A claims-based algorithm to reduce relapse and cost in schizophrenia.

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A claims-based algorithm to reduce relapse and cost in schizophrenia.

Am J Manag Care. 2019 Dec 01;25(12):e373-e378

Authors: Waters HC, Ruetsch C, Tkacz J

Abstract
OBJECTIVES: To refine a payer algorithm identifying patients with schizophrenia at high risk of relapse within a managed Medicaid population and evaluate its effectiveness in a case management (CM) program.
STUDY DESIGN: Cross-sectional and longitudinal study design.
METHODS: The algorithm used a single payer's Medicaid medical and pharmacy claims (August 1, 2009, to July 31, 2014) for patients with schizophrenia (N = 12,353) to predict those at high risk for hospitalization. The final algorithm was used in a CM program (outbound communication to providers) at 3 payer service centers in 3 states. Based on the algorithm, 60 patients (20 from each site) with the highest risk scores were targeted for CM (CM group) and 60 (those patients ranked 21st-40th most at-risk at each site) comprised the control group. Chi-square tests compared groups on frequency measures (hospitalizations, emergency department [ED] visits). Pre- to postimplementation differences were tested using McNemar's test. A pre-post analysis of variance assessed mean numbers of inpatient admissions, inpatient days, and ED visits for both groups.
RESULTS: The algorithm had good positive predictive power (64.0%), negative predictive power (94.7%), sensitivity (40.2%), and specificity (97.9%). Following CM, the proportion of patients with at least 1 inpatient admission in the CM group decreased (23.3% to 13.3%), as did the rate of ED visits per month (by approximately 15%), whereas increases were observed in the control group.
CONCLUSIONS: Although not all of these differences were statistically significant, they suggest that the algorithm may be an effective case-finding tool for plans attempting to mitigate hospitalizations among high-risk patients with schizophrenia.

PMID: 31860231 [PubMed - in process]

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