Static outliers for White Horse Medical Practice
There is substantial variation in prescribing behaviours, across various different areas of medicine. Some variation can be explained by demographic changes, or local policies or guidelines, but much of the remaining variation is less easy to explain. At OpenPrescribing we are piloting a number of data-driven approaches to identify unusual prescribing and collect feedback on this prescribing to inform development of new tools to support prescribers and organisations to audit and review prescribing.
This report has been developed to automatically identify prescribing patterns at a chemical level which are furthest away from “typical prescribing” and can be classified as an “outlier”. We calculate the number of prescriptions for each chemical in the BNF coding system using the BNF subparagraph as a denominator, for prescriptions dispensed between April 2021 and August 2021. We then calculate the mean and standard deviation for each numerator and denominator pair across all practices/CCGs/PCNs/STPs. From this we can calculate the “z-score”, which is a measure of how many standard deviations a given practice/CCG/PCN/STP is from the population mean. We then rank your “z-scores” to find the top 5 results where prescribing is an outlier for prescribing higher than its peers and those where it is an outlier for prescribing lower than its peers.
It is important to remember that this information was generated automatically and it is therefore likely that some of the behaviour is warranted. This report seeks only to collect information about where this variation may be warranted and where it might not. Our full analytical method code is openly available on GitHub here.
The DataLab is keen to hear your feedback on the results. You can do this by completing the following survey or emailing us at ebmdatalab@phc.ox.ac.uk. Please DO NOT INCLUDE IDENTIFIABLE PATIENT information in your feedback. All feedback is helpful, you can send short or detailed feedback.
Prescribing where White Horse Medical Practice is higher than most
| BNF Chemical | Chemical Items | BNF Subparagraph | Subparagraph Items | Ratio | Mean | std | Z_Score | Plots |
|---|---|---|---|---|---|---|---|---|
| Meningococcal B vaccine | 7 | Vaccines and antisera | 36 | 0.19 | 0.0 | 0.01 | 22.51 |  |
| Eprosartan | 239 | Angiotensin-II receptor antagonists | 3002 | 0.08 | 0.0 | 0.01 | 13.70 |  |
| Erythromycin stearate | 11 | Macrolides | 228 | 0.05 | 0.0 | 0.01 | 5.10 |  |
| Meningococcal A + C + W135 + Y vaccine | 3 | Vaccines and antisera | 36 | 0.08 | 0.0 | 0.02 | 4.55 |  |
| Bupropion hydrochloride | 6 | Nicotine dependence | 6 | 1.00 | 0.1 | 0.20 | 4.47 |  |
Prescribing where White Horse Medical Practice is lower than most
| BNF Chemical | Chemical Items | BNF Subparagraph | Subparagraph Items | Ratio | Mean | std | Z_Score | Plots |
|---|---|---|---|---|---|---|---|---|
| Amlodipine | 2495 | Calcium-channel blockers | 6289 | 0.40 | 0.75 | 0.11 | -3.15 |  |
| Hydralazine hydrochloride | 0 | Vasodilator antihypertensive drugs | 4 | 0.00 | 0.85 | 0.28 | -3.01 |  |
| Urea hydrogen peroxide | 0 | Removal of ear wax and other substances | 2 | 0.00 | 0.83 | 0.34 | -2.47 |  |
| Ramipril | 2380 | Angiotensin-converting enzyme inhibitors | 7060 | 0.34 | 0.69 | 0.17 | -2.12 |  |
| Medroxyprogesterone acetate | 68 | Parenteral progestogen-only contraceptives | 117 | 0.58 | 0.88 | 0.16 | -1.85 |  |