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Exploring computer-aided health decision-making on cervical cancer interventions through deliberative interviews in Ethiopia.

Frithjof SyAstrid Berner-RodoredaTakelech AsnakeMisrak GetnetWondwossen Amogne DeguHermann BussmannHelen AberaTill BärnighausenAndreas Deckert
Published in: NPJ digital medicine (2023)
Cervical cancer is a significant disease burden in Ethiopia. Mathematical models and computer simulations on disease dynamics can support effective resource allocation. The objectives of this work are (i) to explore the perspectives of health decision-makers on computer-aided predictions supporting cervical cancer interventions, (ii) to identify their information needs from these predictions, and (iii) their willingness to apply the results in their work. We conducted deliberative interviews with 15 health decision-makers and advisors in Ethiopia in autumn 2019. We analyze the data using a five steps framework approach drawing on thematic analysis and find that Ethiopian health decision-makers are willing to use computer-aided predictions in their decisions. Data on HPV prevalence and the cervical cancer burden are scarce but valued highly and decision-makers are particularly interested in the identification of local HPV hotspots. Data-driven mathematical models and computer simulations may increasingly influence health decision-making in Ethiopia.
Keyphrases
  • public health
  • decision making
  • healthcare
  • health information
  • mental health
  • physical activity
  • risk factors
  • big data
  • health promotion
  • deep learning
  • molecular dynamics
  • machine learning
  • artificial intelligence