Past speculations of the future: a review of the methods used for forecasting emerging health technologies

Lucy Doos, Claire Packer, Derek Ward, Susan Simpson, Andrew Stevens

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
179 Downloads (Pure)


Objectives: Forecasting can support rational decision making around the introduction and use of emerging health technologies and prevent investment in technologies that have limited long-term potential. However, forecasting methods need to be credible. We performed a systematic search to identify the methods used in forecasting studies to predict future health technologies within a 3-20 year timeframe. Identification and retrospective assessment of such methods potentially offer a route to more reliable prediction.
Design: Systematic search of the literature to identify studies reported on methods of forecasting in healthcare.
Participants: People are not needed in this study.
Data sources: The authors searched MEDLINE, EMBASE, PsychINFO, and grey literature sources, and included articles published in English that reported their methods and a list of identified technologies.
Main outcome measure: Studies reporting methods used to predict future health technologies within a 3- 20 year timeframe with an identified list of individual health care technologies. Commercially sponsored reviews, long-term futurology studies (with over 20 year timeframes) and speculative editorials were excluded.
Results: Fifteen studies met our inclusion criteria. Our results showed that the majority of studies (13/15) consulted experts either alone or in combination with other methods such as literature searching. Only two studies used more complex forecasting tools such as scenario building.
Conclusion: The methodological fundamentals of formal 3- 20 year prediction are consistent but vary in details. Further research needs to be conducted to ascertain if the predictions made were accurate and whether accuracy varies by the methods used or by the types of technologies identified.
Original languageEnglish
Article numbere010479
JournalBMJ open
Publication statusPublished - 10 Mar 2016


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