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 t...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Royal College of General Practitioners
2018-04-01
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| Series: | BJGP Open |
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| Online Access: | https://bjgpopen.org/content/2/1/bjgpopen18X101277 |
| _version_ | 1828768831653281792 |
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| author | Mary Halter Louise Joly Simon de Lusignan Robert L Grant Heather Gage Vari M Drennan |
| author_facet | Mary Halter Louise Joly Simon de Lusignan Robert L Grant Heather Gage Vari M Drennan |
| author_sort | Mary Halter |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T13:36:51Z |
| format | Article |
| id | doaj.art-04b275be9fd84feaa5996f5856d86c74 |
| institution | Directory Open Access Journal |
| issn | 2398-3795 |
| language | English |
| last_indexed | 2024-12-11T13:36:51Z |
| publishDate | 2018-04-01 |
| publisher | Royal College of General Practitioners |
| record_format | Article |
| series | BJGP Open |
| spelling | doaj.art-04b275be9fd84feaa5996f5856d86c742022-12-22T01:04:59ZengRoyal College of General PractitionersBJGP Open2398-37952018-04-012110.3399/bjgpopen18X101277Capturing complexity in clinician case-mix: classification system development using GP and physician associate dataMary Halter0Louise Joly1Simon de Lusignan2Robert L Grant3Heather Gage4Vari M Drennan5Faculty of Health, Social Care & Education, Kingston University & St George's, University of London, London, UKSocial Care Workforce Unit, King's College London, London, UKDepartment of Clinical and Experimental Medicine, University of Surrey, Guildford, UKFaculty of Health, Social Care & Education, Kingston University & St George's, University of London, London, UKSchool of Economics, University of Surrey, Guildford, UKFaculty of Health, Social Care & Education, Kingston University & St George's, University of London, London, UKBackground: 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.https://bjgpopen.org/content/2/1/bjgpopen18X101277classificationmethodscase-mixgeneral practicephysician assistantsphysician associate |
| spellingShingle | Mary Halter Louise Joly Simon de Lusignan Robert L Grant Heather Gage Vari M Drennan Capturing complexity in clinician case-mix: classification system development using GP and physician associate data BJGP Open classification methods case-mix general practice physician assistants physician associate |
| title | Capturing complexity in clinician case-mix: classification system development using GP and physician associate data |
| title_full | Capturing complexity in clinician case-mix: classification system development using GP and physician associate data |
| title_fullStr | Capturing complexity in clinician case-mix: classification system development using GP and physician associate data |
| title_full_unstemmed | Capturing complexity in clinician case-mix: classification system development using GP and physician associate data |
| title_short | Capturing complexity in clinician case-mix: classification system development using GP and physician associate data |
| title_sort | capturing complexity in clinician case mix classification system development using gp and physician associate data |
| topic | classification methods case-mix general practice physician assistants physician associate |
| url | https://bjgpopen.org/content/2/1/bjgpopen18X101277 |
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