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012 Development of an Analytic Framework for Making Evidence-Based Coverage Policy Decisions
  1. V King1,3,
  2. S Vandegriff1,
  3. A Little1,3,
  4. D Coffman2,
  5. C Livingston2,3,
  6. W Shaffer2,
  7. J Gingerich2
  1. 1Center for Evidence-based Policy, Oregon Health & Science University, Portland, USA
  2. 2Office of Oregon Health Policy and Research, Oregon Health Authority, Salem, USA
  3. 3Department of Family Medicine, Oregon Health & Science University, Portland, USA


Background Comprehensive health reform legislation in 2009 directed a US state to develop processes by which evidence can be translated into coverage guidance, and be applied rapidly and uniformly across public and private settings.

Context Topics for development of coverage guidance were chosen if they represented a significant burden of disease, had important uncertainty with regard to efficacy or harms, had important variation or controversy in clinical care, significant economic impact and/or were of high public interest.

Description of Best Practice A list of evidence sources was developed and vetted through the Governor-appointed committee that manages the state Medicaid benefit package. An analytic framework algorithm was developed to guide coverage decisions that consider six stepwise decision points: sufficiency of evidence; effectiveness of the treatment and availability of alternatives; treatment risk; cost; prevalence of treatment and feasibility of clinical research studies. The GRADE process was also used to specify the addition of patient values and preferences as a factor. The algorithm allows the committee to determine whether a service is recommended or not, with two levels of strength of recommendation (strong and weak). Using a public process, the committee has reviewed the evidence and has made coverage policy recommendations for 15 topics, to date. Decisions have been applied to Medicaid, and are also made available to other public and private payers.

Lessons for Guideline Developers, Adaptors, Implementers, and/or Users Use of a discreet analytic framework can aid in the development of coverage decisions, and may accelerate the dissemination of research evidence into clinical practice.

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