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.
BNF Chemical | Chemical Items | BNF Subparagraph | Subparagraph Items | Ratio | Mean | std | Z_Score | Plots |
---|---|---|---|---|---|---|---|---|
Acarbose | 40 | Other antidiabetic drugs | 508 | 0.08 | 0.00 | 0.01 | 14.89 | |
Prazosin hydrochloride | 41 | Alpha-adrenoceptor blocking drugs | 245 | 0.17 | 0.00 | 0.01 | 13.40 | |
Levomepromazine maleate | 34 | Antipsychotic drugs | 398 | 0.09 | 0.00 | 0.01 | 11.12 | |
Alkyl sulfate | 2 | Shampoos and some other scalp preparations | 40 | 0.05 | 0.00 | 0.01 | 6.59 | |
Rotigotine | 40 | Dopaminergic drugs used in parkinsonism | 164 | 0.24 | 0.03 | 0.04 | 5.42 | |
BNF Chemical | Chemical Items | BNF Subparagraph | Subparagraph Items | Ratio | Mean | std | Z_Score | Plots |
---|---|---|---|---|---|---|---|---|
Doxazosin mesilate | 204 | Alpha-adrenoceptor blocking drugs | 245 | 0.83 | 0.99 | 0.02 | -8.00 | |
Estradiol with progestogen | 41 | Oestrogens and Hormone Replacement Therapy | 263 | 0.16 | 0.39 | 0.12 | -1.95 | |
Estradiol 1mg / Norethisterone acetate 500microgram tablets : 2 Estradiol 1mg / Dydrogesterone 5mg tablets : 1 Estradiol 500micrograms / Dydrogesterone 2.5mg tablets : 1 Kliofem tablets : 1 Femoston 1/10mg tablets : 1 Femoston-conti 0.5mg/2.5mg tablets : 1 Elleste Duet 2mg tablets : 1 FemSeven Conti patches : 1 Novofem tablets : 4 Evorel Sequi patches : 8 Evorel Conti patches : 15 Femoston-conti 1mg/5mg tablets : 5 | ||||||||
Atenolol | 61 | Beta-adrenoceptor blocking drugs | 1239 | 0.05 | 0.13 | 0.05 | -1.59 | |
Allopurinol | 203 | Gout and cytotoxic induced hyperiuicaemia | 249 | 0.82 | 0.89 | 0.05 | -1.48 | |
Gabapentin | 112 | Control of epilepsy | 947 | 0.12 | 0.24 | 0.08 | -1.47 | |