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Using predictive modeling to evaluate the financial effect of disease management
Terry Whitlock1, Kenton Johnston
1BlueCross BlueShield of Tennessee, Chattanooga 37402, USA. terry_whitlock@bcbst.com
Managed Care Interface
|October 5, 2006
Summary
Diagnostic cost grouping (DCG) predictive modeling offers a more accurate financial impact assessment for disease management (DM) programs than traditional pre-post methods. This study demonstrates its effectiveness in evaluating DM programs for chronic conditions.
Area of Science:
- Health Economics
- Health Services Research
- Predictive Analytics
Background:
- Disease management (DM) programs aim to improve outcomes for chronically ill patients.
- Accurate financial impact assessment is crucial for DM program evaluation.
- Current methods, like the population-based pre-post design, have limitations in financial effect measurement.
Purpose of the Study:
- To evaluate the financial impact of a disease management program on a chronically ill population.
- To compare the accuracy of diagnostic cost grouping (DCG) predictive modeling against the population-based pre-post method for assessing DM program financial effects.
Main Methods:
- Utilized diagnostic cost grouping (DCG) predictive modeling.
- Analyzed claims data and DM program-specific data over a three-year period (2001-2003).
- Computed mean differences between predicted and actual total claims costs, applying inflation factors.
Main Results:
- DCG predictive modeling provided a more accurate financial impact evaluation.
- Preliminary findings indicate superior accuracy compared to the population-based pre-post method.
- Inflation factors were developed and applied for precise financial effect evaluation.
Conclusions:
- DCG predictive modeling is a more accurate approach for evaluating the financial impact of disease management programs.
- This method offers improved financial insights for DM programs targeting chronic conditions like heart failure and coronary artery disease.
- The study highlights the limitations of current pre-post methods and advocates for advanced modeling techniques.
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