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J Am Heart Assoc. 2013 April; 2(2): e000116.
Published online 2013 April 24. doi:  10.1161/JAHA.113.000116
PMCID: PMC3647271

Postdischarge Environment Following Heart Failure Hospitalization: Expanding the View of Hospital Readmission

Introduction

Readmission after hospitalization for heart failure (HF) has received increasing attention due to the significant burden it places on patients and payers.12 Among Medicare beneficiaries, readmission within 30 days following heart failure hospitalization approaches 25%.2 Even after adjusting for case mix, significant variation in hospital readmission rates exists. This hospital‐level variation suggests that many of these readmissions may be preventable.3 HF readmission rates adjusted for risk using a claims‐based model are now publicly reported as a measure of institutional quality (www.HospitalCompare.hhs.gov). As of October 2012, the Patient Protection and Affordable Care Act's (PPACA) value‐based purchasing policies began reducing Medicare payments to hospitals with “excess” HF readmissions and offered new funding opportunities for innovative approaches to reduce HF readmissions.4

Despite the obvious value of reducing unnecessary readmissions, the way forward is not as clear as these policies might suggest. An increasing segment of the medical community is voicing concern with the extent to which public reporting and financial penalties positively influence institutional HF readmission rates.5 Value‐based purchasing may unfairly punish hospitals that provide care to socioeconomically disadvantaged patients and incentivize the avoidance of high‐risk patients69 due to perceived inadequacies of current risk standardization models.10 In addition, effective interventions to prevent unnecessary readmissions remain elusive.11

Prior efforts to identify risk factors for HF readmission have put an inordinate priority on the convenience of data collection. The vast majority of existing risk models employ administrative billing and inpatient clinical data from a single episode of care that are not designed to fully elucidate the breadth of potential causes of readmission. Notably missing are factors reflecting the patient's postdischarge environment. Recent literature suggests that “social instability”—a term which reflects a relative lack of social support, education, economic stability, access to care, and safety in the patient's environment—is an important mediator of readmission risk.1213

Within this context, we set out to (1) review what is known about the postdischarge environment and its relationship to HF readmission, and (2) propose a new conceptual model for HF readmission that integrates patient, provider, health system, and environmental factors. Doing so has the potential to improve the predictive capacity of HF readmission risk models, thereby making quality measures fairer, and to guide us in improving transitions of care, and ultimately leading toward reductions in unnecessary readmissions.

Literature Search

The concept of the postdischarge environment has not been a clearly defined domain in current readmission literature. Therefore, the approach taken was to systematically identify all readmission models and then manually extract factors that were perceived to represent the postdischarge environment. Systematic reviews of the literature regarding HF readmission risk models have been performed previously by Kansagara et al10 (2011) and Ross et al14 (2008). We used the published Kansagara search alogrithm to capture newer literature published up to November 15, 2012. In addition, we supplemented the Kansagara search algorithm with an additional search focusing specifically on the postdischarge environment using the terms “postdischarge environment, environment, social, social instability, education, poverty, economic, and socioeconomic” in combination with “readmission and/or rehospitalization” and any medical or surgical condition. We then reviewed abstracts and included studies which explored the relationship of readmission to one or more aspects of the postdischarge environment.

Models identified from these searches that included any factor representing the postdischarge environment are summarized in the Table.

Table 1.
Selected Heart Failure and General Readmission Risk Models Focusing on Patient, System, and Environmental Level Covariates

Using these results we then attempted to synthesize the information into a conceptual model of HF readmission (Figure), paying special attention to postdischarge environmental factors.

Figure 1.
Proposed conceptual model for heart failure readmission, emphasizing that the patient and health care provider work within their environment.

The State of Heart Failure Readmission Risk Modeling

Prediction models play a vital role in our understanding, interpretation, and reaction to HF readmissions. They provide insight into the primary factors that underlie readmission, and as such, point toward new and more focused interventions. Furthermore, an understanding of individual patient risk allows hospitals to triage costly, high‐intensity interventions to those patients most likely to derive benefit from them. Finally, readmission rates, adjusted for patient factors, have been used to measure institutional quality of care. Thus appropriate risk modeling is vital for creating “apples‐to‐apples” comparisons between different institutions, as well as within a single institution over time.

Risk factors and associated prediction models for HF readmission have been systematically described elsewhere.10,14 Although several HF readmission risk models have been validated and published, the state of risk prediction in HF readmission remains crude. The ability to discriminate patients who will be readmitted from those who will not is significantly lower than it is for postdischarge mortality, with C‐indices for HF readmission models rarely exceeding 0.70.13,22,24,26,30,37 Likewise the ability of providers to predict HF readmission via “clinical gestalt” appears similarly limited.36 The reasons are multiple. First, a relatively high proportion of readmissions may be inherently stochastic events, and therefore, models of readmission will have some “ceiling” of predictive performance. Second, variation in readmission risk following adjustment for patient‐level factors may be partially attributable to provider and system‐level differences in care delivery (ie, differences in quality). Third, existing models might fail to reliably predict some readmissions because they are missing key domains that drive its occurrence.

Existing prediction models have relied heavily on data collected during hospitalization, typically from inpatient clinical registries and claims‐based administrative data (Table). This “data first” approach uses readily available data to dictate the hypotheses to be tested, rather than the other way around. It largely neglects some difficult‐to‐measure, but logical, domains. These include complex comorbid disease, frailty, subclinical depression and anxiety, substance abuse, cognitive limitations, lack of formal and informal education (health literacy, numeracy), acculturation, suboptimal patient adherence, inability to provide self‐care, caregiver support, and social networks.

The Importance of the Postdischarge Environment

Although easily captured measures of a patient's postdischarge environment have been considered in some existing models (eg, income, marital status, insurance status; Table), a systematic approach to this domain has been largely absent from the HF readmission discussion. New data are emerging to indicate that stability in the postdischarge environment plays a critical part in HF readmission.

Amarasingham et al derived and validated an HF readmission risk model within a large, inner city, safety‐net hospital, using a wide range of automated data gleaned from the electronic medical record.13 In this multivariable analysis, several factors emerged that were associated with 30‐day readmission, including being single, male, using Medicaid, having an increased number of address changes, average income level for zip code of residence, and time of presentation to the ED (between 6 am and 6 pm). When these markers of “social instability” were included as a group into a previously validated model, the 30‐day risk prediction improved markedly (C‐statistic from 0.61 to 0.72). This suggests that social environmental factors are important determinants of readmission risk.

A second study by Arbaje et al further supports this hypothesis.12 Using Medicare claims data as well as the Current Beneficiary Survey, this group looked at the relationship between socioeconomics, the postdischarge environment, and the likelihood of early hospital readmission over a range of diseases, including HF. In the study's population, being unmarried, living alone, lacking “self‐management skills”, and having an unmet activity of daily living and lower level of education put a patient at increased risk for readmission. Interestingly, after adjusting for these other factors, no direct relationship was found between income and risk.

A variety of studies have shown that indigent populations tend to have higher rates of HF readmission. An analysis of national Medicare data showed that 30‐day HF readmission rates for Medicare beneficiaries were higher among black patients than white patients, and that patients from minority‐serving hospitals had higher readmission rates than those from nonminority‐serving hospitals.39 Even after adjustment for measured clinical factors, Medicaid populations had higher HF readmission rates than their commercially insured counterparts.40 Some portion of these differences may be due to inferior health care for these populations, but differences in patient and environmental factors not captured by existing models are likely to contribute as well. At least among the Medicare population, community measures explain far more of the variance in institutional HF readmission rates than do hospital process performance measures.41

Recent analyses that have specifically collected data on social factors not captured by traditional databases (a “hypothesis first” approach) have helped expand our view of the mediators of readmission. Peterson et al showed in a series of papers derived from prospective health survey information that health literacy42 and acculturation43 were strong predictors of adverse outcomes after discharge among patients hospitalized with HF, and Tao et al44 suggest a scoring system that might be used to predict patients whose social situation place them at higher risk for readmission.

As further evidence of the influence of the postdischarge environment on readmission, successful interventions that have effectively reduced readmissions have generally done so by altering the patient's postdischarge environment or the patient's ability to manage his/her own environment. For example, comprehensive discharge planning (including education of the patient and family), social‐service consultation, and intensive follow‐up were components of the earliest successful HF readmission interventions.4546 More recently, transition coaches who go directly into the home environment to support a variety of patient needs have been shown to be effective.47 Unlike successful interventions that use trained personnel to broadly support patients in their transition to home, unimodal interventions11 and those focused primarily on the physiology of HF48 have consistently failed to reduce HF readmission rates.

A New Conceptual Model for HF Readmission

HF readmission is an event that occurs, by definition, in the postdischarge environment. As such, it is reasonable to surmise then that this environment would act as a mediator. Based on our current understanding of readmissions, we propose a new explicit paradigm of HF readmission that positions patient and health system factors within their relevant environment (Figure). The patient interacts with the provider and health system all within the context of the surrounding environment. This conceptualization moves the postdischarge environment from a peripheral (or ignored) role to an encompassing one. Changing our conceptualization transforms our view of readmission from a biological, hospital‐based event to a “sociobiological” process. This new model also helps reconcile how patient factors and provider/health system factors relate to each other through the postdischarge environment. Concretely, this reframing suggests how new lines of research into the postdischarge environment may lead to further improvements in our ability to predict and mitigate risk of readmission.

The question of how the postdischarge environment affects readmissions is important. Readmission is typically a multifactorial process.49 We hypothesize that increased stability in the postdischarge environment can positively affect a variety of domains related to readmission. Social stability has the potential to improve dietary compliance and fluid restriction, increase medication adherence, increase access to health care and improve compliance with appointments, raise levels of exercise, reduce tobacco and alcohol use, etc. Together, these factors may positively influence HF severity and disease progression. In addition, they may decrease comorbidity number and severity and even help bolster a patient's physiologic reserve. These domains may remove barriers to, or combine with, provider and systems‐based factors to synergistically influence rates of readmission.

Environmental Factors and Public Policy

It has been has been argued that socioeconomic factors have a limited place in risk modeling because adjusting for them may “excuse” substandard care for indigent and impoverished populations.59,50 To the contrary, acknowledging that the patient and health system reside within a larger environment counters this argument. Including environmental factors in risk‐standardization models for public reporting and value‐based purchasing recognizes the unique challenges posed by patients with significant environmental instability. In addition, this perspective lends support to incentives that would foster the development of innovative transitional care programs in order to accommodate social instability or directly enhance the patient's ability to navigate the postdischarge environment. Moving from the overly simplistic, dichotomous, patient‐hospital construct to consideration of the patient, clinician, and hospital as members of the community in which they all reside promotes a more integrated approach to health. Ultimately, major improvements in the health of patients with chronic, progressive diseases (like HF) will require coordinated efforts among patients, families, providers, health systems, governmental agencies, and community organizations. This integrated approach should be properly incentivized by sound public policy.

As the Centers for Medicare and Medicaid Services scale up performance‐based payments, it must consider the potential influence of socioeconomic factors on outcomes to ensure that hospital payment penalties do not exacerbate disparities in care. Although outcome measures designed to reduce unnecessary hospital readmissions may be an important step forward in advancing quality in some respects, the failure to incorporate environmental factors could influence hospitals' ability and willingness to serve vulnerable populations.51 Stratifying institutional readmission results by important environmental factors may be one way to “level the playing field” when assessing hospital performance and encourage hospitals to maintain access to care for vulnerable populations.

Future Research

Factors related to the postdischarge environment need to be better explored, measured, and integrated into risk models and interventions. Without a comprehensive and systematic analysis of the postdischarge environment, we are unlikely to realize reductions in unnecessary HF readmissions. Such an approach would involve a number of steps, including the development of definitions and an associated taxonomy around relevant factors in the postdischarge environment followed by surveillance of these factors through an explicit mechanism.52

Research by Ross et al14 and Arbaje et al12 provides an example of how to assess the incremental value of “factors of social instability” by assessing risk model performance before and after inclusion of these factors. In the meantime, institutions that are seriously working to improve their HF readmission rates should recognize that interventions that ignore the environment into which a patient is discharged are unlikely to significantly impact their readmission rates.

Conclusions

A variety of forces, including passage of the PPACA and its linkage of HF readmission to reimbursement, have placed HF readmissions at the forefront of quality improvement efforts in medicine. However, the poor performance of existing HF readmission risk models combined with our failure to significantly impact HF readmission rates53 should give us pause. HF readmission consists of a complex interplay between patient, health system, and the environment. We believe that conceptualizing HF readmission as a sociobiological process rather than a discrete physiologic occurrence will help us to better characterize, predict, and ultimately mitigate risk. Further research into the exact mechanisms by which the postdischarge environment affects readmission will improve quality measures and future interventions designed to keep HF patients out of the hospital.

Sources of Funding

Dr. Allen is currently supported by grant 1K23HL105896‐01A1 from the National Heart, Lung and Blood Institute.

Disclosures

None.

Acknowledgments

We sincerely thank C. David Kosakowski for his technical revision of the text.

References

1. Roger VL, Go AS, Lloyd‐Jones DM, Benjamin EJ, Berry JD, Borden WB, Bravata DM, Dai S, Ford ES, Fox CS, Fullerton HJ, Gillespie C, Hailpern SM, Heit JA, Howard VJ, Kissela BM, Kittner SJ, Lackland DT, Lichtman JH, Lisabeth LD, Makuc DM, Marcus GM, Marelli A, Matchar DB, Moy CS, Mozaffarian D, Mussolino ME, Nichol G, Paynter NP, Soliman EZ, Sorlie PD, Sotoodehnia N, Turan TN, Virani SS, Wong ND, Woo D, Turner MB. Heart disease and stroke statistics—2012 update: a report from the american heart association. Circulation. 2012; 125:e2-e220. [PubMed]
2. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the medicare fee‐for‐service program. N Engl J Med. 2009; 360:1418-1428. [PubMed]
3. Keenan PS, Normand SL, Lin Z, Drye EE, Bhat KR, Ross JS, Schuur JD, Stauffer BD, Bernheim SM, Epstein AJ, Wang Y, Herrin J, Chen J, Federer JJ, Mattera JA, Krumholz HM. An administrative claims measure suitable for profiling hospital performance on the basis of 30‐day all‐cause readmission rates among patients with heart failure. Circ Cardiovasc Qual Outcomes. 2008; 1:29-37. [PubMed]
4. Patient Protection and Affordable Care Act, Pub. L. No. 111‐148, §2702, 124 Stat. 119, 318–319 2010.
5. Joynt KE, Jha AK. Thirty‐day readmissions–truth and consequences. N Engl J Med. 2012; 366:1366-1369. [PubMed]
6. Lubell J. Hospitals cry foul. Preventable readmission penalty brings concerns. Mod Healthc. 2010; 40:10-11. [PubMed]
7. Weinick RM, Hasnain‐Wynia R. Quality improvement efforts under health reform: how to ensure that they help reduce disparities–not increase them. Health Aff (Millwood). 2011; 30:1837-1843. [PubMed]
8. Joynt KE, Rosenthal MB. Hospital value‐based purchasing: will medicare's new policy exacerbate disparities? Circ Cardiovasc Qual Outcomes. 2012; 5:148-149. [PubMed]
9. Bhalla R, Kalkut G. Could medicare readmission policy exacerbate health care system inequity? Ann Intern Med. 2010; 152:114-117. [PubMed]
10. Kansagara D, Englander H, Salanitro A, Kagen D, Theobald C, Freeman M, Kripalani S. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011; 306:1688-1698. [PMC free article] [PubMed]
11. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011; 155:520-528. [PubMed]
12. Arbaje AI, Wolff JL, Yu Q, Powe NR, Anderson GF, Boult C. Postdischarge environmental and socioeconomic factors and the likelihood of early hospital readmission among community‐dwelling medicare beneficiaries. Gerontologist. 2008; 48:495-504. [PubMed]
13. Amarasingham R, Moore BJ, Tabak YP, Drazner MH, Clark CA, Zhang S, Reed WG, Swanson TS, Ma Y, Halm EA. An automated model to identify heart failure patients at risk for 30‐day readmission or death using electronic medical record data. Med Care. 2010; 48:981-988. [PubMed]
14. Ross JS, Mulvey GK, Stauffer B, Patlolla V, Bernheim SM, Keenan PS, Krumholz HM. Statistical models and patient predictors of readmission for heart failure: a systematic review. Arch Intern Med. 2008; 168:1371-1386. [PubMed]
15. Anderson GF, Steinberg EP. Predicting hospital readmissions in the medicare population. Inquiry. 1985; 22:251-258. [PubMed]
16. Evans RL, Hendricks RD, Lawrence KV, Bishop DS. Identifying factors associated with health care use: a hospital‐based risk screening index. Soc Sci Med. 1988; 27:947-954. [PubMed]
17. Smith DM, Weinberger M, Katz BP, Moore PS. Postdischarge care and readmissions. Med Care. 1988; 26:699-708. [PubMed]
18. Holloway JJ, Medendorp SV, Bromberg J. Risk factors for early readmission among veterans. Health Serv Res. 1990; 25:213-237. [PMC free article] [PubMed]
19. Burns R, Nichols LO. Factors predicting readmission of older general medicine patients. J Gen Intern Med. 1991; 6:389-393. [PubMed]
20. Naessens JM, Leibson CL, Krishan I, Ballard DJ. Contribution of a measure of disease complexity (complex) to prediction of outcome and charges among hospitalized patients. Mayo Clin Proc. 1992; 67:1140-1149. [PubMed]
21. Chin MH, Goldman L. Correlates of early hospital readmission or death in patients with congestive heart failure. Am J Cardiol. 1997; 79:1640-1644. [PubMed]
22. Philbin EF, DiSalvo TG. Prediction of hospital readmission for heart failure: development of a simple risk score based on administrative data. J Am Coll Cardiol. 1999; 33:1560-1566. [PubMed]
23. Smith DM, Giobbie‐Hurder A, Weinberger M, Oddone EZ, Henderson WG, Asch DA, Ashton CM, Feussner JR, Ginier P, Huey JM, Hynes DM, Loo L, Mengel CE. Predicting non‐elective hospital readmissions: a multi‐site study. Department of veterans affairs cooperative study group on primary care and readmissions. J Clin Epidemiol. 2000; 53:1113-1118. [PubMed]
24. Krumholz HM, Chen YT, Wang Y, Vaccarino V, Radford MJ, Horwitz RI. Predictors of readmission among elderly survivors of admission with heart failure. Am Heart J. 2000; 139:72-77. [PubMed]
25. Coleman EA, Min SJ, Chomiak A, Kramer AM. Posthospital care transitions: patterns, complications, and risk identification. Health Serv Res. 2004; 39:1449-1465. [PMC free article] [PubMed]
26. Felker GM, Leimberger JD, Califf RM, Cuffe MS, Massie BM, Adams KF, Jr, Gheorghiade M, O'Connor CM. Risk stratification after hospitalization for decompensated heart failure. J Cardiac Fail. 2004; 10:460-466. [PubMed]
27. Bottle A, Aylin P, Majeed A. Identifying patients at high risk of emergency hospital admissions: a logistic regression analysis. J R Soc Med. 2006; 99:406-414. [PMC free article] [PubMed]
28. Halfon P, Eggli Y, Pretre‐Rohrbach I, Meylan D, Marazzi A, Burnand B. Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care. Med Care. 2006; 44:972-981. [PubMed]
29. Billings J, Mijanovich T. Improving the management of care for high‐cost medicaid patients. Health Aff (Millwood). 2007; 26:1643-1654. [PubMed]
30. Yamokoski LM, Hasselblad V, Moser DK, Binanay C, Conway GA, Glotzer JM, Hartman KA, Stevenson LW, Leier CV. Prediction of rehospitalization and death in severe heart failure by physicians and nurses of the escape trial. J Cardiac Fail. 2007; 13:8-13. [PubMed]
31. Krumholz HM, Normand SL, Keenan PS, Lin Z, Drye EE, Bhat KR, Wang Y. Hospital 30‐day heart failure readmission measure methodology. 2008. A report prepared for the Centers for Medicare & Medicaid Services: Apr.
32. Silverstein MD, Qin H, Mercer SQ, Fong J, Haydar Z. Risk factors for 30‐day hospital readmission in patients >/=65 years of age. Proc (Bayl Univ Med Cent). 2008; 21:363-372. [PMC free article] [PubMed]
33. Howell S, Coory M, Martin J, Duckett S. Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Serv Res. 2009; 9:96. [PMC free article] [PubMed]
34. van Walraven C, Dhalla IA, Bell C, Etchells E, Stiell IG, Zarnke K, Austin PC, Forster AJ. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010; 182:551-557. [PMC free article] [PubMed]
35. Hasan O, Meltzer DO, Shaykevich SA, Bell CM, Kaboli PJ, Auerbach AD, Wetterneck TB, Arora VM, Zhang J, Schnipper JL. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010; 25:211-219. [PMC free article] [PubMed]
36. Allaudeen N, Schnipper JL, Orav EJ, Wachter RM, Vidyarthi AR. Inability of providers to predict unplanned readmissions. J Gen Intern Med. 2011; 26:771-776. [PMC free article] [PubMed]
37. Hammill BG, Curtis LH, Fonarow GC, Heidenreich PA, Yancy CW, Peterson ED, Hernandez AF. Incremental value of clinical data beyond claims data in predicting 30‐day outcomes after heart failure hospitalization. Circ Cardiovasc Qual Outcomes. 2011; 4:60-67. [PubMed]
38. Gruneir A, Dhalla IA, van Walraven C, Fischer HD, Camacho X, Rochon PA, Anderson GM. Unplanned readmissions after hospital discharge among patients identified as being at high risk for readmission using a validated predictive algorithm. Open Med. 2011; 5:e104-e111. [PMC free article] [PubMed]
39. Joynt KE, Orav EJ, Jha AK. Thirty‐day readmission rates for medicare beneficiaries by race and site of care. JAMA. 2011; 305:675-681. [PMC free article] [PubMed]
40. Allen LA, Smoyer‐Tomic KE, Smith DM, Wilson KL, Agodoa I. Rates and predictors of 30‐day readmission among commercially insured and medicaid‐enrolled patients hospitalized with systolic heart failure. Circ Heart Fail. 2012; 5:672-679. [PubMed]
41. Joynt KE, Orav EJ, Jha AK. Impact of community factors on readmission rates. Circ Cardiovasc Qual Outcomes. 2012; 5:A12.
42. Peterson PN, Shetterly SM, Clarke CL, Bekelman DB, Chan PS, Allen LA, Matlock DD, Magid DJ, Masoudi FA. Health literacy and outcomes among patients with heart failure. JAMA. 2011; 305:1695-1701. [PubMed]
43. Peterson PN, Campagna EJ, Maravi M, Allen LA, Bull S, Steiner JF, Havranek EP, Dickinson LM, Masoudi FA. Acculturation and outcomes among patients with heart failure. Circulation. Heart failure. 2012; 5:160-166. [PubMed]
44. Tao H, Ellenbecker CH, Chen J, Zhan L, Dalton J. The influence of social environmental factors on rehospitalization among patients receiving home health care services. ANS Adv Nurs Sci. 2012; 35:346-358. [PubMed]
45. Rich MW, Beckham V, Wittenberg C, Leven CL, Freedland KE, Carney RM. A multidisciplinary intervention to prevent the readmission of elderly patients with congestive heart failure. N Engl J Med. 1995; 333:1190-1195. [PubMed]
46. Naylor M, Brooten D, Jones R, Lavizzo‐Mourey R, Mezey M, Pauly M. Comprehensive discharge planning for the hospitalized elderly. A randomized clinical trial. Ann Intern Med. 1994; 120:999-1006. [PubMed]
47. Coleman EA, Parry C, Chalmers S, Min SJ. The care transitions intervention: results of a randomized controlled trial. Arch Intern Med. 2006; 166:1822-1828. [PubMed]
48. Chaudhry SI, Mattera JA, Curtis JP, Spertus JA, Herrin J, Lin Z, Phillips CO, Hodshon BV, Cooper LS, Krumholz HM. Telemonitoring in patients with heart failure. N Engl J Med. 2010; 363:2301-2309. [PMC free article] [PubMed]
49. Retrum J, Boggs J, Hersh A, Wright M, Main D, Magid D, Allan L. Patient‐identified factors related to heart failure readmission. Circ Cardiovasc Qual Outcomes . [PubMed]
50. Krumholz HM, Normand SL, Spertus JA, Shahian DM, Bradley EH. Measuring performance for treating heart attacks and heart failure: the case for outcomes measurement. Health Aff (Millwood). 2007; 26:75-85. [PubMed]
51. Chatterjee P, Joynt KE, Orav EJ, Jha AK. Patient experience in safety‐net hospitals: implications for improving care and value‐based purchasing. Arch Intern Med. 2012; 172:1204-1210. [PubMed]
52. Goff DC, Jr, Brass L, Braun LT, Croft JB, Flesch JD, Fowkes FG, Hong Y, Howard V, Huston S, Jencks SF, Luepker R, Manolio T, O'Donnell C, Robertson RM, Rosamond W, Rumsfeld J, Sidney S, Zheng ZJ. Essential features of a surveillance system to support the prevention and management of heart disease and stroke: a scientific statement from the american heart association councils on epidemiology and prevention, stroke, and cardiovascular nursing and the interdisciplinary working groups on quality of care and outcomes research and atherosclerotic peripheral vascular disease. Circulation. 2007; 115:127-155. [PubMed]
53. Rau J. Hospitals' readmission rates not budging. Kaiser Health News. 2012.

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