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Capturing complexity in clinician case-mix: classification system development using GP and physician associate data

Halter, M, Joly, L, de Lusignan, Simon, Grant, R, Gage, Heather and Drennan, V (2018) Capturing complexity in clinician case-mix: classification system development using GP and physician associate data BJGP Open.

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There are limited case-mix classification systems for primary care settings which are applicable when considering the optimal clinical skill mix to provide services.


To 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 & setting

Secondary analysis of controlled observational data from six general practices employing PAs and six matched practices not employing PAs in England.


Routinely-collected patient consultation records (PA n = 932, GP n = 1154) were used to design the CMCS (combining problem codes, disease register data, and free text); to describe the case-mix; and to assess impact of statistical adjustment for the CMCS on comparison of outcomes of consultations with PAs and with GPs.


A 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.


This 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.

Item Type: Article
Divisions : Faculty of Health and Medical Sciences > School of Biosciences and Medicine
Authors :
Halter, M
Joly, L
de Lusignan,
Grant, R
Drennan, V
Date : 9 January 2018
DOI : 10.3399/bjgpopen18X101277
Copyright Disclaimer : Copyright © 2018, BJGP Open. Published under a Creative Commons CC BY-NC 4.0 License
Uncontrolled Keywords : Classification; Methods; Case-mix; General practice; Physician assistants; Physician associate
Depositing User : Melanie Hughes
Date Deposited : 15 Jan 2018 15:51
Last Modified : 16 Jan 2019 19:07

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