Medicaid Patients at High Risk for Frequent Hospital Admission: Real-Time Identification and Remediable Risks
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Patients with frequent hospitalizations generate a disproportionate share of hospital visits and costs. Accurate determination of patients who might benefit from interventions is challenging: most patients with frequent admissions in 1 year would not continue to have them in the next. Our objective was to employ a validated regression algorithm to case-find Medicaid patients at high-risk for hospitalization in the next 12 months and identify intervention-amenable characteristics to reduce hospitalization risk. We obtained encounter data for 36,457 Medicaid patients with any visit to an urban public hospital from 2001 to 2006 and generated an algorithm-based score for hospitalization risk in the subsequent 12 months for each patient (0 = lowest, 100 = highest). To determine medical and social contributors to the current admission, we conducted in-depth interviews with high-risk hospitalized patients (scores >50) and analyzed associated Medicaid claims data. An algorithm-based risk score >50 was attained in 2,618 (7.2%) patients. The algorithm’s positive predictive value was equal to 0.67. During the study period, 139 high-risk patients were admitted: 60 met inclusion criteria and 50 were interviewed. Fifty-six percent cited the Emergency Department as their usual source of care or had none. Sixty-eight percent had >1 chronic medical conditions, and 42% were admitted for conditions related to substance use. Sixty percent were homeless or precariously housed. Mean Medicaid expenditures for the interviewed patients were $39,188 and $84,040 per patient for the years immediately prior to and following study participation, respectively. Findings including high rates of substance use, homelessness, social isolation, and lack of a medical home will inform the design of interventions to improve community-based care and reduce hospitalizations and associated costs.
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- Medicaid Patients at High Risk for Frequent Hospital Admission: Real-Time Identification and Remediable Risks
Journal of Urban Health
Volume 86, Issue 2 , pp 230-241
- Cover Date
- Print ISSN
- Online ISSN
- Springer US
- Additional Links
- Frequent hospitalization
- High risk
- Identifying patients
- Case-finding algorithm
- Social risk factors
- Substance use
- Author Affiliations
- 1. Department of Emergency Medicine, NYU School of Medicine, 462 1st Avenue, New York, NY, 10016, USA
- 3. Bellevue Hospital Center, New York, NY, USA
- 2. New York University Wagner School of Public Service, New York, NY, USA
- 4. Division of General Internal Medicine, New York University School of Medicine, New York, NY, USA