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

Abstract

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.

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Identifiers

ORCID for Mozhdeh Shiranirad: orcid.org/0000-0003-4346-3059

ORCID for Zlatko Zlatev: orcid.org/0000-0001-6275-5626

ORCID for Emilia Holland: orcid.org/0000-0001-5722-3836

ORCID for Jakub Dylag: orcid.org/0000-0001-6263-7339

ORCID for Nisreen A. Alwan: orcid.org/0000-0002-4134-8463

ORCID for Ann Berrington: orcid.org/0000-0002-1683-6668

ORCID for Michael Boniface: orcid.org/0000-0002-9281-6095

ORCID for Rebecca B. Hoyle: orcid.org/0000-0002-1645-1071

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