- Top of page
- Why a Real-World Data Task Force?
- Task Force Objectives and Scope
- Types and Sources of RW Data
- Key Findings
Objectives: Health decision-makers involved with coverage and payment policies are increasingly developing policies that seek information on “real-world” (RW) outcomes. Motivated by these initiatives, the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) created a Task Force on Real-World Data to develop a framework to assist health-care decision-makers in dealing with RW data, especially related to coverage and payment decisions.
Methods: Task Force cochairs were selected by the ISPOR Board of Directors. Cochairs selected chairs for four working groups on: clinical outcomes, economic outcomes, patient-reported outcomes, and evidence hierarchies. Task Force members included representatives from academia, the pharmaceutical industry, and health insurers. The Task Force met on several occasions, conducted frequent correspondence and exchanges of drafts, and solicited comments on three drafts from a core group of external reviewers and from the ISPOR membership.
Results: We defined RW data as data used for decision-making that are not collected in conventional randomized controlled trials (RCTs). We considered several characterizations: by type of outcome (clinical, economic, and patient-reported), by hierarchies of evidence (which rank evidenceaccording to the strength of research design), and by type of data source (supplementary data collection alongside RCTs, large simple trials, patient registries, administrative claims database, surveys, and medical records). Our report discusses eight key issues: 1) the importance of RW data; 2) limitations of RW data; 3) the fact that the level of evidence required depends on the circumstance; 4) the need for good research practices for collecting and reporting RW data; 5) the need for good process in using RW data in coverage and reimbursement decisions; 6) the need to consider costs and benefits of data collection; 7) the ongoing need for modeling; and 8) the need for continued stakeholder dialogue on these topics.
Conclusions: Real-world data are essential for sound coverage and reimbursement decisions. The types and applications of such data are varied, and context matters greatly in determining the value of a particular type in any circumstance. It is critical that policymakers recognize the benefits, limitations, and methodological challenges in using RW data, and the need to consider carefully the costs and benefits of different forms of data collection in different situations.