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Prototype Prescribing Outlier Dashboard for South Yorkshire And Bassetlaw STP

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 STPs. From this we can calculate the “z-score”, which is a measure of how many standard deviations a given STP 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 STP's chemical:subparagraph ratios is provided, with this STP'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 South Yorkshire And Bassetlaw STP is higher than most

BNF Chemical Chemical Items BNF Subparagraph Subparagraph Items Ratio Mean std Z_Score Plots
Capecitabine 1 Antimetabolites 2,083 0.00 0.00 0.00 6.33
Sorafenib 1 Other antineoplastic drugs 128 0.01 0.00 0.00 6.33
Doravirine 1 HIV infection 37 0.03 0.00 0.00 6.25
Dolutegravir/lamivudine 1 HIV infection 37 0.03 0.00 0.00 6.10
Clonazepam 13 Drugs used in status epilepticus 1,546 0.01 0.00 0.00 5.51
Deferasirox 8 Hypoplastic/haemolytic and renal anaemias 12 0.67 0.05 0.14 4.49
Pantoprazole 106,601 Proton pump inhibitors 1,539,177 0.07 0.02 0.01 4.39
Ampicillin 47 Broad-spectrum penicillins 116,699 0.00 0.00 0.00 4.20
Isotretinoin 1 Topical preparations for acne 27,377 0.00 0.00 0.00 4.20
Potassium bicarbonate 13 Oral bicarbonate 5,116 0.00 0.00 0.00 3.98

Prescribing where South Yorkshire And Bassetlaw STP is lower than most

BNF Chemical Chemical Items BNF Subparagraph Subparagraph Items Ratio Mean std Z_Score Plots
Sodium bicarbonate 5,103 Oral bicarbonate 5,116 1.00 1.00 0.00 -3.98
Cabergoline 1,107 Bromocriptine and other dopaminergic drugs 1,623 0.68 0.84 0.05 -2.92
Venlafaxine 59,198 Other antidepressant drugs 364,771 0.16 0.27 0.04 -2.59
Estradiol 9,654 Preparations for vaginal/vulval changes 24,016 0.40 0.59 0.08 -2.54
Potassium citrate 264 Drugs used in urological pain 310 0.85 0.95 0.04 -2.39
Glycopyrronium bromide 11 Antimuscarinic drugs 387 0.03 0.74 0.31 -2.27
Fluorometholone 358 Corticosteroids 11,777 0.03 0.09 0.03 -2.16
Emollient bath and shower preparations 12,049 Emollient bath and shower preparations 25,842 0.47 0.64 0.08 -2.13
Other amino acidandnutritional agent preparations 278 Amino acids and nutritional agents 344 0.81 0.97 0.08 -2.07
Lamivudine 1 HIV infection 37 0.03 0.58 0.27 -2.05