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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; and 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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Lihao Chu, PhD; Neeraj Sood, PhD; Michael Tu, MS; Katrina Miller, MD; Lhasa Ray, MD; and Jennifer N. Sayles, MD
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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.

Objectives: To identify characteristics and readmission risks associated with opting out of a social work–driven transition intervention.

Study Design: Secondary data analysis of a randomized controlled pilot study at a large nonprofit urban community hospital.

Methods: Hospitalized English-speaking, cognitively intact adults 65 years or older with expected discharge back to the community were eligible for enrollment. Additionally, patients met at least 1 of the 3 criteria: 1) 75 years or older, 2) taking 5 or more medications, or 3) had 1 or more prior inpatient stays or emergency department visits in the previous 6 months. The transition intervention consisted of up to 2 in-home visits (the first occurring within 48 hours after discharge) and up to 4 telephone follow-up calls (for a maximum of 6 total contacts) by a transition social worker. This study analyzed participants randomized to the intervention arm on measures including demographics, medical diagnoses, presence of advance directive, and all-cause 30-day hospital readmissions. 

Results: Of the 90 patients randomized to the Social Work Intervention Focused on Transitions intervention group, 10% were readmitted within 30 days and nearly one-third refused (ie, opted out of) the home visit component of the intervention. Multivariate analyses revealed that those opting out of the intervention had 3 times greater odds of having a respiratory condition compared with intervention recipients (odds ratio [OR], 3.10; 95% CI, 1.09-8.80; P = .034). Additionally, opting out of the intervention (OR, 6.75; 95% CI, 1.05-43.52; P = .045) and having a diagnosis of cancer (OR, 29.59; 95% CI, 2.01-435.45; P = .014) significantly predicted readmission.

Conclusions: Findings suggest that some at-risk patients may not be receptive to services and programs aimed at improving care transitions, resulting in a higher risk for readmission.

Am J Manag Care. 2017;23(12):e394-e401
Takeaway Points

Current policies have charged hospitals with the task of reducing 30-day readmission rates; however, results of the present study contribute to a growing body of research suggesting that it may not be reasonable to place this burden solely on hospitals. 
  • Some patients at risk for hospital readmission may not be receptive to in-home transition interventions, yet those who refuse interventions may experience greater odds of being readmitted within 30 days. 
  • Participants who opted out of the Social Work Intervention Focused on Transitions intervention or those diagnosed with cancer were more likely to be readmitted within 30 days.
  • Participants with a respiratory condition were more likely to opt out of the intervention.
Transitions between care settings have been identified as vulnerable exchange points that are associated with increased risk for hospital readmissions,1-5 medication errors,6,7 lapses in care and safety,8 poor satisfaction with care,9 unmet needs,10 and subsequent high rates of additional costly health service use, many of which may have been avoided.5,7 Older adults are at highest risk for poor transitions and subsequent hospital readmissions. A benchmark study found that 20% of hospitalized Medicare beneficiaries were readmitted within 30 days and 34% within 90 days of their index hospitalization.11 This high rate of readmission among older adults served as the impetus for the Hospital Readmissions Reduction Program (HRRP) implemented as part of the Affordable Care Act (ACA) in 2012, which imposes penalties on hospitals with higher than expected rates of 30-day readmissions among older adults with certain conditions (heart attack, pneumonia, and heart failure, during this study period). A study conducted following implementation of HRRP payment penalties found that readmission rates declined to 17.8% for HRRP-targeted conditions.12 Although some have challenged these findings by attributing reductions in readmission rates to rising observation rates,13 it is clear that hospitals have made some improvements in 30-day readmission rates.12,14,15 

Although many hospitals have implemented transitional care interventions targeting older adults,16-18 the penalties have raised hospital interest in identifying additional mechanisms to further reduce readmissions. While some hospitals have seen significant reductions in 30-day rehospitalization rates,16-18 recent studies and reports highlight several challenges in the ability of hospitals to impact readmission rates and associated HRRP penalties. These concerns include patient-level factors (eg, sociodemographic factors, patient preference, and access to community-based supportive services) beyond the hospital’s control influencing recidivism,19-23 readmissions that are appropriate and unavoidable,23 and lack of risk adjustment for uncontrollable factors that influence recidivism (resulting in disproportionate penalties on safety-net hospitals).15,19,23 Administrators and researchers are calling for HRRP reform; in the meantime, hospitals continue to grapple with which patients constitute the high-risk pool that should be targeted for transitional care services and be provided with interventions aimed at reducing readmissions and improving quality.23

This study is a secondary analysis of patients randomized to the care transition intervention arm of the Social Work Intervention Focused on Transitions (SWIFT) pilot study conducted among older adults identified as being at high risk for readmission. This analysis aims to identify the characteristics and risk factors associated with opting out of a social work–driven transition intervention. Increased knowledge of the factors associated with intervention refusal can help hospitals identify those patients who appear resistant to interventions. With hospitals accountable for 30-day readmissions, information from this study may provide insight to hospitals on patient groups who could benefit from alternate strategies to reduce readmissions, such as education and interventions provided during the hospital stay or in the primary care setting following discharge. Findings from this study also may inform hospital practice and CMS policies and funding priorities.

The question guiding this research was: what characteristics and risk factors are associated with opting out of transition intervention services? 


This study was a secondary data analysis of the SWIFT randomized controlled pilot study conducted between February 2011 and September 2013 at a large (625-bed) nonprofit teaching hospital located in the Los Angeles area. The study was approved by the institutional review board at the hospital study site (Huntington Memorial Hospital) and the academic research institution (University of Southern California) executing the study.

Eligibility and Recruitment

Hospitalized patients eligible for the study were English-speaking community-dwelling adults 65 years or older living within a 20-mile radius of the hospital. Additionally, participants had to meet at least 1 of the following criteria: 1) advanced age (75 years or older), 2) taking 5 or more prescription medications, and/or 3) having 1 or more hospitalizations or emergency department (ED) visits in the previous 6 months. These criteria have been used in previous social work case management24 and hospital-to-home care transition interventions25 to identify individuals at high risk for hospital readmission. Patients were ineligible for the SWIFT study if they were homeless, lived in an environment where they received skilled care (ie, long-term care or hospice recipient), were cognitively impaired (as determined by a Short Portable Mental Status Questionnaire [SPMSQ]26-28 score of 5 or more errors in replies to 10 questions), or were diagnosed with Alzheimer disease, severe dementia, or end-stage renal disease. Patients with end-stage renal disease were excluded from the SWIFT study due to their elevated risk of death and the associated level of need for skilled nursing care, which is outside the skillset of social workers.29 

We identified potentially eligible patients 65 and older by reviewing daily hospital census reports (excluding the intensive/critical care unit) 1 to 2 times per day Monday through Friday. Direct referrals also were made by a social worker conducting rounds in the nursing units. Electronic health records (EHRs) were reviewed to determine number of medications being taken and previous medical service use. Patients meeting the eligibility criteria were approached at hospital bedside by master’s-level research assistants who administered the SPMSQ to establish mental competency. Initial (and, if needed, subsequent) patient contact was made at varying points during the patient’s hospitalization course due to noncontinuous census screening, patients sometimes being out of their room for a procedure or test, and some patients requesting a revisit (eg, because they were feeling unwell or requested family be present during discussion). Eligible patients were invited to participate and asked to sign informed consent and Health Insurance Portability and Accountability Act authorization documents. 

SWIFT Intervention

The SWIFT intervention builds on previous transitional care research16,25,30 and integrates social work practice approaches. The intervention consists of in-home visits (a maximum of 2 in-home visits) and telephone follow-up calls (up to 4 telephone contacts) conducted by the study social worker. Patients receive a minimum of 2 contacts (1 in person and 1 phone call) and a maximum of 6 contacts with the social worker. The purpose of the first home visit is to conduct an initial assessment and develop and implement a plan of care. Activities performed by the social worker during this visit are guided by an intervention checklist and include a psychosocial evaluation, home safety check, medication inventory for reconciliation, review of hospital discharge instructions, health goal setting and problem solving, coaching around scheduling follow-up physician appointments, and referrals to home- and community-based services. The second in-home visit is conducted if problems identified at the initial home visit are not sufficiently resolved or are extensive enough that telephone contact is not adequate to resolve the problems. 

In addition to the in-home visit(s), SWIFT intervention patients receive up to 4 telephone calls from the social worker. The aim of these calls is to follow up on issues identified at the home visit(s), discuss outcomes from physician office visits, review established health goals (and draft new ones, when applicable), determine success of linkages or referrals to community-based services, and problem-solve around new issues.


The study researchers collected data from the hospital electronic database and through patient surveys. To ensure reliability, they were formally trained in methods for gathering and extracting data and the safe and ethical conduct of human subjects research. They used the hospital’s EHR to obtain data on previous ED visits and hospitalizations, all-cause 30-day readmissions (planned and unplanned), and presence of an advance directive. Research assistants collected patient demographics and other characteristics, including age, gender, marital status, and disease diagnoses (via a “yes/no” inventory of 10 common conditions) through patient surveys conducted at bedside. Asthma and chronic obstructive pulmonary disease (COPD) were consolidated into a single variable renamed “respiratory disease.” Similarly, “cardiac disease” represented a consolidation of heart disease and chronic heart failure, and all cancers were included in a single “cancer” variable. Following hospital discharge, research assistants obtained EHR data on hospital length of stay and whether home health care services were ordered at discharge. 

Study participants randomized to the SWIFT intervention who did not receive in-home intervention visits were considered “opt-outs.” Opt-outs were identified by social workers in their outreach to SWIFT intervention participants. SWIFT social workers documented patients’ stated reasons for opting out of SWIFT intervention home visits during their initial patient contact following hospital discharge.


Descriptive statistics were used to describe the sample, and bivariate tests (χ2 and Mann-Whitney U) were performed to analyze differences between the participants who opted out of the intervention and those who received the intervention. Two logistic regressions were performed to identify characteristics associated with opting out of the SWIFT intervention and to determine risk factors for 30-day readmission. We used results of the bivariate analyses and findings from previous research to guide inclusion of variables in the regressions to maintain the most parsimonious model, given the small sample size. Regression models to determine predictors of opting out of the SWIFT intervention included the following independent variables: respiratory condition, cardiac condition, cancer, length of stay (index hospitalization), presence of advance directive, and discharge to home without home health care services (self-care). We included the same variables, with the addition of the intervention opt-out variable, in the second logistic regression to determine predictors of 30-day readmission.



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