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Prototype Prescribing Outlier Dashboard for Whittington Moor Surgery

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 Whittington Moor Surgery is higher than most

BNF Chemical Chemical Items BNF Subparagraph Subparagraph Items Ratio Mean std Z_Score Plots
Low protein risotto 1 Foods for special diets 252 0.00 0.00 0.00 13.05
Lixisenatide 54 Other antidiabetic drugs 1,162 0.05 0.00 0.01 8.18
Low protein desserts 11 Foods for special diets 252 0.04 0.00 0.01 7.92
Low protein meals 15 Foods for special diets 252 0.06 0.00 0.01 6.74
Cefradine 18 Cephalosporins 46 0.39 0.02 0.08 4.65
Other soothing haemorrhoidal preparations 3 Soothing haemorrhoidal preparations 17 0.18 0.00 0.04 4.57
Calcitriol 13 Preparations for psoriasis 109 0.12 0.01 0.02 4.40
Low protein miscellaneous 2 Foods for special diets 252 0.01 0.00 0.00 4.24
Gluten free/low protein cereals 5 Foods for special diets 252 0.02 0.00 0.00 4.06
Benzoyl peroxide 58 Topical preparations for acne 163 0.36 0.08 0.07 4.04

Prescribing where Whittington Moor Surgery is lower than most

BNF Chemical Chemical Items BNF Subparagraph Subparagraph Items Ratio Mean std Z_Score Plots
Cefalexin 27 Cephalosporins 46 0.59 0.96 0.10 -3.90
Potassium citrate 0 Drugs used in urological pain 7 0.00 0.92 0.25 -3.67
    Bisoprolol fumarate 1,807 Beta-adrenoceptor blocking drugs 4,168 0.43 0.65 0.08 -2.78
    Indapamide 42 Thiazides and related diuretics 1,395 0.03 0.43 0.16 -2.51
    Calcipotriol 78 Preparations for psoriasis 109 0.72 0.87 0.08 -1.94
    Apixaban 243 Oral anticoagulants 1,797 0.14 0.41 0.14 -1.93
    Tramadol hydrochloride 373 Opioid analgesics 3,664 0.10 0.27 0.09 -1.83
    Mupirocin 0 Antibacterial preparations only used topically 3 0.00 0.64 0.36 -1.79
      Gentamicin sulfate 0 Aminoglycosides 6 0.00 0.75 0.42 -1.77
        Insulin glargine 58 Intermediate and long-acting insulins 587 0.10 0.33 0.13 -1.76