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Capturing the human impact of living with multiple long-term conditions in routine electronic health records - lost in translation?

Simon D S Fraser Orcid Logo, Emilia Holland Orcid Logo, Lynn Laidlaw Orcid Logo, Nick A Francis Orcid Logo, Sara Macdonald, Frances S Mair Orcid Logo, Nisreen A Alwan, Michael Boniface Orcid Logo, Rebecca B Hoyle Orcid Logo, Nic Fair, Jakub J Dylag, Mozhdeh Shiranirad, Roberta Chiovoloni, Sebastian Stannard Orcid Logo, Robin Poole Orcid Logo, Ashley Akbari Orcid Logo, Mark Ashworth, Alex Dregan

Journal of Multimorbidity and Comorbidity, Volume: 15

Swansea University Authors: Roberta Chiovoloni, Ashley Akbari Orcid Logo

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DOI (Published version): 10.1177/26335565251329869

Abstract

Background: Living with multiple long-term conditions (MLTCs) involves ‘work’. A recent qualitative synthesis identified eight patient-centred work themes: ‘learning and adapting’, ‘accumulation and complexity’, ‘investigation and monitoring’, ‘health service and administration’ and ‘symptom’, ‘emot...

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Published in: Journal of Multimorbidity and Comorbidity
ISSN: 2633-5565 2633-5565
Published: SAGE Publications 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa69276
Abstract: Background: Living with multiple long-term conditions (MLTCs) involves ‘work’. A recent qualitative synthesis identified eight patient-centred work themes: ‘learning and adapting’, ‘accumulation and complexity’, ‘investigation and monitoring’, ‘health service and administration’ and ‘symptom’, ‘emotional’, ‘medication’ and ‘financial’ work. These themes may be underrepresented in electronic health records (EHRs). This study aimed to evaluate the representation of these themes and their constituent concepts in EHR data in a general population and among individuals with history of a mental health condition. Methods: Using the OpenCodelists builder from OpenSAFELY, clinical code lists corresponding to work concepts were developed using Systematised Nomenclature of Medicine Clinical Terms (SNOMED CT) and validated by two clinicians. Additional concepts were engineered within the Clinical Practice Research Datalink (CPRD) and the Secure Anonymised Information Linkage (SAIL) Databank. We analysed trends in recording rates over 20 years across a SAIL general population cohort (n=5,180,602) and a CPRD cohort comprising individuals with a mental health diagnosis (n=3,616,776) and matched controls (n=4,457,225). Results: 55 code lists and seven engineered concepts were developed across the themes. The proportion of patients with codes related to ‘investigation and monitoring’ exceeded 40%, while ‘accumulation and complexity’ and ‘financial work’ were poorly represented (<2% and <1% of the study population respectively). Recording was generally higher among individuals with a mental health diagnosis history. Conclusion: While EHR data captures some aspects of MLTC work, patient-centred concepts are under-represented. Future research should explore reasons behind variability in coding practices, and innovative methods for enriching structured records with patient-centred data.
Keywords: multimorbidity, long-term conditions, electronic health records, clinical coding, lived experience
College: Faculty of Medicine, Health and Life Sciences
Funders: This study is independent research funded by the National Institute for Health and Care Research (NIHR) Artificial Intelligence for Multiple Long-Term Conditions (AIM) ‘Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B)’, (reference number NIHR203988). The MELD-B study also received some funding from the NIHR Applied Research Collaboration Wessex.

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