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Prototype Prescribing Outlier Dashboard for Turton PCN

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. These pilot results are provided for the interest of advanced users, although we don't know how relevant they are in 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.

The DataLab is keen to hear your feedback on the results. You can do this by completing the following survey or emailing us at [email protected]. Please DO NOT INCLUDE IDENTIFIABLE PATIENT information in your feedback. All feedback is helpful, you can send short or detailed feedback.

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, the count of all prescriptions within that chemical's BNF subparagraph, for prescriptions dispensed between June 2021 and December 2021. We then calculate the ratio of these counts along with the mean and standard deviation of those ratios across all PCNs. From this we can calculate the “z-score”, which is a measure of how many standard deviations a given PCN is from the population mean. We then rank your “z-scores” to find the top 10 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.

For each outlier chemical, a kernel density estimation plot of all PCN's chemical:subparagraph ratios is provided, with this PCN's ratio overlaid in red.

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, to inform research on this topic. Our full analytical method code is openly available on GitHub here.

This is a new, experimental feature. We'd love to .

Prescribing where Turton PCN is higher than most

BNF Chemical Chemical Items BNF Subparagraph Subparagraph Items Ratio Mean std Z_Score Plots
Carglumic acid 1 Drugs used in metabolic disorders 2 0.50 0.00 0.04 14.05
Levomepromazine maleate 180 Antipsychotic drugs 3,825 0.05 0.00 0.00 11.43
Other otitis external preparations 3 Otitis externa 703 0.00 0.00 0.00 10.65
Ospemifene 8 Preparations for vaginal/vulval changes 562 0.01 0.00 0.00 10.07
Hydrocortisone 1 Corticosteroids 79 0.01 0.00 0.00 9.82
Promazine hydrochloride 918 Antipsychotic drugs 3,825 0.24 0.01 0.03 7.18
Nebivolol 1,631 Beta-adrenoceptor blocking drugs 17,346 0.09 0.02 0.01 5.99
Camellia sinensis extract 1 Preparations for warts and calluses 50 0.02 0.00 0.00 4.80
Verapamil hydrochloride 647 Calcium-channel blockers 17,668 0.04 0.01 0.01 4.04
Reboxetine 62 Other antidepressant drugs 8,282 0.01 0.00 0.00 3.95

Prescribing where Turton PCN is lower than most

BNF Chemical Chemical Items BNF Subparagraph Subparagraph Items Ratio Mean std Z_Score Plots
Zinc sulfate monohydrate 0 Zinc 7 0.00 0.74 0.33 -2.26
    Aspirin 7,016 Antiplatelet drugs 12,515 0.56 0.65 0.04 -2.08
    Glycopyrronium bromide 5 Antimuscarinic drugs 64 0.08 0.78 0.36 -1.93
    Ciprofloxacin 110 Quinolones 157 0.70 0.86 0.08 -1.90
    Carbamazepine 521 Control of epilepsy 11,991 0.04 0.07 0.02 -1.82
    Amitriptyline hydrochloride 5,009 Tricyclic and related antidepressant drugs 6,737 0.74 0.84 0.05 -1.81
    Indapamide 1,243 Thiazides and related diuretics 5,099 0.24 0.42 0.10 -1.79
    Quetiapine 810 Antipsychotic drugs 3,825 0.21 0.36 0.08 -1.78
    Furosemide 3,519 Loop diuretics 4,818 0.73 0.85 0.07 -1.71
    Losartan potassium 2,044 Angiotensin-II receptor antagonists 8,521 0.24 0.50 0.16 -1.67