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)

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

Pretrained Language Models for Semantics-Aware Data Harmonisation of Observational Clinical Studies in the Era of Big Data

“…However, exploiting this potential requires significant effort in harmonising source datasets and curating the study data [3]. In observational studies, such as the Cluster-AIM study for development and validation of population clusters for integrating health and social care for patients with MLTCs [4], datasets must be curated from various cohort study databases or routine healthcare data databases. Such studies have complex and multi-faceted domains with 10s of thousands of variables to be curated for the specific research task at hand.…”

Clustering by Multiple Long-Term Conditions and Social Care Needs: A cohort study amongst 10,025 older adults in England

“…Clustering by both MLTC and SCN may allow more precise identification of those who could benefit most from preventive interventions and increased resource allocation in a holistic way (10,18). Although some advances have been made in MLTC clustering research (19,20), there is a scarcity of evidence considering SCN in combination with MLTC (21,22). This study aimed to classify people by MLTC and SCN into distinct clusters and quantify the association between derived clusters and care outcomes.…”