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

Mary Halter1, Louise Joly2, Simon de Lusignan3

  • 1Associate Professor, Faculty of Health, Social Care & Education, Kingston University & St George's, University of London, London, UK.

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Summary
This summary is machine-generated.

A new case-mix classification system (CMCS) helps assess patient complexity in primary care. This system allows for fairer comparisons of clinician roles, like physician associates and GPs, aiding in task shifting discussions.

Keywords:
case-mixclassificationgeneral practicemethodsphysician assistantsphysician associate

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Area of Science:

  • Primary care research
  • Health services research
  • Clinical informatics

Background:

  • Limited case-mix classification systems exist for primary care.
  • Optimal clinical skill mix is crucial for service delivery.

Purpose of the Study:

  • Develop a case-mix classification system (CMCS).
  • Test CMCS impact on patient outcome analyses by clinician type (physician associates vs. GPs).

Main Methods:

  • Secondary analysis of controlled observational data from English general practices.
  • Used routinely-collected patient consultation records (PA n=932, GP n=1154).
  • Designed CMCS combining problem codes, disease registers, and free text.

Main Results:

  • Developed CMCS classifying patient need/complexity (acute, chronic, minor, prevention, process).
  • CMCS improved classification of acuity/complexity for 30.6% of cases compared to consultation level alone.
  • CMCS adjustment was key in modeling repeat consultation rates.

Conclusions:

  • CMCS aids in classifying case-mix differences between healthcare professions.
  • Facilitates fairer assessment for role substitution and task shifting in primary care.
  • Further validation of the CMCS is required.