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Free-text Analysis of General Practice Out-of-hours (GPOOH) Use by People with Advanced Cancer: an Analysis of Coded and Uncoded Free-text Data

Overview
Journal Br J Gen Pract
Specialty Public Health
Date 2023 Jan 26
PMID 36702608
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Abstract

Background: People with advanced cancer frequently use the GP out-of-hours (GPOOH) service. Considerable amounts of routine GPOOH data are uncoded. Therefore, these data are omitted from existing healthcare datasets.

Aim: To conduct a free-text analysis of a GPOOH dataset, to identify reasons for attendance and care delivered through GPOOH to people with advanced cancer.

Design And Setting: An analysis of a GPOOH healthcare dataset was undertaken. It contained all coded and free- text information for 5749 attendances from a cohort of 2443 people who died from cancer in Tayside, Scotland, from 2013-2015.

Method: Random sampling methods selected 575 consultations for free-text analysis. Each consultation was analysed by two independent reviewers to determine the following: assigned presenting complaints; key and additional palliative care symptoms recorded in free text; evidence of anticipatory care planning; and free-text recording of dispensed medications. Inter-rater reliability concordance was established through Kappa testing.

Results: More than half of all coded reasons for attendance ( = 293; 51.0%) were 'other' or 'missing'. Free-text analysis demonstrated that nearly half ( = 284; 49.4%) of GPOOH attendances by people with advanced cancer were for pain or palliative care. More than half of GPOOH attendances ( = 325; 56.5%) recorded at least one key or additional palliative care symptom in free text, with the commonest being breathlessness, vomiting, cough, and nausea. Anticipatory care planning was poorly recorded in both coded and uncoded records. Uncoded medications were dispensed in more than one- quarter of GPOOH consultations.

Conclusion: GPOOH delivers a substantial amount of pain management and palliative care, much of which is uncoded. Therefore, it is unrecognised and under-reported in existing large healthcare data analyses.

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PMID: 38643000 PMC: 11033633. DOI: 10.1136/bmjopen-2023-081930.

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