Background Minimising prescription costs while maintaining quality is a core element of delivering high-value healthcare. There are various strategies to achieve savings, but almost no research to date on determining the most effective approach. We describe a new method of identifying potential savings due to large national variations in drug cost, including variation in generic drug cost, and compare these with potential savings from an established method (generic prescribing).
Methods We used English National Health Service (NHS) Digital prescribing data, from October 2015 to September 2016. Potential cost savings were calculated by determining the price per unit (eg, pill, millilitre) for each drug and dose within each general practice. This was compared against the same cost for the practice at the lowest cost decile to determine achievable savings. We compared these price-per-unit savings to the savings possible from generic switching and determined the chemicals with the highest savings nationally. A senior pharmacist manually assessed whether a random sample of savings were practically achievable.
Results We identified a theoretical maximum of £410 million of savings over 12 months. £273 million of these savings were for individual prescribing changes worth over £50 per practice per month (mean annual saving £33 433 per practice); this compares favourably with generic switching, where only £35 million of achievable savings were identified. The biggest savings nationally were on glucose blood testing reagents (£12 million), fluticasone propionate (£9 million) and venlafaxine (£8 million). Approximately half of all savings were deemed practically achievable.
Discussion We have developed a new method to identify and enable large potential cost savings within NHS community prescribing. Given the current pressures on the NHS, it is vital that these potential savings are realised. Our tool enabling doctors to achieve these savings is now launched in pilot form at OpenPrescribing.net. However, savings could potentially be achieved more simply through national policy change.
- cost saving
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Contributors RC, SB, AJW, BG and HJC conceived and designed the study. SB and AJW collected and analysed the data with input from RC, HJC and BG. AJW drafted the manuscript. All authors contributed and approved the final manuscript. SB was lead engineer on the associated website resource with input from RC, AJW, BG, HJC and LF (who led on user testing). BG supervised the project and is guarantor. Lead engineer on the original OpenPrescribing tool was Anna Powell-Smith.
Funding This work was supported by the NIHR Biomedical Research Centre, Oxford; the Health Foundation grant (Unique Award Reference Number 7599) and by an National Institute for Health Research (NIHR) School of Primary Care Research (SPCR) grant ref number: 327.
Competing interests BG has received research funding from the Laura and John Arnold Foundation, the Wellcome Trust, the NHS National Institute for Health Research, the Health Foundation and WHO; he also receives personal income from speaking and writing for lay audiences on the misuse of science. AJW, HJC, SB, RC and LF are employed on BG’s grant from the Health Foundation. RC reports personal fees as a paid member of an advisory board from Galen Pharmaceuticals, Martindale Pharma, Galderma (UK), ProStraken Group PLC, Menarini Farmaceutica Internazionale SRL, Stirling Anglian Pharmaceuticals , outside the submitted work; and RC is employed by a CCG to optimise prescribing.
Patient consent Detail has been removed from this case description/these case descriptions to ensure anonymity. The editors and reviewers have seen the detailed information available and are satisfied that the information backs up the case the authors are making.
Provenance and peer review Not commissioned; externally peer reviewed.
Data sharing statement All analytic data and code are available online at https://figshare.com/s/39a4301a29316bc86b35. All codes for the OpenPrescribing tool and the associated PPU tool are shared under an open license and is available on Github https://github.com/ebmdatalab/price-per-dose
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