Algorithm development to facilitate rapid detection of low value interventions in response to health service savings requirement: South East London experience
PHE ePoster Library. Giddings R. Apr 10, 2019; 257489; 15360
Dr. Rebecca Giddings
Dr. Rebecca Giddings
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Abstract Introduction
With rapidly evolving and increasingly costly healthcare interventions becoming a major pressure on health service budgets, developing a robust and values-driven approach to prioritisation is an important healthcare public health skill. Yet the huge range of interventions now possible, oftentimes amid a paucity of high quality scientific evidence, presents challenges to healthcare public health capacity. CHALLENGEFaced with a menu of 51 interventions and asked by NHS colleagues to identify candidate interventions for deeper review, the Southwark healthcare public health team was asked to rapidly review and prioritise the interventions for further investigation. The team devised an algorithm to rapidly interrogate each intervention with the intention to reduce review time for each intervention to 10 minutes. APPROACHBy identifying a hierarchy of evidence sources ranked in quality order, and integrating this into an approved algorithm, the team was able to review all 51 interventions and report in a matter of days rather than weeks. All interventions were categorised into one of four mutually exclusive and collectively exhaustive groups: i. typically for funding; ii. funded in specific circumstances; iii. not typically funded and iv. further investigation required. We reviewed 51 interventions in 10 hours, averaging 12 minutes per intervention. Our methodology revealed the majority of items (47/73, 64.4%) had sufficiently high quality evidence available to make a funding recommendation without further workup. CONCLUSIONThis approach demonstrated that our algorithm was able to improve efficiency thereby preserving capacity to undertake more in-depth analysis of some of the more challenging interventions. All of this was achieved while maintaining a robust methodology, a trail of accountability and meeting urgent timelines. In the current financial environment for the health service and increasingly constrained healthcare public health capacity, developing robust and scalable algorithms is an opportunity to ensure evidence continues to steer health service investment.
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