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Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

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Interactive Cost-benefit Analysis: Providing Real-World Financial Context to Predictive Analytics.

Mark G Weiner¹, Wasiq Sheikh¹, Harold P Lehmann²

¹Lewis Katz School of Medicine at Temple University, Philadelphia, PA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|March 1, 2019

Summary

Predictive analytics for high-cost health outcomes may not always save money. A cost-benefit analysis is crucial to determine if using predictive models is financially favorable compared to intervening on all patients.

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Area of Science:

Health economics
Clinical informatics
Predictive modeling

Background:

Predictive analytics are used to identify high-risk patients for interventions, assuming cost savings.
The cost-effectiveness of this strategy requires rigorous financial evaluation.

Purpose of the Study:

To assess the financial viability of using predictive analytics for high-cost outcome interventions.
To develop a tool for cost-benefit analysis of predictive analytics implementation.

Main Methods:

Designed an interactive web-based cost-benefit calculator.
Incorporated predictive model accuracy, clinical, and financial factors.
Applied the tool to analyze interventions for Congestive Heart Failure (CHF) readmissions.

Main Results:

Implementing predictive analytics may not always be more cost-effective than intervening on all patients.
Predictive model accuracy influences cost savings, but outcome prevalence and intervention effectiveness are critical.
Other factors like intervention cost and success rate significantly impact the favored strategy.

Conclusions:

Interactive cost-benefit analysis is essential for evaluating the financial implications of predictive analytics in clinical settings.
The decision to implement predictive analytics should consider a comprehensive financial assessment beyond model accuracy.