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Development and Validation of Population Clusters for Integrating Health and Social Care: Protocol for a Mixed Methods Study in Multiple Long-Term Conditions (Cluster-Artificial Intelligence for Multiple Long-Term Conditions)

Dambha-Miller, Hajira; Simpson, Glenn; Akyea, Ralph K; Hounkpatin, Hilda; Morrison, Leanne; Gibson, Jon; Stokes, Jonathan; Islam, Nazrul; Chapman, Adriane; Stuart, Beth; Zaccardi, Francesco; Zlatev, Zlatko; Jones, Karen; Roderick, Paul; Boniface, Michael; Santer, Miriam; Farmer, Andrew


Hajira Dambha-Miller

Glenn Simpson

Hilda Hounkpatin

Leanne Morrison

Jon Gibson

Jonathan Stokes

Nazrul Islam

Adriane Chapman

Beth Stuart

Francesco Zaccardi

Zlatko Zlatev

Karen Jones

Paul Roderick

Michael Boniface

Miriam Santer

Andrew Farmer


Background: Multiple long-term health conditions (multimorbidity) (MLTC-M) are increasingly prevalent and associated with high rates of morbidity, mortality, and health care expenditure. Strategies to address this have primarily focused on the biological aspects of disease, but MLTC-M also result from and are associated with additional psychosocial, economic, and environmental barriers. A shift toward more personalized, holistic, and integrated care could be effective. This could be made more efficient by identifying groups of populations based on their health and social needs. In turn, these will contribute to evidence-based solutions supporting delivery of interventions tailored to address the needs pertinent to each cluster. Evidence is needed on how to generate clusters based on health and social needs and quantify the impact of clusters on long-term health and costs. Objective: We intend to develop and validate population clusters that consider determinants of health and social care needs for people with MLTC-M using data-driven machine learning (ML) methods compared to expert-driven approaches within primary care national databases, followed by evaluation of cluster trajectories and their association with health outcomes and costs. Methods: The mixed methods program of work with parallel work streams include the following: (1) qualitative semistructured interview studies exploring patient, caregiver, and professional views on clinical and socioeconomic factors influencing experiences of living with or seeking care in MLTC-M; (2) modified Delphi with relevant stakeholders to generate variables on health and social (wider) determinants and to examine the feasibility of including these variables within existing primary care databases; and (3) cohort study with expert-driven segmentation, alongside data-driven algorithms. Outputs will be compared, clusters characterized, and trajectories over time examined to quantify associations with mortality, additional long-term conditions, worsening frailty, disease severity, and 10-year health and social care costs. Results: The study will commence in October 2021 and is expected to be completed by October 2023. Conclusions: By studying MLTC-M clusters, we will assess how more personalized care can be developed, how accurate costs can be provided, and how to better understand the personal and medical profiles and environment of individuals within each cluster. Integrated care that considers “whole persons” and their environment is essential in addressing the complex, diverse, and individual needs of people living with MLTC-M.


Dambha-Miller, H., Simpson, G., Akyea, R. K., Hounkpatin, H., Morrison, L., Gibson, J., …Farmer, A. (2022). Development and Validation of Population Clusters for Integrating Health and Social Care: Protocol for a Mixed Methods Study in Multiple Long-Term Conditions (Cluster-Artificial Intelligence for Multiple Long-Term Conditions). JMIR Research Protocols, 11(6), Article e34405.

Journal Article Type Article
Acceptance Date Apr 21, 2022
Online Publication Date Jun 16, 2022
Publication Date Jun 1, 2022
Deposit Date Jul 18, 2022
Publicly Available Date Jul 18, 2022
Journal JMIR Research Protocols
Print ISSN 1929-0748
Electronic ISSN 1929-0748
Publisher JMIR Publications
Peer Reviewed Peer Reviewed
Volume 11
Issue 6
Article Number e34405
Public URL
Publisher URL


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