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Past speculations of future health technologies: a description of technologies predicted in 15 forecasting studies published between 1986 and 2010
  1. Lucy Doos1,
  2. Claire Packer1,
  3. Derek Ward1,
  4. Sue Simpson1,
  5. Andrew Stevens2
  1. 1 NIHR Horizon Scanning Research and Intelligence Centre, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
  2. 2 Institute of Applied Health Research,University of Birmingham, Birmingham, UK
  1. Correspondence to Dr Lucy Doos; l.doos{at}bham.ac.uk

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.

  • Forecasting
  • prediction
  • health technology
  • innovation
  • healthcare

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/

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Footnotes

  • Contributors All authors have directly participated in the planning and execution of the study. LD extracted the study data. LD and CP independently reviewed the technologies to be included. LD and CP drafted the paper and DW, SS and AS critically revised the manuscript. All authors agreed the final version of the paper. All authors had full access to all of the data.

  • Funding At the time of the study, LD, CP, DW and SS were funded by the UK's NIHR and AS was partly funded by the UK's NIHR. This report presents independent research funded by the NIHR.

  • Disclaimer The views expressed in this publication are those of the authors and not necessarily those of the National Health Service, the NIHR or the Department of Health.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement A full list of health technologies included in the study is available in a supplemental document submitted with this article. More details on data are available from the corresponding author at l.doos@bham.ac.uk.

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