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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)

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)
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)

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.

artificial intelligence, big data, long-term health, mixed method, multimorbidity, protocol, social care
1929-0748
Dambha-Miller, Hajira
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Simpson, Glenn
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Akyea, Ralph K
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Hounkpatin, Hilda
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Morrison, Leanne
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Gibson, Jon
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Stokes, Jonathan
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Islam, Nazrul
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Chapman, Adriane
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Stuart, Beth
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Zaccardi, Francesco
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Zlatev, Zlatko
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Jones, Karen
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Roderick, Paul
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Boniface, Michael
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Santer, Miriam
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Farmer, Andrew
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Dambha-Miller, Hajira
58961db5-31aa-460e-9394-08590c4b7ba1
Simpson, Glenn
802b50d9-aa00-4cca-9eaf-238385f8481c
Akyea, Ralph K
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Hounkpatin, Hilda
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Morrison, Leanne
920a4eda-0f9d-4bd9-842d-6873b1afafef
Gibson, Jon
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Stokes, Jonathan
f993fbb9-62ff-4718-a19b-f738b24c5e70
Islam, Nazrul
e5345196-7479-438f-b4f6-c372d2135586
Chapman, Adriane
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Stuart, Beth
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Zaccardi, Francesco
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Zlatev, Zlatko
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Jones, Karen
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Roderick, Paul
dbb3cd11-4c51-4844-982b-0eb30ad5085a
Boniface, Michael
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Santer, Miriam
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Farmer, Andrew
c384123c-1276-4d06-a2b5-d5419bd83b1d

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 and Farmer, Andrew (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). Journal of Medical Internet Research (JMIR) Research Protocols, 11 (6), [e34405]. (doi:10.2196/34405).

Record type: Article

Abstract

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.

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Accepted/In Press date: 21 April 2022
Published date: 16 June 2022
Additional Information: ©Hajira Dambha-Miller, Glenn Simpson, Ralph K Akyea, 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. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 16.06.2022.
Keywords: artificial intelligence, big data, long-term health, mixed method, multimorbidity, protocol, social care

Identifiers

Local EPrints ID: 468250
URI: http://eprints.soton.ac.uk/id/eprint/468250
ISSN: 1929-0748
PURE UUID: 45452913-4e32-4508-a7bb-5255a013012e
ORCID for Hajira Dambha-Miller: ORCID iD orcid.org/0000-0003-0175-443X
ORCID for Glenn Simpson: ORCID iD orcid.org/0000-0002-1753-942X
ORCID for Leanne Morrison: ORCID iD orcid.org/0000-0002-9961-551X
ORCID for Nazrul Islam: ORCID iD orcid.org/0000-0003-3982-4325
ORCID for Adriane Chapman: ORCID iD orcid.org/0000-0002-3814-2587
ORCID for Beth Stuart: ORCID iD orcid.org/0000-0001-5432-7437
ORCID for Paul Roderick: ORCID iD orcid.org/0000-0001-9475-6850
ORCID for Michael Boniface: ORCID iD orcid.org/0000-0002-9281-6095
ORCID for Miriam Santer: ORCID iD orcid.org/0000-0001-7264-5260

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Date deposited: 08 Aug 2022 17:00
Last modified: 11 Nov 2022 03:02

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Contributors

Author: Glenn Simpson ORCID iD
Author: Ralph K Akyea
Author: Leanne Morrison ORCID iD
Author: Jon Gibson
Author: Jonathan Stokes
Author: Nazrul Islam ORCID iD
Author: Adriane Chapman ORCID iD
Author: Beth Stuart ORCID iD
Author: Francesco Zaccardi
Author: Zlatko Zlatev
Author: Karen Jones
Author: Paul Roderick ORCID iD
Author: Miriam Santer ORCID iD
Author: Andrew Farmer

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