An informatics approach to profiling patient experiences using electronic health records: constructing and clustering the burden space of individuals under 65 years of age with multiple long-term conditions

Living with multiple long-term conditions (MLTC) profoundly impacts patients lives, affecting not only their health but also their financial, emotional, and social well-being. It can impose a significant burden on people. Here we take a novel approach, exploring the lived experience of individuals with MLTC by identifying patterns of burden--spanning physical, emotional, social, and financial domains--using machine learning techniques applied to electronic health records (EHR). We constructed a cohort of 310,990 individuals born between January 1, 1958, and December 31, 1967, all with two or more long-term conditions. Proxy indicators of burden were extracted from EHR data. Using k-means clustering, we identified subgroups of individuals with distinct burden profiles and analyzed the distribution of burden indicators within each cluster. Several large clusters were characterized by high prevalence of one or more of pain, anxiety, and depression. Most clusters were predominantly female, with females over-represented compared to the overall burden cohort. Socioeconomic disparities were evident: clusters marked by pain had a higher proportion of individuals from the most deprived areas, while clusters characterised by stress or anxiety alone had a higher proportion of those from the least deprived areas. Certain combinations of burden indicators tended to be over-represented in the same clusters, such as pain with mobility problems, and depression with very high A&E arrivals, and separation/divorce. This study demonstrates the utility of machine learning for uncovering nuanced, patient-centered patterns in the experience of living with MLTC. The clustering approach reveals how different burdens intersect and vary across demographic and socioeconomic lines, offering insights that could inform more tailored and equitable care strategies. Author summaryAlthough a growing number of people are living with multiple long-term conditions (MLTCs), the nature of the burden faced by individuals and the common patterns of such person-centred burdens remain largely unknown. Previous MLTC studies have often clustered people by their long-term conditions to uncover how these conditions group together in electronic health records (EHRs). However, this approach does not capture the true complexity of MLTCs or their impact on patient experience. In this study, we identified a series of proxy burden indicators, highlighted the challenges of extracting them from EHRs, and developed data-driven methods to uncover important patterns of patient-centred burden within this large, complex space--opening new insights and a fresh research direction for understanding MLTCs. Health systems, policymakers, and clinicians stand to benefit from this studys findings by gaining clearer insight into the expected challenges faced by different groups living with MLTCs, potentially informing more targeted support, smarter resource allocation, and better care outcomes. Researchers, in turn, benefit from a systematic methodology for clustering patient burden.