The Peer Models Network: A Novel Mechanism to Support Transformative Model Interrogation in Patient-Oriented Health Economics

Project Status: Active

Project Summary

In BC's public health care system, decision-makers have to choose what treatments and health programs to invest in, as the provincial budget cannot fund everything. To help make these tough choices, decision-makers often rely on information from 'health economists', researchers that specialize in health care costs and outcomes. To know which treatments and health programs provide the best value for money, health economists look closely at health care costs and outcomes using computer models.

Recently, many people have agreed that these computer models should be made publicly available, since British Columbians deserve to know what evidence informs decisions around health care funding. At UBC, a research group is currently working on a way to make these computer models available to the public, on a website called PRISM. However, the group is concerned that just making these models available is not enough, for two reasons:

  • One, computer models are very complex, so it would be good to develop educational tools to help people to understand them.
  • Two, how computer models are developed can affect the results they give, so it would be good to give people an opportunity to give feedback to model developers.

The goal of this project is to 'pilot test' a process for filling these gaps.

  1. First, it will determine whether social media can be used to create a network of people, including patients, who are willing to help review and improve health economics models as they are developed.
  2. Second, it will test a rapid way to create educational videos to help people understand models on the PRISM website.
  3. Third, it will explore whether network members prefer to give feedback online or in-person, and whether model developers make changes to models based on the feedback they receive.

Visit the Peer Models Network website

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Team

Stephanie Harvard, co-lead
Mohsen Sadatsafavi, co-lead
Amin Adibi, co-lead
Zhina Madjzade
Leah Grantham
Don Grant
Alison McLean
Greg Werker
David Murphy
Diego Silva