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.
Conclusion: 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.