{"title":"Unravelling the dynamics of referral-to-treatment in the NHS.","authors":"Richard M Wood","doi":"10.1080/20476965.2019.1700764","DOIUrl":null,"url":null,"abstract":"<p><p>Despite being the principal measure of elective performance in Great Britain's National Health Service, there is little on-the-ground awareness of the dynamics at play behind the referral-to-treatment (RTT) standard. Through a simple worked analogy, it is shown how this performance measure - calculated as the proportion of unresolved RTT pathways within 18 weeks from referral - is dependent on the interplay between elective demand and capacity. Bringing in activity (cost) and waiting list size, the presented theory unifies the five key components of the pathway dynamics for the first time within the published literature. A computer simulation model based on these principles is thereafter constructed as part of a more quantitative analysis using publicly available national data for 2017-2018. In this, referral rates and capacity are varied in line with a range of \"what if\" scenarios known to be of interest to service planners, with the effect on performance and cost objectively assessed.</p>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2019-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/20476965.2019.1700764","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/20476965.2019.1700764","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 4
Abstract
Despite being the principal measure of elective performance in Great Britain's National Health Service, there is little on-the-ground awareness of the dynamics at play behind the referral-to-treatment (RTT) standard. Through a simple worked analogy, it is shown how this performance measure - calculated as the proportion of unresolved RTT pathways within 18 weeks from referral - is dependent on the interplay between elective demand and capacity. Bringing in activity (cost) and waiting list size, the presented theory unifies the five key components of the pathway dynamics for the first time within the published literature. A computer simulation model based on these principles is thereafter constructed as part of a more quantitative analysis using publicly available national data for 2017-2018. In this, referral rates and capacity are varied in line with a range of "what if" scenarios known to be of interest to service planners, with the effect on performance and cost objectively assessed.
尽管RTT是衡量英国国民健康服务(National Health Service)选择性表现的主要标准,但人们对RTT标准背后的动态机制知之甚少。通过一个简单的工作类比,显示了这种绩效衡量——以转诊后18周内未解决的RTT路径的比例计算——是如何依赖于可选需求和能力之间的相互作用的。引入活动(成本)和等候名单大小,本文提出的理论在已发表的文献中首次统一了路径动力学的五个关键组成部分。随后,基于这些原则构建了计算机模拟模型,作为使用2017-2018年公开国家数据进行更定量分析的一部分。在这方面,转诊率和能力根据服务规划人员感兴趣的一系列“假设”情况而变化,并客观地评估对业绩和成本的影响。
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.