The Relationship Between Quality of Care and Choice of Clinical Computing System: Retrospective Analysis of Family Practice Performance Under the UK’s Quality and Outcomes Framework
Kontopantelis, E., Buchan, I., Reeves, D., Checkland, K. & Doran, T.
Objectives: To investigate the relationship between performance on the UK Quality and Outcomes Framework pay-for-performance scheme and choice of clinical computer system. Design: Retrospective longitudinal study. Setting: Data for 2007-8 to 2010-11, extracted from the clinical computer systems of general practices in England. Participants: All English practices participating in the pay-for-performance scheme: average 8257 each year, covering over 99% of the English population registered with a general practice. Main outcome measures: Levels of achievement on 62 quality of care indicators, measured as: reported achievement (levels of care after excluding inappropriate patients); population achievement (levels of care for all patients with the relevant condition); and percentage of available quality points attained. Multilevel mixed effects multiple linear regression models were used to identify population, practice, and clinical computing system predictors of achievement. Results: Seven clinical computer systems were consistently active in the study period, collectively holding approximately 99% of the market share. Of all population and practice characteristics assessed, choice of clinical computing system was the strongest predictor of performance across all three outcome measures. Differences between systems were greatest for intermediate outcomes indicators (for example, control of cholesterol levels). Conclusions: Under the UK’s pay-for-performance scheme, differences in practice performance were associated with choice of clinical computing system. This raises the question of whether particular system characteristics facilitate higher quality of care, better data recording, or both. Inconsistencies across systems need to be understood and addressed, and researchers need to be cautious when generalising findings from samples of providers using a single computing system.