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The work of the consultation in general practice: a comparison of affluent and deprived areas of Scotland using a novel consultation workload index

2025/10/08 by Kieran Sweeney, Lauren Ng, Stewart W Mercer · 1 voice
Business, Management and Accounting · Health Professions · #Healthcare Systems and Technology #Patient-Provider Communication in Healthcare #Primary Care and Health Outcomes

paper · pdf · doi:10.3399/bjgpo.2025.0103

openalex publication_date 2025/10/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

BACKGROUND: The workload within GP consultations, and how this varies by deprivation, is not well known. AIM: To examine how deprivation influences GP consultation workload by developing and applying a novel consultation workload index (CWI). DESIGN & SETTING: Secondary analysis of a cross-sectional postal survey of patients who had recently consulted a GP in deprived and affluent areas of Scotland. METHOD: The CWI was developed using patient-reported data on: (1) whether more than one problem was discussed in the consultation; (2) whether a complex problem (defined as involving both physical and psychosocial issues) was discussed; and (3) the presence of a disability or limiting long-term condition. Results were analysed by area-level deprivation and consultation modality (face-to-face versus telephone consultations). RESULTS: Analysis included 721 patients. Correlations between the three variables of the CWI were low (rho<0.2), suggesting that each was capturing a distinct aspect of consultation workload. Using the CWI, more than half of all consultations in deprived areas had 'high' (29%) or 'very high' (25%) workload, compared with approximately one-quarter in affluent areas ('high' 20%, 'very high' 6%). This was evident across both face-to-face and telephone consultations. CONCLUSION: Greater patient need and complexity in deprived areas is reflected in higher GP workload in the consultation as measured by the CWI. Ways of operationalising the CWI routinely - for example, through real-time artificial intelligence (AI) analysis of consultations - should be explored. If robust, these could be used to inform the resource allocation to general practice to help address the inverse care law.

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