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A prediction model for targeting low-cost, high-risk members of managed care organizations
Henry G Dove1, Ian Duncan, Arthur Robb
1Division of Health Policy and Administration, Department of Epidemiology and Public Health, Yale University, New Haven, Conn 06520-8034, USA. dove@worldnet.att.net
Insights
This study developed a predictive model to identify health maintenance organization (HMO) members at high risk for future high healthcare costs. The model accurately targets low-cost individuals likely to incur significant expenses within 12 months.
Area of Science:
- Health Services Research
- Predictive Analytics
- Healthcare Management
Background:
- Identifying high-cost healthcare consumers is crucial for effective resource allocation and cost containment within health plans.
- Predictive modeling offers a data-driven approach to proactively identify at-risk individuals before significant healthcare expenditures occur.
Purpose of the Study:
- To develop and validate a predictive model for identifying health maintenance organization (HMO) members likely to incur high healthcare costs.
- To enable targeted interventions for cost management and improved patient outcomes.
Main Methods:
- A split-sample multivariate regression analysis was employed using claims data from a 350,000-member HMO.
- The model incorporated clinical and behavioral variables from 1998 and 1999 claims data to predict costs in subsequent years.
- Prospective testing involved applying the model to identify low-cost patients (costing <$2000) at high risk for exceeding $2000 in costs the following year.
Main Results:
- The predictive model identified 47.8% of high-cost patients among top-ranked low-cost individuals in 1998, compared to 14.2% in a randomly selected control group.
- In a prospective validation, 39.7% of top-ranked low-cost patients in 1999 incurred high costs in 2000, versus 12.2% of the control group.
Conclusions:
- The developed predictive model effectively identifies low-cost, high-risk HMO members with a high likelihood of incurring substantial medical expenses within the next 12 months.
- This capability supports targeted disease management and cost-saving strategies for health plans.
Objective:
To describe the development and validation of a predictive model designed to identify and target HMO members who are likely to incur high costs.
Study Design:
Split-sample multivariate regression analysis.
Patients And Methods:
We studied enrollees in a 350000-member HMO with > or = 1 claim in 1998 and 1999. The prediction model uses a combination of clinical and behavioral vaiables and 1998 and 1999 claims data. The prediction model was applied and used to rank low-cost patients (1998 cost < dollars 2000) according to their estimated probability of incurring costs > or = dollars 2000 in 1999. For prospective testing, we applied our models to data that are not available in advance. The same prediction model was applied to rank a different set of low-cost patients (1999 cost < dollars 2000) according to estimated probability of incurring costs > or = dollars 2000 in 2000. Because the predictions were used for disease management purposes, the outcomes of a randomly selected control group not intervened on for the disease management program was analyzed. The predictive accuracy of the model was tested by comparing the percentages of "targeted" vs all low-cost patients who incurred high costs in the subsequent year.
Results:
Of the low-cost, top-ranked 1998 patients, 47.8% incurred high (> or = dollars 2000) medical expenses in 1999 vs 14.2% of randomly selected patients who were low cost in 1998. Of the top-ranked 1999 patients, 39.7% incurred high costs in 2000 vs 12.2% of the randomly selected low-ranked patients.
Conclusions:
The prediction model successfully identifies low-cost, high-risk patients who are likely to incur high costs in the next 12 months.