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Prototype Prescribing Outlier Dashboard for Birbeck Medical Group

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 Practices. From this we can calculate the “z-score”, which is a measure of how many standard deviations a given Practice 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 Practice's chemical:subparagraph ratios is provided, with this Practice'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 Birbeck Medical Group is higher than most

BNF Chemical Chemical Items BNF Subparagraph Subparagraph Items Ratio Mean std Z_Score Plots
Rifampicin combined preparations 1 Antituberculosis drugs 1 1.00 0.02 0.12 8.00
Dronabinol/cannabidiol 16 Skeletal muscle relaxants 230 0.07 0.00 0.01 7.41
Dulaglutide 453 Other antidiabetic drugs 1,280 0.35 0.06 0.05 5.59
Fluticasone furoate (Inhalation) 1,016 Corticosteroids (respiratory) 2,958 0.34 0.09 0.08 3.18
Lodoxamide trometamol 8 Other anti-inflammatory preparations 66 0.12 0.01 0.04 2.82
Umeclidinium bromide 177 Antimuscarinic bronchodilators 370 0.48 0.12 0.13 2.81
Lisinopril 3,404 Angiotensin-converting enzyme inhibitors 5,893 0.58 0.18 0.14 2.81
Codeine phosphate 1,536 Opioid analgesics 3,082 0.50 0.21 0.11 2.70
Quinine bisulfate 62 Antimalarials 134 0.46 0.10 0.13 2.68
Triamcinolone acetonide 48 Drugs used in nasal allergy 1,033 0.05 0.01 0.02 2.56

Prescribing where Birbeck Medical Group is lower than most

BNF Chemical Chemical Items BNF Subparagraph Subparagraph Items Ratio Mean std Z_Score Plots
Quinine sulfate 72 Antimalarials 134 0.54 0.90 0.14 -2.64
Tiotropium bromide 105 Antimuscarinic bronchodilators 370 0.28 0.71 0.18 -2.38
Co-amilofruse (Amiloride hydrochloride/frusemide) 0 Potassium sparing diuretics and compounds 3 0.00 0.69 0.33 -2.10
    Ramipril 2,214 Angiotensin-converting enzyme inhibitors 5,893 0.38 0.69 0.17 -1.92
    Rifampicin 0 Antituberculosis drugs 1 0.00 0.74 0.40 -1.88
      Medroxyprogesterone acetate 154 Parenteral progestogen-only contraceptives 253 0.61 0.88 0.15 -1.78
      Beclometasone dipropionate 1,185 Corticosteroids (respiratory) 2,958 0.40 0.58 0.12 -1.53
      Mercaptopurine 6 Antimetabolites 22 0.27 0.80 0.35 -1.53
      Famotidine 61 H2-Receptor antagonists 123 0.50 0.81 0.21 -1.51
      Zinc oxide 0 Barrier preparations 8 0.00 0.49 0.33 -1.49