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Prototype Prescribing Outlier Dashboard for Queens Walk 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 Queens Walk Practice is higher than most

BNF Chemical Chemical Items BNF Subparagraph Subparagraph Items Ratio Mean std Z_Score Plots
Tapentadol hydrochloride 139 Opioid analgesics 619 0.22 0.01 0.02 12.58
Pridinol mesilate 1 Skeletal muscle relaxants 6 0.17 0.00 0.01 12.21
Oxycodone hydrochloride/naloxone hydrochloride 28 Opioid analgesics 619 0.05 0.00 0.00 9.14
Hydrochlorothiazide 2 Thiazides and related diuretics 344 0.01 0.00 0.00 7.14
Prazosin hydrochloride 28 Alpha-adrenoceptor blocking drugs 372 0.08 0.00 0.01 5.86
Rupatadine fumarate 3 Antihistamines 595 0.01 0.00 0.00 5.16
Phenytoin 8 Control of epilepsy 1,105 0.01 0.00 0.00 4.61
Primidone 35 Control of epilepsy 1,105 0.03 0.00 0.01 4.57
Bumetanide 509 Loop diuretics 940 0.54 0.14 0.09 4.47
Haloperidol 68 Antipsychotic drugs 439 0.15 0.03 0.03 4.33

Prescribing where Queens Walk Practice is lower than most

BNF Chemical Chemical Items BNF Subparagraph Subparagraph Items Ratio Mean std Z_Score Plots
Furosemide 431 Loop diuretics 940 0.46 0.85 0.09 -4.44
Doxazosin mesilate 339 Alpha-adrenoceptor blocking drugs 372 0.91 0.99 0.02 -4.12
Water for injection 3 Electrolytes and water 12 0.25 0.82 0.25 -2.32
Chloramphenicol 59 Antibacterials 104 0.57 0.81 0.11 -2.23
Other emollient preparations 133 Emollients 177 0.75 0.89 0.07 -2.03
Zopiclone 74 Hypnotics 216 0.34 0.60 0.14 -1.88
Baclofen 3 Skeletal muscle relaxants 6 0.50 0.83 0.18 -1.81
Quinine sulfate 81 Antimalarials 123 0.66 0.90 0.14 -1.75
Naftidrofuryl oxalate 0 Peripheral vasodilators and related drugs 2 0.00 0.67 0.39 -1.73
    Lamotrigine 46 Control of epilepsy 1,105 0.04 0.11 0.04 -1.69