ArticleBMJ open2024
Exploration of understanding of impactibility analysis and application through workshops to inform model design for population health management policy: a qualitative assessment.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Population health management within the primary care context - scoping review.The European journal of general practice · 2026Article
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Authors and funding
8 authors.
Funding
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Abstract
objectivesAssess understanding of impactibility modelling definitions, benefits, challenges and approaches.
designQualitative assessment.
settingTwo workshops were developed. Workshop 1 was to consider impactibility definitions and terminology through moderated open discussion, what the potential pros and cons might be, and what factors would be best to assess. In workshop 2, participants appraised five approaches to impactibility modelling identified in the literature.
participantsNational Health Service (NHS) analysts, policy-makers, academics and members of non-governmental think tank organisations identified through existing networks and via a general announcement on social media. Interested participants could enrol after signing informed consent. OUTCOME MEASURES: Descriptive assessment of responses to gain understanding of the concept of impactibility (defining impactibility analysis), the benefits and challenges of using this type of modelling and most relevant approach to building an impactibility model for the NHS.
results37 people attended 1 or 2 workshops in small groups (maximum 10 participants): 21 attended both workshops, 6 only workshop 1 and 10 only workshop 2. Discussions in workshop 1 illustrated that impactibility modelling is not clearly understood, with it generally being viewed as a cross-sectional way to identify patients rather than considering patients by iterative follow-up. Recurrent factors arising from workshop 2 were the shortage of benchmarks; incomplete access to/recording of primary care data and social factors (which were seen as important to understanding amenability to treatment); the need for outcome/action suggestions as well as providing the data and the risk of increasing healthcare inequality.
conclusionsUnderstanding of impactibility modelling was poor among our workshop attendees, but it is an emerging concept for which few studies have been published. Implementation would require formal planning and training and should be performed by groups with expertise in the procurement and handling of the most relevant health-related real-world data.
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