Overcoming the challenges of using automated technologies for public health evidence synthesis.

Lucy Hocking, Sarah Parkinson, Avery Adams, Emmanuel Molding Nielsen, Cecilia Ang, Helena de Carvalho Gomes
Author Information
  1. Lucy Hocking: RAND Europe, Cambridge, United Kingdom.
  2. Sarah Parkinson: RAND Europe, Cambridge, United Kingdom.
  3. Avery Adams: RAND Europe, Cambridge, United Kingdom.
  4. Emmanuel Molding Nielsen: RAND Europe, Cambridge, United Kingdom.
  5. Cecilia Ang: RAND Europe, Cambridge, United Kingdom.
  6. Helena de Carvalho Gomes: European Centre for Disease Prevention and Control (ECDC), Stockholm, Sweden.

Abstract

Many organisations struggle to keep pace with public health evidence due to the volume of published literature and length of time it takes to conduct literature reviews. New technologies that help automate parts of the evidence synthesis process can help conduct reviews more quickly and efficiently to better provide up-to-date evidence for public health decision making. To date, automated approaches have seldom been used in public health due to significant barriers to their adoption. In this Perspective, we reflect on the findings of a study exploring experiences of adopting automated technologies to conduct evidence reviews within the public health sector. The study, funded by the European Centre for Disease Prevention and Control, consisted of a literature review and qualitative data collection from public health organisations and researchers in the field. We specifically focus on outlining the challenges associated with the adoption of automated approaches and potential solutions and actions that can be taken to mitigate these. We explore these in relation to actions that can be taken by tool developers (e.g. improving tool performance and transparency), public health organisations (e.g. developing staff skills, encouraging collaboration) and funding bodies/the wider research system (e.g. researchers, funding bodies, academic publishers and scholarly journals).

Keywords

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MeSH Term

Humans
Public Health
Data Collection

Word Cloud

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