- Email: [email protected]
The Readmissions Obsession and Magical Numbers
Journal of Cardiac Failure Vol. 21 No. 5 2015
Editor’s Page
The Readmissions Obsession and Magical Numbers PAUL J. HAUPTMAN, MD Saint Louis, Missouri
that length of stay and risk for readmissions may be inversely correlated6,7 and that readmissions may not be a reflection of quality seem not to matter. The only other magical number to keep in mind is 3%, the maximum penalty meted out by the Centers for Medicare and Medicaid Services. None of this is news to the sophisticated reader of the Journal of Cardiac Failure. We know that some readmissions are unplanned, others unavoidable. We know that while we focus on readmissions for heart failure, many readmissions are with heart failure, and that is a big difference when contemplating policy. The use of multiple interventions may not buy you much.8 Besides, each intervention has both a cost and a 3-pronged metric to consider: generalizability, scalability, and sustainability. Just because an intervention sounds good (like telemanagement) does not mean it works.9,10 But even if we knew what to do, we don’t know enough about the truly at risk. Most would agree with the statement that ‘‘patients with multiple chronic conditions . are especially vulnerable to breakdown in care transitions.’’11 We may even extend what we know about mortality risk factors from mostly physiologically oriented models12,13 to our thinking about readmissions. But on a day-to-day basis, we know that there are other factors at play that have less to do with physiology than sociodemography. What if we sent our medical students out to the drive-ins of every fast food establishment within a 2-mile radius of the patient’s home and demanded a B-type natriuretic peptide measurement before the occupants of the car could place an order? Would that be more effective in preventing readmissions than, say, a reading from an implantable hemodynamic monitor? If we focus on nonphysiologic variables, we should consider the work of Amarasingham et al, who demonstrated that factors such as number of home address changes, missed office visits, and residence in a neighborhood in the lowest socioeconomic quartile were powerful predictors of readmission.14 In the current issue of the Journal, Huynh et al have evaluated both clinical and nonclinical models and note that combining both provides the greatest discrimination (as judged by the c-statistic).15 The authors included variables such as distance to nearest liquor store and distance to nearest
Magic Number: Any of the numbers, 2, 8, 20, 28, 50, 82, or 126, that represent the number of neutrons or protons in strongly bound and exceptionally stable atomic nuclei.1 Much has been written about what can only be termed ‘‘The Readmissions Obsession.’’ We all know the drill. The magic number is 30. On day 29, a patient readmission is toxic to a hospital’s financial bottom line. On day 31, the admission is just fine. Yes, that is true, the proponents will say, but after all, ‘‘the line had to be drawn somewhere,’’ and clearly a financial penalty is a great motivator. Indeed, no one would argue that being out of hospital is best for patients and that as a consequence, it is reasonable to expend considerable energy to achieve that goal. Termed ‘‘the revolving door at the hospital’’ by Ron Winslow of the Wall Street Journal,2 and estimated to be 26.9% in the Medicare population,3 readmissions have been in the line of sight by regulators for quite some time. With passage of the Affordable Care Act, the ever shifting paradigm for care delivery at US hospitals shifted more rapidly. As a consequence, the mandate is no longer just about acute episodic care. For at least 30 days after discharge, that entity called a hospital becomes social worker, telemonitor, telephone operator, and more. It is obvious that identifying the patient at risk for readmission would allow more focused use of resources. How best to stratify risk and how best to intervene once risk is identified are the ultimate ‘‘devil in the details’’ questions. Clearly, heart failure is the major driver of the readmission penalty,4 and certain structural characteristics appear to place specific hospital types at higher risk.5 As outlined in Table 1, certain additional themes pervade the discussions about readmissions. The facts
From the Saint Louis University School of Medicine, Saint Louis, Missouri. Manuscript received April 6, 2015. Reprint requests: Paul J. Hauptman, MD, Saint Louis University Hospital, 3635 Vista Avenue, Saint Louis, MO 63110. Tel: 314 268 5293; Fax: 314 577 8861. E-mail: [email protected] See page 366 for disclosure information. 1071-9164/$ - see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.cardfail.2015.04.001
365
366 Journal of Cardiac Failure Vol. 21 No. 5 May 2015 Table 1. Hospital Readmission Themes
Hospitals at risk fit a pattern Regional differences exist Length of stay is not divorced from readmission Interventions work Not all interventions work Rates of readmissions may not be an indicator of quality of hospital care Will decreased readmissions lead to lower mortality, or is that not the point?
pharmacy. From a more expansive vantage point, this type of analysis brings us to a more sensible place where realistic appraisal of the patient’s social milieu is evaluated, without judgment, but with acknowledgement that disparities in health care delivery and quality start somewhere. It is therefore comforting that this issue has been recognized by a few wise politicians who are now proposing the ‘‘Establishing Beneficiary Equity in the Hospital Readmissions Program Act of 2015.’’ According to the American Association of Medical Colleges, the Act is designed ‘‘to ensure that hospitals treating the nation’s most medically complex and vulnerable patients are not disproportionately penalized by the Medicare Hospital Readmission Reduction Program.’’16 Where does this leave us? Cynics would argue that next steps will include a further increase in the overall penalty or an extension beyond 30 days or the inclusion of many other conditions. We should be prepared to adapt, but given the constraints of our health care system and costs associated with additional manpower and technology, it is not clear how far we can go. Patients need to empower themselves as much as possible. Our efforts need to be adequately resourced and sustained, but at the end of the day, I think of the reductive approach that Karl Swedberg adopted on the issue of improving outcomes in patients with heart failure (I paraphrase): ‘‘just take the medicines, they work.’’ With this axiom in mind, the only magic number that counts is the number of medication refills. Disclosures None.
References 1. American Heritage Science Dictionary. Magic-number. Houghton Mifflin Company. Available at: http://dictionary.reference.com/browse/ magic-number. Accessed April 3, 2015. 2. Winslow Ron. The revolving door at the hospital: while patient stays shorten, readmission rates rise; where’s the savings? Wall Street Journal, June 2, 2010. Available at: www.wsj.com/articles/ SB10001424052748703961204575280903681581516. Accessed April 3, 2015. 3. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare Fee-for-Service Program. N Engl J Med 2009; 360:1418e28. 4. Vidic A, Chibnall J, Hauptman PJ. Heart Failure is a major contributor to hospital readmission penalties. J Card Fail 2015;21:134e7. 5. Joynt KE, Jha AK. Who has higher readmission rates for heart failure and why? Circ Cardiovasc Qual Outcomes 2011;4:53e9. 6. Cleland JCF, Swedberg K, Follath F, Komajda M, Cohen-Solal A, Aguilar JC, et al. The EuroHeart Survey programmeda survey on the quality of care among patients with heart failure in Europe. Eur Heart J 2000;24:442e63. 7. Eapen ZJ, Reed SD, Li Y, Kociol RD, Armstrong PW, Starling RC, et al. Do countries or hospitals with longer hospital stays for acute heart failure have lower readmission rates? Findings from ASCEND-HF. Circ Heart Fail 2013;6:727e32. 8. Bradley EH, Curry L, Horwitz LI, Sipsma H, Wang Y, Walsh MN, et al. Hospital strategies associated with 30-day readmission rates for patients with heart failure. Circ Cardiovasc Qual Outcomes 2013;6:444e50. 9. Desai A. Home monitoring heart failure care does not improve outcomes. Circulation 2012;125:828e36. 10. Chaudhry SI, Mattera JA, Curtis JP, Spertus JA, Herrin J, Lin Z, et al. Telemonitoring in patients with heart failure. N Engl J Med 2010;363: 2301e9. 11. Balicer RD, Shadmi E, Israeli A. Interventions for reducing readmissionsdare we barking up the right tree? Isr J Health Policy Res 2013;2:2. 12. Levy WC, Mozaffarian D, Linker DT, Sutradhar SC, Anker SD, Cropp AB, et al. The Seattle Heart Failure Model: prediction of survival in heart failure. Circulation 2006;113:1424e33. 13. Lee DS, Austin PC, Rouleau JL, Liu PP, Naimark D, Tu JV. Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model. JAMA 2003;290:2581e7. 14. Amarasingham R, Moore BJ, Tabak YP, Drazner MH, Clark CA, Zhang S, et al. An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. Med Care 2010;48:981e8. 15. Huynh QL, Saito M, Blizzard CL, Eskandari M, Johnson B, Adabi G, et al. Prediction of 30-day rehospitalization or death among heart failure patients: Roles of non-clinical and clinical data. J Card Fail 2015;21:374e81. 16. AAMC Applauds Members of Congress for Addressing Flaw in Hospital Readmissions Reduction Program (news release). Accessed on April 3, 2015. Available at: https://www.aamc.org/newsroom/ newsreleases/427020/20151003.html
Editor’s Page
The Readmissions Obsession and Magical Numbers PAUL J. HAUPTMAN, MD Saint Louis, Missouri
that length of stay and risk for readmissions may be inversely correlated6,7 and that readmissions may not be a reflection of quality seem not to matter. The only other magical number to keep in mind is 3%, the maximum penalty meted out by the Centers for Medicare and Medicaid Services. None of this is news to the sophisticated reader of the Journal of Cardiac Failure. We know that some readmissions are unplanned, others unavoidable. We know that while we focus on readmissions for heart failure, many readmissions are with heart failure, and that is a big difference when contemplating policy. The use of multiple interventions may not buy you much.8 Besides, each intervention has both a cost and a 3-pronged metric to consider: generalizability, scalability, and sustainability. Just because an intervention sounds good (like telemanagement) does not mean it works.9,10 But even if we knew what to do, we don’t know enough about the truly at risk. Most would agree with the statement that ‘‘patients with multiple chronic conditions . are especially vulnerable to breakdown in care transitions.’’11 We may even extend what we know about mortality risk factors from mostly physiologically oriented models12,13 to our thinking about readmissions. But on a day-to-day basis, we know that there are other factors at play that have less to do with physiology than sociodemography. What if we sent our medical students out to the drive-ins of every fast food establishment within a 2-mile radius of the patient’s home and demanded a B-type natriuretic peptide measurement before the occupants of the car could place an order? Would that be more effective in preventing readmissions than, say, a reading from an implantable hemodynamic monitor? If we focus on nonphysiologic variables, we should consider the work of Amarasingham et al, who demonstrated that factors such as number of home address changes, missed office visits, and residence in a neighborhood in the lowest socioeconomic quartile were powerful predictors of readmission.14 In the current issue of the Journal, Huynh et al have evaluated both clinical and nonclinical models and note that combining both provides the greatest discrimination (as judged by the c-statistic).15 The authors included variables such as distance to nearest liquor store and distance to nearest
Magic Number: Any of the numbers, 2, 8, 20, 28, 50, 82, or 126, that represent the number of neutrons or protons in strongly bound and exceptionally stable atomic nuclei.1 Much has been written about what can only be termed ‘‘The Readmissions Obsession.’’ We all know the drill. The magic number is 30. On day 29, a patient readmission is toxic to a hospital’s financial bottom line. On day 31, the admission is just fine. Yes, that is true, the proponents will say, but after all, ‘‘the line had to be drawn somewhere,’’ and clearly a financial penalty is a great motivator. Indeed, no one would argue that being out of hospital is best for patients and that as a consequence, it is reasonable to expend considerable energy to achieve that goal. Termed ‘‘the revolving door at the hospital’’ by Ron Winslow of the Wall Street Journal,2 and estimated to be 26.9% in the Medicare population,3 readmissions have been in the line of sight by regulators for quite some time. With passage of the Affordable Care Act, the ever shifting paradigm for care delivery at US hospitals shifted more rapidly. As a consequence, the mandate is no longer just about acute episodic care. For at least 30 days after discharge, that entity called a hospital becomes social worker, telemonitor, telephone operator, and more. It is obvious that identifying the patient at risk for readmission would allow more focused use of resources. How best to stratify risk and how best to intervene once risk is identified are the ultimate ‘‘devil in the details’’ questions. Clearly, heart failure is the major driver of the readmission penalty,4 and certain structural characteristics appear to place specific hospital types at higher risk.5 As outlined in Table 1, certain additional themes pervade the discussions about readmissions. The facts
From the Saint Louis University School of Medicine, Saint Louis, Missouri. Manuscript received April 6, 2015. Reprint requests: Paul J. Hauptman, MD, Saint Louis University Hospital, 3635 Vista Avenue, Saint Louis, MO 63110. Tel: 314 268 5293; Fax: 314 577 8861. E-mail: [email protected] See page 366 for disclosure information. 1071-9164/$ - see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.cardfail.2015.04.001
365
366 Journal of Cardiac Failure Vol. 21 No. 5 May 2015 Table 1. Hospital Readmission Themes
Hospitals at risk fit a pattern Regional differences exist Length of stay is not divorced from readmission Interventions work Not all interventions work Rates of readmissions may not be an indicator of quality of hospital care Will decreased readmissions lead to lower mortality, or is that not the point?
pharmacy. From a more expansive vantage point, this type of analysis brings us to a more sensible place where realistic appraisal of the patient’s social milieu is evaluated, without judgment, but with acknowledgement that disparities in health care delivery and quality start somewhere. It is therefore comforting that this issue has been recognized by a few wise politicians who are now proposing the ‘‘Establishing Beneficiary Equity in the Hospital Readmissions Program Act of 2015.’’ According to the American Association of Medical Colleges, the Act is designed ‘‘to ensure that hospitals treating the nation’s most medically complex and vulnerable patients are not disproportionately penalized by the Medicare Hospital Readmission Reduction Program.’’16 Where does this leave us? Cynics would argue that next steps will include a further increase in the overall penalty or an extension beyond 30 days or the inclusion of many other conditions. We should be prepared to adapt, but given the constraints of our health care system and costs associated with additional manpower and technology, it is not clear how far we can go. Patients need to empower themselves as much as possible. Our efforts need to be adequately resourced and sustained, but at the end of the day, I think of the reductive approach that Karl Swedberg adopted on the issue of improving outcomes in patients with heart failure (I paraphrase): ‘‘just take the medicines, they work.’’ With this axiom in mind, the only magic number that counts is the number of medication refills. Disclosures None.
References 1. American Heritage Science Dictionary. Magic-number. Houghton Mifflin Company. Available at: http://dictionary.reference.com/browse/ magic-number. Accessed April 3, 2015. 2. Winslow Ron. The revolving door at the hospital: while patient stays shorten, readmission rates rise; where’s the savings? Wall Street Journal, June 2, 2010. Available at: www.wsj.com/articles/ SB10001424052748703961204575280903681581516. Accessed April 3, 2015. 3. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare Fee-for-Service Program. N Engl J Med 2009; 360:1418e28. 4. Vidic A, Chibnall J, Hauptman PJ. Heart Failure is a major contributor to hospital readmission penalties. J Card Fail 2015;21:134e7. 5. Joynt KE, Jha AK. Who has higher readmission rates for heart failure and why? Circ Cardiovasc Qual Outcomes 2011;4:53e9. 6. Cleland JCF, Swedberg K, Follath F, Komajda M, Cohen-Solal A, Aguilar JC, et al. The EuroHeart Survey programmeda survey on the quality of care among patients with heart failure in Europe. Eur Heart J 2000;24:442e63. 7. Eapen ZJ, Reed SD, Li Y, Kociol RD, Armstrong PW, Starling RC, et al. Do countries or hospitals with longer hospital stays for acute heart failure have lower readmission rates? Findings from ASCEND-HF. Circ Heart Fail 2013;6:727e32. 8. Bradley EH, Curry L, Horwitz LI, Sipsma H, Wang Y, Walsh MN, et al. Hospital strategies associated with 30-day readmission rates for patients with heart failure. Circ Cardiovasc Qual Outcomes 2013;6:444e50. 9. Desai A. Home monitoring heart failure care does not improve outcomes. Circulation 2012;125:828e36. 10. Chaudhry SI, Mattera JA, Curtis JP, Spertus JA, Herrin J, Lin Z, et al. Telemonitoring in patients with heart failure. N Engl J Med 2010;363: 2301e9. 11. Balicer RD, Shadmi E, Israeli A. Interventions for reducing readmissionsdare we barking up the right tree? Isr J Health Policy Res 2013;2:2. 12. Levy WC, Mozaffarian D, Linker DT, Sutradhar SC, Anker SD, Cropp AB, et al. The Seattle Heart Failure Model: prediction of survival in heart failure. Circulation 2006;113:1424e33. 13. Lee DS, Austin PC, Rouleau JL, Liu PP, Naimark D, Tu JV. Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model. JAMA 2003;290:2581e7. 14. Amarasingham R, Moore BJ, Tabak YP, Drazner MH, Clark CA, Zhang S, et al. An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. Med Care 2010;48:981e8. 15. Huynh QL, Saito M, Blizzard CL, Eskandari M, Johnson B, Adabi G, et al. Prediction of 30-day rehospitalization or death among heart failure patients: Roles of non-clinical and clinical data. J Card Fail 2015;21:374e81. 16. AAMC Applauds Members of Congress for Addressing Flaw in Hospital Readmissions Reduction Program (news release). Accessed on April 3, 2015. Available at: https://www.aamc.org/newsroom/ newsreleases/427020/20151003.html