Detecting change in comparison to peers in NHS prescribing data: a novel application of cumulative sum methodology
| Autor: | Richard Croker, Ben Goldacre, Seb Bacon, Alex J Walker |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Percentile
National Health Programs Computer science Population Health Informatics CUSUM 030204 cardiovascular system & hematology lcsh:Computer applications to medicine. Medical informatics Drug Prescriptions Medical Order Entry Systems Peer Group Standard deviation 03 medical and health sciences 0302 clinical medicine Humans Operations management 030212 general & internal medicine Practice Patterns Physicians' skin and connective tissue diseases Set (psychology) education Prescribing data education.field_of_study Health Policy Statistical process control Summary statistics United Kingdom 3. Good health Computer Science Applications Change detection lcsh:R858-859.7 sense organs Algorithms Research Article |
| Zdroj: | BMC Medical Informatics and Decision Making, Vol 18, Iss 1, Pp 1-10 (2018) BMC Medical Informatics and Decision Making |
| Popis: | Background The widely used OpenPrescribing.net service provides standard measures which compare prescribing of Clinical Commissioning Groups (CCGs) and English General Practices against that of their peers. Detecting changes in prescribing behaviour compared with peers can help identify missed opportunities for medicines optimisation. Automating the process of detecting these changes is necessary due to the volume of data, but challenging due to variation in prescribing volume for different measures and locations. We set out to develop and implement a method of detecting change on all individual prescribing measures, in order to notify CCGs and practices of such changes in a timely manner. Methods We used the statistical process control method CUSUM to detect prescribing behaviour changes in relation to population trends for the individual standard measures on OpenPrescribing. Increases and decreases in percentile were detected separately, using a multiple of standard deviation as the threshold for detecting change. The algorithm was modified to continue re-triggering when trajectory persists. It was deployed, user-tested, and summary statistics generated on the number of alerts by CCG and practice. Results The algorithm detected changes in prescribing for 32 prespecified measures, across a wide range of CCG and practice sizes. Across the 209 English CCGs, a mean of 2.5 increase and 2.4 decrease alerts were triggered per CCG, per month. For the 7578 practices, a mean of 1.3 increase and 1.4 decrease alerts were triggered per practice, per month. Conclusions The CUSUM method appears to effectively discriminate between random noise and sustained change in prescribing behaviour. This method aims to allow practices and CCGs to be informed of important changes quickly, with a view to improve their prescribing behaviour. The number of alerts triggered for CCGs and practices appears to be appropriate. Prescribing behaviour after users are alerted to changes will be monitored in order to assess the impact of these alerts. Electronic supplementary material The online version of this article (10.1186/s12911-018-0642-6) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
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