ArticleBMJ health & care informatics2025
Identifying long-term conditions in New Zealand general practice using structured and unstructured data: a cross-sectional study.
Article in BMJ health & care informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Multimorbidity and cardiovascular prevention in primary care: a cohort study in New Zealand.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study examined whether incorporating free-text entries into structured general practice records improves the detection of long-term conditions (LTCs) and multimorbidity (MM) in New Zealand (NZ) general practices.
methodsData from 374 071 deidentified individuals in general practices were analysed to identify 61 LTCs. Structured data were extracted using Read codes from a national master list, and clinical raters independently identified condition-related free-text, including synonyms, negation terms and common misspellings in randomised samples. Keywords were categorised and refined through ten iterative tests. Programmatic text classification was developed and assessed against gold-standard clinician ratings, using sensitivity, specificity, positive predictive value (PPV) and F
resultsA quarter of general practitioner classifications contained either unrecognised Read codes or consisted of free-text only. Clinician inter-rater reliability was high (kappa ≥0.9). Compared with clinical gold standard, text classification yielded an average sensitivity of 88%, specificity of 99% and PPV of 95%, with an F DISCUSSION: In the course of workflow, general practitioners face barriers to accurate LTC coding or may simply annotate with text-based descriptions. Programmatic text classification has demonstrated high performance and identified many more patients receiving LTC care.
conclusionsCombining structured and unstructured data optimises MM detection in NZ general practices and has the potential to improve case management, follow-up care and allocation of healthcare resources.
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