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Prototype Prescribing Outlier Dashboard for Hawthorn Medical Practice

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 Hawthorn Medical Practice is higher than most

BNF Chemical Chemical Items BNF Subparagraph Subparagraph Items Ratio Mean std Z_Score Plots
Nicotinamide 8 Topical preparations for acne 159 0.05 0.00 0.01 7.60
Bupivacaine hydrochloride 51 Local anaesthetics 143 0.36 0.01 0.05 7.54
Nabilone 4 Drugs used in nausea and vertigo 1,508 0.00 0.00 0.00 5.93
Epinastine hydrochloride 7 Other anti-inflammatory preparations 152 0.05 0.00 0.01 4.01
Cinnarizine 275 Drugs used in nausea and vertigo 1,508 0.18 0.05 0.04 3.12
Sodium fusidate 3 Some other antibacterials 15 0.20 0.01 0.06 2.96
Nizatidine 11 H2-Receptor antagonists 22 0.50 0.10 0.16 2.57
Sodium zirconium cyclosilicate 4 Oral potassium 16 0.25 0.02 0.10 2.44
Varenicline tartrate 17 Nicotine dependence 22 0.77 0.18 0.24 2.42
Lactulose 1,510 Osmotic laxatives 3,007 0.50 0.28 0.10 2.34

Prescribing where Hawthorn Medical Practice is lower than most

BNF Chemical Chemical Items BNF Subparagraph Subparagraph Items Ratio Mean std Z_Score Plots
Macrogol 3350 1,486 Osmotic laxatives 3,007 0.49 0.71 0.10 -2.20
Lidocaine hydrochloride 90 Local anaesthetics 143 0.63 0.90 0.13 -2.04
Nicotine 0 Nicotine dependence 22 0.00 0.65 0.33 -1.95
    Methylprednisolone acetate 0 Local corticosteroid injections 51 0.00 0.74 0.39 -1.89
      Sodium picosulfate 0 Bowel cleansing preparations 2 0.00 0.69 0.43 -1.61
        Light liquid paraffin 1 Emollient bath and shower preparations 118 0.01 0.34 0.20 -1.61
        Latanoprost 404 Treatment of glaucoma 2,269 0.18 0.31 0.08 -1.58
        Potassium chloride 12 Oral potassium 16 0.75 0.96 0.14 -1.53
        Lamotrigine 557 Control of epilepsy 11,200 0.05 0.11 0.04 -1.49
        Famotidine 11 H2-Receptor antagonists 22 0.50 0.81 0.21 -1.49