Past speculations of future health technologies: a description of technologies predicted in 15 forecasting studies published between 1986 and 2010. Issue 7 (31st July 2017)

• Record Type: Journal Article
• Authors: Doos, Lucy; Packer, Claire; Ward, Derek; Simpson, Sue; Stevens, Andrew
• Is Part Of: BMJ open. Volume 7:Issue 7(2017)
• Publication Date: 2017-07-31
• Subjects: Forecasting -- prediction -- health technology -- innovation -- healthcare; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2017-016206
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objective: To describe and classify health technologies predicted in forecasting studies. Design and methods: A portrait describing health technologies predicted in 15 forecasting studies published between 1986 and 2010 that were identified in a previous systematic review. Health technologies are classified according to their type, purpose and clinical use; relating these to the original purpose and timing of the forecasting studies. Data sources: All health-related technologies predicted in 15 forecasting studies identified in a previously published systematic review. Main outcome measure: Outcomes related to (1) each forecasting study including country, year, intention and forecasting methods used and (2) the predicted technologies including technology type, purpose, targeted clinical area and forecast timeframe. Results: Of the 896 identified health-related technologies, 685 (76.5%) were health technologies with an explicit or implied health application and included in our study. Of these, 19.1% were diagnostic or imaging tests, 14.3% devices or biomaterials, 12.6% information technology systems, eHealth or mHealth and 12% drugs. The majority of the technologies were intended to treat or manage disease (38.1%) or diagnose or monitor disease (26.1%). The most frequent targeted clinical areas were infectious diseases followed by cancer, circulatory and nervous system disorders. The most frequent technology types were for: infectious diseases—prophylactic vaccines (45.8%), cancer—drugs (40%), circulatory disease—devices and biomaterials (26.3%), and diseases of the nervous system—equally devices and biomaterials (25%) and regenerative medicine (25%). The mean timeframe for forecasting was 11.6 years (range 0–33 years, median=10, SD=6.6). The forecasting timeframe significantly differed by technology type (p=0.002), the intent of the forecasting group (p<0.001) and the methods used (p<001). Conclusion: While description and classification of predicted health-related technologies is crucial in preparing healthcare systems for adopting new innovations, further work is needed to test the accuracy of predictions made.