Background Approaches to addressing unwarranted variation in healthcare service delivery have traditionally relied on the prospective identification of activities and outcomes, based on a hypothesis, with subsequent reporting against defined measures. There is an opportunity to adopt a more data-driven approach by applying hypothesis free data driven algorithms to national datasets to capture variability and identify outliers. Objectives To develop and apply a hypothesis free algorithm to identify unusual prescribing behaviour in primary care data at multiple administrative levels in the NHS in England, and to visualise these results using organisation-specific interactive dashboards. Methods Here we report a new data-driven approach to quantify how "unusual" prescribing rates of a particular chemical within an organisation are as compared to peer organisations, over a period of six months (June-December 2021). This is followed by ranking to identify which chemicals are the most notable outliers in each organisation. These outlying chemicals are calculated for all practices, primary care networks, clinical commissioning groups and sustainability and transformation partnerships in England. Results are presented via organisation-specific interactive dashboards, the iterative development of which has been informed by user feedback. Results User feedback and internal review of case studies demonstrate that our methodology identifies chemicals that are in line with local policies and internal reporting. Conclusions Data-driven approaches overcome existing biases with regards to the planning and execution of audits, interventions and policy-making within NHS organisations, potentially revealing new targets for improved healthcare service delivery. We provide our dashboards as a candidate list for the consideration of expert users to prioritise for further interpretation and qualitative research in terms of potential targets for improved performance.

BG has received research funding from the Laura and John Arnold Foundation, the NHS National Institute for Health Research (NIHR), the NIHR School of Primary Care Research, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research UK, the Health Foundation, the World Health Organisation, UKRI, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme; he also receives personal income from speaking and writing for lay audiences on the misuse of science.

This project is funded by the National Institute for Health Research (NIHR) under its Research for Patient Benefit (RfPB) Programme (Grant Reference Number PB-PG-0418-20036).

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The study used openly available human data as provided by NHS BSA (https://www.nhsbsa.nhs.uk/prescription-data/prescribing-data/english-prescribing-data-epd). These data are processed, summarised and visualised using our OpenPrescribing platform as described on our website (https://openprescribing.net/about/).

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