Abstract

The burden of serious and persistent mental illness such as schizophrenia is substantial and requires health-care organizations to have adequate risk adjustment models to effectively allocate their resources to managing patients who are at the greatest risk. Currently available models underestimate health-care costs for those with mental or behavioral health conditions. The study aimed to develop and evaluate predictive models for identification of future high-cost schizophrenia patients using advanced supervised machine learning methods. This was a retrospective study using a payer administrative database. The study cohort consisted of 97,862 patients diagnosed with schizophrenia (ICD9 code 295.*) from January 2009 to June 2014. Training (n = 34,510) and study evaluation (n = 30,077) cohorts were derived based on 12-month observation and prediction windows (PWs). The target was average total cost/patient/month in the PW. Three models (baseline, intermediate, final) were developed to assess the value of different variable categories for cost prediction (demographics, coverage, cost, health-care utilization, antipsychotic medication usage, and clinical conditions). Scalable orthogonal regression, significant attribute selection in high dimensions method, and random forests regression were used to develop the models. The trained models were assessed in the evaluation cohort using the regression R2, patient classification accuracy (PCA), and cost accuracy (CA). The model performance was compared to the Centers for Medicare & Medicaid Services Hierarchical Condition Categories (CMS-HCC) model. At top 10% cost cutoff, the final model achieved 0.23 R2, 43% PCA, and 63% CA; in contrast, the CMS-HCC model achieved 0.09 R2, 27% PCA with 45% CA. The final model and the CMS-HCC model identified 33 and 22%, respectively, of total cost at the top 10% cost cutoff. Using advanced feature selection leveraging detailed health care, medication utilization features, and supervised machine learning methods improved the ability to predict and identify future high-cost patients with schizophrenia when compared with the CMS-HCC model.

Highlights

  • Schizophrenia is a chronic and costly condition estimated to have annual direct and indirect costs of up to US$102 billion [1, 2]

  • With minimum demographic and total per member per month (PMPM) cost variables in observation window (OW), the Baseline Model using linear regression significantly improved the performance measures of R2 by 10%, patient classification accuracy (PCA) and cost accuracy (CA) by 13% at top 10% setting, and 15%, 9% at top 20% setting, compared to the Centers for Medicare & Medicaid Services (CMS)-Hierarchical Condition Categories (HCCs) model

  • Whereas the CMS-HCC achieved an R2 of only 0.09 in the schizophrenic population, our Baseline Model achieved an R2 of 0.19, and adding coverage, health-care utilization, antipsychotic medication usage, and detailed costs increased the predictive value of this model (0.24 R2)

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Summary

Introduction

Schizophrenia is a chronic and costly condition estimated to have annual direct and indirect costs of up to US$102 billion [1, 2]. Given the significant cost and disease burden of serious and persistent mental illness, health-care organizations require adequate risk adjustment models to effectively allocate their resources to managing patients who are at the greatest risk. Health-care organizations have historically used diagnosisbased and data-driven risk adjustment models that cover general medical conditions. These models include the Hierarchical Condition Categories (HCCs) and the Adjusted Clinical Groups (ACGs) systems. The burden of serious and persistent mental illness such as schizophrenia is substantial and requires health-care organizations to have adequate risk adjustment models to effectively allocate their resources to managing patients who are at the greatest risk. Available models underestimate health-care costs for those with mental or behavioral health conditions

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