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The American Journal of Managed Care December 2017
Chronic Disease Outcomes From Primary Care Population Health Program Implementation
Jeffrey M. Ashburner, PhD, MPH; Daniel M. Horn, MD; Sandra M. O’Keefe, MPH; Adrian H. Zai, MD, PhD; Yuchiao Chang, PhD; Neil W. Wagle, MD, MBA; and Steven J. Atlas, MD, MPH
Expanding the "Safe Harbor" in High-Deductible Health Plans: Better Coverage and Lower Healthcare Costs
A. Mark Fendrick, MD, and Rashna Soonavala
Impact of Consumer-Directed Health Plans on Low-Value Healthcare
Rachel O. Reid, MD, MS; Brendan Rabideau, BA; and Neeraj Sood, PhD
Insurance Switching and Mismatch Between the Costs and Benefits of New Technologies
David Cutler, PhD; Michael Ciarametaro, MBA; Genia Long, MPP; Noam Kirson, PhD; and Robert Dubois, MD, PhD
ED-Based Care Coordination Reduces Costs for Frequent ED Users
Michelle P. Lin, MD, MPH; Bonnie B. Blanchfield, ScD, CPA; Rose M. Kakoza, MD, MPH; Vineeta Vaidya, MS; Christin Price, MD; Joshua S. Goldner, MD; Michelle Higgins, PA-C; Elisabeth Lessenich, MD, MPH; Karl Laskowski, MD, MBA; & Jeremiah D. Schuur, MD, MHS
Evaluation of the Quality Blue Primary Care Program on Health Outcomes
Qian Shi, PhD, MPH; Thomas J. Yan, MS; Peter Lee, BS; Paul Murphree, MD, MHA; Xiaojing Yuan, MPH; Hui Shao, PhD, MHA; William H. Bestermann, MD; Selina Loupe, BS; Dawn Cantrell, BA; David Carmouche, MD; John Strapp, BA; and Lizheng Shi, PhD, MSPharm
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Investigating the Impact of Intervention Refusal on Hospital Readmission
Alexis Coulourides Kogan, PhD; Eileen Koons, MSW, ACSW; and Susan Enguidanos, PhD
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Investigating the Impact of Intervention Refusal on Hospital Readmission

Alexis Coulourides Kogan, PhD; Eileen Koons, MSW, ACSW; and Susan Enguidanos, PhD
Findings suggest that some at-risk patients may not be receptive to in-home transition interventions and that opting out may be associated with higher odds of hospital readmission.
Overall, 90 participants were randomized to the SWIFT intervention group (see Figure). SWIFT intervention group participants were mostly Caucasian (63.6%), male (56.1%), and living in their own houses or apartments (89.7%). The average (SD) age of participants was 78.4 (7.8) years. Educational attainment was high; the vast majority (87.6%) completed high school or beyond and 46.3% held a bachelor’s degree or higher (Table 1). 

Among the 90 participants randomized to the intervention group, nearly one-third (31.1%) opted out. Participants identified the following reasons for opting out of the SWIFT intervention: felt home visit/follow-up care was not needed (n = 12; 42.9%), did not want a home visit (n = 8; 28.6%), were hoping to be randomized to the usual care arm (n = 6; 21.4%), and were unreachable (n = 2; 7.1%). We queried the reachable subsample of participants (n = 26) to further understand their reasons for opting out of the home intervention. Several reported not needing the home visit because they were feeling very good (n = 7; 26.9%) or were already well cared for by family members and/or caregivers (n = 2; 7.7%). Three indicated that they were not usually “sick” and did not need home visits. Others no longer wanted the SWIFT home visit because they were prescribed visits from home health (n = 6; 23.1%) and felt that additional visitors were unnecessary. Two participants reported “fatigue” with clinicians and medical encounters in general. The 6 participants who had hoped to be randomized to the usual care arm of the study unanimously stated that they only enrolled to either “help the hospital” or “help the researchers,” explaining that this “level of care” (ie, home visits) was unnecessary for them but could help “someone who could really use it” in the future if it became “normal care for all seniors.” 

Comparison of characteristics of those enrolled in the SWIFT intervention and those opting out revealed few differences: opt-outs were more likely to have a respiratory disease compared with intervention recipients (52.2% of opt-outs vs 28.3% of intervention; P = .04). A greater proportion of opt-outs were readmitted to the hospital within 30 days (n = 5 [18.5%] vs n = 4 [6.3%] of intervention recipients), but this trend did not reach statistical significance (P = .08). Of the 9 readmissions among all participants randomized to the SWIFT intervention, only 1 was planned (an intervention recipient with a planned hospitalization for a fiber-optic bronchoscopy procedure).

Intervention Opt-out 

Using binary logistic regression, we investigated characteristics associated with opting out of the SWIFT intervention. Results revealed that the odds of opting out of the SWIFT intervention were significantly higher for participants with respiratory conditions (odds ratio [OR], 3.29; 95% CI, 1.09-9.90; P = .034) (Table 2). No other variables were significantly associated with opting out.

Any 30-Day Readmission

We conducted a second logistic regression to examine variables associated with all-cause readmission to the hospital within 30 days of the index hospital discharge. Results revealed that, while controlling for other confounding variables, having a cancer diagnosis and opting out of the SWIFT intervention both predicted 30-day hospital readmission (P = .01 and P = .05, respectively). SWIFT participants diagnosed with cancer had nearly 30 times higher odds of being readmitted within 30 days of discharge (OR, 29.59; 95% CI, 2.01-435.45), and those who opted out of the home intervention had greater than 6 times higher odds of being readmitted within 30 days of discharge (OR, 6.75; 95% CI, 1.05-43.53) (Table 3).

DISCUSSION

This study aimed to identify the characteristics and risk factors associated with opting out of a social work–driven transition intervention. Findings suggest that some at-risk patients may not be receptive to in-home transition interventions, with nearly one-third of patients opting out of a home visit after consenting to participate in the study. 

We found that participants who opted out of the intervention were significantly more likely to have a respiratory condition. There is a dearth of literature pertaining to the medical diagnoses of older adults who decline or drop out of interventions. However, the results of a study by Voss and colleagues examining recruitment of hospitalized Medicare patients for behavioral research show that patients who reported a perceived inability to control important life domains (ie, “In the last week, how often have you felt that you are unable to control the important things in your life?”31), had low expectations of recovery, or reported confusion with the researcher’s questions were significantly less likely to consent to the research.31 The authors suggested that stress, self-expectations for recovery, and health literacy are potential influences on older adults’ decision to participate in behavioral research.31 

Although participants in the present study did originally consent to the research, with some later opting out of the home intervention aspect of the study, the constructs that Voss et al describe may be particularly prevalent among older adults with respiratory conditions. For example, COPD is a common respiratory condition most prevalent among older adults aged 65 to 74 years32 and was the second leading overall cause of death for all ages in 2015.32 Exacerbations of COPD can be significant events that can cause patients to be hospitalized and may require any number of inpatient interventions, each of which is considered to have fatal risks.33,34 The constructs of perceived stress, expected recovery, and health literacy offered by Voss et al could be impacted by the severity of COPD exacerbations and hospital course of care, in addition to psychosocial risk factors such as depression and socioeconomic status,35 and later translate to intervention opt-outs.31,36,37 

Another reason why participants with a respiratory condition may have opted out of the SWIFT intervention was because they were feeling better after their hospital stay. The most common reason for opting out of the SWIFT intervention was that the home visits were not needed because the participant was feeling good or did not consider themselves to be a “sick” person who needed home visits. This possibility is corroborated by previous research by van Grunsven et al.36 Interestingly, although we found that respiratory disease was significantly associated with opting out of the intervention but not with having a 30-day readmission, others have found high readmission rates (within 30 days and beyond) among patients with COPD.38-40 Further research among older patients with COPD and other respiratory conditions is needed. 

All but one 30-day readmission among our SWIFT intervention study participants was unplanned. We found significantly higher odds of 30-day readmission among participants diagnosed with cancer. Studies of hospitalized patients with cancer also found significantly higher 30-day rehospitalization rates compared with patients without cancer.41,42 These findings and those of the present study suggest that the effects of cancer and its treatment may place patients with cancer at increased risk for unplanned 30-day rehospitalization. Additionally, our finding of higher odds of 30-day readmission among participants opting out of our SWIFT intervention lends to the question of the role of patient self-determination in hospital readmissions. Patients cited several reasons for opting out of the SWIFT intervention—lack of perceived need, lack of interest, health provider fatigue—all reflecting personal preference toward less or no additional care. This finding is consistent with another study that found that patients who did not keep their outpatient appointments following hospital discharge had higher readmission rates.43 Other studies also have highlighted the variability in 30-day hospitalization rates driven by the risk and composition of the patient population they serve, patient access to care, and the availability of community resources.20-22 Studies have found that demographic factors (eg, older age, low income) and psychosocial factors (eg, baseline depression) are related to higher rates of hospital readmissions.35 

With patient-level and community factors accounting for a high portion of readmission rates, penalizing hospitals for aspects they cannot control may be misguided. Moreover, although many hospitals have successfully undertaken efforts to improve transitional care provided to patients, some interventions have been found to be associated with an increase in readmission rates believed to be caused by improved access to care and patient satisfaction.22,44

Limitations

Results of the study may be limited in several ways. First, the sample size may weaken the statistical power to detect differences between intervention opt-outs and intervention recipients. Secondly, study participants were recruited from a single, large, nonprofit, urban hospital, and the results may not be generalizable to other areas. Similarly, although our inclusion and exclusion criteria may have introduced selection bias that could impact the generalizability of findings (ie, excluding those with advanced dementia or Alzheimer disease, homeless individuals, etc), these criteria are appropriate for the skillset of social workers conducting a home-based intervention. Noncontinuous patient eligibility screening and enrollment efforts, resulting in a large number of patients being discharged before they could determine participation, and the use of a single EHR are limitations of the present study. Self-reported patient data posed limitations as well: we could not fully understand the reasons that at-risk patients opted out of the intervention, and we did not obtain patient self-reports to document ED visits and inpatient hospital stays to supplement the EHRs. Additional EHR and/or Medicare claims data would strengthen these findings. Also, due to the nature of the pilot study, only cognitively intact English-speaking older adults were eligible to participate and, therefore, this sample may not be representative of hospitalized older adult patients.

CONCLUSIONS

 
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