Patient-centred workload clustering in adults under 65 with long-term conditions from longitudinal primary care data
Year: 2026
Type: article
Study design: Observational Study
Abstract: Introduction: Although patient workload is increasingly recognised as a relevant dimension of the patient experience, it is not satisfactorily captured in current electronic health records. We set out to describe the distribution of patient workload patterns across long-term conditions and demographic groups using electronic health records annotated to reflect patient-centred workload derived from a qualitative evidence synthesis of patient experience. Methods: We used a predefined compendium of 63 workload indicators, derived from a previously published qualitative evidence synthesis of patient experience and mapped to electronic health record codes, classified into eight themes. We computed counts of these indicators in 765 930 patients born between 1956 and 1965 with two or more recorded long-term conditions, randomly sampled from the Clinical Practice Research Datalink. We then used an unsupervised learning approach to construct a typology of patients based on these counts. Results: Median age at first record was 31 years with 28 years of follow-up. The cohort was 49.4% (n=378 154) male and predominantly white (68.1%, n=521 859). Patients had a median of 15 workload indicators and 4 long-term conditions. Nine clusters were identified, with marked contrasts in workload intensity, long-term conditions, demographic profiles and size. While large clusters were less burdensome, smaller clusters showed more complex profiles, characterised by cardiometabolic or mental health long-term conditions, higher female-to-male ratios and greater ethnic diversity. The most burdensome cluster, characterised by a broad spectrum of rarer long-term conditions and dominated by indicators associated with investigation and treatment monitoring, appeared comparatively less complex. Conclusions: This unsupervised approach to patient workload revealed patient clusters with marked disparities in burden, long-term conditions, demographic characteristics and complexity, supporting future research directions aimed at better understanding and supporting patients with elevated workload demands.
Source: BMJ Public Health
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
Awards: Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B), G1240776402, G2210647123, Prevention of Risks and Onset of Mental Health problems through Interdisciplinary Stakeholder Engagement-PROMISE Population Health Improvement Cluster, Mechanisms and consequences of depression-related multimorbidity over the life course: coordinated analysis of population and primary care data
OpenAlex ID: W7224890539