title: Medicaid patients at high risk for frequent hospital admission: real-time identification and remediable risks.
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.
authors.affiliation: Department of Emergency Medicine, NYU School of Medicine, New York, NY 10016, USA. maria.raven@nyumc.org
authors.forename: Maria C
authors.initials: MC
authors.lastname: Raven
authors.forename: John C
authors.initials: JC
authors.lastname: Billings
authors.forename: Lewis R
authors.initials: LR
authors.lastname: Goldfrank
authors.forename: Eric D
authors.initials: ED
authors.lastname: Manheimer
authors.forename: Marc N
authors.initials: MN
authors.lastname: Gourevitch
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D005260:Female; D006703:Ill-Housed Persons; D006779:Hospitals, Public; D006801:Humans; D007345:Insurance Claim Review; D007407:Interviews as Topic; D008297:Male; D008484:Medicaid / Q000191:economics; D008875:Middle Aged; D009519:New York City; D010343:Patient Admission / Q000706:statistics & numerical data*; D018570:Risk Assessment / Q000706:statistics & numerical data; D040242:Risk Reduction Behavior; D014481:United States; D055815:Young Adult