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14.09.17 - A new research paper by Prof. Thomas Weber (EPFL) and Prof. Lauren Cipriano (University of Westen Ontario) “Population-Level Intervention and Information Collection in Dynamic Healthcare Policy,” examines when and how much costly information should be collected before deciding whether to optimally start or stop a new healthcare program. As an application of their theoretical insights authors consider the evaluation of hepatitis C virus screening and make public policy recommendations.

Abstract

We develop a general framework for optimal health policy design in a dynamic setting. We consider a hypothetical medical intervention for a cohort of patients where one parameter varies across cohorts with imperfectly observable linear dynamics. We seek to identify the optimal time to change the current health intervention policy and the optimal time to collect decision-relevant information. We formulate this problem as a discrete-time, infinite-horizon Markov decision process and we establish structural properties in terms of first and second-order monotonicity. We demonstrate that it is generally optimal to delay information acquisition until an effect on decisions is sufficiently likely. We apply this framework to the evaluation of hepatitis C virus (HCV) screening in the general population determining which birth cohorts to screen for HCV and when to collect information about HCV prevalence.

Reference

Cipriano, L.E., Weber, T.A. (2017) “Population-Level Intervention and Information Collection in Dynamic Healthcare Policy,” Health Care Management Science, in press. [DOI 10.1007/s10729-017-9415-5]

[Available freely via open access at http://link.springer.com/article/10.1007/s10729-017-9415-5 or at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3034885 ]

Author:Carole BonardiSource:CDM | Management of Technology
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