Medicaid patients at high risk for frequent hospital admission: Real-time identification and remediable risks

Maria C. Raven, John C. Billings, Lewis R. Goldfrank, Eric D. Manheimer, Marc N. Gourevitch

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)230-241
Number of pages12
JournalJournal of Urban Health
Volume86
Issue number2
DOIs
StatePublished - Mar 2009

Keywords

  • Case-finding algorithm
  • Frequent hospitalization
  • High risk
  • Homelessness
  • Identifying patients
  • Medicaid
  • Social risk factors
  • Substance use

ASJC Scopus subject areas

  • Health(social science)
  • Urban Studies
  • Public Health, Environmental and Occupational Health

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