Burgess, R (2022) Benchmarking community and primary care musculoskeletal services: Developing recommendations using evidence syntheses, consensus methods and secondary data analysis. Doctoral thesis, Keele University.

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Introduction: High quality data on service performance is essential in healthcare. There is however a paucity of publicly reported data in community/primary care musculoskeletal (MSK) services. There is also a lack of guidance on which metrics services should be collecting and reporting, and how to adjust data to make fair comparisons across services. This thesis aims to address these gaps, and to develop benchmarking capabilities in this area.

Method: a) a systematic review to identify existing MSK case-mix adjustment models; b) an umbrella review of predictors of MSK functional outcome; c) a systematic review identifying key MSK cost drivers; d) an online survey to develop consensus around a core MSK dataset; e) a secondary analysis of data to test identified case-mix models; f) development of benchmarking recommendations.

Results: Two existing case-mix models were identified from the UK and US. Predictors of MSK function were identified; baseline function, baseline pain severity, mental wellbeing, comorbidities, age and body mass index. Key community/primary care cost drivers were identified; visits to GP, Physiotherapy, and Medical Specialists. Consensus on a MSK core dataset was captured from 166 healthcare professionals and 25 patients across the UK. Secondary analysis of a primary care cohort testing modified versions of the two existing case-mix models showed the US model gave slightly higher predictive power than the UK model (44% and 41% respectively). Finally, the thesis findings were triangulated to develop data collection recommendations for future MSK service benchmarking.

Conclusions: This thesis has generated a body of evidence to inform community/primary care MSK service benchmarking and provides recommendations for future routine data collection of MSK metrics, including; complexity factors for case-mix adjustment, demographics, clinical factors, PROMs, PREMs, and optional cost indicators. The next steps involve the provision of support and guidance for services to successfully implement these recommendations into practice.

Item Type: Thesis (Doctoral)
Subjects: R Medicine > RC Internal medicine
Divisions: Faculty of Medicine and Health Sciences > School of Medicine
Contributors: Hill, JC (Thesis advisor)
Depositing User: Lisa Bailey
Date Deposited: 23 Jun 2022 13:55
Last Modified: 23 Jun 2022 13:55
URI: https://eprints.keele.ac.uk/id/eprint/11061

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