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

Background: There are limited case-mix classification systems for primary care settings which are applicable when considering the optimal clinical skill mix to provide services. Aim: 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. Method: 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. Results: 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. Conclusion: 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. Halter M et al. BJGP Open 2018; DOI: 10.3399/bjgpopen18X101277 1 of 10 RESEARCH