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Maxim Topaz, PhD, MA, RN; Youjeong Kang, PhD, MPH, RN; Diane E. Holland, PhD, RN; Brenda Ohta, PhD, MSW; Kathy Rickard, MSN, RN; and Kathryn H. Bowles, PhD, RN, FAAN, FACMI
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Higher 30-Day and 60-Day Readmissions Among Patients Who Refuse Post Acute Care Services

Maxim Topaz, PhD, MA, RN; Youjeong Kang, PhD, MPH, RN; Diane E. Holland, PhD, RN; Brenda Ohta, PhD, MSW; Kathy Rickard, MSN, RN; and Kathryn H. Bowles, PhD, RN, FAAN, FACMI
Although patients who refuse post acute care services are relatively young, well educated, and healthy, they are twice as likely to have 30- and 60-day readmissions compared with acceptors of services.
Sociodemographic and Clinical Characteristics, Quality of Life, and Health-Related Problems and Unmet Needs Among PAC Acceptors and Refusers

On average, refusers were significantly younger than acceptors (aged 68 years vs 73 years, respectively; P <.001). Refusers were significantly more likely to be married (62% vs 46%; P <.001), have private or other insurance versus straight or managed Medicare or Medicaid (35% vs 18%; P <.001), be admitted electively (30% vs 14% emergently or transfer; P <.001), have lower risk of mortality/severity of illness (eg, 31% were in the “major/extreme risk” category vs 53%; P <.001) and have shorter lengths of hospital stay (4.8 days vs 7.5 days; P = .001) than acceptors. See Table 1 for more details.

Very little was documented about the reasons for refusal. Almost half of the refusers (46%) gave no explanation, 34% said they did not see the need for nursing services, and 8% said they would rely on their caregivers.

Quality of life. Refusers reported a higher overall quality-of-life index compared with acceptors (0.83 vs 0.73 respectively; P <.001) (Table 2). Refusers also experienced fewer limitations on almost all quality of life subscales, including fewer problems with mobility (44% vs 49%; P <.001), self-care (13% vs 30%; P <.001), and usual activities (39% vs 58%; P <.001), and they reported less pain and discomfort (33% vs 44%; P = .03).

Problems after discharge. Refusers reported significantly fewer problems, except for physical problems (82% vs 86% respectively; P = .29) (Table 3). For instance, refusers experienced fewer problems with personal care (14% vs 39%; P <.001), household activities (47% vs 75%; P <.001), mobility (52% vs 73%; P <.001), psychological issues (38% vs 55%; P <.001), and aids or equipment (2% vs 9%; P = .01) than acceptors.

Unmet needs. Refusers reported fewer unmet needs overall (6.6% vs 10.5% respectively; P <.001) and on almost all the unmet needs subscales (Table 4). For instance, only 5% of refusers reported 1 or more psychological unmet needs compared with 18% among acceptors (P <.001). Similar significant differences were found on the following subscales: personal care (3% vs 7%; P = .02); household activities (7% vs 18%, P <.001); mobility (3% vs 16%; P <.001); physical (14% vs 27%; P <.001); aids or equipment (3% vs 10%; P = .01); and instructions and directions for unmet needs (4% vs 14%; P <.001).

Association Between PAC Refusal and Risk For 30/60-Day Readmission

Nearly 21% (n = 29) of refusers experienced 30-day readmission compared with 16% (n = 56) of acceptors. Although this was a 5% clinically significant difference, the difference was not statistically significant (P = .17). At 60 days, 31% (n = 43) of refusers experienced readmission compared with 25% (n = 89) of acceptors (P = .18). Bivariate analysis (unadjusted; only significant associations reported) of 30-day readmission showed that readmitted patients had longer length of index hospital stay (9.7 days vs 6.2; P <.001); had higher incidence of hospital admissions within the last 6 months (35% were readmitted 2 or more times vs 24%; P = .01); and lower quality of life index scores (0.67 vs 0.78; P <.001). Bivariate analysis of 60-day readmission showed that readmitted patients were significantly younger (aged 69.4 years vs 71.8; P = .02); had longer length of index hospital stay (8.6 days vs 6.1; P <.001); had slightly higher number of prescribed medications (10.6 vs 9.1; P = .01); and had higher incidence of hospital admissions within the last 6 months (40% were readmitted 2 or more times vs 21%; P <.001).

In the logistic regression model of 30-day readmission adjusted for those factors and quality-of-life index, percent of problems, and percent of unmet needs, 4 significant factors emerged: 1) refusers were more than twice as likely to be readmitted (OR [odds ratio], 2.13; 95% CI, 1.21-3.75; P = .01) than acceptors; 2) patients with better quality of life (higher indices) were less likely to be readmitted (OR, 0.10; 95% CI, 0.02-0.43; P <.001); 3) patients with longer index hospitalization stays were more likely to be readmitted (OR, 1.03; 95% CI, 1.01-1.06; P = .01); and 4) patients with more previous overnight hospitalizations in the past 6 months were more likely to be readmitted (P = .04) (Table 5).

In the adjusted logistic regression model of 60-day readmission, 2 significant factors emerged: 1) refusers were almost twice as likely to be readmitted (OR, 1.80; 95% CI, 1.11-3.02; P = .02) than acceptors; and 2) patients with more previous overnight hospitalizations in the past 6 months were more likely to be readmitted (P <.001) (Table 5). Ad hoc power analysis confirmed that the sample size (n = 495) was adequate to provide 80% power to answer the study questions.25

DISCUSSION

This study showed that 28% of patients who were offered post acute care refused the services. This alarming finding validates other anecdotal reports on the high rates (6% to 27%) of PAC refusal.12-14 Typically, about 30% of hospitalized patients receive referrals for PAC,2 but in our study, 71% were offered a referral. This high rate was consistent across the 2 hospital sites and is either a reflection of the use of screening tools that highlighted high-risk patients, or over-referral due to efforts to deter readmissions by using PAC support.

In an attempt to better understand the characteristics of the PAC refusers, we identified that they were younger and more likely to be married and have private medical insurance. From the clinical perspective, refusers were less medically complex, had shorter index hospital stays, and were more likely to be admitted electively rather than by emergent admissions or transfer from other facilities. At 7 to 14 days after hospital discharge, refusers reported better quality of life and fewer health-related problems and unmet needs than acceptors.

From the patient perspective, refusers may also see themselves as less medically complex. Chart review of the documentation of the reasons for refusal revealed that the most frequently reported reasons were, “I don’t need it” and “My wife will take care of me.” If we can better understand the barriers to PAC acceptance, we can tailor interventions to overcome those barriers. The reasons for refusal also indicate the importance of including the caregivers in the conversations or confirming the patient’s responses to make sure the patient’s caregiver really agrees and is capable of meeting the patient’s healthcare needs. In nearly half the refusals, no details were documented as to why the service was refused. Understanding the reasons for refusals and patient preferences regarding care after discharge will help clinicians to better match services to what patients and caregivers will accept. Clinicians must probe to make sure patients and caregivers understand what is offered and why it is offered, and they must document why patients and caregivers are refusing.

Patients may refuse services because they do not understand the value of those services. Gregory26 studied stroke patients during hospitalization to assess their preferences about rehabilitation. Eighty-five percent preferred rehabilitation in their homes to inpatient rehabilitation or skilled nursing facility care, even though more aggressive inpatient rehabilitation may result in improved functional outcomes.26 Three explanations for disparities in patients’ acceptance of rehabilitation services were found: 1) a lack of financial or functional eligibility, 2) a failure to recognize the need for services during the acute care stay, and 3) a patient’s preference to return home and not pursue therapy elsewhere. Further research is needed to validate the reasons for PAC refusal on a larger patient sample and to match services to patient preferences.

Refusers were more likely to be readmitted. Since we did not find other studies examining the characteristics and readmission rates of PAC refusers, further research to validate these results is warranted. We may discover that factors such as adherence, health literacy, and level of engagement are as important as clinical risk factors in identifying which patients are likely to fail after discharge.27,28 While the appearance of refusers may seem to be healthier, and their reassurances that they can manage their post acute needs with only informal supports are convincing, our data have shown that they suffer more readmissions and therefore we need to understand why.

Our findings also showed that patients with better self-reported quality of life after discharge were less likely to experience 30-day readmission. Those results match current reports of the positive association of quality of life and health outcomes.29-32 This finding indicates the need to carefully evaluate and support any unmet needs in pain management, self-care, and activities of daily living functions to optimize quality of life. In agreement with several recent studies,33-35 we found that shorter hospital stays were not associated with higher readmission rates. On the contrary, patients with longer hospital stays had higher odds of 30-day readmission, even when controlling for several indicators of clinical severity. Lastly, our finding of a positive association between previous overnight hospitalizations in the last 6 months and 30- and 60-day readmission is not new; several other studies have reported the same.36-38

Among acceptors, the high levels of problems, unmet needs, and quality-of-life issues reported are notable. Twenty-seven percent, 44%, and 49% reported quality-of-life issues with anxiety or depression, pain, and mobility, respectively, and 74% had unmet information needs. This may indicate that discharge plans or post acute care settings are not adequately meeting their needs, or perhaps our assessment was too early in the episode to show improvement. Nonetheless, instruments such as the PADQ-E and EQ-5D appear helpful to PAC providers to focus on the exact problems and unmet needs patients are experiencing.

Limitations

One limitation of this cross-sectional study is our limited ability to control for the quality/quantity of the PAC services offered to acceptors, which may have influenced readmission rates. The study was limited to medical units of 2 large urban academic hospitals in the northeast United States. 

CONCLUSIONS

We found that patients identified as high risk for poor discharge outcomes and referred for PAC services, who nonetheless refused such post acute services as home care, experienced 5% higher readmission rates and were twice as likely to experience readmission at 30 and 60 days than patients who accepted the services—despite the fact that acceptors had higher severity-of-illness scores, lower quality of life, and more problems and unmet needs after discharge. These findings suggest the powerful effect of post acute care support in preventing readmission, and lead us to call for more research into the reasons for service refusal, and for why refusers are readmitted more often although they appear to be less in need of post acute support. 

Author Affiliations: Harvard Medical School & Brigham Women’s Health Hospital (MT), Boston; University of Pennsylvania School of Nursing (YK, KHB), Philadelphia; Hospital of the University of Pennsylvania (KR), Philadelphia; Mayo Clinic Department of Nursing, College of Medicine (DEH), Rochester, MN; Langone Medical Center, New York University (BO), New York, NY.

Source of Funding: The investigators acknowledge grant support from the National Institute of Nursing Research (RO1-NR07674); this grant supported the original development of the D2S2. The Edna G. Kynett Foundation, the NewCourtland Center for Transitions and Health, the Leonard Davis Institute of Health Economics, and the Frank Morgan Jones Fund all supported data collection and data analyses for the primary study. The Center for Integrative Science in Aging (University of Pennsylvania School of Nursing) provided funding for the secondary analysis.

Author Disclosures: Dr Bowles owns equity in RightCare Solutions, a company that commercialized some aspects of this work. The original study was conducted and completed before the equity was obtained. The University of Pennsylvania conflict of interest committee reviewed the study and assigned a management plan that required an independent statistician complete the study analysis and lead the interpretation of study results. The plan was followed exactly. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (KHB, DEH); analysis and interpretation of data (MT, YK); drafting of the manuscript (MT, YK); critical revision of the manuscript for important intellectual content (MT, KHB, YK, DEH, BO, KR); statistical analysis (MT, YK); and supervision (KB).

Address correspondence to: Kathryn H. Bowles, PhD, RN, FAAN, FACMI, University of Pennsylvania School of Nursing, Philadelphia, PA 19104. E-mail: bowles@nursing.upenn.edu.

REFERENCES
 
1. Number, rate, and average length of stay for discharges from short-stay hospitals, by age, region, and sex: United States, 2010. CDC website. http://www.cdc.gov/nchs/data/nhds/1general/2010gen1_agesexalos.pdf. Accessed August 14, 2013.
 
2. Use of home health care and other care services among Medicare beneficiaries. Alliance for Home Health Quality and Innovation website. http://www.ahhqi.org/images/pdf/cacep-wp2-baselines.pdf. Published April 4, 2012. Accessed August 14, 2013.
 
3. Key features of the healthcare reform. HHS website. http://www.hhs.gov/healthcare/facts/timeline/. Reviewed November 18, 2014. Accessed December 21, 2012.
 
4. Readmissions Reduction Program. CMS website. http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html. Updated August 4, 2014. Accessed May 22, 2015.
 
5. Pezzin LE, Feldman PH, Mongoven JM, McDonald MV, Gerber LM, Peng TR. Improving blood pressure control: results of home-based post-acute care interventions. J Gen Intern Med. 2011;26(3):280-286.
 
6. Chen LK, Chen YM, Hwang SJ, et al; Longitudinal Older Veterans Study Group. Effectiveness of community hospital-based post-acute care on functional recovery and 12-month mortality in older patients: a prospective cohort study. Ann Med. 2010;42(8):630-636.
 
7. Vincent HK, Seay AN, Montero C, Vincent KR. Outpatient rehabilitation outcomes in obese patients with orthopedic conditions. Eur J Phys Rehabil Med. 2013;49(3):419-429. Review.
 
8. Fleming MO, Haney TT. Improving patient outcomes with better care transitions: the role for home health. Cleve Clin J Med. 2013;80 electronic suppl 1:eS2-eS6.
 
9. Bowles KH, Ratcliffe SJ, Holmes JH, Liberatore M, Nydick R, Naylor MD. Post-acute referral decisions made by multidisciplinary experts compared to hospital clinicians and the patients’ 12-week outcomes. Med Care. 2008;46(2):158-166.
 
10. Bowles KH, Naylor MD, Foust JB. Patient characteristics at hospital discharge and a comparison of home care referral decisions. J Am Geriatr Soc. 2002;50(2):336-342.
 
11. Wong W, Anderson KM, Dankwa-Mullan I, Simon MA, Vega WA. The patient-centered medical home: a path toward health equity? Institute of Medicine website. http://www.iom.edu/~/media/Files/Perspectives-Files/2012/Discussion-Papers/PatientCenteredMedicalHome.pdf. Published September 2012. Accessed Accessed August 14, 2013.
 
12. Steiner A, Walsh B, Pickering RM, Wiles R, Ward J, Brooking JI; Southampton NLU Evaluation Team. Therapeutic nursing or unblocking beds? a randomised controlled trial of a post-acute intermediate care unit. BMJ. 2001;322(7284):453-460.
 
13. Coleman EA, Smith JD, Frank JC, Min SJ, Parry C, Kramer AM. Preparing patients and caregivers to participate in care delivered across settings: the Care Transitions Intervention. J Am Geriatr Soc. 2004;52(11):1817-1825.
 
14. Sharp HealthCare. Reducing 30-day hospital readmissions for chronic obstructive pulmonary disorder (COPD) patients by using mobile health technology and personalized health coaching. Center for Technology and Aging website. http://www.techandaging.org/mHealth_Sharp.html. Published 2012. Accessed August 16, 2013.
 
15. Bowles KH, Hanlon A, Holland D, Potashnik SL, Topaz M. Impact of discharge planning decision support on time to readmission among older adult medical patients. Prof Case Manag. 2014;19(1):29-38.
 
16. Holland DE, Knafl GJ, Bowles KH. Targeting hospitalised patients for early discharge planning intervention. J Clin Nurs. 2013;22(19-20):2696-2703.
 
17. Holland DE, Harris MR, Leibson CL, Pankratz VS, Krichbaum KE. Development and validation of a screen for specialized discharge planning services. Nurs Res. 2006;55(1):62-71.
 
18. Bowles KH, Holmes JH, Ratcliffe SJ, Liberatore M, Nydick R, Naylor MD. Factors identified by experts to support decision making for post acute referral. Nurs Res. 2009;58(2):115-122.
 
19. Sebaldt R, Dalziel W, Massoud F, et al. Detection of cognitive impairment and dementia using the animal fluency test: the DECIDE study. Can J Neurol Sci. 2009;36(5):599-604.
 
20. Averill R, Goldfield N, Hughes J, McCullough C, Steinbeck B, Mullin R, Tang A; 3M Health Information Systems. All patient refined diagnosis related groups (APR-DRGs). Healthcare Cost and Utilization Project website. https://www.hcup-us.ahrq.gov/db/nation/nis/APR-DRGsV20MethodologyOverviewandBibliography.pdf. Published 2003. Accessed May 2015.
 
21. Duijnhouwer E, Mistiaen P. Content validation of the mailed dismissal problems questionnaire by qualitative research with the elderly. Verpleegkunde. 1999;14(2):76-83.
 
22. EQ-5D-3L user guide. 2013. EuroQol Group website. http://www.euroqol.org/fileadmin/user_upload/Documenten/PDF/Folders_Flyers/EQ-5D-3L_UserGuide_2013_v5.0_October_2013.pdf. Accessed February 2, 2014.
 
23. Xie F, Gaebel K, Perampaladas K, Doble B, Pullenayegum E. Comparing EQ-5D valuation studies: a systematic review and methodological reporting checklist. Med Decis Making. 2014;34(1):8-20.
 
24. Calculating the U.S. population-based EQ-5D index score. Agency for Healthcare Research and Quality website. http://archive.ahrq.gov/professionals/clinicians-providers/resources/rice/EQ5Dscore.html. Published August 2005. Accessed February 2, 2014.
 
25. Hintze J. PASS version 11 [software]. NCSS, LLC. Kaysville, Utah, USA; 2011.
 
26. Gregory P, Edwards L, Faurot K, Williams SW, Felix AC. Patient preferences for stroke rehabilitation. Top Stroke Rehabil. 2010;17(5):394-400.
 
27. Annema C, Luttik ML, Jaarsma T. Reasons for readmission in heart failure: perspectives of patients, caregivers, cardiologists, and heart failure nurses. Heart Lung. 2009;38(5):427-434.
 
28. Coleman EA, Chugh A, Williams MV, et al. Understanding and execution of discharge instructions. Am J Med Qual. 2013;28(5):383-391.
 
29. O’Loughlin C, Murphy NF, Conlon C, O’Donovan A, Ledwidge M, McDonald K. Quality of life predicts outcome in a heart failure disease management program. Int J Cardiol. 2010;139(1):60-67.
 
30. Moser DK, Yamokoski L, Sun JL, et al; Escape Investigators. Improvement in health-related quality of life after hospitalization predicts event-free survival in patients with advanced heart failure. J Card Fail. 2009;15(9):763-769.
 
31. Steer J, Gibson GJ, Bourke SC. Predicting outcomes following hospitalization for acute exacerbations of COPD. QJM. 2010;103(11):817-829.
 
32. Zuluaga MC, Guallar-Castillón P, López-Garcia E, et al. Generic and disease-specific quality of life as a predictor of long-term mortality in heart failure. Eur J Heart Fail. 2010;12(12):1372-1378.

33. Vorhies JS, Wang Y, Herndon J, Maloney WJ, Huddleston JI. Readmission and length of stay after total hip arthroplasty in a national Medicare sample. J Arthroplasty. 2011;26(6 suppl):119-123.
 
34. Saczynski JS, Lessard D, Spencer FA, et al. Declining length of stay for patients hospitalized with AMI: impact on mortality and readmissions. Am J Med. 2010;123(11):1007-1015.
 
35. Kaboli PJ, Go JT, Hockenberry J, et al. Associations between reduced hospital length of stay and 30-day readmission rate and mortality: 14-year experience in 129 Veterans Affairs hospitals. Ann Intern Med. 2012;157(12):837-845.
 
36. Hasan O, Meltzer DO, Shaykevich SA, et al. Hospital readmission in general medicine patients: a prediction model. J Gen Intern Med. 2010;25(3):211-219.
 
37. Lemke KW, Weiner JP, Clark JM. Development and validation of a model for predicting inpatient hospitalization. Med Care. 2012;50(2):131-139.
 
38. Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA. 2011;306(15):1688-1698.
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