Integrated Primary Care and Social Services for Older Adults With Multimorbidity in England: A Scoping Review

Dambha‐Miller, Hajira; Simpson, Glenn; Hobson, Lucy; Roderick, Paul; Little, Paul; Everitt, Hazel; Santer, Miriam

doi:10.21203/rs.3.rs-140720/v1

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…To address this gap in the evidence base, we carried out a mixed methods programme of research. This included a scoping review of the literature, 6 and a qualitative interview study to elicit key stakeholder views on drivers and barriers to integration of primary care and social services in England. Stakeholders included patients, care service users, carers, primary and secondary care clinicians, social prescribers, community nurses, social workers, voluntary sector workers, and multiple other relevant individuals.…”

Section: Introductionmentioning

confidence: 99%

Integrating primary care and social services for older adults with multimorbidity: policy implications

et al. 2021

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Section: Introductionmentioning

confidence: 99%

Integrating primary care and social services for older adults with multimorbidity: policy implications

et al. 2021

Self Cite

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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‐Miller¹,

Simpson²,

Akyea³

et al. 2022

JMIR Res Protoc

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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. International Registered Report Identifier (IRRID) PRR1-10.2196/34405

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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) (Preprint)

Dambha‐Miller¹,

Simpson²,

Akyea³

et al. 2021

Preprint

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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 tackle this have primarily focused on addressing biological aspects of disease, but MLTC-M are also the result of and associated with additional psycho-social, economic and environmental barriers. A shift towards more personalised, holistic and integrated care could be effective. This could be made more efficient by identifying groups of the population based on their health and social need. These will, in turn, 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 need and to quantify the impact of clusters on long-term health and costs. OBJECTIVE To develop and validate population clusters that consider determinants of health and social care need for people with MLTC-M using data-driven ML(machine-learning) 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 A mixed-methods programme of work with parallel work streams including; 1) Qualitative semi-structured interview study exploring patient, carer and professional views on clinical and socio-economic 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 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 characterised and trajectories over time examined, to quantify associations with mortality, additional long-term conditions, worsening frailty, disease severity and ten-year health/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 personalised care can be developed, accurate costs provided, better understand the personal and medical profiles and environment of individuals within each cluster. Integrated care that considers the ‘whole person’ and their environment is essential in addressing the complex, diverse and individual needs of people living with MLTC-M. CLINICALTRIAL Not applicable

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Integrated Primary Care and Social Services for Older Adults With Multimorbidity in England: A Scoping Review

Cited by 3 publications

References 40 publications

Integrating primary care and social services for older adults with multimorbidity: policy implications

Integrating primary care and social services for older adults with multimorbidity: policy implications

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) (Preprint)

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