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Am J Manag Care ; 25(8): 388-395, 2019 08.
Article in English | MEDLINE | ID: mdl-31419096

ABSTRACT

OBJECTIVES: To determine whether self-identified social needs, such as financial assistance with utilities, food programs, housing support, transportation, and medication assistance, collected using a passive social health surveillance system were associated with inpatient readmissions. STUDY DESIGN: Cross-sectional, retrospective observational study. METHODS: This retrospective observational study linked social service referral data collected from a call center-based passive social health surveillance system with healthcare claims data extracted from a managed care organization (MCO). Mixed-effects logistic regression models calculated the odds of all-cause hospital readmissions within 30, 90, and 180 days among individuals with self-identified social service needs compared with those without. RESULTS: Individuals who identified social service needs had 68% (odds ratio [OR], 1.68; 95% CI, 1.51-1.86), 89% (OR, 1.89; 95% CI, 1.74-2.05), and 101% (OR, 2.01; 95% CI, 1.87-2.17) higher odds of readmission within 30, 90, and 180 days, respectively, after controlling for other study variables. Examining each social service need separately, individuals had higher odds of hospital readmission within 30 days of discharge if they identified a financial (OR, 1.19; 95% CI, 1.07-1.33), food (OR, 1.32; 95% CI, 1.17-1.48), housing (OR, 1.31; 95% CI, 1.09-1.57), or transportation (OR, 1.21; 95% CI, 1.08-1.36) need compared with those without those social needs. In all study outcomes, medication assistance was not associated with readmissions. CONCLUSIONS: An MCO created a passive social health surveillance program to more effectively integrate medical and social care. Understanding individual-level social health needs provides the insights needed to develop interventions to prevent hospital readmissions.


Subject(s)
Patient Readmission/statistics & numerical data , Social Work/statistics & numerical data , Adult , Aged , Cross-Sectional Studies , Female , Humans , Insurance Claim Review , Logistic Models , Male , Middle Aged , Retrospective Studies , Risk Factors , Socioeconomic Factors
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