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Optimal Information Collection Policies in a Markov Decision Process Framework.

Lauren E Cipriano1,2,3,4, Jeremy D Goldhaber-Fiebert1,2,3,4, Shan Liu1,2,3,4

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Medical Decision Making : an International Journal of the Society for Medical Decision Making
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Summary

Delaying health information collection can be optimal, especially when parameters like hepatitis C virus (HCV) prevalence change. This strategy maximizes the value of information for better decision-making and cost-effectiveness.

Keywords:
Markov decision processesdynamic programminghepatitis Coptimal stoppingvalue-of-information analysis

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Area of Science:

  • Health economics
  • Decision analysis
  • Public health

Background:

  • The value of health technology information can change due to evolving patient populations and interventions.
  • Optimal timing for data collection is crucial for cost-effectiveness, even for seemingly static parameters.

Purpose of the Study:

  • To develop a stochastic dynamic programming approach for optimizing intervention and information collection policies.
  • To evaluate hepatitis C virus (HCV) screening policies considering time-varying parameters like prevalence.

Main Methods:

  • Utilized stochastic dynamic programming to identify optimal intervention and information collection strategies.
  • Modeled HCV screening for birth cohorts, focusing on the impact of time-varying HCV prevalence on information collection for liver fibrosis staging.

Main Results:

  • Demonstrated that delaying information collection can be optimal until it significantly impacts decision-making.
  • For HCV screening, delaying liver fibrosis information collection by 12 years increased expected net monetary benefit by $169.5 million compared to current guidelines.

Conclusions:

  • Delaying information collection until it influences decisions enhances efficiency.
  • A dynamic programming framework effectively assesses the marginal value of information and determines optimal collection policies.