Capturing complexity in clinician case-mix: classification system development using GP and physician associate data
<p><strong>Background:</strong> There are limited case-mix classification systems for primary care settings which are applicable when considering the optimal clinical skill mix to provide services.</p> <p><strong>Aim:</strong> To develop a case-mix classific...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Royal College of General Practitioners
2018
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| _version_ | 1797101771935449088 |
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| author | Halter, M Joly, L De Lusignan, S Grant, R Gage, H Drennan, V |
| author_facet | Halter, M Joly, L De Lusignan, S Grant, R Gage, H Drennan, V |
| author_sort | Halter, M |
| collection | OXFORD |
| description | <p><strong>Background:</strong> There are limited case-mix classification systems for primary care settings which are applicable when considering the optimal clinical skill mix to provide services.</p> <p><strong>Aim:</strong> 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.</p> <p><strong>Design & setting:</strong> 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.</p> <p><strong>Results:</strong> 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.</p> <p><strong>Conclusion:</strong> 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.</p> |
| first_indexed | 2024-03-07T05:56:36Z |
| format | Journal article |
| id | oxford-uuid:eab8224d-5788-48cb-8248-4ca4b92d5fe4 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T05:56:36Z |
| publishDate | 2018 |
| publisher | Royal College of General Practitioners |
| record_format | dspace |
| spelling | oxford-uuid:eab8224d-5788-48cb-8248-4ca4b92d5fe42022-03-27T11:04:24ZCapturing complexity in clinician case-mix: classification system development using GP and physician associate dataJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:eab8224d-5788-48cb-8248-4ca4b92d5fe4EnglishSymplectic Elements at OxfordRoyal College of General Practitioners2018Halter, MJoly, LDe Lusignan, SGrant, RGage, HDrennan, V<p><strong>Background:</strong> There are limited case-mix classification systems for primary care settings which are applicable when considering the optimal clinical skill mix to provide services.</p> <p><strong>Aim:</strong> 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.</p> <p><strong>Design & setting:</strong> 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.</p> <p><strong>Results:</strong> 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.</p> <p><strong>Conclusion:</strong> 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.</p> |
| spellingShingle | Halter, M Joly, L De Lusignan, S Grant, R Gage, H Drennan, V Capturing complexity in clinician case-mix: classification system development using GP and physician associate data |
| 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 |
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