Journal of Urban Health

, Volume 86, Issue 2, pp 230–241 | Cite as

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


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.


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



This research was supported by a grant from the United Hospital Fund and by a research fellowship training grant: CDC T01 CD000146. An abstract of this research was presented at the Society for General Internal Medicine (SGIM) Annual Research Meeting in 2007 and at the Academy Health Annual Research Meeting in 2007.

Supplementary material

11524_2008_9336_MOESM1_ESM.doc (106 kb)
Appendix A (DOC 106 KB )


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Copyright information

© The New York Academy of Medicine 2008

Authors and Affiliations

  • Maria C. Raven
    • 1
    • 3
  • John C. Billings
    • 2
  • Lewis R. Goldfrank
    • 1
    • 3
  • Eric D. Manheimer
    • 3
  • Marc. N. Gourevitch
    • 4
  1. 1.Department of Emergency MedicineNYU School of MedicineNew YorkUSA
  2. 2.New York University Wagner School of Public ServiceNew YorkUSA
  3. 3.Bellevue Hospital CenterNew YorkUSA
  4. 4.Division of General Internal MedicineNew York University School of MedicineNew YorkUSA

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