JOURNAL ARTICLE

Health technology assessment and primary data collection for reducing uncertainty in decision making

Ron Goeree, Les Levin, Kiran Chandra, James M Bowen, Gord Blackhouse, Jean-Eric Tarride, Natasha Burke, Matthias Bischof, Feng Xie, Daria O'Reilly
Journal of the American College of Radiology: JACR 2009, 6 (5): 332-42
19394574

BACKGROUND: Health care expenditures continue to escalate, and pressures for increased spending will continue. Health care decision makers from publicly financed systems, private insurance companies, or even from individual health care institutions, will continue to be faced with making difficult purchasing, access, and reimbursement decisions. As a result, decision makers are increasingly turning to evidence-based platforms to help control costs and make the most efficient use of existing resources. Most tools used to assist with evidence-based decision making focus on clinical outcomes.

HEALTH TECHNOLOGY ASSESSMENT: Health technology assessment (HTA) is increasing in popularity because it also considers other factors important for decision making, such as cost, social and ethical values, legal issues, and factors such as the feasibility of implementation. In some jurisdictions, HTAs have also been supplemented with primary data collection to help address uncertainty that may still exist after conducting a traditional HTA.

ROLE OF PRIMARY DATA COLLECTION: The HTA process adopted in Ontario, Canada, is unique in that assessments are also made to determine what primary data research should be conducted and what should be collected in these studies. In this article, concerns with the traditional HTA process are discussed, followed by a description of the HTA process that has been established in Ontario, with a particular focus on the data collection program followed by the Programs for Assessment of Technology in Health Research Institute. An illustrative example is used to show how the Ontario HTA process works and the role value of information analyses plays in addressing decision uncertainty, determining research feasibility, and determining study data collection needs.

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