Evidence map›Paper›PMID 40044181›Full record

ArticleThe British journal of general practice : the journal of the Royal College of General Practitioners2025

Patterns in GP appointment systems: a cluster analysis of 3480 English practices.

James Scuffell, Stevo Durbaba

Abstract read
In one paragraph

Article in The British journal of general practice : the journal of the Royal College of General Practitioners, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Appointments aren't Access: The Hidden Work of Getting Care.Journal of primary care & community health
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

James ScuffellSchool of Life Course and Population Sciences, King's College London, Guy's campus, London SE1 1UL, UK Jamie.scuffell@kcl.ac.uk.ORCID 0000-0002-1468-0989
Stevo DurbabaSchool of Life Course and Population Sciences, King's College London, Guy's campus, London SE1 1UL, UK.ORCID 0000-0002-4098-5399

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn response to increasing demand for appointments, UK general practices have adopted a range of appointment systems. These systems vary widely in implementation. These changes have not yet been clearly described.

aimTo characterise patterns of primary care delivery in English general practices. DESIGN AND

settingCross-sectional study using NHS Appointments in General Practice data from 3480 English GP practices, totalling 56 million appointments between August and October 2023.

methodTwelve measures associated with consultation modality, waiting time, clinician type, and triage use were derived. Practices with similar characteristics for those 12 variables were clustered together using an ensemble machine learning approach. Clustering was validated using December 2023 data. The characteristics of each practice grouping were described using 2021 Census and NHS workforce data.

resultsTwo main models of care emerged. 'Routine care' practices (

conclusionThis study identified two dominant models of primary care delivery in England, reflecting differing approaches to managing patient access. These differences could have an impact on continuity of care and equity of access to primary care.

Indexed as

Appointments and SchedulesGeneral PracticePractice Patterns, Physicians'Primary Health CareCluster AnalysisCross-Sectional StudiesEnglandHealth Services AccessibilityHumansReferral and ConsultationState MedicineTriageWaiting Listsappointment and schedulescluster analysishealth workforceprimary health careremote consultationtelemedicine

Identifiers

PMID40044181
PMCPMC12729048

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.