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Random fluctuations and validity in measuring disease management effectiveness for small populations
J Ramsay Farah1, Kyahn Kamali, Jeffrey Harner
1Calvert Health Partners, LLC, Baltimore, Maryland, USA.
Population Health Management
|December 26, 2008
Summary
To ensure accurate disease management (DM) program savings, this study determines the necessary sample size to overcome random fluctuations in claims costs. It also offers alternative measures for smaller groups to validate outcomes.
Area of Science:
- Health Economics
- Biostatistics
- Healthcare Management
Background:
- Disease management (DM) programs aim to reduce member claims costs.
- Standardizing DM outcome measurement is ongoing, but random fluctuations are often overlooked.
- Large random fluctuations can obscure actual DM program savings, impacting validity.
Purpose of the Study:
- To measure fluctuations in DM savings within a large commercial population.
- To determine the minimum sample size needed for credible DM outcomes measurement.
- To propose alternative utilization-based measures for smaller groups.
Main Methods:
- Utilized an adjusted historical control methodology, the industry standard.
- Calculated fluctuations in DM savings and determined necessary sample sizes for various confidence levels.
- Modeled outcome fluctuations based on trend selection, truncation levels, and savings estimates.
Main Results:
- Identified sample size requirements to reliably demonstrate DM program savings.
- Provided confidence intervals for both claims- and utilization-based savings estimates.
- Addressed outcome measurement for group sizes from 1,000 to 100,000 members.
Conclusions:
- Random fluctuations significantly impact the perceived savings of DM programs.
- Specific sample sizes are crucial for statistically valid DM outcomes.
- Utilization-based measures serve as a viable proxy for smaller populations lacking sufficient claims data.
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