ArticleBJGP open2018
Capturing complexity in clinician case-mix: classification system development using GP and physician associate data.
Article in BJGP open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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.
Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Physician associates and anaesthetic associates in UK: rapid systematic review of recent UK based research.BMJ (Clinical research ed.) · 2025Pooled it
- Review
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundThere are limited case-mix classification systems for primary care settings which are applicable when considering the optimal clinical skill mix to provide services.
aimTo develop a case-mix classification system (CMCS) and test its impact on analyses of patient outcomes by clinician type, using example data from physician associates' (PAs) and GPs' consultations with same-day appointment patients. DESIGN &
settingSecondary analysis of controlled observational data from six general practices employing PAs and six matched practices not employing PAs in England.
methodRoutinely-collected patient consultation records (PA
resultsA CMCS was developed by extending a system that only classified 18.6% (213/1147) of the presenting problems in this study's data. The CMCS differentiated the presenting patient's level of need or complexity as: acute, chronic, minor problem or symptom, prevention, or process of care, applied hierarchically. Combination of patient and consultation-level measures resulted in a higher classification of acuity and complexity for 639 (30.6%) of patient cases in this sample than if using consultation level alone. The CMCS was a key adjustment in modelling the study's main outcome measure, that is rate of repeat consultation.
conclusionThis CMCS assisted in classifying the differences in case-mix between professions, thereby allowing fairer assessment of the potential for role substitution and task shifting in primary care, but it requires further validation.
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Registered trials
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.