OP06 Past Speculations Of Future Health Technologies: What Did They Predict?. (2017)

• Record Type: Journal Article
• Authors: Doos, Lucy; Packer, Claire; Ward, Derek; Simpson, Sue; Stevens, Andrew
• Is Part Of: International journal of technology assessment in health care. Volume 33:Supplement 1(2017)
• Page Start: 4
• Page End: 4
• Publication Date: 2017
• Subjects: Medical technology -- Periodicals; Technology assessment -- Periodicals; 610.28
• DOI: 10.1017/S0266462317001167
• Languages: English
• ISSNs: 0266-4623

Abstract

Abstract : INTRODUCTION: Rapid technological innovation is leading to new health technologies and interventions becoming available to healthcare markets at increasing speed; these often cost more than current alternatives and significantly affect the cost of healthcare services and delivery (1). Identifying future technologies supports service preparedness, long-term planning, and strategic decision making. The aim of this study was to describe and classify health technologies predicted in fifteen forecasting studies according to their type, purpose and clinical use, and relate these to the original purpose and timing of the forecasting studies. METHODS: This was a descriptive study of predicted healthcare technologies identified in fifteen forecasting studies included in a previously published systematic review (2). Outcomes related to (i) each forecast study including country, year, intent and forecasting methods used, and (ii) the predicted technology type, purpose, targeted clinical area and forecast timeframe. RESULTS: We identified 896 predicted health-related topics, of which 685 were health technologies. Of these, 19.1 percent were diagnostic or imaging tests and 14.3 percent devices or biomaterials; 38.1 percent were intended to treat or manage disease and 21.6 percent to diagnose or monitor disease. The most frequent targeted clinical areas were infectious diseases followed by cancer, circulatory and nervous system disorders. The mean timeframe for technology forecast was 11.6 years (Standard Deviation, SD = 6.6). The forecasting timeframe significantly differed by technology type (p = .002), the intent of the forecasting group (p < .0001), and the methods used (p < .0001). CONCLUSIONS: Our description and classification of predicted health-related technologies from prior forecasting studies provides an overview of the technological and clinical frontiers of innovation in health and healthcare provision.