-time Project BOOST facilitator at Valley Presbyterian Hospital in Van Nuys. “They see a lot of our patients in their offices.”
Three Valley Presbyterian physicians who work part-time as hospitalists and maintain office practices have agreed to carve out time to see patients who are going home without scheduled appointments with their primary-care physicians (PCPs) within seven days of discharge.
“We find that many of our discharged patients do not call their primary-care physicians for post-discharge appointments,” says Quintero, adding that such patients often decline the hospital team’s offers for help. The readmissions team at Valley Presbyterian is redesigning its clinical multidisciplinary rounds using a rounding script focusing more on discharge planning in rounding.
Larry Beresford is a freelance writer in Oakland, Calif.
Seven Southern California hospitals participating in the yearlong Readmissions Reduction Collaborative, modeled after Project BOOST and sponsored by SHM and the Hospital Association of Southern California (HASC), reported on their experience at a June meeting in Montebello, Calif. Quality teams from four of the seven hospitals demonstrated reductions in readmission rates ranging from 24% to 55%. The other three hospitals are still implementing quality processes and are just now starting to see measurable results.
Several of the participating hospitals do not employ traditional hospitalist services. However, all seven benefit from mentoring by Project BOOST experts and have adopted a number of its approaches and techniques: 72-hour follow-up calls to discharged patients, the use of discharge advocates, medication reconciliation at time of discharge, enhanced discharge planning, and BOOST’s “8Ps” patient risk stratification tool. Another popular approach in use is the “teachback” communication technique, in which patients are asked to repeat in their own words what they understand the professional has told them about their condition and self-care.
One reason many Southern California hospitals do not have a strong hospitalist presence is the widespread prevalence of independent practice associations (IPAs), which often designate members of their medical groups to fill the hospitalist role for patients at a given hospital, says Z. Joseph Wanski, MD, FA
At Harbor UCLA Medical Center in Torrance, a major safety-net facility for Los Angeles County, the readmissions team initially focused on heart failure patients and was able to demonstrate a 5.5% decrease in readmissions for all heart failure patients at a time when readmissions for the hospital as a whole remained the same. The team built relationships with outside partners, including a nearby adult daycare center, home health agencies, and a care-transitions coach while emphasizing early identification of patients for referral to a heart failure disease management registry. The readmissions team also was instrumental in developing the Cardiovascular Open Access Rapid Evaluation (CORE) service, an observation unit for heart failure patients aimed at allieviating ED overcrowding.
“Hospitalists have been very cooperative with our project,” reports Adriana Quintero, MSW, the full-time Project BOOST facilitator at Valley Presbyterian Hospital in Van Nuys. “They see a lot of our patients in their offices.”
Three Valley Presbyterian physicians who work part-time as hospitalists and maintain office practices have agreed to carve out time to see patients who are going home without scheduled appointments with their primary-care physicians (PCPs) within seven days of discharge.
“We find that many of our discharged patients do not call their primary-care physicians for post-discharge appointments,” says Quintero, adding that such patients often decline the hospital team’s offers for help. The readmissions team at Valley Presbyterian is redesigning its clinical multidisciplinary rounds using a rounding script focusing more on discharge planning in rounding.
Larry Beresford is a freelance writer in Oakland, Calif.
Antegrade Beats Retrograde Enteroscopy in Small Bowel Disease
Antegrade enteroscopy had a significantly greater diagnostic and therapeutic yield in small bowel disease, compared with retrograde enteroscopy, reported Dr. Madhusudhan R. Sanaka and colleagues in the August issue of Clinical Gastroenterology and Hepatology.
Moreover, antegrade enteroscopy had a significantly shorter mean duration, with a greater mean depth of maximal insertion, the authors added.
In what the researchers called "the first study ... to compare the efficacy of all three available enteroscopy systems between antegrade and retrograde approach" in small bowel disease, Dr. Sanaka, of the Digestive Disease Institute at the Cleveland Clinic, studied 250 such procedures performed at that institution between January 2008 and August 2009.
A total of 182 procedures were antegrade (91 with a single-balloon enteroscope, 52 with a double-balloon enteroscope, and 39 with a spiral enteroscope), and 68 were retrograde (23 with a single balloon, 37 with a double balloon, and 8 with a spiral enteroscope).
The mean age of all participants was 61.5 years, and the antegrade and retrograde groups did not differ significantly on any of the demographic factors or history of prior capsule endoscopies.
Although obscure gastrointestinal bleeding was the most common indication in both groups, "abdominal pain or suspected Crohn’s disease was a much more common indication for antegrade enteroscopy when compared to retrograde (18.7% vs. 4.4%, P less than .001)," wrote the authors.
Overall, the diagnostic yield of antegrade enteroscopy was significantly greater, at 63.7%, than the yield of the retrograde procedures (39.7%), with P less than .001 (Clin. Gastroenterol. Hepatol. 2012 [doi: 10.1016/j.cgh.2012.04.020]).
The investigators then looked at the therapeutic yield of the two procedures. "With the antegrade approach, in 59 procedures (32.4%), a therapeutic intervention was performed," including argon plasma coagulation in 52 cases (28.6%), dilatation in 1 (0.6%), and polypectomy in 4 cases (2.2%).
With the retrograde approach, therapies were initiated in just 14.7% of cases, which was significantly lower than the percentage for the antegrade approach (P less than .001).
The authors also compared the technical aspects of the different procedure types. In this study, antegrade enteroscopies lasted 44.3 minutes on average, versus 58.9 minutes for the retrograde procedures (P less than .001).
Antegrade procedures also achieved a significantly greater depth of maximal insertion on average, at 231.8 cm, compared with 103.4 cm for retrograde procedures (P less than .001).
The authors conceded that the study had several limitations. Not only was it retrospective, they wrote, "there was no randomization and hence there could have been a significant bias in patient selection and use of a particular enteroscopy approach in individual cases, particularly in patients in whom the source of small bowel disorder was not known."
Nevertheless, "our findings of higher diagnostic and therapeutic yields with antegrade enteroscopy compared to retrograde enteroscopy support the expert opinion to consider antegrade enteroscopy as a default initial approach for suspected small bowel disease," the authors concluded.
"Retrograde enteroscopy may be considered when the antegrade enteroscopy is either nondiagnostic or if the abnormalities identified are unlikely to account for the patient’s symptoms," or when capsule endoscopy or radiologic imaging studies indicate that distal small bowel disease is likely, such as in suspected Crohn’s disease.
One of the authors, Dr. John Vargo, declared that he is a consultant for Olympus America, maker of enteroscopes and other devices. The authors stated that there was no outside funding.
Antegrade enteroscopy had a significantly greater diagnostic and therapeutic yield in small bowel disease, compared with retrograde enteroscopy, reported Dr. Madhusudhan R. Sanaka and colleagues in the August issue of Clinical Gastroenterology and Hepatology.
Moreover, antegrade enteroscopy had a significantly shorter mean duration, with a greater mean depth of maximal insertion, the authors added.
In what the researchers called "the first study ... to compare the efficacy of all three available enteroscopy systems between antegrade and retrograde approach" in small bowel disease, Dr. Sanaka, of the Digestive Disease Institute at the Cleveland Clinic, studied 250 such procedures performed at that institution between January 2008 and August 2009.
A total of 182 procedures were antegrade (91 with a single-balloon enteroscope, 52 with a double-balloon enteroscope, and 39 with a spiral enteroscope), and 68 were retrograde (23 with a single balloon, 37 with a double balloon, and 8 with a spiral enteroscope).
The mean age of all participants was 61.5 years, and the antegrade and retrograde groups did not differ significantly on any of the demographic factors or history of prior capsule endoscopies.
Although obscure gastrointestinal bleeding was the most common indication in both groups, "abdominal pain or suspected Crohn’s disease was a much more common indication for antegrade enteroscopy when compared to retrograde (18.7% vs. 4.4%, P less than .001)," wrote the authors.
Overall, the diagnostic yield of antegrade enteroscopy was significantly greater, at 63.7%, than the yield of the retrograde procedures (39.7%), with P less than .001 (Clin. Gastroenterol. Hepatol. 2012 [doi: 10.1016/j.cgh.2012.04.020]).
The investigators then looked at the therapeutic yield of the two procedures. "With the antegrade approach, in 59 procedures (32.4%), a therapeutic intervention was performed," including argon plasma coagulation in 52 cases (28.6%), dilatation in 1 (0.6%), and polypectomy in 4 cases (2.2%).
With the retrograde approach, therapies were initiated in just 14.7% of cases, which was significantly lower than the percentage for the antegrade approach (P less than .001).
The authors also compared the technical aspects of the different procedure types. In this study, antegrade enteroscopies lasted 44.3 minutes on average, versus 58.9 minutes for the retrograde procedures (P less than .001).
Antegrade procedures also achieved a significantly greater depth of maximal insertion on average, at 231.8 cm, compared with 103.4 cm for retrograde procedures (P less than .001).
The authors conceded that the study had several limitations. Not only was it retrospective, they wrote, "there was no randomization and hence there could have been a significant bias in patient selection and use of a particular enteroscopy approach in individual cases, particularly in patients in whom the source of small bowel disorder was not known."
Nevertheless, "our findings of higher diagnostic and therapeutic yields with antegrade enteroscopy compared to retrograde enteroscopy support the expert opinion to consider antegrade enteroscopy as a default initial approach for suspected small bowel disease," the authors concluded.
"Retrograde enteroscopy may be considered when the antegrade enteroscopy is either nondiagnostic or if the abnormalities identified are unlikely to account for the patient’s symptoms," or when capsule endoscopy or radiologic imaging studies indicate that distal small bowel disease is likely, such as in suspected Crohn’s disease.
One of the authors, Dr. John Vargo, declared that he is a consultant for Olympus America, maker of enteroscopes and other devices. The authors stated that there was no outside funding.
Antegrade enteroscopy had a significantly greater diagnostic and therapeutic yield in small bowel disease, compared with retrograde enteroscopy, reported Dr. Madhusudhan R. Sanaka and colleagues in the August issue of Clinical Gastroenterology and Hepatology.
Moreover, antegrade enteroscopy had a significantly shorter mean duration, with a greater mean depth of maximal insertion, the authors added.
In what the researchers called "the first study ... to compare the efficacy of all three available enteroscopy systems between antegrade and retrograde approach" in small bowel disease, Dr. Sanaka, of the Digestive Disease Institute at the Cleveland Clinic, studied 250 such procedures performed at that institution between January 2008 and August 2009.
A total of 182 procedures were antegrade (91 with a single-balloon enteroscope, 52 with a double-balloon enteroscope, and 39 with a spiral enteroscope), and 68 were retrograde (23 with a single balloon, 37 with a double balloon, and 8 with a spiral enteroscope).
The mean age of all participants was 61.5 years, and the antegrade and retrograde groups did not differ significantly on any of the demographic factors or history of prior capsule endoscopies.
Although obscure gastrointestinal bleeding was the most common indication in both groups, "abdominal pain or suspected Crohn’s disease was a much more common indication for antegrade enteroscopy when compared to retrograde (18.7% vs. 4.4%, P less than .001)," wrote the authors.
Overall, the diagnostic yield of antegrade enteroscopy was significantly greater, at 63.7%, than the yield of the retrograde procedures (39.7%), with P less than .001 (Clin. Gastroenterol. Hepatol. 2012 [doi: 10.1016/j.cgh.2012.04.020]).
The investigators then looked at the therapeutic yield of the two procedures. "With the antegrade approach, in 59 procedures (32.4%), a therapeutic intervention was performed," including argon plasma coagulation in 52 cases (28.6%), dilatation in 1 (0.6%), and polypectomy in 4 cases (2.2%).
With the retrograde approach, therapies were initiated in just 14.7% of cases, which was significantly lower than the percentage for the antegrade approach (P less than .001).
The authors also compared the technical aspects of the different procedure types. In this study, antegrade enteroscopies lasted 44.3 minutes on average, versus 58.9 minutes for the retrograde procedures (P less than .001).
Antegrade procedures also achieved a significantly greater depth of maximal insertion on average, at 231.8 cm, compared with 103.4 cm for retrograde procedures (P less than .001).
The authors conceded that the study had several limitations. Not only was it retrospective, they wrote, "there was no randomization and hence there could have been a significant bias in patient selection and use of a particular enteroscopy approach in individual cases, particularly in patients in whom the source of small bowel disorder was not known."
Nevertheless, "our findings of higher diagnostic and therapeutic yields with antegrade enteroscopy compared to retrograde enteroscopy support the expert opinion to consider antegrade enteroscopy as a default initial approach for suspected small bowel disease," the authors concluded.
"Retrograde enteroscopy may be considered when the antegrade enteroscopy is either nondiagnostic or if the abnormalities identified are unlikely to account for the patient’s symptoms," or when capsule endoscopy or radiologic imaging studies indicate that distal small bowel disease is likely, such as in suspected Crohn’s disease.
One of the authors, Dr. John Vargo, declared that he is a consultant for Olympus America, maker of enteroscopes and other devices. The authors stated that there was no outside funding.
FROM CLINICAL GASTROENTEROLOGY AND HEPATOLOGY
Secondary prophylaxis reduces bleeding in hemophilia

PARIS—Results of a phase 3 study indicate that the recombinant antihemophilic factor octocog alfa is effective as secondary bleeding prophylaxis in patients with severe hemophilia A.
The product appeared to be well-tolerated, and it reduced bleeding frequency as secondary prophylaxis (ie, treatment after multiple bleeding episodes have occurred), when compared to on-demand treatment.
These results were presented as a late-breaking abstract at the World Federation of Hemophilia 2012 World Congress, which took place July 8-12. The study—called SPINART—was sponsored by Bayer Healthcare, the makers of octocog alfa (marketed as Kogenate).
“Patients on the prophylactic regimen experienced significantly fewer bleeds than those using on-demand treatment,” said the study’s principal investigator, Marilyn Manco-Johnson, MD, of the University of Colorado at Denver.
“Those bleeds that did occur on the prophylactic regimen were predominantly mild-to-moderate.”
Dr Manco-Johnson and her colleagues had randomized 84 patients with hemophilia A to receive either on-demand treatment or secondary prophylaxis at 25 IU/kg 3 times per week. The total follow-up was 3 years.
After a median follow-up of 1.7 years, the researchers observed significantly fewer total bleeding events per year with prophylaxis vs on-demand treatment. The median number of bleeding events were 0 and 27.9, respectively. However, 48% of patients in the prophylaxis arm did experience at least 1 bleeding event.
There were significantly fewer joint bleeds with prophylaxis than with on-demand treatment. The median number of joint bleeds were 0 and 21.2, respectively. But 38% of patients in the prophylaxis arm did experience joint bleeds.
In patients on prophylaxis who did experience bleeding, 20% of the episodes were severe, 44% were mild, and 36% were moderate. In patients receiving on-demand treatment, 19% of bleeding episodes were severe, 23% were mild, and 58% were moderate.
The researchers did not observe inhibitor formation in any of the patients. And adverse events were consistent with those observed in previous studies, including skin-associated hypersensitivity reactions, infusion site reactions, and central venous access device line-associated infections.

PARIS—Results of a phase 3 study indicate that the recombinant antihemophilic factor octocog alfa is effective as secondary bleeding prophylaxis in patients with severe hemophilia A.
The product appeared to be well-tolerated, and it reduced bleeding frequency as secondary prophylaxis (ie, treatment after multiple bleeding episodes have occurred), when compared to on-demand treatment.
These results were presented as a late-breaking abstract at the World Federation of Hemophilia 2012 World Congress, which took place July 8-12. The study—called SPINART—was sponsored by Bayer Healthcare, the makers of octocog alfa (marketed as Kogenate).
“Patients on the prophylactic regimen experienced significantly fewer bleeds than those using on-demand treatment,” said the study’s principal investigator, Marilyn Manco-Johnson, MD, of the University of Colorado at Denver.
“Those bleeds that did occur on the prophylactic regimen were predominantly mild-to-moderate.”
Dr Manco-Johnson and her colleagues had randomized 84 patients with hemophilia A to receive either on-demand treatment or secondary prophylaxis at 25 IU/kg 3 times per week. The total follow-up was 3 years.
After a median follow-up of 1.7 years, the researchers observed significantly fewer total bleeding events per year with prophylaxis vs on-demand treatment. The median number of bleeding events were 0 and 27.9, respectively. However, 48% of patients in the prophylaxis arm did experience at least 1 bleeding event.
There were significantly fewer joint bleeds with prophylaxis than with on-demand treatment. The median number of joint bleeds were 0 and 21.2, respectively. But 38% of patients in the prophylaxis arm did experience joint bleeds.
In patients on prophylaxis who did experience bleeding, 20% of the episodes were severe, 44% were mild, and 36% were moderate. In patients receiving on-demand treatment, 19% of bleeding episodes were severe, 23% were mild, and 58% were moderate.
The researchers did not observe inhibitor formation in any of the patients. And adverse events were consistent with those observed in previous studies, including skin-associated hypersensitivity reactions, infusion site reactions, and central venous access device line-associated infections.

PARIS—Results of a phase 3 study indicate that the recombinant antihemophilic factor octocog alfa is effective as secondary bleeding prophylaxis in patients with severe hemophilia A.
The product appeared to be well-tolerated, and it reduced bleeding frequency as secondary prophylaxis (ie, treatment after multiple bleeding episodes have occurred), when compared to on-demand treatment.
These results were presented as a late-breaking abstract at the World Federation of Hemophilia 2012 World Congress, which took place July 8-12. The study—called SPINART—was sponsored by Bayer Healthcare, the makers of octocog alfa (marketed as Kogenate).
“Patients on the prophylactic regimen experienced significantly fewer bleeds than those using on-demand treatment,” said the study’s principal investigator, Marilyn Manco-Johnson, MD, of the University of Colorado at Denver.
“Those bleeds that did occur on the prophylactic regimen were predominantly mild-to-moderate.”
Dr Manco-Johnson and her colleagues had randomized 84 patients with hemophilia A to receive either on-demand treatment or secondary prophylaxis at 25 IU/kg 3 times per week. The total follow-up was 3 years.
After a median follow-up of 1.7 years, the researchers observed significantly fewer total bleeding events per year with prophylaxis vs on-demand treatment. The median number of bleeding events were 0 and 27.9, respectively. However, 48% of patients in the prophylaxis arm did experience at least 1 bleeding event.
There were significantly fewer joint bleeds with prophylaxis than with on-demand treatment. The median number of joint bleeds were 0 and 21.2, respectively. But 38% of patients in the prophylaxis arm did experience joint bleeds.
In patients on prophylaxis who did experience bleeding, 20% of the episodes were severe, 44% were mild, and 36% were moderate. In patients receiving on-demand treatment, 19% of bleeding episodes were severe, 23% were mild, and 58% were moderate.
The researchers did not observe inhibitor formation in any of the patients. And adverse events were consistent with those observed in previous studies, including skin-associated hypersensitivity reactions, infusion site reactions, and central venous access device line-associated infections.
Families Help Addicts Enter Treatment
Ms. A. arrived at the office for a routine medication visit with her psychiatrist. She was visibly tense and dejected, with swollen eyes from a night of crying. "I am so hurt! My son is so bright – he really has potential – but he’s drinking way too much. He took a leave from college after getting bad grades last semester, and now his girlfriend broke up with him!"
She continued, looking down at the floor. "A few years ago, his father died, and they were so close. Now, it’s just the two of us living in our home, as my daughter is out of state. Anyway, my son has distanced her as well. They aren’t as close as they used to be.
"I want to help him so badly but he doesn’t think he has a problem. He’s not in school, but he won’t even look for a job. I feel responsible ... and ashamed. I can’t even tell my family. What should I do?"
Family-Focused Interventions
Most individuals with substance use disorders resist engaging in treatment despite the negative consequences of their addictions. (NIDA Res. Monogr. 1997;165:44-84). People who misuse substances typically have calamitous effects on their families, who then need to reach out to mental health professionals for advice, support, empathy, and direction – yet family members often do not seek help. In the families of addicts, marital distress, social problems, financial woes, legal problems, criminality, aggression, and interpersonal violence commonly arise (Int. J. Addict. 1992;27:1-14), often leading to feelings of intense anger, sadness, anxiety, shame, guilt, and social isolation (Drugs in the Family: The Impact on Parents and Siblings. University of Glasgow, Scotland, 2005). Providing support to families of addicts is crucial, along with getting the substance abuser into treatment.
Family-focused interventions can lead to positive outcomes for both the substance misuser and his or her family members. Alcoholics Anonymous (families group)/Narcotics Anonymous (families group) are good family-support groups. Family therapy, such as the Behavioral Couples Therapy (BCT) of Fals-Stewart, is very effective. But the question often is: How do I persuade my relative to seek help? Two evidence-based treatments designed to help family members persuade their loved ones to seek treatment are profiled here.
Community Reinforcement Approach Family Training (CRAFT)
CRAFT uses a positive approach that doesn’t involve confrontation. This program encourages family members to identify the addict’s triggers, to assist him in breaking the patterns that lead to his drinking. Once these triggers are identified, CRAFT helps the family learn how to reward nondrinking through positive reinforcement.
Family members learn how to improve their communication skills in order to more effectively express their needs and also to reestablish good self-care. In a recent study, CRAFT resulted in three times more patient engagement than do Al-Anon/Nar-Anon, and two times more patient engagement than does the Johnson Institute Intervention. CRAFT also encouraged two-thirds of treatment-resistant patients to attend treatment (Addiction 2010;105:1729-38).
A Relational Intervention Sequence for Engagement (ARISE)
ARISE engages the patient in a family-centered process. The assumption with ARISE is that families are competent and have the capacity to heal. The therapist looks for strengths within family relationships. An "intervention recovery network" within the family functions like a board of directors, so that the addict cannot manipulate people one-on-one.
The process of ARISE is as follows. First, the telephone call: The therapist coaches the caller to include all the family members and as many friends as possible for the intervention. Next, the "identified loved one," or substance abuser, is invited into a conversation that will occur in the form of a family meeting. By the time of the family meeting, each participant has become clear on their "eyewitness account" of how the crisis has affected their loved one and the whole group.
Ideally, before the family meeting, the family members and friends cooperate to plan and write a "change message" that will be shared with their loved one at the family meeting. At the meeting, the group talks, and then signs the change agreement. The individual with substance abuse/dependence also signs the change plan. The therapist’s goal is to get the substance abuser into treatment. If successful, the therapist then collaborates with the treatment provider, and family and friends, through weekly phone calls for 6 months. In an NIH-funded study, ARISE resulted in 83% of substance abusers entering treatment (Am. J. Drug Alcohol Abuse 2004;30:711-48).
Beyond this overall framework, the ARISE program offers tips and guidance for families, to maximize the odds of success:
• Raising the subject. There is no perfect time or place to bring up the issue, but do not do it while the person is drunk or drinking. Wait until he or she is sober. Sometimes, a confrontation is more productive when facilitated by a professional who is knowledgeable about alcoholism and alcohol abuse and who can arrange a therapeutic intervention.
• Explaining the consequences. Convey the following message to the substance abuser, in a kind but firm tone: You need to get help or suffer the consequences. These consequences could include loss of your job, chronic illness, divorce, and breakup of the family or friendships. I will no longer cover-up for you.
• Don’t be brushed off. If you are seriously concerned about a person’s drinking, do not allow her to distract you from your concerns. If you are constantly bailing her out of trouble or giving her another chance, the alcoholic or alcohol abuser is likely to interpret this pattern as permission to keep drinking.
• Blame is counterproductive. Someone with an alcohol problem is likely to feel misunderstood. Try to put blame aside because it only feeds such feelings. Remember that alcohol addiction is a disease, not a moral weakness.
• One on one, the alcoholic wins. It is very common to become isolated in the effort of trying to get the alcoholic to accept help. Once you are isolated into one-on-one confrontations, the alcoholic almost always wins because he has the power to manipulate with promises, short-term efforts to improve, and blaming you as the cause of the problem. It is important to build a support network, such an intervention group, to avoid the pitfalls of getting caught in a one-on-one confrontation.
• Don’t wait until it’s too late. Putting off the discussion or confrontation increases the risk of serious health and social problems. As with any disease, the earlier the person gets treatment, the better. The alcoholic does not have to "hit bottom" in order to get help.
• Don’t neglect your own needs. It’s easy for the alcohol problems of one person to overwhelm an entire family. Family or personal stresses often show up as problems with emotional, economic, physical, and social functioning from living with alcoholism. You may feel anger, resentment, depression, betrayal, and disillusionment.
• Counseling may be necessary. You may need counseling to help you understand alcoholism and learn appropriate actions to protect your own well-being. Intervention is a proven method to both get you support and help a loved one get started in treatment. One way to help the alcoholic or alcohol abuser is to attend to your own needs and those of other family members. Going to alcohol support groups such as Al-Anon can be very helpful.
Psychiatrists see patients like Ms. A. in their clinical practices every day. While the importance of quiet, compassionate, and involved listening with patients is crucial, psychiatrists can also help their patients by providing new psychoeducation and treatment options, such as CRAFT or ARISE. These treatments can have profoundly positive effects and bring relief to the family and the person with substance dependence.
Dr. Heru is in the department of psychiatry at the University of Colorado at Denver. She has been a member of the Association of Family Psychiatrists since 2002 and currently serves as the organization’s treasurer. In addition, she is the coauthor of two books on working with families and is the author of numerous articles on this topic. Dr. Ascher is a resident in psychiatry at Beth Israel Medical Center and a candidate in the postdoctoral program in psychotherapy and psychoanalysis at New York University. He is a Sol W. Ginsburg Fellow in the Group for the Advancement of Psychiatry (GAP) Family Committee.
Ms. A. arrived at the office for a routine medication visit with her psychiatrist. She was visibly tense and dejected, with swollen eyes from a night of crying. "I am so hurt! My son is so bright – he really has potential – but he’s drinking way too much. He took a leave from college after getting bad grades last semester, and now his girlfriend broke up with him!"
She continued, looking down at the floor. "A few years ago, his father died, and they were so close. Now, it’s just the two of us living in our home, as my daughter is out of state. Anyway, my son has distanced her as well. They aren’t as close as they used to be.
"I want to help him so badly but he doesn’t think he has a problem. He’s not in school, but he won’t even look for a job. I feel responsible ... and ashamed. I can’t even tell my family. What should I do?"
Family-Focused Interventions
Most individuals with substance use disorders resist engaging in treatment despite the negative consequences of their addictions. (NIDA Res. Monogr. 1997;165:44-84). People who misuse substances typically have calamitous effects on their families, who then need to reach out to mental health professionals for advice, support, empathy, and direction – yet family members often do not seek help. In the families of addicts, marital distress, social problems, financial woes, legal problems, criminality, aggression, and interpersonal violence commonly arise (Int. J. Addict. 1992;27:1-14), often leading to feelings of intense anger, sadness, anxiety, shame, guilt, and social isolation (Drugs in the Family: The Impact on Parents and Siblings. University of Glasgow, Scotland, 2005). Providing support to families of addicts is crucial, along with getting the substance abuser into treatment.
Family-focused interventions can lead to positive outcomes for both the substance misuser and his or her family members. Alcoholics Anonymous (families group)/Narcotics Anonymous (families group) are good family-support groups. Family therapy, such as the Behavioral Couples Therapy (BCT) of Fals-Stewart, is very effective. But the question often is: How do I persuade my relative to seek help? Two evidence-based treatments designed to help family members persuade their loved ones to seek treatment are profiled here.
Community Reinforcement Approach Family Training (CRAFT)
CRAFT uses a positive approach that doesn’t involve confrontation. This program encourages family members to identify the addict’s triggers, to assist him in breaking the patterns that lead to his drinking. Once these triggers are identified, CRAFT helps the family learn how to reward nondrinking through positive reinforcement.
Family members learn how to improve their communication skills in order to more effectively express their needs and also to reestablish good self-care. In a recent study, CRAFT resulted in three times more patient engagement than do Al-Anon/Nar-Anon, and two times more patient engagement than does the Johnson Institute Intervention. CRAFT also encouraged two-thirds of treatment-resistant patients to attend treatment (Addiction 2010;105:1729-38).
A Relational Intervention Sequence for Engagement (ARISE)
ARISE engages the patient in a family-centered process. The assumption with ARISE is that families are competent and have the capacity to heal. The therapist looks for strengths within family relationships. An "intervention recovery network" within the family functions like a board of directors, so that the addict cannot manipulate people one-on-one.
The process of ARISE is as follows. First, the telephone call: The therapist coaches the caller to include all the family members and as many friends as possible for the intervention. Next, the "identified loved one," or substance abuser, is invited into a conversation that will occur in the form of a family meeting. By the time of the family meeting, each participant has become clear on their "eyewitness account" of how the crisis has affected their loved one and the whole group.
Ideally, before the family meeting, the family members and friends cooperate to plan and write a "change message" that will be shared with their loved one at the family meeting. At the meeting, the group talks, and then signs the change agreement. The individual with substance abuse/dependence also signs the change plan. The therapist’s goal is to get the substance abuser into treatment. If successful, the therapist then collaborates with the treatment provider, and family and friends, through weekly phone calls for 6 months. In an NIH-funded study, ARISE resulted in 83% of substance abusers entering treatment (Am. J. Drug Alcohol Abuse 2004;30:711-48).
Beyond this overall framework, the ARISE program offers tips and guidance for families, to maximize the odds of success:
• Raising the subject. There is no perfect time or place to bring up the issue, but do not do it while the person is drunk or drinking. Wait until he or she is sober. Sometimes, a confrontation is more productive when facilitated by a professional who is knowledgeable about alcoholism and alcohol abuse and who can arrange a therapeutic intervention.
• Explaining the consequences. Convey the following message to the substance abuser, in a kind but firm tone: You need to get help or suffer the consequences. These consequences could include loss of your job, chronic illness, divorce, and breakup of the family or friendships. I will no longer cover-up for you.
• Don’t be brushed off. If you are seriously concerned about a person’s drinking, do not allow her to distract you from your concerns. If you are constantly bailing her out of trouble or giving her another chance, the alcoholic or alcohol abuser is likely to interpret this pattern as permission to keep drinking.
• Blame is counterproductive. Someone with an alcohol problem is likely to feel misunderstood. Try to put blame aside because it only feeds such feelings. Remember that alcohol addiction is a disease, not a moral weakness.
• One on one, the alcoholic wins. It is very common to become isolated in the effort of trying to get the alcoholic to accept help. Once you are isolated into one-on-one confrontations, the alcoholic almost always wins because he has the power to manipulate with promises, short-term efforts to improve, and blaming you as the cause of the problem. It is important to build a support network, such an intervention group, to avoid the pitfalls of getting caught in a one-on-one confrontation.
• Don’t wait until it’s too late. Putting off the discussion or confrontation increases the risk of serious health and social problems. As with any disease, the earlier the person gets treatment, the better. The alcoholic does not have to "hit bottom" in order to get help.
• Don’t neglect your own needs. It’s easy for the alcohol problems of one person to overwhelm an entire family. Family or personal stresses often show up as problems with emotional, economic, physical, and social functioning from living with alcoholism. You may feel anger, resentment, depression, betrayal, and disillusionment.
• Counseling may be necessary. You may need counseling to help you understand alcoholism and learn appropriate actions to protect your own well-being. Intervention is a proven method to both get you support and help a loved one get started in treatment. One way to help the alcoholic or alcohol abuser is to attend to your own needs and those of other family members. Going to alcohol support groups such as Al-Anon can be very helpful.
Psychiatrists see patients like Ms. A. in their clinical practices every day. While the importance of quiet, compassionate, and involved listening with patients is crucial, psychiatrists can also help their patients by providing new psychoeducation and treatment options, such as CRAFT or ARISE. These treatments can have profoundly positive effects and bring relief to the family and the person with substance dependence.
Dr. Heru is in the department of psychiatry at the University of Colorado at Denver. She has been a member of the Association of Family Psychiatrists since 2002 and currently serves as the organization’s treasurer. In addition, she is the coauthor of two books on working with families and is the author of numerous articles on this topic. Dr. Ascher is a resident in psychiatry at Beth Israel Medical Center and a candidate in the postdoctoral program in psychotherapy and psychoanalysis at New York University. He is a Sol W. Ginsburg Fellow in the Group for the Advancement of Psychiatry (GAP) Family Committee.
Ms. A. arrived at the office for a routine medication visit with her psychiatrist. She was visibly tense and dejected, with swollen eyes from a night of crying. "I am so hurt! My son is so bright – he really has potential – but he’s drinking way too much. He took a leave from college after getting bad grades last semester, and now his girlfriend broke up with him!"
She continued, looking down at the floor. "A few years ago, his father died, and they were so close. Now, it’s just the two of us living in our home, as my daughter is out of state. Anyway, my son has distanced her as well. They aren’t as close as they used to be.
"I want to help him so badly but he doesn’t think he has a problem. He’s not in school, but he won’t even look for a job. I feel responsible ... and ashamed. I can’t even tell my family. What should I do?"
Family-Focused Interventions
Most individuals with substance use disorders resist engaging in treatment despite the negative consequences of their addictions. (NIDA Res. Monogr. 1997;165:44-84). People who misuse substances typically have calamitous effects on their families, who then need to reach out to mental health professionals for advice, support, empathy, and direction – yet family members often do not seek help. In the families of addicts, marital distress, social problems, financial woes, legal problems, criminality, aggression, and interpersonal violence commonly arise (Int. J. Addict. 1992;27:1-14), often leading to feelings of intense anger, sadness, anxiety, shame, guilt, and social isolation (Drugs in the Family: The Impact on Parents and Siblings. University of Glasgow, Scotland, 2005). Providing support to families of addicts is crucial, along with getting the substance abuser into treatment.
Family-focused interventions can lead to positive outcomes for both the substance misuser and his or her family members. Alcoholics Anonymous (families group)/Narcotics Anonymous (families group) are good family-support groups. Family therapy, such as the Behavioral Couples Therapy (BCT) of Fals-Stewart, is very effective. But the question often is: How do I persuade my relative to seek help? Two evidence-based treatments designed to help family members persuade their loved ones to seek treatment are profiled here.
Community Reinforcement Approach Family Training (CRAFT)
CRAFT uses a positive approach that doesn’t involve confrontation. This program encourages family members to identify the addict’s triggers, to assist him in breaking the patterns that lead to his drinking. Once these triggers are identified, CRAFT helps the family learn how to reward nondrinking through positive reinforcement.
Family members learn how to improve their communication skills in order to more effectively express their needs and also to reestablish good self-care. In a recent study, CRAFT resulted in three times more patient engagement than do Al-Anon/Nar-Anon, and two times more patient engagement than does the Johnson Institute Intervention. CRAFT also encouraged two-thirds of treatment-resistant patients to attend treatment (Addiction 2010;105:1729-38).
A Relational Intervention Sequence for Engagement (ARISE)
ARISE engages the patient in a family-centered process. The assumption with ARISE is that families are competent and have the capacity to heal. The therapist looks for strengths within family relationships. An "intervention recovery network" within the family functions like a board of directors, so that the addict cannot manipulate people one-on-one.
The process of ARISE is as follows. First, the telephone call: The therapist coaches the caller to include all the family members and as many friends as possible for the intervention. Next, the "identified loved one," or substance abuser, is invited into a conversation that will occur in the form of a family meeting. By the time of the family meeting, each participant has become clear on their "eyewitness account" of how the crisis has affected their loved one and the whole group.
Ideally, before the family meeting, the family members and friends cooperate to plan and write a "change message" that will be shared with their loved one at the family meeting. At the meeting, the group talks, and then signs the change agreement. The individual with substance abuse/dependence also signs the change plan. The therapist’s goal is to get the substance abuser into treatment. If successful, the therapist then collaborates with the treatment provider, and family and friends, through weekly phone calls for 6 months. In an NIH-funded study, ARISE resulted in 83% of substance abusers entering treatment (Am. J. Drug Alcohol Abuse 2004;30:711-48).
Beyond this overall framework, the ARISE program offers tips and guidance for families, to maximize the odds of success:
• Raising the subject. There is no perfect time or place to bring up the issue, but do not do it while the person is drunk or drinking. Wait until he or she is sober. Sometimes, a confrontation is more productive when facilitated by a professional who is knowledgeable about alcoholism and alcohol abuse and who can arrange a therapeutic intervention.
• Explaining the consequences. Convey the following message to the substance abuser, in a kind but firm tone: You need to get help or suffer the consequences. These consequences could include loss of your job, chronic illness, divorce, and breakup of the family or friendships. I will no longer cover-up for you.
• Don’t be brushed off. If you are seriously concerned about a person’s drinking, do not allow her to distract you from your concerns. If you are constantly bailing her out of trouble or giving her another chance, the alcoholic or alcohol abuser is likely to interpret this pattern as permission to keep drinking.
• Blame is counterproductive. Someone with an alcohol problem is likely to feel misunderstood. Try to put blame aside because it only feeds such feelings. Remember that alcohol addiction is a disease, not a moral weakness.
• One on one, the alcoholic wins. It is very common to become isolated in the effort of trying to get the alcoholic to accept help. Once you are isolated into one-on-one confrontations, the alcoholic almost always wins because he has the power to manipulate with promises, short-term efforts to improve, and blaming you as the cause of the problem. It is important to build a support network, such an intervention group, to avoid the pitfalls of getting caught in a one-on-one confrontation.
• Don’t wait until it’s too late. Putting off the discussion or confrontation increases the risk of serious health and social problems. As with any disease, the earlier the person gets treatment, the better. The alcoholic does not have to "hit bottom" in order to get help.
• Don’t neglect your own needs. It’s easy for the alcohol problems of one person to overwhelm an entire family. Family or personal stresses often show up as problems with emotional, economic, physical, and social functioning from living with alcoholism. You may feel anger, resentment, depression, betrayal, and disillusionment.
• Counseling may be necessary. You may need counseling to help you understand alcoholism and learn appropriate actions to protect your own well-being. Intervention is a proven method to both get you support and help a loved one get started in treatment. One way to help the alcoholic or alcohol abuser is to attend to your own needs and those of other family members. Going to alcohol support groups such as Al-Anon can be very helpful.
Psychiatrists see patients like Ms. A. in their clinical practices every day. While the importance of quiet, compassionate, and involved listening with patients is crucial, psychiatrists can also help their patients by providing new psychoeducation and treatment options, such as CRAFT or ARISE. These treatments can have profoundly positive effects and bring relief to the family and the person with substance dependence.
Dr. Heru is in the department of psychiatry at the University of Colorado at Denver. She has been a member of the Association of Family Psychiatrists since 2002 and currently serves as the organization’s treasurer. In addition, she is the coauthor of two books on working with families and is the author of numerous articles on this topic. Dr. Ascher is a resident in psychiatry at Beth Israel Medical Center and a candidate in the postdoctoral program in psychotherapy and psychoanalysis at New York University. He is a Sol W. Ginsburg Fellow in the Group for the Advancement of Psychiatry (GAP) Family Committee.
Localizing General Medical Teams
Localizing inpatient general medical teams to nursing units has high intuitive validity for improving physician productivity, hospital efficiency, and patient outcomes. Motion or the moving of personnel between tasksso prominent if teams are not localizedis 1 of the 7 wastes in lean thinking.1 In a timemotion study, where hospitalists cared for patients on up to 5 different wards, O'Leary et al2 have reported large parts of hospitalists' workdays spent in indirect patient care (69%), paging (13%), and travel (3%). Localization could increase the amount of time available for direct patient care, decrease time spent for (and interruptions due to) paging, and decrease travel time, all leading to greater productivity.
O'Leary et al3 have also reported the beneficial effects of localization of medical inpatients on communication between nurses and physicians, who could identify each other more often, and reported greater communication (specifically face‐to‐face communication) with each other following localization. This improvement in communication and effective multidisciplinary rounds could lead to safer care4 and better outcomes.
Further investigations about the effect of localization are limited. Roy et al5 have compared the outcomes of patients localized to 2 inpatient pods medically staffed by hospitalists and physician assistants (PAs) to geographically dispersed, but structurally different, house staff teams. They noticed significantly lower costs, slight but nonsignificant increase in length of stay, and no difference in mortality or readmissions, but it is impossible to tease out the affect of localization versus the affect of team composition. In a before‐and‐after study, Findlay et al6 have reported a decrease in mortality and complication rates in clinically homogenous surgical patients (proximal hip fractures) when cared for by junior trainee physicians localized to a unit, but their experience cannot be extrapolated to the much more diverse general medical population.
In our hospital, each general medical team could admit patients dispersed over 14 different units. An internal group, commissioned to evaluate our hospitalist practice, recommended reducing this dispersal to improve physician productivity, hospital efficiency, and outcomes of care. We therefore conducted a project to evaluate the impact of localizing general medical inpatient teams to a single nursing unit.
METHODS
Setting
We conducted our project at a 490 bed, urban academic medical center in the midwestern United States where of the 10 total general medical teams, 6 were traditional resident‐based teams and 4 consisted of a hospitalist paired with a PA (H‐PA teams). We focused our study on the 4 H‐PA teams. The hospitalists could be assigned to any H‐PA team and staffed them for 2 weeks (including weekends). The PAs were always assigned to the same team but took weekends off. An in‐house hospitalist provided overnight cross‐coverage for the H‐PA teams. Prior to our intervention, these teams could admit patients to any of the 14 nursing units at our hospital. They admitted patients from 7 AM to 3 PM, and also accepted care of patients admitted overnight after the resident teams had reached their admission limits (overflow). A Faculty Admitting Medical Officer (AMO) balanced the existing workload of the teams against the number and complexity of incoming patients to decide team assignment for the patients. The AMO was given guidelines (soft caps) to limit total admissions to H‐PA teams to 5 per team per day (3 on a weekend), and to not exceed a total patient census of 16 for an H‐PA team.
Intervention
Starting April 1, 2010, until July 15, 2010, we localized patients admitted to 2 of our 4 H‐PA teams on a single 32‐bed nursing unit. The patients of the other 2 H‐PA teams remained dispersed throughout the hospital.
Transition
April 1, 2010 was a scheduled switch day for the hospitalists on the H‐PA teams. We took advantage of this switch day and reassigned all patients cared for by H‐PA teams on our localized unit to the 2 localized teams. Similarly, all patients on nonlocalized units cared for by H‐PA teams were reassigned to the 2 nonlocalized teams. All patients cared for by resident teams on the localized unit, that were anticipated to be discharged soon, stayed until discharge; those that had a longer stay anticipated were transferred to a nonlocalized unit.
Patient Assignment
The 4 H‐PA teams continued to accept patients between 7 AM and 3 PM, as well as overflow patients. Patients with sickle cell crises were admitted exclusively to the nonlocalized teams, as they were cared for on a specialized nursing unit. No other patient characteristic was used to decide team assignment.
The AMO balanced the existing workload of the teams against the number and complexity of incoming patients to decide team assignment for the patients, but if these factors were equivocal, the AMO was now asked to preferentially admit to the localized teams. The admission soft cap for the H‐PA teams remained the same (5 on weekdays and 3 on weekends). The soft cap on the total census of 16 patients for the nonlocalized teams remained, but we imposed hard caps on the total census for the localized teams. These hard caps were 16 for each localized team for the month of April (to fill a 32‐bed unit), then decreased to 12 for the month of May, as informal feedback from the teams suggested a need to decrease workload, and then rebalanced to 14 for the remaining study period.
Evaluation
Clinical Outcomes
Using both concurrent and historical controls, we evaluated the impact of localization on the following clinical outcome measures: length of stay (LOS), charges, and 30‐day readmission rates.
Inclusion Criteria
We included all patients assigned to localized and nonlocalized teams between the period April 1, 2010 to July 15, 2010, and discharged before July 16, 2010, in our intervention group and concurrent control group, respectively. We included all patients assigned to any of the 4 H‐PA teams during the period January 1, 2010 and March 31, 2010 in the historical control group.
Exclusion Criteria
From the historical control group, we excluded patients assigned to one particular H‐PA team during the period January 1, 2010 to February 28, 2010, during which the PA assigned to that team was on leave. We excluded, from all groups, patients with a diagnosis of sickle cell disease and hospitalizations that straddled the start of the intervention. Further, we excluded repeat admissions for each patient.
Data Collection
We used admission logs to determine team assignment and linked them to our hospital's discharge abstract database to get patient level data. We grouped the principal diagnosis, International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes into clinically relevant categories using the Healthcare Cost and Utilization Project Clinical Classification Software for ICD‐9‐CM (Rockville, MD, www.hcup‐us.ahrq.gov/toolssoftware/ccs/ccs.jsp). We created comorbidity measures using Healthcare Cost and Utilization Project Comorbidity Software, version 3.4 (Rockville, MD, www.hcup‐us.ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp).
We calculated LOS by subtracting the discharge day and time from the admission day and time. We summed all charges accrued during the entire hospital stay, but did not include professional fees. The LOS and charges included time spent and charges accrued in the intensive care unit (ICU). As ICU care was not under the control of the general medical teams and could have a significant impact on outcomes reflecting resource utilization, we compared LOS and charges only for 2 subsets of patients: patients not initially admitted to ICU before care by medical teams, and patients never requiring ICU care. We considered any repeat hospitalization to our hospital within 30 days following a discharge to be a readmission, except those for a planned procedure or for inpatient rehabilitation. We compared readmission rates for all patients irrespective of ICU stay, as discharge planning for all patients was under the direct control of the general medical teams.
Data Analysis
We performed unadjusted descriptive statistics using medians and interquartile ranges for continuous variables, and frequencies and percentages for categorical variables. We used chi‐square tests of association, and KruskalWallis analysis of variance, to compare baseline characteristics of patients assigned to localized and control teams.
We used regression models with random effects to risk adjust for a wide variety of variables. We included age, gender, race, insurance, admission source, time, day of week, discharge time, and total number of comorbidities as fixed effects in all models. We then added individual comorbidity measures one by one as fixed effects, including them only if significant at P < 0.01. We always added a variable identifying the admitting physician as a random effect, to account for dependence between admissions to the same physician. We log transformed LOS and charges because they were extremely skewed in nature. We analyzed readmissions after excluding patients who died. We evaluated the affect of our intervention on clinical outcomes using both historical and concurrent controls. We report P values for both overall 3‐way comparisons, as well as each of the 2‐way comparisonsintervention versus historical control and intervention versus concurrent control.
Productivity and Workflow Measures
We also evaluated the impact of localization on the following productivity and workflow measures: number of pages received, number of patient encounters, relative value units (RVUs) generated, and steps walked by PAs.
Data Collection
We queried our in‐house paging systems for the number of pages received by intervention and concurrent control teams between 7 AM and 6 PM (usual workday). We queried our professional billing data to determine the number of encounters per day and RVUs generated by the intervention, as well as historical and concurrent control teams, as a measure of productivity.
During the last 15 days of our intervention (July 1 July 15, 2010), we received 4 pedometers and we asked the PAs to record the number of steps taken during their workday. We chose PAs, rather than physicians, as the PAs had purely clinical duties and their walking activity would reflect activity for solely clinical purposes.
Data Analysis
For productivity and workflow measures, we adjusted for the day of the week and used random effects models to adjust for clustering of data by physician and physician assistant.
Statistical Software
We performed the statistical analysis using R software, versions 2.9.0 (The R Project for Statistical Computing, Vienna, Austria, http://www.R‐project.org).
Ethical Concerns
The study protocol was approved by our institutional review board.
RESULTS
Study Population
There were 2431 hospitalizations to the 4 H‐PA teams during the study period. Data from 37 hospitalizations was excluded because of missing data. After applying all exclusion criteria, our final study sample consisted of a total of 1826 first hospitalizations for patients: 783 historical controls, 478 concurrent controls, and 565 localized patients.
Patients in the control groups and intervention group were similar in age, gender, race, and insurance status. Patients in the intervention group were more likely to be admitted over the weekend, but had similar probability of being discharged over the weekend or having had an ICU stay. Historical controls were admitted more often between 6 AM and 12 noon, while during the intervention period, patients were more likely to be admitted between midnight and 6 AM. The discharge time was similar across all groups. The 5 most common diagnoses were similar across the groups (Table 1).
Historical Control | Intervention Localized Teams | Concurrent Control | P Value | |
---|---|---|---|---|
| ||||
Patients | 783 | 565 | 478 | |
Age median (IQR) | 57 (4575) | 57 (4573) | 56 (4470) | 0.186 |
Age groups, n (%) | ||||
<30 | 65 (8.3) | 37 (6.6) | 46 (9.6) | |
3039 | 76 (9.7) | 62 (11.0) | 47 (9.8) | |
4049 | 114 (14.6) | 85 (15.0) | 68 (14.2) | |
5059 | 162 (20.7) | 124 (22.0) | 118 (24.7) | 0.145 |
6069 | 119 (15.2) | 84 (14.9) | 76 (16.0) | |
7079 | 100 (12.8) | 62 (11.0) | 58 (12.1) | |
8089 | 113 (14.4) | 95 (16.8) | 51 (10.7) | |
>89 | 34 (4.3) | 16 (2.88) | 14 (2.9) | |
Female gender, n (%) | 434 (55.4) | 327 (57.9) | 264 (55.2) | 0.602 |
Race: Black, n (%) | 285 (36.4) | 229 (40.5) | 200 (41.8) | 0.111 |
Observation status, n (%) | 165 (21.1) | 108 (19.1) | 108 (22.6) | 0.380 |
Insurance, n (%) | ||||
Commercial | 171 (21.8) | 101 (17.9) | 101 (21.1) | |
Medicare | 376 (48.0) | 278 (49.2) | 218 (45.6) | 0.225 |
Medicaid | 179 (22.8) | 126 (22.3) | 117 (24.5) | |
Uninsured | 54 (7.3) | 60 (10.6) | 42 (8.8) | |
Weekend admission, n (%) | 137 (17.5) | 116 (20.5) | 65 (13.6) | 0.013 |
Weekend discharge, n (%) | 132 (16.9) | 107 (18.9) | 91 (19.0) | 0.505 |
Source of admission | ||||
ED, n (%) | 654 (83.5) | 450 (79.7) | 370 (77.4) | 0.022 |
No ICU stay, n (%) | 600 (76.6) | 440 (77.9) | 383 (80.1) | 0.348 |
Admission time, n (%) | ||||
00000559 | 239 (30.5) | 208 (36.8) | 172 (36.0) | |
06001159 | 296 (37.8) | 157 (27.8) | 154 (32.2) | 0.007 |
12001759 | 183 (23.4) | 147 (26.0) | 105 (22.0) | |
18002359 | 65 (8.3) | 53 (9.4) | 47 (9.8) | |
Discharge time, n (%) | ||||
00001159 | 67 (8.6) | 45 (8.0) | 43 (9.0) | |
12001759 | 590 (75.4) | 417 (73.8) | 364 (76.2) | 0.658 |
18002359 | 126 (16.1) | 103 (18.2) | 71 (14.9) | |
Inpatient deaths, n | 13 | 13 | 6 | |
Top 5 primary diagnoses (%) | ||||
1 | Chest pain (11.5) | Chest pain (13.3) | Chest pain (11.9) | |
2 | Septicemia (6.4) | Septicemia (5.1) | Septicemia (3.8) | |
3 | Diabetes w/cm (4.6) | Pneumonia (4.9) | Diabetes w/cm (3.3) | n/a |
4 | Pneumonia (2.8) | Diabetes w/cm (4.1) | Pneumonia (3.3) | |
5 | UTI (2.7) | COPD (3.2) | UTI (2.9) |
Clinical Outcomes
Unadjusted Analyses
The risk of 30‐day readmission was no different between the intervention and control groups. In patients without an initial ICU stay, and without any ICU stay, charges incurred and LOS were no different between the intervention and control groups (Table 2).
Historical Control | Intervention Localized Teams | Concurrent Control | P Value | |
---|---|---|---|---|
| ||||
30‐day readmissions n (%) | 118 (15.3) | 69 (12.5) | 66 (14.0) | 0.346 |
Charges: excluding patients initially admitted to ICU | ||||
Median (IQR) in $ | 9346 (621614,520) | 9724 (665715,390) | 9902 (661115,670) | 0.393 |
Charges: excluding all patients with an ICU stay | ||||
Median (IQR) in $ | 9270 (618713,990) | 9509 (660114,940) | 9846 (658015,400) | 0.283 |
Length of stay: excluding patients initially admitted to ICU | ||||
Median (IQR) in days | 1.81 (1.223.35) | 2.16 (1.214.02) | 1.89 (1.193.50) | 0.214 |
Length of stay: excluding all patients with an ICU stay | ||||
Median (IQR) in days | 1.75 (1.203.26) | 2.12 (1.203.74) | 1.84 (1.193.42) | 0.236 |
Adjusted Analysis
The risk of 30‐day readmission was no different between the intervention and control groups. In patients without an initial ICU stay, and without any ICU stay, charges incurred were no different between the intervention and control groups; LOS was about 11% higher in the localized group as compared to historical controls, and about 9% higher as compared to the concurrent control group. The difference in LOS was not statistically significant on an overall 3‐way comparison (Table 3).
Localized Teams in Comparison to | |||
---|---|---|---|
Historical Control | Concurrent Control | Overall P Value | |
| |||
30‐day risk of readmission OR (CI) | 0.85 (0.611.19) | 0.94 (0.651.37) | 0.630 |
P value | 0.351 | 0.751 | |
Charges: excluding patients initially admitted to ICU | |||
% change | 2% higher | 4% lower | 0.367 |
(CI) | (6% lower to 11% higher) | (12% lower to 5%higher) | |
P value | 0.572 | 0.427 | |
Charges: excluding all patients with an ICU stay | |||
% change | 2% higher | 5% lower | 0.314 |
(CI) | (6% lower to 10% higher) | (13% lower to 4% higher) | |
P value | 0.695 | 0.261 | |
Length of stay: excluding patients initially admitted to ICU | |||
% change | 11% higher | 9% higher | 0.105 |
(CI) | (1% to 22% higher) | (3% lower to 21% higher) | |
P value | 0.038 | 0.138 | |
Length of stay: excluding all patients with an ICU stay | |||
% change | 10% higher | 8% higher | 0.133 |
(CI) | (0% to 22% higher) | (3% lower to 20% higher) | |
P value | 0.047 | 0.171 |
Productivity and Workflow Measures
Unadjusted Analyses
The localized teams received fewer pages as compared to concurrently nonlocalized teams. Localized teams had more patient encounters per day and generated more RVUs per day as compared to both historical and concurrent control groups. Physician assistants on localized teams took fewer steps during their work day (Table 4).
Historical Control | Intervention Localized Teams | Concurrent Control | P Value | |
---|---|---|---|---|
| ||||
Pages received/day (7 AM6 PM) Median (IQR) | No data | 15 (921) | 28 (12.540) | <0.001 |
Total encounters/day Median (IQR) | 10 (813) | 12 (1013) | 11 (913) | <0.001 |
RVU/day | ||||
Mean (SD) | 19.9 (6.76) | 22.6 (5.6) | 21.2 (6.7) | <0.001 |
Steps/day Median (IQR) | No data | 4661 (3922 5166) | 5554 (50606544) | <0.001 |
Adjusted Analysis
On adjusting for clustering by physician and day of week, the significant differences in pages received, total patient encounters, and RVUs generated persisted, while the difference in steps walked by PAs was attenuated to a statistically nonsignificant level (Table 5). The increase in RVU productivity was sustained through various periods of hard caps (data not shown).
Localized Teams in Comparison to | |||
---|---|---|---|
Historical Control | Concurrent Control | Overall P Value | |
| |||
Pages received (7 AM 6 PM) %(CI) | No data | 51% fewer (4854) | |
P value | P < 0.001 | ||
Total encounters | 0.89 more | 1.02 more | |
N (CI) | (0.371.41) | (0.461.58) | |
P value | P < 0.001 | P < 0.001 | P < 0.001 |
RVU/day | 2.20 more | 1.36 more | |
N (CI) | (1.103.29) | (0.172.55) | |
P value | P < 0.001 | P = 0.024 | P < 0.001 |
Steps/day | 1186 fewer (791 more to | ||
N (CI) | No data | 3164 fewer) | |
P value | P = 0.240 |
DISCUSSION
We found that general medical patients admitted to H‐PA teams and localized to a single nursing unit had similar risk of 30‐day readmission and charges, but may have had a higher length of stay compared to historical and concurrent controls. The localized teams received far fewer pages, had more patient encounters, generated more RVUs, and walked less during their work day. Taken together, these findings imply that in our study, localization led to greater team productivity and a possible decrease in hospital efficiency, with no significant impact on readmissions or charges incurred.
The higher productivity was likely mediated by the preferential assignments of more patients to the localized teams, and improvements in workflow (such as fewer pages and fewer steps walked), which allowed them to provide more care with the same resources as the control teams. Kocher and Sahni7 recently pointed out that the healthcare sector has experienced no gains in labor productivity in the past 20 years. Our intervention fits their prescription for redesigning healthcare delivery models to achieve higher productivity.
The possibility of a higher LOS associated with localization was a counterintuitive finding, and similar to that reported by Roy et al.5 We propose 3 hypotheses to explain this:
Selection bias: Higher workload of the localized teams led to compromised efficiency and a higher length of stay (eg, localized teams had fewer observation admissions, more hospitalizations with an ICU stay, and the AMO was asked to preferentially admit patients to localized teams).
Localization provided teams the opportunity to spend more time with their patients (by decreasing nonvalue‐added tasks) and to consequently address more issues before transitioning to outpatient care, or to provide higher quality of care.
Gaming: By having a hard cap on total number of occupied beds, we provided a perverse incentive to the localized teams to retain patients longer to keep assigned beds occupied, thereby delaying new admissions to avoid higher workload.
Our study cannot tell us which of these hypotheses represents the dominant phenomenon that led to this surprising finding. Hypothesis 3 is most worrying, and we suggest that others looking to localize their medical teams consider the possibility of unintended perverse incentives.
Differences were more pronounced between the historical control group and the intervention group, as opposed to the intervention group and concurrent controls. This may have occurred if we contaminated the concurrent control by decreasing the number of units they had to go to, by sequestering 1 unit for the intervention team.
Our report has limitations. It is a nonrandomized, quasi‐experimental investigation using a single institution's administrative databases. Our intervention was small in scale (localizing 2 out of 10 general medical teams on 1 out of 14 nursing units). What impact a wider implementation of localization may have on emergency department throughput and hospital occupancy remains to be studied. Nevertheless, our research is the first report, to our knowledge, investigating a wide variety of outcomes of localizing inpatient medical teams, and adds significantly to the limited research on this topic. It also provides significant operational details for other institutions to use when localizing medical teams.
We conclude that our intervention of localization of medical teams to a single nursing unit led to higher productivity and better workflow, but did not impact readmissions or charges incurred. We caution others designing similar localization interventions to protect against possible perverse incentives for inefficient care.
Acknowledgements
Disclosure: Nothing to report.
- Reducing waste in US health care systems. JAMA. 2007;297(8):871–874. .
- How hospitalists spend their time: insights on efficiency and safety. J Hosp Med. 2006;1(2):88–93. , , .
- Impact of localizing physicians to hospital units on nurse–physician communication and agreement on the plan of care. J Gen Intern Med. 2009;24(11):1223–1227. , , , et al.
- Structured interdisciplinary rounds in a medical teaching unit: improving patient safety. Arch Intern Med. 2011;171(7):678–684. , , , et al.
- Implementation of a physician assistant/hospitalist service in an academic medical center: impact on efficiency and patient outcomes. J Hosp Med. 2008;3(5):361–368. , , , et al.
- Ward‐based rather than team‐based junior surgical doctors reduce mortality for patients with a fracture of the proximal femur: results from a two‐year observational study. J Bone Joint Surg Br. 2011;93‐B(3):393–398. , , , , .
- Rethinking health care labor. N Engl J Med. 2011;365(15):1370–1372. , .
Localizing inpatient general medical teams to nursing units has high intuitive validity for improving physician productivity, hospital efficiency, and patient outcomes. Motion or the moving of personnel between tasksso prominent if teams are not localizedis 1 of the 7 wastes in lean thinking.1 In a timemotion study, where hospitalists cared for patients on up to 5 different wards, O'Leary et al2 have reported large parts of hospitalists' workdays spent in indirect patient care (69%), paging (13%), and travel (3%). Localization could increase the amount of time available for direct patient care, decrease time spent for (and interruptions due to) paging, and decrease travel time, all leading to greater productivity.
O'Leary et al3 have also reported the beneficial effects of localization of medical inpatients on communication between nurses and physicians, who could identify each other more often, and reported greater communication (specifically face‐to‐face communication) with each other following localization. This improvement in communication and effective multidisciplinary rounds could lead to safer care4 and better outcomes.
Further investigations about the effect of localization are limited. Roy et al5 have compared the outcomes of patients localized to 2 inpatient pods medically staffed by hospitalists and physician assistants (PAs) to geographically dispersed, but structurally different, house staff teams. They noticed significantly lower costs, slight but nonsignificant increase in length of stay, and no difference in mortality or readmissions, but it is impossible to tease out the affect of localization versus the affect of team composition. In a before‐and‐after study, Findlay et al6 have reported a decrease in mortality and complication rates in clinically homogenous surgical patients (proximal hip fractures) when cared for by junior trainee physicians localized to a unit, but their experience cannot be extrapolated to the much more diverse general medical population.
In our hospital, each general medical team could admit patients dispersed over 14 different units. An internal group, commissioned to evaluate our hospitalist practice, recommended reducing this dispersal to improve physician productivity, hospital efficiency, and outcomes of care. We therefore conducted a project to evaluate the impact of localizing general medical inpatient teams to a single nursing unit.
METHODS
Setting
We conducted our project at a 490 bed, urban academic medical center in the midwestern United States where of the 10 total general medical teams, 6 were traditional resident‐based teams and 4 consisted of a hospitalist paired with a PA (H‐PA teams). We focused our study on the 4 H‐PA teams. The hospitalists could be assigned to any H‐PA team and staffed them for 2 weeks (including weekends). The PAs were always assigned to the same team but took weekends off. An in‐house hospitalist provided overnight cross‐coverage for the H‐PA teams. Prior to our intervention, these teams could admit patients to any of the 14 nursing units at our hospital. They admitted patients from 7 AM to 3 PM, and also accepted care of patients admitted overnight after the resident teams had reached their admission limits (overflow). A Faculty Admitting Medical Officer (AMO) balanced the existing workload of the teams against the number and complexity of incoming patients to decide team assignment for the patients. The AMO was given guidelines (soft caps) to limit total admissions to H‐PA teams to 5 per team per day (3 on a weekend), and to not exceed a total patient census of 16 for an H‐PA team.
Intervention
Starting April 1, 2010, until July 15, 2010, we localized patients admitted to 2 of our 4 H‐PA teams on a single 32‐bed nursing unit. The patients of the other 2 H‐PA teams remained dispersed throughout the hospital.
Transition
April 1, 2010 was a scheduled switch day for the hospitalists on the H‐PA teams. We took advantage of this switch day and reassigned all patients cared for by H‐PA teams on our localized unit to the 2 localized teams. Similarly, all patients on nonlocalized units cared for by H‐PA teams were reassigned to the 2 nonlocalized teams. All patients cared for by resident teams on the localized unit, that were anticipated to be discharged soon, stayed until discharge; those that had a longer stay anticipated were transferred to a nonlocalized unit.
Patient Assignment
The 4 H‐PA teams continued to accept patients between 7 AM and 3 PM, as well as overflow patients. Patients with sickle cell crises were admitted exclusively to the nonlocalized teams, as they were cared for on a specialized nursing unit. No other patient characteristic was used to decide team assignment.
The AMO balanced the existing workload of the teams against the number and complexity of incoming patients to decide team assignment for the patients, but if these factors were equivocal, the AMO was now asked to preferentially admit to the localized teams. The admission soft cap for the H‐PA teams remained the same (5 on weekdays and 3 on weekends). The soft cap on the total census of 16 patients for the nonlocalized teams remained, but we imposed hard caps on the total census for the localized teams. These hard caps were 16 for each localized team for the month of April (to fill a 32‐bed unit), then decreased to 12 for the month of May, as informal feedback from the teams suggested a need to decrease workload, and then rebalanced to 14 for the remaining study period.
Evaluation
Clinical Outcomes
Using both concurrent and historical controls, we evaluated the impact of localization on the following clinical outcome measures: length of stay (LOS), charges, and 30‐day readmission rates.
Inclusion Criteria
We included all patients assigned to localized and nonlocalized teams between the period April 1, 2010 to July 15, 2010, and discharged before July 16, 2010, in our intervention group and concurrent control group, respectively. We included all patients assigned to any of the 4 H‐PA teams during the period January 1, 2010 and March 31, 2010 in the historical control group.
Exclusion Criteria
From the historical control group, we excluded patients assigned to one particular H‐PA team during the period January 1, 2010 to February 28, 2010, during which the PA assigned to that team was on leave. We excluded, from all groups, patients with a diagnosis of sickle cell disease and hospitalizations that straddled the start of the intervention. Further, we excluded repeat admissions for each patient.
Data Collection
We used admission logs to determine team assignment and linked them to our hospital's discharge abstract database to get patient level data. We grouped the principal diagnosis, International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes into clinically relevant categories using the Healthcare Cost and Utilization Project Clinical Classification Software for ICD‐9‐CM (Rockville, MD, www.hcup‐us.ahrq.gov/toolssoftware/ccs/ccs.jsp). We created comorbidity measures using Healthcare Cost and Utilization Project Comorbidity Software, version 3.4 (Rockville, MD, www.hcup‐us.ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp).
We calculated LOS by subtracting the discharge day and time from the admission day and time. We summed all charges accrued during the entire hospital stay, but did not include professional fees. The LOS and charges included time spent and charges accrued in the intensive care unit (ICU). As ICU care was not under the control of the general medical teams and could have a significant impact on outcomes reflecting resource utilization, we compared LOS and charges only for 2 subsets of patients: patients not initially admitted to ICU before care by medical teams, and patients never requiring ICU care. We considered any repeat hospitalization to our hospital within 30 days following a discharge to be a readmission, except those for a planned procedure or for inpatient rehabilitation. We compared readmission rates for all patients irrespective of ICU stay, as discharge planning for all patients was under the direct control of the general medical teams.
Data Analysis
We performed unadjusted descriptive statistics using medians and interquartile ranges for continuous variables, and frequencies and percentages for categorical variables. We used chi‐square tests of association, and KruskalWallis analysis of variance, to compare baseline characteristics of patients assigned to localized and control teams.
We used regression models with random effects to risk adjust for a wide variety of variables. We included age, gender, race, insurance, admission source, time, day of week, discharge time, and total number of comorbidities as fixed effects in all models. We then added individual comorbidity measures one by one as fixed effects, including them only if significant at P < 0.01. We always added a variable identifying the admitting physician as a random effect, to account for dependence between admissions to the same physician. We log transformed LOS and charges because they were extremely skewed in nature. We analyzed readmissions after excluding patients who died. We evaluated the affect of our intervention on clinical outcomes using both historical and concurrent controls. We report P values for both overall 3‐way comparisons, as well as each of the 2‐way comparisonsintervention versus historical control and intervention versus concurrent control.
Productivity and Workflow Measures
We also evaluated the impact of localization on the following productivity and workflow measures: number of pages received, number of patient encounters, relative value units (RVUs) generated, and steps walked by PAs.
Data Collection
We queried our in‐house paging systems for the number of pages received by intervention and concurrent control teams between 7 AM and 6 PM (usual workday). We queried our professional billing data to determine the number of encounters per day and RVUs generated by the intervention, as well as historical and concurrent control teams, as a measure of productivity.
During the last 15 days of our intervention (July 1 July 15, 2010), we received 4 pedometers and we asked the PAs to record the number of steps taken during their workday. We chose PAs, rather than physicians, as the PAs had purely clinical duties and their walking activity would reflect activity for solely clinical purposes.
Data Analysis
For productivity and workflow measures, we adjusted for the day of the week and used random effects models to adjust for clustering of data by physician and physician assistant.
Statistical Software
We performed the statistical analysis using R software, versions 2.9.0 (The R Project for Statistical Computing, Vienna, Austria, http://www.R‐project.org).
Ethical Concerns
The study protocol was approved by our institutional review board.
RESULTS
Study Population
There were 2431 hospitalizations to the 4 H‐PA teams during the study period. Data from 37 hospitalizations was excluded because of missing data. After applying all exclusion criteria, our final study sample consisted of a total of 1826 first hospitalizations for patients: 783 historical controls, 478 concurrent controls, and 565 localized patients.
Patients in the control groups and intervention group were similar in age, gender, race, and insurance status. Patients in the intervention group were more likely to be admitted over the weekend, but had similar probability of being discharged over the weekend or having had an ICU stay. Historical controls were admitted more often between 6 AM and 12 noon, while during the intervention period, patients were more likely to be admitted between midnight and 6 AM. The discharge time was similar across all groups. The 5 most common diagnoses were similar across the groups (Table 1).
Historical Control | Intervention Localized Teams | Concurrent Control | P Value | |
---|---|---|---|---|
| ||||
Patients | 783 | 565 | 478 | |
Age median (IQR) | 57 (4575) | 57 (4573) | 56 (4470) | 0.186 |
Age groups, n (%) | ||||
<30 | 65 (8.3) | 37 (6.6) | 46 (9.6) | |
3039 | 76 (9.7) | 62 (11.0) | 47 (9.8) | |
4049 | 114 (14.6) | 85 (15.0) | 68 (14.2) | |
5059 | 162 (20.7) | 124 (22.0) | 118 (24.7) | 0.145 |
6069 | 119 (15.2) | 84 (14.9) | 76 (16.0) | |
7079 | 100 (12.8) | 62 (11.0) | 58 (12.1) | |
8089 | 113 (14.4) | 95 (16.8) | 51 (10.7) | |
>89 | 34 (4.3) | 16 (2.88) | 14 (2.9) | |
Female gender, n (%) | 434 (55.4) | 327 (57.9) | 264 (55.2) | 0.602 |
Race: Black, n (%) | 285 (36.4) | 229 (40.5) | 200 (41.8) | 0.111 |
Observation status, n (%) | 165 (21.1) | 108 (19.1) | 108 (22.6) | 0.380 |
Insurance, n (%) | ||||
Commercial | 171 (21.8) | 101 (17.9) | 101 (21.1) | |
Medicare | 376 (48.0) | 278 (49.2) | 218 (45.6) | 0.225 |
Medicaid | 179 (22.8) | 126 (22.3) | 117 (24.5) | |
Uninsured | 54 (7.3) | 60 (10.6) | 42 (8.8) | |
Weekend admission, n (%) | 137 (17.5) | 116 (20.5) | 65 (13.6) | 0.013 |
Weekend discharge, n (%) | 132 (16.9) | 107 (18.9) | 91 (19.0) | 0.505 |
Source of admission | ||||
ED, n (%) | 654 (83.5) | 450 (79.7) | 370 (77.4) | 0.022 |
No ICU stay, n (%) | 600 (76.6) | 440 (77.9) | 383 (80.1) | 0.348 |
Admission time, n (%) | ||||
00000559 | 239 (30.5) | 208 (36.8) | 172 (36.0) | |
06001159 | 296 (37.8) | 157 (27.8) | 154 (32.2) | 0.007 |
12001759 | 183 (23.4) | 147 (26.0) | 105 (22.0) | |
18002359 | 65 (8.3) | 53 (9.4) | 47 (9.8) | |
Discharge time, n (%) | ||||
00001159 | 67 (8.6) | 45 (8.0) | 43 (9.0) | |
12001759 | 590 (75.4) | 417 (73.8) | 364 (76.2) | 0.658 |
18002359 | 126 (16.1) | 103 (18.2) | 71 (14.9) | |
Inpatient deaths, n | 13 | 13 | 6 | |
Top 5 primary diagnoses (%) | ||||
1 | Chest pain (11.5) | Chest pain (13.3) | Chest pain (11.9) | |
2 | Septicemia (6.4) | Septicemia (5.1) | Septicemia (3.8) | |
3 | Diabetes w/cm (4.6) | Pneumonia (4.9) | Diabetes w/cm (3.3) | n/a |
4 | Pneumonia (2.8) | Diabetes w/cm (4.1) | Pneumonia (3.3) | |
5 | UTI (2.7) | COPD (3.2) | UTI (2.9) |
Clinical Outcomes
Unadjusted Analyses
The risk of 30‐day readmission was no different between the intervention and control groups. In patients without an initial ICU stay, and without any ICU stay, charges incurred and LOS were no different between the intervention and control groups (Table 2).
Historical Control | Intervention Localized Teams | Concurrent Control | P Value | |
---|---|---|---|---|
| ||||
30‐day readmissions n (%) | 118 (15.3) | 69 (12.5) | 66 (14.0) | 0.346 |
Charges: excluding patients initially admitted to ICU | ||||
Median (IQR) in $ | 9346 (621614,520) | 9724 (665715,390) | 9902 (661115,670) | 0.393 |
Charges: excluding all patients with an ICU stay | ||||
Median (IQR) in $ | 9270 (618713,990) | 9509 (660114,940) | 9846 (658015,400) | 0.283 |
Length of stay: excluding patients initially admitted to ICU | ||||
Median (IQR) in days | 1.81 (1.223.35) | 2.16 (1.214.02) | 1.89 (1.193.50) | 0.214 |
Length of stay: excluding all patients with an ICU stay | ||||
Median (IQR) in days | 1.75 (1.203.26) | 2.12 (1.203.74) | 1.84 (1.193.42) | 0.236 |
Adjusted Analysis
The risk of 30‐day readmission was no different between the intervention and control groups. In patients without an initial ICU stay, and without any ICU stay, charges incurred were no different between the intervention and control groups; LOS was about 11% higher in the localized group as compared to historical controls, and about 9% higher as compared to the concurrent control group. The difference in LOS was not statistically significant on an overall 3‐way comparison (Table 3).
Localized Teams in Comparison to | |||
---|---|---|---|
Historical Control | Concurrent Control | Overall P Value | |
| |||
30‐day risk of readmission OR (CI) | 0.85 (0.611.19) | 0.94 (0.651.37) | 0.630 |
P value | 0.351 | 0.751 | |
Charges: excluding patients initially admitted to ICU | |||
% change | 2% higher | 4% lower | 0.367 |
(CI) | (6% lower to 11% higher) | (12% lower to 5%higher) | |
P value | 0.572 | 0.427 | |
Charges: excluding all patients with an ICU stay | |||
% change | 2% higher | 5% lower | 0.314 |
(CI) | (6% lower to 10% higher) | (13% lower to 4% higher) | |
P value | 0.695 | 0.261 | |
Length of stay: excluding patients initially admitted to ICU | |||
% change | 11% higher | 9% higher | 0.105 |
(CI) | (1% to 22% higher) | (3% lower to 21% higher) | |
P value | 0.038 | 0.138 | |
Length of stay: excluding all patients with an ICU stay | |||
% change | 10% higher | 8% higher | 0.133 |
(CI) | (0% to 22% higher) | (3% lower to 20% higher) | |
P value | 0.047 | 0.171 |
Productivity and Workflow Measures
Unadjusted Analyses
The localized teams received fewer pages as compared to concurrently nonlocalized teams. Localized teams had more patient encounters per day and generated more RVUs per day as compared to both historical and concurrent control groups. Physician assistants on localized teams took fewer steps during their work day (Table 4).
Historical Control | Intervention Localized Teams | Concurrent Control | P Value | |
---|---|---|---|---|
| ||||
Pages received/day (7 AM6 PM) Median (IQR) | No data | 15 (921) | 28 (12.540) | <0.001 |
Total encounters/day Median (IQR) | 10 (813) | 12 (1013) | 11 (913) | <0.001 |
RVU/day | ||||
Mean (SD) | 19.9 (6.76) | 22.6 (5.6) | 21.2 (6.7) | <0.001 |
Steps/day Median (IQR) | No data | 4661 (3922 5166) | 5554 (50606544) | <0.001 |
Adjusted Analysis
On adjusting for clustering by physician and day of week, the significant differences in pages received, total patient encounters, and RVUs generated persisted, while the difference in steps walked by PAs was attenuated to a statistically nonsignificant level (Table 5). The increase in RVU productivity was sustained through various periods of hard caps (data not shown).
Localized Teams in Comparison to | |||
---|---|---|---|
Historical Control | Concurrent Control | Overall P Value | |
| |||
Pages received (7 AM 6 PM) %(CI) | No data | 51% fewer (4854) | |
P value | P < 0.001 | ||
Total encounters | 0.89 more | 1.02 more | |
N (CI) | (0.371.41) | (0.461.58) | |
P value | P < 0.001 | P < 0.001 | P < 0.001 |
RVU/day | 2.20 more | 1.36 more | |
N (CI) | (1.103.29) | (0.172.55) | |
P value | P < 0.001 | P = 0.024 | P < 0.001 |
Steps/day | 1186 fewer (791 more to | ||
N (CI) | No data | 3164 fewer) | |
P value | P = 0.240 |
DISCUSSION
We found that general medical patients admitted to H‐PA teams and localized to a single nursing unit had similar risk of 30‐day readmission and charges, but may have had a higher length of stay compared to historical and concurrent controls. The localized teams received far fewer pages, had more patient encounters, generated more RVUs, and walked less during their work day. Taken together, these findings imply that in our study, localization led to greater team productivity and a possible decrease in hospital efficiency, with no significant impact on readmissions or charges incurred.
The higher productivity was likely mediated by the preferential assignments of more patients to the localized teams, and improvements in workflow (such as fewer pages and fewer steps walked), which allowed them to provide more care with the same resources as the control teams. Kocher and Sahni7 recently pointed out that the healthcare sector has experienced no gains in labor productivity in the past 20 years. Our intervention fits their prescription for redesigning healthcare delivery models to achieve higher productivity.
The possibility of a higher LOS associated with localization was a counterintuitive finding, and similar to that reported by Roy et al.5 We propose 3 hypotheses to explain this:
Selection bias: Higher workload of the localized teams led to compromised efficiency and a higher length of stay (eg, localized teams had fewer observation admissions, more hospitalizations with an ICU stay, and the AMO was asked to preferentially admit patients to localized teams).
Localization provided teams the opportunity to spend more time with their patients (by decreasing nonvalue‐added tasks) and to consequently address more issues before transitioning to outpatient care, or to provide higher quality of care.
Gaming: By having a hard cap on total number of occupied beds, we provided a perverse incentive to the localized teams to retain patients longer to keep assigned beds occupied, thereby delaying new admissions to avoid higher workload.
Our study cannot tell us which of these hypotheses represents the dominant phenomenon that led to this surprising finding. Hypothesis 3 is most worrying, and we suggest that others looking to localize their medical teams consider the possibility of unintended perverse incentives.
Differences were more pronounced between the historical control group and the intervention group, as opposed to the intervention group and concurrent controls. This may have occurred if we contaminated the concurrent control by decreasing the number of units they had to go to, by sequestering 1 unit for the intervention team.
Our report has limitations. It is a nonrandomized, quasi‐experimental investigation using a single institution's administrative databases. Our intervention was small in scale (localizing 2 out of 10 general medical teams on 1 out of 14 nursing units). What impact a wider implementation of localization may have on emergency department throughput and hospital occupancy remains to be studied. Nevertheless, our research is the first report, to our knowledge, investigating a wide variety of outcomes of localizing inpatient medical teams, and adds significantly to the limited research on this topic. It also provides significant operational details for other institutions to use when localizing medical teams.
We conclude that our intervention of localization of medical teams to a single nursing unit led to higher productivity and better workflow, but did not impact readmissions or charges incurred. We caution others designing similar localization interventions to protect against possible perverse incentives for inefficient care.
Acknowledgements
Disclosure: Nothing to report.
Localizing inpatient general medical teams to nursing units has high intuitive validity for improving physician productivity, hospital efficiency, and patient outcomes. Motion or the moving of personnel between tasksso prominent if teams are not localizedis 1 of the 7 wastes in lean thinking.1 In a timemotion study, where hospitalists cared for patients on up to 5 different wards, O'Leary et al2 have reported large parts of hospitalists' workdays spent in indirect patient care (69%), paging (13%), and travel (3%). Localization could increase the amount of time available for direct patient care, decrease time spent for (and interruptions due to) paging, and decrease travel time, all leading to greater productivity.
O'Leary et al3 have also reported the beneficial effects of localization of medical inpatients on communication between nurses and physicians, who could identify each other more often, and reported greater communication (specifically face‐to‐face communication) with each other following localization. This improvement in communication and effective multidisciplinary rounds could lead to safer care4 and better outcomes.
Further investigations about the effect of localization are limited. Roy et al5 have compared the outcomes of patients localized to 2 inpatient pods medically staffed by hospitalists and physician assistants (PAs) to geographically dispersed, but structurally different, house staff teams. They noticed significantly lower costs, slight but nonsignificant increase in length of stay, and no difference in mortality or readmissions, but it is impossible to tease out the affect of localization versus the affect of team composition. In a before‐and‐after study, Findlay et al6 have reported a decrease in mortality and complication rates in clinically homogenous surgical patients (proximal hip fractures) when cared for by junior trainee physicians localized to a unit, but their experience cannot be extrapolated to the much more diverse general medical population.
In our hospital, each general medical team could admit patients dispersed over 14 different units. An internal group, commissioned to evaluate our hospitalist practice, recommended reducing this dispersal to improve physician productivity, hospital efficiency, and outcomes of care. We therefore conducted a project to evaluate the impact of localizing general medical inpatient teams to a single nursing unit.
METHODS
Setting
We conducted our project at a 490 bed, urban academic medical center in the midwestern United States where of the 10 total general medical teams, 6 were traditional resident‐based teams and 4 consisted of a hospitalist paired with a PA (H‐PA teams). We focused our study on the 4 H‐PA teams. The hospitalists could be assigned to any H‐PA team and staffed them for 2 weeks (including weekends). The PAs were always assigned to the same team but took weekends off. An in‐house hospitalist provided overnight cross‐coverage for the H‐PA teams. Prior to our intervention, these teams could admit patients to any of the 14 nursing units at our hospital. They admitted patients from 7 AM to 3 PM, and also accepted care of patients admitted overnight after the resident teams had reached their admission limits (overflow). A Faculty Admitting Medical Officer (AMO) balanced the existing workload of the teams against the number and complexity of incoming patients to decide team assignment for the patients. The AMO was given guidelines (soft caps) to limit total admissions to H‐PA teams to 5 per team per day (3 on a weekend), and to not exceed a total patient census of 16 for an H‐PA team.
Intervention
Starting April 1, 2010, until July 15, 2010, we localized patients admitted to 2 of our 4 H‐PA teams on a single 32‐bed nursing unit. The patients of the other 2 H‐PA teams remained dispersed throughout the hospital.
Transition
April 1, 2010 was a scheduled switch day for the hospitalists on the H‐PA teams. We took advantage of this switch day and reassigned all patients cared for by H‐PA teams on our localized unit to the 2 localized teams. Similarly, all patients on nonlocalized units cared for by H‐PA teams were reassigned to the 2 nonlocalized teams. All patients cared for by resident teams on the localized unit, that were anticipated to be discharged soon, stayed until discharge; those that had a longer stay anticipated were transferred to a nonlocalized unit.
Patient Assignment
The 4 H‐PA teams continued to accept patients between 7 AM and 3 PM, as well as overflow patients. Patients with sickle cell crises were admitted exclusively to the nonlocalized teams, as they were cared for on a specialized nursing unit. No other patient characteristic was used to decide team assignment.
The AMO balanced the existing workload of the teams against the number and complexity of incoming patients to decide team assignment for the patients, but if these factors were equivocal, the AMO was now asked to preferentially admit to the localized teams. The admission soft cap for the H‐PA teams remained the same (5 on weekdays and 3 on weekends). The soft cap on the total census of 16 patients for the nonlocalized teams remained, but we imposed hard caps on the total census for the localized teams. These hard caps were 16 for each localized team for the month of April (to fill a 32‐bed unit), then decreased to 12 for the month of May, as informal feedback from the teams suggested a need to decrease workload, and then rebalanced to 14 for the remaining study period.
Evaluation
Clinical Outcomes
Using both concurrent and historical controls, we evaluated the impact of localization on the following clinical outcome measures: length of stay (LOS), charges, and 30‐day readmission rates.
Inclusion Criteria
We included all patients assigned to localized and nonlocalized teams between the period April 1, 2010 to July 15, 2010, and discharged before July 16, 2010, in our intervention group and concurrent control group, respectively. We included all patients assigned to any of the 4 H‐PA teams during the period January 1, 2010 and March 31, 2010 in the historical control group.
Exclusion Criteria
From the historical control group, we excluded patients assigned to one particular H‐PA team during the period January 1, 2010 to February 28, 2010, during which the PA assigned to that team was on leave. We excluded, from all groups, patients with a diagnosis of sickle cell disease and hospitalizations that straddled the start of the intervention. Further, we excluded repeat admissions for each patient.
Data Collection
We used admission logs to determine team assignment and linked them to our hospital's discharge abstract database to get patient level data. We grouped the principal diagnosis, International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes into clinically relevant categories using the Healthcare Cost and Utilization Project Clinical Classification Software for ICD‐9‐CM (Rockville, MD, www.hcup‐us.ahrq.gov/toolssoftware/ccs/ccs.jsp). We created comorbidity measures using Healthcare Cost and Utilization Project Comorbidity Software, version 3.4 (Rockville, MD, www.hcup‐us.ahrq.gov/toolssoftware/comorbidity/comorbidity.jsp).
We calculated LOS by subtracting the discharge day and time from the admission day and time. We summed all charges accrued during the entire hospital stay, but did not include professional fees. The LOS and charges included time spent and charges accrued in the intensive care unit (ICU). As ICU care was not under the control of the general medical teams and could have a significant impact on outcomes reflecting resource utilization, we compared LOS and charges only for 2 subsets of patients: patients not initially admitted to ICU before care by medical teams, and patients never requiring ICU care. We considered any repeat hospitalization to our hospital within 30 days following a discharge to be a readmission, except those for a planned procedure or for inpatient rehabilitation. We compared readmission rates for all patients irrespective of ICU stay, as discharge planning for all patients was under the direct control of the general medical teams.
Data Analysis
We performed unadjusted descriptive statistics using medians and interquartile ranges for continuous variables, and frequencies and percentages for categorical variables. We used chi‐square tests of association, and KruskalWallis analysis of variance, to compare baseline characteristics of patients assigned to localized and control teams.
We used regression models with random effects to risk adjust for a wide variety of variables. We included age, gender, race, insurance, admission source, time, day of week, discharge time, and total number of comorbidities as fixed effects in all models. We then added individual comorbidity measures one by one as fixed effects, including them only if significant at P < 0.01. We always added a variable identifying the admitting physician as a random effect, to account for dependence between admissions to the same physician. We log transformed LOS and charges because they were extremely skewed in nature. We analyzed readmissions after excluding patients who died. We evaluated the affect of our intervention on clinical outcomes using both historical and concurrent controls. We report P values for both overall 3‐way comparisons, as well as each of the 2‐way comparisonsintervention versus historical control and intervention versus concurrent control.
Productivity and Workflow Measures
We also evaluated the impact of localization on the following productivity and workflow measures: number of pages received, number of patient encounters, relative value units (RVUs) generated, and steps walked by PAs.
Data Collection
We queried our in‐house paging systems for the number of pages received by intervention and concurrent control teams between 7 AM and 6 PM (usual workday). We queried our professional billing data to determine the number of encounters per day and RVUs generated by the intervention, as well as historical and concurrent control teams, as a measure of productivity.
During the last 15 days of our intervention (July 1 July 15, 2010), we received 4 pedometers and we asked the PAs to record the number of steps taken during their workday. We chose PAs, rather than physicians, as the PAs had purely clinical duties and their walking activity would reflect activity for solely clinical purposes.
Data Analysis
For productivity and workflow measures, we adjusted for the day of the week and used random effects models to adjust for clustering of data by physician and physician assistant.
Statistical Software
We performed the statistical analysis using R software, versions 2.9.0 (The R Project for Statistical Computing, Vienna, Austria, http://www.R‐project.org).
Ethical Concerns
The study protocol was approved by our institutional review board.
RESULTS
Study Population
There were 2431 hospitalizations to the 4 H‐PA teams during the study period. Data from 37 hospitalizations was excluded because of missing data. After applying all exclusion criteria, our final study sample consisted of a total of 1826 first hospitalizations for patients: 783 historical controls, 478 concurrent controls, and 565 localized patients.
Patients in the control groups and intervention group were similar in age, gender, race, and insurance status. Patients in the intervention group were more likely to be admitted over the weekend, but had similar probability of being discharged over the weekend or having had an ICU stay. Historical controls were admitted more often between 6 AM and 12 noon, while during the intervention period, patients were more likely to be admitted between midnight and 6 AM. The discharge time was similar across all groups. The 5 most common diagnoses were similar across the groups (Table 1).
Historical Control | Intervention Localized Teams | Concurrent Control | P Value | |
---|---|---|---|---|
| ||||
Patients | 783 | 565 | 478 | |
Age median (IQR) | 57 (4575) | 57 (4573) | 56 (4470) | 0.186 |
Age groups, n (%) | ||||
<30 | 65 (8.3) | 37 (6.6) | 46 (9.6) | |
3039 | 76 (9.7) | 62 (11.0) | 47 (9.8) | |
4049 | 114 (14.6) | 85 (15.0) | 68 (14.2) | |
5059 | 162 (20.7) | 124 (22.0) | 118 (24.7) | 0.145 |
6069 | 119 (15.2) | 84 (14.9) | 76 (16.0) | |
7079 | 100 (12.8) | 62 (11.0) | 58 (12.1) | |
8089 | 113 (14.4) | 95 (16.8) | 51 (10.7) | |
>89 | 34 (4.3) | 16 (2.88) | 14 (2.9) | |
Female gender, n (%) | 434 (55.4) | 327 (57.9) | 264 (55.2) | 0.602 |
Race: Black, n (%) | 285 (36.4) | 229 (40.5) | 200 (41.8) | 0.111 |
Observation status, n (%) | 165 (21.1) | 108 (19.1) | 108 (22.6) | 0.380 |
Insurance, n (%) | ||||
Commercial | 171 (21.8) | 101 (17.9) | 101 (21.1) | |
Medicare | 376 (48.0) | 278 (49.2) | 218 (45.6) | 0.225 |
Medicaid | 179 (22.8) | 126 (22.3) | 117 (24.5) | |
Uninsured | 54 (7.3) | 60 (10.6) | 42 (8.8) | |
Weekend admission, n (%) | 137 (17.5) | 116 (20.5) | 65 (13.6) | 0.013 |
Weekend discharge, n (%) | 132 (16.9) | 107 (18.9) | 91 (19.0) | 0.505 |
Source of admission | ||||
ED, n (%) | 654 (83.5) | 450 (79.7) | 370 (77.4) | 0.022 |
No ICU stay, n (%) | 600 (76.6) | 440 (77.9) | 383 (80.1) | 0.348 |
Admission time, n (%) | ||||
00000559 | 239 (30.5) | 208 (36.8) | 172 (36.0) | |
06001159 | 296 (37.8) | 157 (27.8) | 154 (32.2) | 0.007 |
12001759 | 183 (23.4) | 147 (26.0) | 105 (22.0) | |
18002359 | 65 (8.3) | 53 (9.4) | 47 (9.8) | |
Discharge time, n (%) | ||||
00001159 | 67 (8.6) | 45 (8.0) | 43 (9.0) | |
12001759 | 590 (75.4) | 417 (73.8) | 364 (76.2) | 0.658 |
18002359 | 126 (16.1) | 103 (18.2) | 71 (14.9) | |
Inpatient deaths, n | 13 | 13 | 6 | |
Top 5 primary diagnoses (%) | ||||
1 | Chest pain (11.5) | Chest pain (13.3) | Chest pain (11.9) | |
2 | Septicemia (6.4) | Septicemia (5.1) | Septicemia (3.8) | |
3 | Diabetes w/cm (4.6) | Pneumonia (4.9) | Diabetes w/cm (3.3) | n/a |
4 | Pneumonia (2.8) | Diabetes w/cm (4.1) | Pneumonia (3.3) | |
5 | UTI (2.7) | COPD (3.2) | UTI (2.9) |
Clinical Outcomes
Unadjusted Analyses
The risk of 30‐day readmission was no different between the intervention and control groups. In patients without an initial ICU stay, and without any ICU stay, charges incurred and LOS were no different between the intervention and control groups (Table 2).
Historical Control | Intervention Localized Teams | Concurrent Control | P Value | |
---|---|---|---|---|
| ||||
30‐day readmissions n (%) | 118 (15.3) | 69 (12.5) | 66 (14.0) | 0.346 |
Charges: excluding patients initially admitted to ICU | ||||
Median (IQR) in $ | 9346 (621614,520) | 9724 (665715,390) | 9902 (661115,670) | 0.393 |
Charges: excluding all patients with an ICU stay | ||||
Median (IQR) in $ | 9270 (618713,990) | 9509 (660114,940) | 9846 (658015,400) | 0.283 |
Length of stay: excluding patients initially admitted to ICU | ||||
Median (IQR) in days | 1.81 (1.223.35) | 2.16 (1.214.02) | 1.89 (1.193.50) | 0.214 |
Length of stay: excluding all patients with an ICU stay | ||||
Median (IQR) in days | 1.75 (1.203.26) | 2.12 (1.203.74) | 1.84 (1.193.42) | 0.236 |
Adjusted Analysis
The risk of 30‐day readmission was no different between the intervention and control groups. In patients without an initial ICU stay, and without any ICU stay, charges incurred were no different between the intervention and control groups; LOS was about 11% higher in the localized group as compared to historical controls, and about 9% higher as compared to the concurrent control group. The difference in LOS was not statistically significant on an overall 3‐way comparison (Table 3).
Localized Teams in Comparison to | |||
---|---|---|---|
Historical Control | Concurrent Control | Overall P Value | |
| |||
30‐day risk of readmission OR (CI) | 0.85 (0.611.19) | 0.94 (0.651.37) | 0.630 |
P value | 0.351 | 0.751 | |
Charges: excluding patients initially admitted to ICU | |||
% change | 2% higher | 4% lower | 0.367 |
(CI) | (6% lower to 11% higher) | (12% lower to 5%higher) | |
P value | 0.572 | 0.427 | |
Charges: excluding all patients with an ICU stay | |||
% change | 2% higher | 5% lower | 0.314 |
(CI) | (6% lower to 10% higher) | (13% lower to 4% higher) | |
P value | 0.695 | 0.261 | |
Length of stay: excluding patients initially admitted to ICU | |||
% change | 11% higher | 9% higher | 0.105 |
(CI) | (1% to 22% higher) | (3% lower to 21% higher) | |
P value | 0.038 | 0.138 | |
Length of stay: excluding all patients with an ICU stay | |||
% change | 10% higher | 8% higher | 0.133 |
(CI) | (0% to 22% higher) | (3% lower to 20% higher) | |
P value | 0.047 | 0.171 |
Productivity and Workflow Measures
Unadjusted Analyses
The localized teams received fewer pages as compared to concurrently nonlocalized teams. Localized teams had more patient encounters per day and generated more RVUs per day as compared to both historical and concurrent control groups. Physician assistants on localized teams took fewer steps during their work day (Table 4).
Historical Control | Intervention Localized Teams | Concurrent Control | P Value | |
---|---|---|---|---|
| ||||
Pages received/day (7 AM6 PM) Median (IQR) | No data | 15 (921) | 28 (12.540) | <0.001 |
Total encounters/day Median (IQR) | 10 (813) | 12 (1013) | 11 (913) | <0.001 |
RVU/day | ||||
Mean (SD) | 19.9 (6.76) | 22.6 (5.6) | 21.2 (6.7) | <0.001 |
Steps/day Median (IQR) | No data | 4661 (3922 5166) | 5554 (50606544) | <0.001 |
Adjusted Analysis
On adjusting for clustering by physician and day of week, the significant differences in pages received, total patient encounters, and RVUs generated persisted, while the difference in steps walked by PAs was attenuated to a statistically nonsignificant level (Table 5). The increase in RVU productivity was sustained through various periods of hard caps (data not shown).
Localized Teams in Comparison to | |||
---|---|---|---|
Historical Control | Concurrent Control | Overall P Value | |
| |||
Pages received (7 AM 6 PM) %(CI) | No data | 51% fewer (4854) | |
P value | P < 0.001 | ||
Total encounters | 0.89 more | 1.02 more | |
N (CI) | (0.371.41) | (0.461.58) | |
P value | P < 0.001 | P < 0.001 | P < 0.001 |
RVU/day | 2.20 more | 1.36 more | |
N (CI) | (1.103.29) | (0.172.55) | |
P value | P < 0.001 | P = 0.024 | P < 0.001 |
Steps/day | 1186 fewer (791 more to | ||
N (CI) | No data | 3164 fewer) | |
P value | P = 0.240 |
DISCUSSION
We found that general medical patients admitted to H‐PA teams and localized to a single nursing unit had similar risk of 30‐day readmission and charges, but may have had a higher length of stay compared to historical and concurrent controls. The localized teams received far fewer pages, had more patient encounters, generated more RVUs, and walked less during their work day. Taken together, these findings imply that in our study, localization led to greater team productivity and a possible decrease in hospital efficiency, with no significant impact on readmissions or charges incurred.
The higher productivity was likely mediated by the preferential assignments of more patients to the localized teams, and improvements in workflow (such as fewer pages and fewer steps walked), which allowed them to provide more care with the same resources as the control teams. Kocher and Sahni7 recently pointed out that the healthcare sector has experienced no gains in labor productivity in the past 20 years. Our intervention fits their prescription for redesigning healthcare delivery models to achieve higher productivity.
The possibility of a higher LOS associated with localization was a counterintuitive finding, and similar to that reported by Roy et al.5 We propose 3 hypotheses to explain this:
Selection bias: Higher workload of the localized teams led to compromised efficiency and a higher length of stay (eg, localized teams had fewer observation admissions, more hospitalizations with an ICU stay, and the AMO was asked to preferentially admit patients to localized teams).
Localization provided teams the opportunity to spend more time with their patients (by decreasing nonvalue‐added tasks) and to consequently address more issues before transitioning to outpatient care, or to provide higher quality of care.
Gaming: By having a hard cap on total number of occupied beds, we provided a perverse incentive to the localized teams to retain patients longer to keep assigned beds occupied, thereby delaying new admissions to avoid higher workload.
Our study cannot tell us which of these hypotheses represents the dominant phenomenon that led to this surprising finding. Hypothesis 3 is most worrying, and we suggest that others looking to localize their medical teams consider the possibility of unintended perverse incentives.
Differences were more pronounced between the historical control group and the intervention group, as opposed to the intervention group and concurrent controls. This may have occurred if we contaminated the concurrent control by decreasing the number of units they had to go to, by sequestering 1 unit for the intervention team.
Our report has limitations. It is a nonrandomized, quasi‐experimental investigation using a single institution's administrative databases. Our intervention was small in scale (localizing 2 out of 10 general medical teams on 1 out of 14 nursing units). What impact a wider implementation of localization may have on emergency department throughput and hospital occupancy remains to be studied. Nevertheless, our research is the first report, to our knowledge, investigating a wide variety of outcomes of localizing inpatient medical teams, and adds significantly to the limited research on this topic. It also provides significant operational details for other institutions to use when localizing medical teams.
We conclude that our intervention of localization of medical teams to a single nursing unit led to higher productivity and better workflow, but did not impact readmissions or charges incurred. We caution others designing similar localization interventions to protect against possible perverse incentives for inefficient care.
Acknowledgements
Disclosure: Nothing to report.
- Reducing waste in US health care systems. JAMA. 2007;297(8):871–874. .
- How hospitalists spend their time: insights on efficiency and safety. J Hosp Med. 2006;1(2):88–93. , , .
- Impact of localizing physicians to hospital units on nurse–physician communication and agreement on the plan of care. J Gen Intern Med. 2009;24(11):1223–1227. , , , et al.
- Structured interdisciplinary rounds in a medical teaching unit: improving patient safety. Arch Intern Med. 2011;171(7):678–684. , , , et al.
- Implementation of a physician assistant/hospitalist service in an academic medical center: impact on efficiency and patient outcomes. J Hosp Med. 2008;3(5):361–368. , , , et al.
- Ward‐based rather than team‐based junior surgical doctors reduce mortality for patients with a fracture of the proximal femur: results from a two‐year observational study. J Bone Joint Surg Br. 2011;93‐B(3):393–398. , , , , .
- Rethinking health care labor. N Engl J Med. 2011;365(15):1370–1372. , .
- Reducing waste in US health care systems. JAMA. 2007;297(8):871–874. .
- How hospitalists spend their time: insights on efficiency and safety. J Hosp Med. 2006;1(2):88–93. , , .
- Impact of localizing physicians to hospital units on nurse–physician communication and agreement on the plan of care. J Gen Intern Med. 2009;24(11):1223–1227. , , , et al.
- Structured interdisciplinary rounds in a medical teaching unit: improving patient safety. Arch Intern Med. 2011;171(7):678–684. , , , et al.
- Implementation of a physician assistant/hospitalist service in an academic medical center: impact on efficiency and patient outcomes. J Hosp Med. 2008;3(5):361–368. , , , et al.
- Ward‐based rather than team‐based junior surgical doctors reduce mortality for patients with a fracture of the proximal femur: results from a two‐year observational study. J Bone Joint Surg Br. 2011;93‐B(3):393–398. , , , , .
- Rethinking health care labor. N Engl J Med. 2011;365(15):1370–1372. , .
Copyright © 2012 Society of Hospital Medicine
Alcohol Withdrawal Admissions
Many patients are admitted and readmitted to acute care hospitals with alcohol‐related diagnoses, including alcohol withdrawal syndrome (AWS), and experience significant morbidity and mortality. In patients with septic shock or at risk for acute respiratory distress syndrome (ARDS), chronic alcohol abuse is associated with increased ARDS and severity of multiple organ dysfunction.1 Among intensive care unit (ICU) patients, those with alcohol dependence have higher morbidity, including septic shock, and higher hospital mortality.2 Patients who experience AWS as a result of alcohol dependence may experience life‐threatening complications, such as seizures and delirium tremens.3,4 In‐hospital mortality from AWS is historically high,5 but with benzodiazepines used in a symptom‐driven manner to treat the complications of alcohol use, hospital mortality rates are more recently reported at 2.4%.6
As inpatient outcomes1,2,7 and hospital mortality810 are negatively affected by alcohol abuse, the post‐hospital course of these patients is also of interest. Specifically, patients are often admitted and readmitted with alcohol‐related diagnoses or AWS to acute care hospitals, but relatively little quantitative data exist on readmission factors in this population.11 Patients readmitted to detoxification units or alcohol and substance abuse units have been studied, and factors associated with readmission include psychiatric disorder,1217 female gender,14,15 and delay in rehabilitation aftercare.18
These results cannot be generalized to patients with AWS who are admitted and readmitted to acute‐care hospitals. First, patients hospitalized for alcohol withdrawal symptoms are often medically ill with more severe symptoms, and more frequent coexisting medical and psychiatric illnesses, that complicate the withdrawal syndrome. Detoxification units and substance abuse units require patients to be medically stable before admission, because they do not have the ability to provide a high level of supervision and treatment. Second, much of what we know regarding risk factors for readmission to detoxification centers and substance abuse units comes from studies of administrative data of the Veterans Health Administration,12,13 Medicare Provider Analysis and Review file,16 privately owned outpatient substance abuse centers,14 and publicly funded detoxification centers.18 These results may be difficult to generalize to other patient populations. Accordingly, the objective of this study was to identify demographic and clinical factors associated with multiple admissions to a general medicine service for treatment of AWS over a 3‐year period. Characterization of these high‐risk patients and their hospital course may help focus intervention and reduce these revolving door admissions.
METHODS
The Mayo Clinic Institutional Review Board deemed the study exempt.
Patient Selection
The study was conducted at an 1157‐bed academic tertiary referral hospital, located in the Midwest, that has approximately 15,000 inpatient admissions to general medicine services annually and serves as the main referral center for the region. Patients included in this study were adults admitted to general medicine services and treated with symptom‐triggered Clinical Institute Withdrawal AssessmentAlcohol Revised (CIWA‐Ar) protocol19 between January 1, 2006 and December 31, 2008. Patients were identified using the Mayo Clinic Quality Improvement Resource Center database, as done in a previous CIWA‐Ar study.20 Patients were excluded if the primary diagnosis was a nonalcohol‐related diagnosis (Figure 1).

Patients were placed in 1 of 2 groups based on number of admissions during the study period, either a single‐admission group or a multiple‐admissions group. While most readmission studies use a 30‐day mark from discharge, we used 3 years to better capture relapse and recidivism in this patient population. The 2 groups were then compared retrospectively. To insure that a single admission was not arbitrarily created by the December 2008 cutoff, we reviewed the single‐admission group for additional admissions through June of 2009. If a patient did have a subsequent admission, then the patient was moved to the multiple‐admissions group.
Clinical Variables
Demographic and clinical data was obtained using the Mayo Data Retrieval System (DRS), the Mayo Clinic Life Sciences System (MCLSS) database, and electronic medical records. Clinical data for the multiple‐admissions group was derived from the first admission of the study period, and subsequently referred to as index admission. Specific demographic information collected included age, race, gender, marital status, employment status, and education. Clinical data collected included admitting diagnosis, comorbid medical disorders, psychiatric disorders, and CIWA‐Ar evaluations including highest total score (CIWA‐Ar score [max] and component scores). The CIWA‐Ar protocol is a scale to assess the severity of alcohol withdrawal, based on 10 symptoms of alcohol withdrawal ranging from 0 (not present) to 7 (extremely severe). The protocol requires the scale to be administered hourly, and total scores guide the medication dosing and administration of benzodiazepines to control withdrawal symptoms. Laboratory data collected included serum ammonia, alanine aminotransferase(ALT), and admission urine drug screen. For the purposes of this study, a urine drug screen was considered positive if a substance other than alcohol was present. Length of stay (LOS) and adverse events during hospitalization (delirium tremens, intubations, rapid response team [RRT] calls, ICU transfers, and in‐hospital mortality) were also collected.
Medical comorbidity was measured using the Charlson Comorbidity Index (CCI).21 The CCI was scored electronically using diagnoses in the institution's medical index database dating back 5 years from patient's first, or index, admission. Originally validated as a prognostic tool for mortality 1 year after admission in medical patients, the CCI was chosen as it accounts for most medical comorbidities.21 Data was validated, by another investigator not involved in the initial abstracting process, by randomly verifying 5% of the abstracted data.
Statistical Analysis
Standard descriptive statistics were used for patient characteristics and demographics. Comparing the multiple‐admissions group and single‐admission group, categorical variables were evaluated using the Fisher exact test or Pearson chi‐square test. Continuous variables were evaluated using 2‐sample t test. Multivariate logistic model analyses with stepwise elimination method were used to identify risk factors that were associated with multiple admissions. Age, gender, and variables that were statistically significant in the univariate analysis were used in stepwise regression to get to the final model. A P value of 0.05 was considered statistically significant. All statistical analyses were performed using SAS version 9.3 software (SAS Institute, Cary, NC).
RESULTS
The CIWA‐Ar protocol was ordered on 1199 admissions during the study period. Of these, 411 (34.3%) admissions were excluded because AWS was not the primary diagnosis, leaving 788 (65.7%) admissions for 322 patients, which formed the study population. Of the 322 patients, 180 (56%) had a single admission and 142 (44%) had multiple admissions.
Univariate analyses of demographic and clinical variables are shown in Tables 1 and 2, respectively. Patients with multiple admissions were more likely divorced (P = 0.028), have a high school education or less (P = 0.002), have a higher CCI score (P < 0.0001), a higher CIWA‐Ar score (max) (P < 0.0001), a higher ALT level (P = 0.050), more psychiatric comorbidity (P < 0.026), and a positive urine drug screen (P < 0.001). Adverse events were not significantly different between the 2 groups (Table 2).
Variable | Single Admission N = 180 | Multiple Admissions N = 142 | P Value |
---|---|---|---|
| |||
Age, years (SD) | 47.85 (12.84) | 45.94 (12) | 0.170 |
Male, No. (%) | 122 (68) | 109 (77) | 0.080 |
Race/Ethnicity, No. (%) | 0.270 | ||
White | 168 (93) | 132 (93) | |
African American | 6 (3) | 3 (2) | |
Asian | 0 (0) | 1 (1) | |
Middle Eastern | 3 (2) | 0 (0) | |
Other | 3 (2) | 6 (4) | |
Relationship status, No. (%) | 0.160 | ||
Divorced | 49 (27) | 55 (39) | 0.028* |
Married | 54 (30) | 34 (24) | 0.230 |
Separated | 9 (5) | 4 (3) | 0.323 |
Single | 59 (33) | 38 (27) | 0.243 |
Widowed | 5 (3) | 3 (2) | 0.703 |
Committed | 4 (2) | 7 (5) | 0.188 |
Unknown | 0 (0) | 1 (1) | 0.259 |
Education, No. (%) | 0.002* | ||
High school graduate, GED, or less | 49 (28) | 67 (47) | |
Some college or above | 89 (49) | 60 (42) | |
Unknown | 41 (23) | 15 (11) | |
Employment, No. (%) | 0.290 | ||
Retired | 26 (14) | 12 (8) | |
Employed | 72 (40) | 51 (36) | |
Unemployed | 51 (28) | 51 (36) | |
Homemaker | 9 (5) | 4 (3) | |
Work disabled | 20 (11) | 23 (16) | |
Student | 1 (1) | 0 (0) | |
Unknown | 1 (1) | 1 (1) |
Variable | Single Admission N = 180 | Multiple Admissions N = 142 | P Value |
---|---|---|---|
| |||
LOS, mean (SD) | 3.71 (7.10) | 2.72 (3.40) | 0.130 |
Charlson Comorbidity Index, mean (SD) | 1.7 (2.23) | 2.51 (2.90) | 0.005* |
Medical comorbidity, No. (%) | |||
Diabetes mellitus | 6 (3) | 16 (11) | 0.005* |
Cardiovascular disease | 6 (3) | 15 (11) | 0.050* |
Cerebrovascular disease | 0 (0) | 3 (2) | 0.009* |
Hypertension | 53 (30) | 36 (25) | 0.400 |
Cancer | 17 (7) | 10 (9) | 0.440 |
Psychiatric comorbidity, No. (%) | 97 (54) | 94 (66) | 0.026* |
Adjustment disorder | 0 (0) | 6 (4) | 0.005* |
Depressive disorder | 85 (47) | 76 (54) | 0.260 |
Bipolar disorder | 6 (3) | 10 (7) | 0.130 |
Psychotic disorder | 4 (2) | 6 (4) | 0.030* |
Anxiety disorder | 30 (17) | 25 (18) | 0.820 |
Drug abuse | 4 (2) | 4 (3) | 0.730 |
Eating disorder | 0 (0) | 3 (2) | 0.050* |
CIWA‐Ar scores | |||
CIWA‐Ar score (max), mean (SD) | 15 (8) | 20 (9) | <0.000* |
Component, mean (SD) | |||
Agitation | 20 (11) | 36 (25) | 0.001* |
Anxiety | 23 (13) | 38 (27) | 0.001* |
Auditory disturbance | 4 (2) | 9 (6) | 0.110 |
Headache | 11 (6) | 26 (18) | 0.001* |
Nausea/vomiting | 5 (3) | 17 (12) | 0.003* |
Orientation | 52 (29) | 72 (51) | 0.001* |
Paroxysm/sweats | 9 (5) | 17 (12) | 0.023* |
Tactile disturbance | 25 (14) | 54 (38) | 0.001* |
Tremor | 35 (19) | 47 (33) | 0.004* |
Visual disturbance | 54 (30) | 77 (54) | 0.001* |
ALT (U/L), mean (SD) | 76 (85) | 101 (71) | 0.050* |
Ammonia (mcg N/dl), mean (SD) | 25 (14) | 29 (29) | 0.530 |
Positive urine drug screen, No. (%) | 25 (14) | 49 (35) | <0.001* |
Tetrahydrocannabinol | 14 (56) | 19 (39) | |
Cocaine | 8 (32) | 8 (16) | |
Benzodiazepine | 6 (24) | 11 (22) | |
Opiate | 4 (16) | 13 (26) | |
Amphetamine | 2 (8) | 2 (4) | |
Barbiturate | 1 (4) | 0 (0) | |
Adverse event, No. (%) | |||
RRT | 1 (1) | 1 (1) | 0.866 |
ICU transfer | 32 (18) | 20 (14) | 0.550 |
Intubation | 12 (7) | 4 (3) | 0.890 |
Delirium tremens | 7 (4) | 4 (3) | 0.600 |
In‐hospital mortality | 0 (0) | 0 (0) |
Multivariate logistic model analysis was performed using the variables age, male gender, divorced marital status, high school education or less, CIWA‐Ar score (max), CCI score, psychiatric comorbidity, and positive urine drug screen. With a stepwise elimination process, the final model showed that multiple admissions were associated with high school education or less (P = 0.0071), higher CCI score (P = 0.0010), higher CIWA‐Ar score (max) (P < 0.0001), a positive urine drug screen (P = 0.0002), and psychiatric comorbidity (P = 0.0303) (Table 3).
Variable | Adjusted Odds Ratio (95% CI) | P Value |
---|---|---|
| ||
High school education or less | 2.074 (1.219, 3.529) | 0.0071* |
CIWA‐Ar score (max) | 1.074 (1.042, 1.107) | <0.0001* |
Charlson Comorbidity Index | 1.232 (1.088, 1.396) | 0.0010* |
Psychiatric comorbidity | 1.757 (1.055, 2.928) | 0.0303* |
Positive urine drug screen | 3.180 (1.740, 5.812) | 0.0002* |
DISCUSSION
We provide important information regarding identification of individuals at high risk for multiple admissions to general medicine services for treatment of AWS. This study found that patients with multiple admissions for AWS had more medical comorbidity. They had more cases of diabetes mellitus, cardiovascular disease, and cerebrovascular disease, and their CCI scores were higher. They also had higher CIWA‐Ar (max) scores, as well as higher CIWA‐Ar component scores, indicating a more severe withdrawal.
Further, psychiatric comorbidity was also associated with multiple admissions. Consistent with the high prevalence in alcoholic patients, psychiatric comorbidity was common in both patients with a single admission and multiple admissions. We also found that a positive urine drug screen was associated with multiple admissions. Interestingly, few patients in each group had a diagnosis recorded in the medical record of an additional substance abuse disorder, yet 14% of patients with a single admission and 29% of patients with multiple admissions had a positive urine drug screen for a non‐alcohol substance. Psychiatric comorbidity, including additional substance abuse, is a well‐established risk factor for readmission to detoxification centers.1215, 17,22,23 Also, people with either an alcohol or non‐alcohol drug addiction, are known to be 7 times more likely to have another addiction than the rest of the population.24 This study suggests clinicians may underrecognize additional substance abuse disorders which are common in this patient population.
In contrast to studies of patients readmitted to detoxification units and substance abuse units,14,15,18,22 we found level of education, specifically a high school education or less, to be associated with multiple admissions. In a study of alcoholics, Greenfield and colleagues found that lower education in alcoholics predicts shorter time to relapse.25 A lack of education may result in inadequate healthcare literacy. Poor health behavior choices that follow may lead to relapse and subsequent admissions for AWS. With respect to other demographic variables, patients in our study population were predominantly men, which is not surprising. Gender differences in alcoholism are well established, with alcohol abuse and dependence more prevalent in men.26 We did not find gender associated with multiple admissions.
Our findings have management and treatment implications. First, providers who care for patients with AWS should not simply focus on treating withdrawal signs and symptoms, but also screen for and address other medical issues, which may not be apparent or seem stable at first glance. While a comorbid medical condition may not be the primary reason for hospital admission, comorbid medical conditions are known to be a source of psychological distress27 and have a negative effect on abstinence. Second, all patients should be screened for additional substance abuse. Initial laboratory testing should include a urine drug screen. Third, before discharging the patient, providers should establish primary care follow‐up for continued surveillance of medical issues. There is evidence that primary care services are predictive of better substance abuse treatment outcomes for those with medical problems.28,29
Finally, inpatient psychiatric consultation, upon admission, is essential for several reasons. First, the psychiatric team can help with initial management and treatment of the alcohol withdrawal regardless of stage and severity, obtain a more comprehensive psychiatric history, and assess for the presence of psychiatric comorbidities that may contribute to, aggravate, or complicate the clinical picture. The team can also address other substance abuse issues when detected by drug screen or clinical history. The psychiatric team, along with chemical‐dependency counselors and social workers, can provide valuable input regarding chemical‐dependency resources available on discharge and help instruct the patient in healthy behaviors. Because healthcare illiteracy may be an issue in this patient population, these instructions should be tailored to the patient's educational level. Prior to discharge, the psychiatry team, social workers, or chemical‐dependency counselors can also assist with, or arrange, rehabilitation aftercare for patients. Recent work shows that patients were less likely to be readmitted to crisis detoxification if they entered rehabilitation care within 3 days of discharge.18
Our study has significant limitations. This study was performed with data at a single academic medical center with an ethnically homogeneous patient population, limiting the external validity of its results. Because this is a retrospective study, data analyses are limited by the quality and accuracy of data in the electronic medical record. Also, our follow‐up period may not have been long enough to detect additional admissions, and we did not screen for patient admissions prior to the study period. By limiting data collection to admissions for AWS to general medical services, we may have missed cases of AWS when admitted for other reasons or to subspecialty services, and we may have missed severe cases requiring admission to an intensive care unit. While we believe we were able to capture most admissions, we may underreport this number since we cannot account for those events that may have occurred at other facilities and locations. Lastly, without a control group, this study is limited in its ability to show an association between any variable and readmission.
In our study, 142 patients accounted for 608 admissions during the 3‐year study period, which speaks to the high recidivism rates for patients with AWS. This disease is associated with high morbidity, high medical costs, and high utilization of healthcare. Our study provides insight regarding identification of patients at high risk of multiple admissions with respect to demographic (lower level of education) and clinical characteristics (worse withdrawal severity, more medical and psychiatric comorbidity, and polysubstance abuse). We believe collaboration between social services, chemical‐dependency counselors, psychiatry, and medicine is necessary to effectively treat this population of patients and assist with the crucial transition to the outpatient setting. Future studies should include key social factors, such as health literacy in the readmission risk assessment, as well as primary care follow‐up and rehabilitation aftercare.
- Chronic alcohol abuse, acute respiratory distress syndrome, and multiple organ dysfunction. Crit Care Med. 2003;31(4 suppl):S207–S212. , .
- Alcohol dependence is independently associated with sepsis, septic shock, and hospital mortality among adult intensive care unit patients. Crit Care Med. 2007;35(2):345–350. , , et al.
- Use of an objective clinical scale in the assessment and management of alcohol withdrawal in a large general hospital. Alcohol Clin Exp Res. 1988;12(3):360–364. , , .
- Alcohol withdrawal syndromes in the intensive care unit. Crit Care Med. 2010;38(9 suppl):S494–S501. , .
- Alcoholism at the Boston City Hospital—V. The causes of death among alcoholic patients at the Haymarket Square Relief Station, 1923–year="1938"1938. N Engl J Med. year="1939"1939;221(July 13):58–59. ,
- Epidemiology of alcohol withdrawal syndrome: mortality and factors of poor prognosis [in Spanish]. An Med Interna (Madrid). 2006;23(7):307–309. , , .
- The impact of alcohol‐related diagnoses on pneumonia outcomes. Arch Intern Med. 1997;157(13):1446–1452. , , .
- Analysis of the factors determining survival of alcoholic withdrawal syndrome patients in a general hospital. Alcohol Alcohol. 2010;45(2):151–158. , , , , , .
- Predictors of mortality in patients with delirium tremens. Acad Emerg Med. 2008;15(8):788–790. , , , , .
- Long‐term mortality of patients admitted to the hospital with alcohol withdrawal syndrome. Alcohol Clin Exp Res. 2011;35(6):1180–1186. , , , .
- Substance use treatment barriers for patients with frequent hospital admissions. J Subst Abuse Treat. 2010;38(1):22–30. , , , , , .
- Diagnostic subgroups and predictors of one‐year re‐admission among late‐middle‐aged and older substance abuse patients. J Stud Alcohol. 1994;55(2):173–183. , , .
- Rates and predictors of four‐year readmission among late‐middle‐aged and older substance abuse patients. J Stud Alcohol. 1994;55(5):561–570. , , .
- Readmission among chemical dependency patients in private, outpatient treatment: patterns, correlates and role in long‐term outcome. J Stud Alcohol. 2005;66(6):842–847. , , .
- Predicting readmission to substance abuse treatment using state information systems. The impact of client and treatment characteristics. J Subst Abuse. 2000;12(3):255–270. , , , .
- Elderly Medicare inpatients with substance use disorders: characteristics and predictors of hospital readmissions over a four‐year interval. J Stud Alcohol. 2000;61(6):891–895. , , , .
- The role of psychiatric comorbidity in the prediction of readmission for detoxification. Compr Psychiatry. 1998;39(3):129–136. , .
- Factors associated with frequent utilization of crisis substance use detoxification services. J Addict Dis. 2011;30(2):116–122. , , , , , .
- Assessment of alcohol withdrawal: the revised Clinical Institute Withdrawal Assessment for Alcohol scale (CIWA‐Ar). Br J Addict. 1989;84(11):1353–1357. , , , , .
- Inappropriate use of symptom‐triggered therapy for alcohol withdrawal in the general hospital. Mayo Clin Proc. 2008;83(3):274–279. , , ,
- A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–383. , , , .
- A four‐year follow‐up study of male alcoholics: factors affecting the risk of readmission. Alcohol. 2002;27(2):83–88. , , .
- Diagnosis and hospital readmission rates of female veterans with substance‐related disorders. Psychiatr Serv. 1995;46(9):932–937. , , , et al.
- Comorbidity of mental disorders with alcohol and other drug abuse. Results from the Epidemiologic Catchment Area (ECA) Study. JAMA. 1990;264(19):2511–2518. , , , et al.
- The relationship between educational attainment and relapse among alcohol‐dependent men and women: a prospective study. Alcohol Clin Exp Res. 2003;27(8):1278–1285. , , , , , .
- Prevalence, correlates, disability, and comorbidity of DSM‐IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2007;64(7):830–842. , , , .
- Health‐related quality of life among adults with serious psychological distress and chronic medical conditions. Qual Life Res. 2008;17(4):521–528. , .
- Primary medical care and reductions in addiction severity: a prospective cohort study. Addiction. 2005;100(1):70–78. , , , , .
- The role of medical conditions and primary care services in 5‐year substance use outcomes among chemical dependency treatment patients. Drug Alcohol Depend. 2008;98(1–2):45–53. , , , .
Many patients are admitted and readmitted to acute care hospitals with alcohol‐related diagnoses, including alcohol withdrawal syndrome (AWS), and experience significant morbidity and mortality. In patients with septic shock or at risk for acute respiratory distress syndrome (ARDS), chronic alcohol abuse is associated with increased ARDS and severity of multiple organ dysfunction.1 Among intensive care unit (ICU) patients, those with alcohol dependence have higher morbidity, including septic shock, and higher hospital mortality.2 Patients who experience AWS as a result of alcohol dependence may experience life‐threatening complications, such as seizures and delirium tremens.3,4 In‐hospital mortality from AWS is historically high,5 but with benzodiazepines used in a symptom‐driven manner to treat the complications of alcohol use, hospital mortality rates are more recently reported at 2.4%.6
As inpatient outcomes1,2,7 and hospital mortality810 are negatively affected by alcohol abuse, the post‐hospital course of these patients is also of interest. Specifically, patients are often admitted and readmitted with alcohol‐related diagnoses or AWS to acute care hospitals, but relatively little quantitative data exist on readmission factors in this population.11 Patients readmitted to detoxification units or alcohol and substance abuse units have been studied, and factors associated with readmission include psychiatric disorder,1217 female gender,14,15 and delay in rehabilitation aftercare.18
These results cannot be generalized to patients with AWS who are admitted and readmitted to acute‐care hospitals. First, patients hospitalized for alcohol withdrawal symptoms are often medically ill with more severe symptoms, and more frequent coexisting medical and psychiatric illnesses, that complicate the withdrawal syndrome. Detoxification units and substance abuse units require patients to be medically stable before admission, because they do not have the ability to provide a high level of supervision and treatment. Second, much of what we know regarding risk factors for readmission to detoxification centers and substance abuse units comes from studies of administrative data of the Veterans Health Administration,12,13 Medicare Provider Analysis and Review file,16 privately owned outpatient substance abuse centers,14 and publicly funded detoxification centers.18 These results may be difficult to generalize to other patient populations. Accordingly, the objective of this study was to identify demographic and clinical factors associated with multiple admissions to a general medicine service for treatment of AWS over a 3‐year period. Characterization of these high‐risk patients and their hospital course may help focus intervention and reduce these revolving door admissions.
METHODS
The Mayo Clinic Institutional Review Board deemed the study exempt.
Patient Selection
The study was conducted at an 1157‐bed academic tertiary referral hospital, located in the Midwest, that has approximately 15,000 inpatient admissions to general medicine services annually and serves as the main referral center for the region. Patients included in this study were adults admitted to general medicine services and treated with symptom‐triggered Clinical Institute Withdrawal AssessmentAlcohol Revised (CIWA‐Ar) protocol19 between January 1, 2006 and December 31, 2008. Patients were identified using the Mayo Clinic Quality Improvement Resource Center database, as done in a previous CIWA‐Ar study.20 Patients were excluded if the primary diagnosis was a nonalcohol‐related diagnosis (Figure 1).

Patients were placed in 1 of 2 groups based on number of admissions during the study period, either a single‐admission group or a multiple‐admissions group. While most readmission studies use a 30‐day mark from discharge, we used 3 years to better capture relapse and recidivism in this patient population. The 2 groups were then compared retrospectively. To insure that a single admission was not arbitrarily created by the December 2008 cutoff, we reviewed the single‐admission group for additional admissions through June of 2009. If a patient did have a subsequent admission, then the patient was moved to the multiple‐admissions group.
Clinical Variables
Demographic and clinical data was obtained using the Mayo Data Retrieval System (DRS), the Mayo Clinic Life Sciences System (MCLSS) database, and electronic medical records. Clinical data for the multiple‐admissions group was derived from the first admission of the study period, and subsequently referred to as index admission. Specific demographic information collected included age, race, gender, marital status, employment status, and education. Clinical data collected included admitting diagnosis, comorbid medical disorders, psychiatric disorders, and CIWA‐Ar evaluations including highest total score (CIWA‐Ar score [max] and component scores). The CIWA‐Ar protocol is a scale to assess the severity of alcohol withdrawal, based on 10 symptoms of alcohol withdrawal ranging from 0 (not present) to 7 (extremely severe). The protocol requires the scale to be administered hourly, and total scores guide the medication dosing and administration of benzodiazepines to control withdrawal symptoms. Laboratory data collected included serum ammonia, alanine aminotransferase(ALT), and admission urine drug screen. For the purposes of this study, a urine drug screen was considered positive if a substance other than alcohol was present. Length of stay (LOS) and adverse events during hospitalization (delirium tremens, intubations, rapid response team [RRT] calls, ICU transfers, and in‐hospital mortality) were also collected.
Medical comorbidity was measured using the Charlson Comorbidity Index (CCI).21 The CCI was scored electronically using diagnoses in the institution's medical index database dating back 5 years from patient's first, or index, admission. Originally validated as a prognostic tool for mortality 1 year after admission in medical patients, the CCI was chosen as it accounts for most medical comorbidities.21 Data was validated, by another investigator not involved in the initial abstracting process, by randomly verifying 5% of the abstracted data.
Statistical Analysis
Standard descriptive statistics were used for patient characteristics and demographics. Comparing the multiple‐admissions group and single‐admission group, categorical variables were evaluated using the Fisher exact test or Pearson chi‐square test. Continuous variables were evaluated using 2‐sample t test. Multivariate logistic model analyses with stepwise elimination method were used to identify risk factors that were associated with multiple admissions. Age, gender, and variables that were statistically significant in the univariate analysis were used in stepwise regression to get to the final model. A P value of 0.05 was considered statistically significant. All statistical analyses were performed using SAS version 9.3 software (SAS Institute, Cary, NC).
RESULTS
The CIWA‐Ar protocol was ordered on 1199 admissions during the study period. Of these, 411 (34.3%) admissions were excluded because AWS was not the primary diagnosis, leaving 788 (65.7%) admissions for 322 patients, which formed the study population. Of the 322 patients, 180 (56%) had a single admission and 142 (44%) had multiple admissions.
Univariate analyses of demographic and clinical variables are shown in Tables 1 and 2, respectively. Patients with multiple admissions were more likely divorced (P = 0.028), have a high school education or less (P = 0.002), have a higher CCI score (P < 0.0001), a higher CIWA‐Ar score (max) (P < 0.0001), a higher ALT level (P = 0.050), more psychiatric comorbidity (P < 0.026), and a positive urine drug screen (P < 0.001). Adverse events were not significantly different between the 2 groups (Table 2).
Variable | Single Admission N = 180 | Multiple Admissions N = 142 | P Value |
---|---|---|---|
| |||
Age, years (SD) | 47.85 (12.84) | 45.94 (12) | 0.170 |
Male, No. (%) | 122 (68) | 109 (77) | 0.080 |
Race/Ethnicity, No. (%) | 0.270 | ||
White | 168 (93) | 132 (93) | |
African American | 6 (3) | 3 (2) | |
Asian | 0 (0) | 1 (1) | |
Middle Eastern | 3 (2) | 0 (0) | |
Other | 3 (2) | 6 (4) | |
Relationship status, No. (%) | 0.160 | ||
Divorced | 49 (27) | 55 (39) | 0.028* |
Married | 54 (30) | 34 (24) | 0.230 |
Separated | 9 (5) | 4 (3) | 0.323 |
Single | 59 (33) | 38 (27) | 0.243 |
Widowed | 5 (3) | 3 (2) | 0.703 |
Committed | 4 (2) | 7 (5) | 0.188 |
Unknown | 0 (0) | 1 (1) | 0.259 |
Education, No. (%) | 0.002* | ||
High school graduate, GED, or less | 49 (28) | 67 (47) | |
Some college or above | 89 (49) | 60 (42) | |
Unknown | 41 (23) | 15 (11) | |
Employment, No. (%) | 0.290 | ||
Retired | 26 (14) | 12 (8) | |
Employed | 72 (40) | 51 (36) | |
Unemployed | 51 (28) | 51 (36) | |
Homemaker | 9 (5) | 4 (3) | |
Work disabled | 20 (11) | 23 (16) | |
Student | 1 (1) | 0 (0) | |
Unknown | 1 (1) | 1 (1) |
Variable | Single Admission N = 180 | Multiple Admissions N = 142 | P Value |
---|---|---|---|
| |||
LOS, mean (SD) | 3.71 (7.10) | 2.72 (3.40) | 0.130 |
Charlson Comorbidity Index, mean (SD) | 1.7 (2.23) | 2.51 (2.90) | 0.005* |
Medical comorbidity, No. (%) | |||
Diabetes mellitus | 6 (3) | 16 (11) | 0.005* |
Cardiovascular disease | 6 (3) | 15 (11) | 0.050* |
Cerebrovascular disease | 0 (0) | 3 (2) | 0.009* |
Hypertension | 53 (30) | 36 (25) | 0.400 |
Cancer | 17 (7) | 10 (9) | 0.440 |
Psychiatric comorbidity, No. (%) | 97 (54) | 94 (66) | 0.026* |
Adjustment disorder | 0 (0) | 6 (4) | 0.005* |
Depressive disorder | 85 (47) | 76 (54) | 0.260 |
Bipolar disorder | 6 (3) | 10 (7) | 0.130 |
Psychotic disorder | 4 (2) | 6 (4) | 0.030* |
Anxiety disorder | 30 (17) | 25 (18) | 0.820 |
Drug abuse | 4 (2) | 4 (3) | 0.730 |
Eating disorder | 0 (0) | 3 (2) | 0.050* |
CIWA‐Ar scores | |||
CIWA‐Ar score (max), mean (SD) | 15 (8) | 20 (9) | <0.000* |
Component, mean (SD) | |||
Agitation | 20 (11) | 36 (25) | 0.001* |
Anxiety | 23 (13) | 38 (27) | 0.001* |
Auditory disturbance | 4 (2) | 9 (6) | 0.110 |
Headache | 11 (6) | 26 (18) | 0.001* |
Nausea/vomiting | 5 (3) | 17 (12) | 0.003* |
Orientation | 52 (29) | 72 (51) | 0.001* |
Paroxysm/sweats | 9 (5) | 17 (12) | 0.023* |
Tactile disturbance | 25 (14) | 54 (38) | 0.001* |
Tremor | 35 (19) | 47 (33) | 0.004* |
Visual disturbance | 54 (30) | 77 (54) | 0.001* |
ALT (U/L), mean (SD) | 76 (85) | 101 (71) | 0.050* |
Ammonia (mcg N/dl), mean (SD) | 25 (14) | 29 (29) | 0.530 |
Positive urine drug screen, No. (%) | 25 (14) | 49 (35) | <0.001* |
Tetrahydrocannabinol | 14 (56) | 19 (39) | |
Cocaine | 8 (32) | 8 (16) | |
Benzodiazepine | 6 (24) | 11 (22) | |
Opiate | 4 (16) | 13 (26) | |
Amphetamine | 2 (8) | 2 (4) | |
Barbiturate | 1 (4) | 0 (0) | |
Adverse event, No. (%) | |||
RRT | 1 (1) | 1 (1) | 0.866 |
ICU transfer | 32 (18) | 20 (14) | 0.550 |
Intubation | 12 (7) | 4 (3) | 0.890 |
Delirium tremens | 7 (4) | 4 (3) | 0.600 |
In‐hospital mortality | 0 (0) | 0 (0) |
Multivariate logistic model analysis was performed using the variables age, male gender, divorced marital status, high school education or less, CIWA‐Ar score (max), CCI score, psychiatric comorbidity, and positive urine drug screen. With a stepwise elimination process, the final model showed that multiple admissions were associated with high school education or less (P = 0.0071), higher CCI score (P = 0.0010), higher CIWA‐Ar score (max) (P < 0.0001), a positive urine drug screen (P = 0.0002), and psychiatric comorbidity (P = 0.0303) (Table 3).
Variable | Adjusted Odds Ratio (95% CI) | P Value |
---|---|---|
| ||
High school education or less | 2.074 (1.219, 3.529) | 0.0071* |
CIWA‐Ar score (max) | 1.074 (1.042, 1.107) | <0.0001* |
Charlson Comorbidity Index | 1.232 (1.088, 1.396) | 0.0010* |
Psychiatric comorbidity | 1.757 (1.055, 2.928) | 0.0303* |
Positive urine drug screen | 3.180 (1.740, 5.812) | 0.0002* |
DISCUSSION
We provide important information regarding identification of individuals at high risk for multiple admissions to general medicine services for treatment of AWS. This study found that patients with multiple admissions for AWS had more medical comorbidity. They had more cases of diabetes mellitus, cardiovascular disease, and cerebrovascular disease, and their CCI scores were higher. They also had higher CIWA‐Ar (max) scores, as well as higher CIWA‐Ar component scores, indicating a more severe withdrawal.
Further, psychiatric comorbidity was also associated with multiple admissions. Consistent with the high prevalence in alcoholic patients, psychiatric comorbidity was common in both patients with a single admission and multiple admissions. We also found that a positive urine drug screen was associated with multiple admissions. Interestingly, few patients in each group had a diagnosis recorded in the medical record of an additional substance abuse disorder, yet 14% of patients with a single admission and 29% of patients with multiple admissions had a positive urine drug screen for a non‐alcohol substance. Psychiatric comorbidity, including additional substance abuse, is a well‐established risk factor for readmission to detoxification centers.1215, 17,22,23 Also, people with either an alcohol or non‐alcohol drug addiction, are known to be 7 times more likely to have another addiction than the rest of the population.24 This study suggests clinicians may underrecognize additional substance abuse disorders which are common in this patient population.
In contrast to studies of patients readmitted to detoxification units and substance abuse units,14,15,18,22 we found level of education, specifically a high school education or less, to be associated with multiple admissions. In a study of alcoholics, Greenfield and colleagues found that lower education in alcoholics predicts shorter time to relapse.25 A lack of education may result in inadequate healthcare literacy. Poor health behavior choices that follow may lead to relapse and subsequent admissions for AWS. With respect to other demographic variables, patients in our study population were predominantly men, which is not surprising. Gender differences in alcoholism are well established, with alcohol abuse and dependence more prevalent in men.26 We did not find gender associated with multiple admissions.
Our findings have management and treatment implications. First, providers who care for patients with AWS should not simply focus on treating withdrawal signs and symptoms, but also screen for and address other medical issues, which may not be apparent or seem stable at first glance. While a comorbid medical condition may not be the primary reason for hospital admission, comorbid medical conditions are known to be a source of psychological distress27 and have a negative effect on abstinence. Second, all patients should be screened for additional substance abuse. Initial laboratory testing should include a urine drug screen. Third, before discharging the patient, providers should establish primary care follow‐up for continued surveillance of medical issues. There is evidence that primary care services are predictive of better substance abuse treatment outcomes for those with medical problems.28,29
Finally, inpatient psychiatric consultation, upon admission, is essential for several reasons. First, the psychiatric team can help with initial management and treatment of the alcohol withdrawal regardless of stage and severity, obtain a more comprehensive psychiatric history, and assess for the presence of psychiatric comorbidities that may contribute to, aggravate, or complicate the clinical picture. The team can also address other substance abuse issues when detected by drug screen or clinical history. The psychiatric team, along with chemical‐dependency counselors and social workers, can provide valuable input regarding chemical‐dependency resources available on discharge and help instruct the patient in healthy behaviors. Because healthcare illiteracy may be an issue in this patient population, these instructions should be tailored to the patient's educational level. Prior to discharge, the psychiatry team, social workers, or chemical‐dependency counselors can also assist with, or arrange, rehabilitation aftercare for patients. Recent work shows that patients were less likely to be readmitted to crisis detoxification if they entered rehabilitation care within 3 days of discharge.18
Our study has significant limitations. This study was performed with data at a single academic medical center with an ethnically homogeneous patient population, limiting the external validity of its results. Because this is a retrospective study, data analyses are limited by the quality and accuracy of data in the electronic medical record. Also, our follow‐up period may not have been long enough to detect additional admissions, and we did not screen for patient admissions prior to the study period. By limiting data collection to admissions for AWS to general medical services, we may have missed cases of AWS when admitted for other reasons or to subspecialty services, and we may have missed severe cases requiring admission to an intensive care unit. While we believe we were able to capture most admissions, we may underreport this number since we cannot account for those events that may have occurred at other facilities and locations. Lastly, without a control group, this study is limited in its ability to show an association between any variable and readmission.
In our study, 142 patients accounted for 608 admissions during the 3‐year study period, which speaks to the high recidivism rates for patients with AWS. This disease is associated with high morbidity, high medical costs, and high utilization of healthcare. Our study provides insight regarding identification of patients at high risk of multiple admissions with respect to demographic (lower level of education) and clinical characteristics (worse withdrawal severity, more medical and psychiatric comorbidity, and polysubstance abuse). We believe collaboration between social services, chemical‐dependency counselors, psychiatry, and medicine is necessary to effectively treat this population of patients and assist with the crucial transition to the outpatient setting. Future studies should include key social factors, such as health literacy in the readmission risk assessment, as well as primary care follow‐up and rehabilitation aftercare.
Many patients are admitted and readmitted to acute care hospitals with alcohol‐related diagnoses, including alcohol withdrawal syndrome (AWS), and experience significant morbidity and mortality. In patients with septic shock or at risk for acute respiratory distress syndrome (ARDS), chronic alcohol abuse is associated with increased ARDS and severity of multiple organ dysfunction.1 Among intensive care unit (ICU) patients, those with alcohol dependence have higher morbidity, including septic shock, and higher hospital mortality.2 Patients who experience AWS as a result of alcohol dependence may experience life‐threatening complications, such as seizures and delirium tremens.3,4 In‐hospital mortality from AWS is historically high,5 but with benzodiazepines used in a symptom‐driven manner to treat the complications of alcohol use, hospital mortality rates are more recently reported at 2.4%.6
As inpatient outcomes1,2,7 and hospital mortality810 are negatively affected by alcohol abuse, the post‐hospital course of these patients is also of interest. Specifically, patients are often admitted and readmitted with alcohol‐related diagnoses or AWS to acute care hospitals, but relatively little quantitative data exist on readmission factors in this population.11 Patients readmitted to detoxification units or alcohol and substance abuse units have been studied, and factors associated with readmission include psychiatric disorder,1217 female gender,14,15 and delay in rehabilitation aftercare.18
These results cannot be generalized to patients with AWS who are admitted and readmitted to acute‐care hospitals. First, patients hospitalized for alcohol withdrawal symptoms are often medically ill with more severe symptoms, and more frequent coexisting medical and psychiatric illnesses, that complicate the withdrawal syndrome. Detoxification units and substance abuse units require patients to be medically stable before admission, because they do not have the ability to provide a high level of supervision and treatment. Second, much of what we know regarding risk factors for readmission to detoxification centers and substance abuse units comes from studies of administrative data of the Veterans Health Administration,12,13 Medicare Provider Analysis and Review file,16 privately owned outpatient substance abuse centers,14 and publicly funded detoxification centers.18 These results may be difficult to generalize to other patient populations. Accordingly, the objective of this study was to identify demographic and clinical factors associated with multiple admissions to a general medicine service for treatment of AWS over a 3‐year period. Characterization of these high‐risk patients and their hospital course may help focus intervention and reduce these revolving door admissions.
METHODS
The Mayo Clinic Institutional Review Board deemed the study exempt.
Patient Selection
The study was conducted at an 1157‐bed academic tertiary referral hospital, located in the Midwest, that has approximately 15,000 inpatient admissions to general medicine services annually and serves as the main referral center for the region. Patients included in this study were adults admitted to general medicine services and treated with symptom‐triggered Clinical Institute Withdrawal AssessmentAlcohol Revised (CIWA‐Ar) protocol19 between January 1, 2006 and December 31, 2008. Patients were identified using the Mayo Clinic Quality Improvement Resource Center database, as done in a previous CIWA‐Ar study.20 Patients were excluded if the primary diagnosis was a nonalcohol‐related diagnosis (Figure 1).

Patients were placed in 1 of 2 groups based on number of admissions during the study period, either a single‐admission group or a multiple‐admissions group. While most readmission studies use a 30‐day mark from discharge, we used 3 years to better capture relapse and recidivism in this patient population. The 2 groups were then compared retrospectively. To insure that a single admission was not arbitrarily created by the December 2008 cutoff, we reviewed the single‐admission group for additional admissions through June of 2009. If a patient did have a subsequent admission, then the patient was moved to the multiple‐admissions group.
Clinical Variables
Demographic and clinical data was obtained using the Mayo Data Retrieval System (DRS), the Mayo Clinic Life Sciences System (MCLSS) database, and electronic medical records. Clinical data for the multiple‐admissions group was derived from the first admission of the study period, and subsequently referred to as index admission. Specific demographic information collected included age, race, gender, marital status, employment status, and education. Clinical data collected included admitting diagnosis, comorbid medical disorders, psychiatric disorders, and CIWA‐Ar evaluations including highest total score (CIWA‐Ar score [max] and component scores). The CIWA‐Ar protocol is a scale to assess the severity of alcohol withdrawal, based on 10 symptoms of alcohol withdrawal ranging from 0 (not present) to 7 (extremely severe). The protocol requires the scale to be administered hourly, and total scores guide the medication dosing and administration of benzodiazepines to control withdrawal symptoms. Laboratory data collected included serum ammonia, alanine aminotransferase(ALT), and admission urine drug screen. For the purposes of this study, a urine drug screen was considered positive if a substance other than alcohol was present. Length of stay (LOS) and adverse events during hospitalization (delirium tremens, intubations, rapid response team [RRT] calls, ICU transfers, and in‐hospital mortality) were also collected.
Medical comorbidity was measured using the Charlson Comorbidity Index (CCI).21 The CCI was scored electronically using diagnoses in the institution's medical index database dating back 5 years from patient's first, or index, admission. Originally validated as a prognostic tool for mortality 1 year after admission in medical patients, the CCI was chosen as it accounts for most medical comorbidities.21 Data was validated, by another investigator not involved in the initial abstracting process, by randomly verifying 5% of the abstracted data.
Statistical Analysis
Standard descriptive statistics were used for patient characteristics and demographics. Comparing the multiple‐admissions group and single‐admission group, categorical variables were evaluated using the Fisher exact test or Pearson chi‐square test. Continuous variables were evaluated using 2‐sample t test. Multivariate logistic model analyses with stepwise elimination method were used to identify risk factors that were associated with multiple admissions. Age, gender, and variables that were statistically significant in the univariate analysis were used in stepwise regression to get to the final model. A P value of 0.05 was considered statistically significant. All statistical analyses were performed using SAS version 9.3 software (SAS Institute, Cary, NC).
RESULTS
The CIWA‐Ar protocol was ordered on 1199 admissions during the study period. Of these, 411 (34.3%) admissions were excluded because AWS was not the primary diagnosis, leaving 788 (65.7%) admissions for 322 patients, which formed the study population. Of the 322 patients, 180 (56%) had a single admission and 142 (44%) had multiple admissions.
Univariate analyses of demographic and clinical variables are shown in Tables 1 and 2, respectively. Patients with multiple admissions were more likely divorced (P = 0.028), have a high school education or less (P = 0.002), have a higher CCI score (P < 0.0001), a higher CIWA‐Ar score (max) (P < 0.0001), a higher ALT level (P = 0.050), more psychiatric comorbidity (P < 0.026), and a positive urine drug screen (P < 0.001). Adverse events were not significantly different between the 2 groups (Table 2).
Variable | Single Admission N = 180 | Multiple Admissions N = 142 | P Value |
---|---|---|---|
| |||
Age, years (SD) | 47.85 (12.84) | 45.94 (12) | 0.170 |
Male, No. (%) | 122 (68) | 109 (77) | 0.080 |
Race/Ethnicity, No. (%) | 0.270 | ||
White | 168 (93) | 132 (93) | |
African American | 6 (3) | 3 (2) | |
Asian | 0 (0) | 1 (1) | |
Middle Eastern | 3 (2) | 0 (0) | |
Other | 3 (2) | 6 (4) | |
Relationship status, No. (%) | 0.160 | ||
Divorced | 49 (27) | 55 (39) | 0.028* |
Married | 54 (30) | 34 (24) | 0.230 |
Separated | 9 (5) | 4 (3) | 0.323 |
Single | 59 (33) | 38 (27) | 0.243 |
Widowed | 5 (3) | 3 (2) | 0.703 |
Committed | 4 (2) | 7 (5) | 0.188 |
Unknown | 0 (0) | 1 (1) | 0.259 |
Education, No. (%) | 0.002* | ||
High school graduate, GED, or less | 49 (28) | 67 (47) | |
Some college or above | 89 (49) | 60 (42) | |
Unknown | 41 (23) | 15 (11) | |
Employment, No. (%) | 0.290 | ||
Retired | 26 (14) | 12 (8) | |
Employed | 72 (40) | 51 (36) | |
Unemployed | 51 (28) | 51 (36) | |
Homemaker | 9 (5) | 4 (3) | |
Work disabled | 20 (11) | 23 (16) | |
Student | 1 (1) | 0 (0) | |
Unknown | 1 (1) | 1 (1) |
Variable | Single Admission N = 180 | Multiple Admissions N = 142 | P Value |
---|---|---|---|
| |||
LOS, mean (SD) | 3.71 (7.10) | 2.72 (3.40) | 0.130 |
Charlson Comorbidity Index, mean (SD) | 1.7 (2.23) | 2.51 (2.90) | 0.005* |
Medical comorbidity, No. (%) | |||
Diabetes mellitus | 6 (3) | 16 (11) | 0.005* |
Cardiovascular disease | 6 (3) | 15 (11) | 0.050* |
Cerebrovascular disease | 0 (0) | 3 (2) | 0.009* |
Hypertension | 53 (30) | 36 (25) | 0.400 |
Cancer | 17 (7) | 10 (9) | 0.440 |
Psychiatric comorbidity, No. (%) | 97 (54) | 94 (66) | 0.026* |
Adjustment disorder | 0 (0) | 6 (4) | 0.005* |
Depressive disorder | 85 (47) | 76 (54) | 0.260 |
Bipolar disorder | 6 (3) | 10 (7) | 0.130 |
Psychotic disorder | 4 (2) | 6 (4) | 0.030* |
Anxiety disorder | 30 (17) | 25 (18) | 0.820 |
Drug abuse | 4 (2) | 4 (3) | 0.730 |
Eating disorder | 0 (0) | 3 (2) | 0.050* |
CIWA‐Ar scores | |||
CIWA‐Ar score (max), mean (SD) | 15 (8) | 20 (9) | <0.000* |
Component, mean (SD) | |||
Agitation | 20 (11) | 36 (25) | 0.001* |
Anxiety | 23 (13) | 38 (27) | 0.001* |
Auditory disturbance | 4 (2) | 9 (6) | 0.110 |
Headache | 11 (6) | 26 (18) | 0.001* |
Nausea/vomiting | 5 (3) | 17 (12) | 0.003* |
Orientation | 52 (29) | 72 (51) | 0.001* |
Paroxysm/sweats | 9 (5) | 17 (12) | 0.023* |
Tactile disturbance | 25 (14) | 54 (38) | 0.001* |
Tremor | 35 (19) | 47 (33) | 0.004* |
Visual disturbance | 54 (30) | 77 (54) | 0.001* |
ALT (U/L), mean (SD) | 76 (85) | 101 (71) | 0.050* |
Ammonia (mcg N/dl), mean (SD) | 25 (14) | 29 (29) | 0.530 |
Positive urine drug screen, No. (%) | 25 (14) | 49 (35) | <0.001* |
Tetrahydrocannabinol | 14 (56) | 19 (39) | |
Cocaine | 8 (32) | 8 (16) | |
Benzodiazepine | 6 (24) | 11 (22) | |
Opiate | 4 (16) | 13 (26) | |
Amphetamine | 2 (8) | 2 (4) | |
Barbiturate | 1 (4) | 0 (0) | |
Adverse event, No. (%) | |||
RRT | 1 (1) | 1 (1) | 0.866 |
ICU transfer | 32 (18) | 20 (14) | 0.550 |
Intubation | 12 (7) | 4 (3) | 0.890 |
Delirium tremens | 7 (4) | 4 (3) | 0.600 |
In‐hospital mortality | 0 (0) | 0 (0) |
Multivariate logistic model analysis was performed using the variables age, male gender, divorced marital status, high school education or less, CIWA‐Ar score (max), CCI score, psychiatric comorbidity, and positive urine drug screen. With a stepwise elimination process, the final model showed that multiple admissions were associated with high school education or less (P = 0.0071), higher CCI score (P = 0.0010), higher CIWA‐Ar score (max) (P < 0.0001), a positive urine drug screen (P = 0.0002), and psychiatric comorbidity (P = 0.0303) (Table 3).
Variable | Adjusted Odds Ratio (95% CI) | P Value |
---|---|---|
| ||
High school education or less | 2.074 (1.219, 3.529) | 0.0071* |
CIWA‐Ar score (max) | 1.074 (1.042, 1.107) | <0.0001* |
Charlson Comorbidity Index | 1.232 (1.088, 1.396) | 0.0010* |
Psychiatric comorbidity | 1.757 (1.055, 2.928) | 0.0303* |
Positive urine drug screen | 3.180 (1.740, 5.812) | 0.0002* |
DISCUSSION
We provide important information regarding identification of individuals at high risk for multiple admissions to general medicine services for treatment of AWS. This study found that patients with multiple admissions for AWS had more medical comorbidity. They had more cases of diabetes mellitus, cardiovascular disease, and cerebrovascular disease, and their CCI scores were higher. They also had higher CIWA‐Ar (max) scores, as well as higher CIWA‐Ar component scores, indicating a more severe withdrawal.
Further, psychiatric comorbidity was also associated with multiple admissions. Consistent with the high prevalence in alcoholic patients, psychiatric comorbidity was common in both patients with a single admission and multiple admissions. We also found that a positive urine drug screen was associated with multiple admissions. Interestingly, few patients in each group had a diagnosis recorded in the medical record of an additional substance abuse disorder, yet 14% of patients with a single admission and 29% of patients with multiple admissions had a positive urine drug screen for a non‐alcohol substance. Psychiatric comorbidity, including additional substance abuse, is a well‐established risk factor for readmission to detoxification centers.1215, 17,22,23 Also, people with either an alcohol or non‐alcohol drug addiction, are known to be 7 times more likely to have another addiction than the rest of the population.24 This study suggests clinicians may underrecognize additional substance abuse disorders which are common in this patient population.
In contrast to studies of patients readmitted to detoxification units and substance abuse units,14,15,18,22 we found level of education, specifically a high school education or less, to be associated with multiple admissions. In a study of alcoholics, Greenfield and colleagues found that lower education in alcoholics predicts shorter time to relapse.25 A lack of education may result in inadequate healthcare literacy. Poor health behavior choices that follow may lead to relapse and subsequent admissions for AWS. With respect to other demographic variables, patients in our study population were predominantly men, which is not surprising. Gender differences in alcoholism are well established, with alcohol abuse and dependence more prevalent in men.26 We did not find gender associated with multiple admissions.
Our findings have management and treatment implications. First, providers who care for patients with AWS should not simply focus on treating withdrawal signs and symptoms, but also screen for and address other medical issues, which may not be apparent or seem stable at first glance. While a comorbid medical condition may not be the primary reason for hospital admission, comorbid medical conditions are known to be a source of psychological distress27 and have a negative effect on abstinence. Second, all patients should be screened for additional substance abuse. Initial laboratory testing should include a urine drug screen. Third, before discharging the patient, providers should establish primary care follow‐up for continued surveillance of medical issues. There is evidence that primary care services are predictive of better substance abuse treatment outcomes for those with medical problems.28,29
Finally, inpatient psychiatric consultation, upon admission, is essential for several reasons. First, the psychiatric team can help with initial management and treatment of the alcohol withdrawal regardless of stage and severity, obtain a more comprehensive psychiatric history, and assess for the presence of psychiatric comorbidities that may contribute to, aggravate, or complicate the clinical picture. The team can also address other substance abuse issues when detected by drug screen or clinical history. The psychiatric team, along with chemical‐dependency counselors and social workers, can provide valuable input regarding chemical‐dependency resources available on discharge and help instruct the patient in healthy behaviors. Because healthcare illiteracy may be an issue in this patient population, these instructions should be tailored to the patient's educational level. Prior to discharge, the psychiatry team, social workers, or chemical‐dependency counselors can also assist with, or arrange, rehabilitation aftercare for patients. Recent work shows that patients were less likely to be readmitted to crisis detoxification if they entered rehabilitation care within 3 days of discharge.18
Our study has significant limitations. This study was performed with data at a single academic medical center with an ethnically homogeneous patient population, limiting the external validity of its results. Because this is a retrospective study, data analyses are limited by the quality and accuracy of data in the electronic medical record. Also, our follow‐up period may not have been long enough to detect additional admissions, and we did not screen for patient admissions prior to the study period. By limiting data collection to admissions for AWS to general medical services, we may have missed cases of AWS when admitted for other reasons or to subspecialty services, and we may have missed severe cases requiring admission to an intensive care unit. While we believe we were able to capture most admissions, we may underreport this number since we cannot account for those events that may have occurred at other facilities and locations. Lastly, without a control group, this study is limited in its ability to show an association between any variable and readmission.
In our study, 142 patients accounted for 608 admissions during the 3‐year study period, which speaks to the high recidivism rates for patients with AWS. This disease is associated with high morbidity, high medical costs, and high utilization of healthcare. Our study provides insight regarding identification of patients at high risk of multiple admissions with respect to demographic (lower level of education) and clinical characteristics (worse withdrawal severity, more medical and psychiatric comorbidity, and polysubstance abuse). We believe collaboration between social services, chemical‐dependency counselors, psychiatry, and medicine is necessary to effectively treat this population of patients and assist with the crucial transition to the outpatient setting. Future studies should include key social factors, such as health literacy in the readmission risk assessment, as well as primary care follow‐up and rehabilitation aftercare.
- Chronic alcohol abuse, acute respiratory distress syndrome, and multiple organ dysfunction. Crit Care Med. 2003;31(4 suppl):S207–S212. , .
- Alcohol dependence is independently associated with sepsis, septic shock, and hospital mortality among adult intensive care unit patients. Crit Care Med. 2007;35(2):345–350. , , et al.
- Use of an objective clinical scale in the assessment and management of alcohol withdrawal in a large general hospital. Alcohol Clin Exp Res. 1988;12(3):360–364. , , .
- Alcohol withdrawal syndromes in the intensive care unit. Crit Care Med. 2010;38(9 suppl):S494–S501. , .
- Alcoholism at the Boston City Hospital—V. The causes of death among alcoholic patients at the Haymarket Square Relief Station, 1923–year="1938"1938. N Engl J Med. year="1939"1939;221(July 13):58–59. ,
- Epidemiology of alcohol withdrawal syndrome: mortality and factors of poor prognosis [in Spanish]. An Med Interna (Madrid). 2006;23(7):307–309. , , .
- The impact of alcohol‐related diagnoses on pneumonia outcomes. Arch Intern Med. 1997;157(13):1446–1452. , , .
- Analysis of the factors determining survival of alcoholic withdrawal syndrome patients in a general hospital. Alcohol Alcohol. 2010;45(2):151–158. , , , , , .
- Predictors of mortality in patients with delirium tremens. Acad Emerg Med. 2008;15(8):788–790. , , , , .
- Long‐term mortality of patients admitted to the hospital with alcohol withdrawal syndrome. Alcohol Clin Exp Res. 2011;35(6):1180–1186. , , , .
- Substance use treatment barriers for patients with frequent hospital admissions. J Subst Abuse Treat. 2010;38(1):22–30. , , , , , .
- Diagnostic subgroups and predictors of one‐year re‐admission among late‐middle‐aged and older substance abuse patients. J Stud Alcohol. 1994;55(2):173–183. , , .
- Rates and predictors of four‐year readmission among late‐middle‐aged and older substance abuse patients. J Stud Alcohol. 1994;55(5):561–570. , , .
- Readmission among chemical dependency patients in private, outpatient treatment: patterns, correlates and role in long‐term outcome. J Stud Alcohol. 2005;66(6):842–847. , , .
- Predicting readmission to substance abuse treatment using state information systems. The impact of client and treatment characteristics. J Subst Abuse. 2000;12(3):255–270. , , , .
- Elderly Medicare inpatients with substance use disorders: characteristics and predictors of hospital readmissions over a four‐year interval. J Stud Alcohol. 2000;61(6):891–895. , , , .
- The role of psychiatric comorbidity in the prediction of readmission for detoxification. Compr Psychiatry. 1998;39(3):129–136. , .
- Factors associated with frequent utilization of crisis substance use detoxification services. J Addict Dis. 2011;30(2):116–122. , , , , , .
- Assessment of alcohol withdrawal: the revised Clinical Institute Withdrawal Assessment for Alcohol scale (CIWA‐Ar). Br J Addict. 1989;84(11):1353–1357. , , , , .
- Inappropriate use of symptom‐triggered therapy for alcohol withdrawal in the general hospital. Mayo Clin Proc. 2008;83(3):274–279. , , ,
- A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–383. , , , .
- A four‐year follow‐up study of male alcoholics: factors affecting the risk of readmission. Alcohol. 2002;27(2):83–88. , , .
- Diagnosis and hospital readmission rates of female veterans with substance‐related disorders. Psychiatr Serv. 1995;46(9):932–937. , , , et al.
- Comorbidity of mental disorders with alcohol and other drug abuse. Results from the Epidemiologic Catchment Area (ECA) Study. JAMA. 1990;264(19):2511–2518. , , , et al.
- The relationship between educational attainment and relapse among alcohol‐dependent men and women: a prospective study. Alcohol Clin Exp Res. 2003;27(8):1278–1285. , , , , , .
- Prevalence, correlates, disability, and comorbidity of DSM‐IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2007;64(7):830–842. , , , .
- Health‐related quality of life among adults with serious psychological distress and chronic medical conditions. Qual Life Res. 2008;17(4):521–528. , .
- Primary medical care and reductions in addiction severity: a prospective cohort study. Addiction. 2005;100(1):70–78. , , , , .
- The role of medical conditions and primary care services in 5‐year substance use outcomes among chemical dependency treatment patients. Drug Alcohol Depend. 2008;98(1–2):45–53. , , , .
- Chronic alcohol abuse, acute respiratory distress syndrome, and multiple organ dysfunction. Crit Care Med. 2003;31(4 suppl):S207–S212. , .
- Alcohol dependence is independently associated with sepsis, septic shock, and hospital mortality among adult intensive care unit patients. Crit Care Med. 2007;35(2):345–350. , , et al.
- Use of an objective clinical scale in the assessment and management of alcohol withdrawal in a large general hospital. Alcohol Clin Exp Res. 1988;12(3):360–364. , , .
- Alcohol withdrawal syndromes in the intensive care unit. Crit Care Med. 2010;38(9 suppl):S494–S501. , .
- Alcoholism at the Boston City Hospital—V. The causes of death among alcoholic patients at the Haymarket Square Relief Station, 1923–year="1938"1938. N Engl J Med. year="1939"1939;221(July 13):58–59. ,
- Epidemiology of alcohol withdrawal syndrome: mortality and factors of poor prognosis [in Spanish]. An Med Interna (Madrid). 2006;23(7):307–309. , , .
- The impact of alcohol‐related diagnoses on pneumonia outcomes. Arch Intern Med. 1997;157(13):1446–1452. , , .
- Analysis of the factors determining survival of alcoholic withdrawal syndrome patients in a general hospital. Alcohol Alcohol. 2010;45(2):151–158. , , , , , .
- Predictors of mortality in patients with delirium tremens. Acad Emerg Med. 2008;15(8):788–790. , , , , .
- Long‐term mortality of patients admitted to the hospital with alcohol withdrawal syndrome. Alcohol Clin Exp Res. 2011;35(6):1180–1186. , , , .
- Substance use treatment barriers for patients with frequent hospital admissions. J Subst Abuse Treat. 2010;38(1):22–30. , , , , , .
- Diagnostic subgroups and predictors of one‐year re‐admission among late‐middle‐aged and older substance abuse patients. J Stud Alcohol. 1994;55(2):173–183. , , .
- Rates and predictors of four‐year readmission among late‐middle‐aged and older substance abuse patients. J Stud Alcohol. 1994;55(5):561–570. , , .
- Readmission among chemical dependency patients in private, outpatient treatment: patterns, correlates and role in long‐term outcome. J Stud Alcohol. 2005;66(6):842–847. , , .
- Predicting readmission to substance abuse treatment using state information systems. The impact of client and treatment characteristics. J Subst Abuse. 2000;12(3):255–270. , , , .
- Elderly Medicare inpatients with substance use disorders: characteristics and predictors of hospital readmissions over a four‐year interval. J Stud Alcohol. 2000;61(6):891–895. , , , .
- The role of psychiatric comorbidity in the prediction of readmission for detoxification. Compr Psychiatry. 1998;39(3):129–136. , .
- Factors associated with frequent utilization of crisis substance use detoxification services. J Addict Dis. 2011;30(2):116–122. , , , , , .
- Assessment of alcohol withdrawal: the revised Clinical Institute Withdrawal Assessment for Alcohol scale (CIWA‐Ar). Br J Addict. 1989;84(11):1353–1357. , , , , .
- Inappropriate use of symptom‐triggered therapy for alcohol withdrawal in the general hospital. Mayo Clin Proc. 2008;83(3):274–279. , , ,
- A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–383. , , , .
- A four‐year follow‐up study of male alcoholics: factors affecting the risk of readmission. Alcohol. 2002;27(2):83–88. , , .
- Diagnosis and hospital readmission rates of female veterans with substance‐related disorders. Psychiatr Serv. 1995;46(9):932–937. , , , et al.
- Comorbidity of mental disorders with alcohol and other drug abuse. Results from the Epidemiologic Catchment Area (ECA) Study. JAMA. 1990;264(19):2511–2518. , , , et al.
- The relationship between educational attainment and relapse among alcohol‐dependent men and women: a prospective study. Alcohol Clin Exp Res. 2003;27(8):1278–1285. , , , , , .
- Prevalence, correlates, disability, and comorbidity of DSM‐IV alcohol abuse and dependence in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Arch Gen Psychiatry. 2007;64(7):830–842. , , , .
- Health‐related quality of life among adults with serious psychological distress and chronic medical conditions. Qual Life Res. 2008;17(4):521–528. , .
- Primary medical care and reductions in addiction severity: a prospective cohort study. Addiction. 2005;100(1):70–78. , , , , .
- The role of medical conditions and primary care services in 5‐year substance use outcomes among chemical dependency treatment patients. Drug Alcohol Depend. 2008;98(1–2):45–53. , , , .
Copyright © 2012 Society of Hospital Medicine