PD67 Strengthening And Accelerating Health Technology Assessments Through Artificial Intelligence

• Published in: International journal of technology assessment in health care Vol. 34; no. S1; pp. 154 - 155
• Main Authors: Goebel, Randy, Kim, Mi-Young, Jonsson, Egon, Wolfaardt, Ulli
• Format: Journal Article
• Language: English
• Published: New York, USA Cambridge University Press 2018
• Subjects: Artificial intelligence; Computation; Health; Health care; Health technology assessment; Learning algorithms; Machine learning; Poster Display Presentations; Quality assessment; Quality control; Synthesis; Technology assessment; Canada
• DOI: 10.1017/S0266462318003288
• ISSN: 0266-4623

Introduction:Rising costs and the rapidly increasing volume of findings from research in health care are driving the demand for comprehensive information to inform the allocation of resources. Health technology assessment (HTA) applies rigorous processes to provide high-quality synthesized information to policymakers and healthcare payers. HTA involves combining large amounts of research publications to systematically evaluate the properties, effects, and impacts on a topic of interest.Methods:The time and resources required to complete a full HTA are often demanding. There is an opportunity to apply high-performance computing (inclusive of artificial intelligence and machine learning disciplines) to HTA. This project applied high-computing technology to create a research synthesis tool to support HTA and then developed a service that integrates as much relevant data as possible to strengthen HTA. This was a joint project that combined expertise from the areas of health technology, machine learning, information technology, and innovation.Results:The information gathered for this phased project from HTA subject matter experts and other stakeholders was collated to inform a research synthesis tool and a broader concept of the project.Conclusions:The results of this study will inform the design of a research synthesis tool that covers the entire HTA process (literature search, screening titles and abstracts, data extraction, quality assessment, and analysis). The collaborators included Alberta Innovates, the Alberta Machine Intelligence Institute, the University of Alberta, Cybera, and PolicyWise. Alberta Innovates, which is an accelerator and innovator of research in the province of Alberta, Canada, was the primary source of funding for this project.