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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Predictive modeling in practice: improving the participant identification process for care management programs using

Shannon M E Murphy1, Heather K Castro, Martha Sylvia

  • 1Research and Development Unit, Johns Hopkins HealthCare LLC, Glen Burnie, Maryland 21060, USA. smurphy@jhhc.com

Population Health Management
|January 19, 2011
PubMed
Summary
This summary is machine-generated.

Optimizing participant selection for care management (CM) programs requires evidence-based, condition-specific cut points. This approach improves prediction accuracy and identifies high-cost members more efficiently, reducing healthcare costs.

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Area of Science:

  • Health Services Research
  • Predictive Analytics
  • Healthcare Management

Background:

  • Care management (CM) programs aim to reduce healthcare costs by targeting high-risk individuals.
  • Selecting appropriate participants is crucial for CM program effectiveness.
  • Current methods may use arbitrary thresholds, potentially leading to inefficient targeting.

Purpose of the Study:

  • To optimize participant selection for CM programs by evaluating different cut point selection methods.
  • To compare evidence-based optimal cut points against arbitrary thresholds.
  • To assess condition-specific cut points versus a uniform screening approach.

Main Methods:

  • Utilized Adjusted Clinical Groups Predictive Modeling (ACG-PM) risk scores for adult Medicaid members (n=6459) with chronic conditions.
  • Predicted top 5% highest healthcare expenditures.
  • Compared model performance (c statistic, sensitivity, specificity, PPV) and population size across three cut point approaches: arbitrary, single optimal, and condition-specific optimal.

Main Results:

  • Optimal cut points (single and condition-specific) outperformed arbitrary selection, showing higher prediction accuracy and sensitivity.
  • Condition-specific optimal cut points demonstrated superior performance (higher c statistic, specificity, PPV) compared to a single optimal cut point, while identifying a more manageable population.
  • Evidence-based cut points enhance targeting efficiency for CM programs.

Conclusions:

  • Condition-specific optimal cut points are superior for participant selection in CM programs.
  • Utilizing evidence-based, condition-specific risk variations optimizes targeting algorithms.
  • Efficiently identifying and intervening with high-cost members can significantly reduce healthcare expenditures.