Static outliers for Richmond PCN
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 Richmond PCN is higher than most
| BNF Chemical | Chemical Items | BNF Subparagraph | Subparagraph Items | Ratio | Mean | std | Z_Score | Plots |
|---|---|---|---|---|---|---|---|---|
| Quinapril hydrochloride with diuretic | 4 | Angiotensin-converting enzyme inhibitors | 6920 | 0.00 | 0.00 | 0.00 | 11.36 |  |
| Benzylpenicillin sodium (Penicillin G) | 1 | Benzylpenicillin and phenoxymethylpenicillin | 281 | 0.00 | 0.00 | 0.00 | 7.27 |  |
| Simvastatin and ezetimibe | 30 | Lipid-regulating drugs | 13715 | 0.00 | 0.00 | 0.00 | 6.75 |  |
| Low protein biscuits | 18 | Foods for special diets | 203 | 0.09 | 0.01 | 0.01 | 6.53 |  |
| Diflucortolone valerate | 7 | Topical corticosteroids | 1890 | 0.00 | 0.00 | 0.00 | 6.09 |  |
Prescribing where Richmond PCN is lower than most
| BNF Chemical | Chemical Items | BNF Subparagraph | Subparagraph Items | Ratio | Mean | std | Z_Score | Plots |
|---|---|---|---|---|---|---|---|---|
| Phenoxymethylpenicillin (Penicillin V) | 280 | Benzylpenicillin and phenoxymethylpenicillin | 281 | 1.00 | 1.00 | 0.00 | -7.27 |  |
| Carbimazole | 140 | Antithyroid drugs | 180 | 0.78 | 0.93 | 0.04 | -3.46 |  |
| Water for injection | 4 | Electrolytes and water | 16 | 0.25 | 0.81 | 0.17 | -3.36 |  |
| Gabapentin | 438 | Control of epilepsy | 5435 | 0.08 | 0.23 | 0.06 | -2.53 |  |
| Neomycin sulfate | 83 | Nasal preparations for infection | 125 | 0.66 | 0.84 | 0.07 | -2.53 |  |