Using machine learning tools to investigate factors associated with trends in 'no-shows' in outpatient appointments
Using machine learning tools to investigate factors associated with trends in 'no-shows' in outpatient appointments
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DOI：
10.1016/j.healthplace.2020.102496
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发表时间：
2021-01-01
期刊：
影响因子：
4.8
通讯作者：
Ware, Andrew
中科院分区：
文献类型：
--
作者：
Incze, Eduard;Holborn, Penny;Ware, Andrew
据估计，英国国家医疗服务体系（NHS）每年因错过预约而损失约10亿英镑。因此，对影响这些所谓的“未参与”（dna）的时空模式的因素类型进行更全面的了解的研究是及时的。这项研究阐明了一项研究的结果，该研究使用机器学习方法来调查这些因素在一系列医学专业中是否一致。预测模型用于确定与错过预约相关的风险增加和风险减轻因素，然后用于为每个专科的逐个预约的患者分配风险评分。结果表明，dna的最佳预测因子包括患者的年龄、就诊史和居住地区的剥夺等级。研究人员从地理和医学专业两方面对调查结果进行了分析，结果表明，与dna有关的因素在重要性和相关性方面有所不同。这项研究表明，机器学习技术在为未来与dna相关的干预政策提供信息方面具有真正的价值，这些政策可以帮助减轻NHS的负担，改善患者的护理和福祉。
Missed appointments are estimated to cost the UK National Health Service (NHS) approximately 1 pound billion annually. Research that leads to a fuller understanding of the types of factors influencing spatial and temporal patterns of these so-called "Did-Not-Attends" (DNAs) is therefore timely. This research articulates the results of a study that uses machine learning approaches to investigate whether these factors are consistent across a range of medical specialities. A predictive model was used to determine the risk-increasing and risk-mitigating factors associated with missing appointments, which were then used to assign a risk score to patients on an appointment by-appointment basis for each speciality. Results show that the best predictors o f DNAs include the patient's age, appointment history, and the deprivation rank o f their area of residence. Findings have been analysed at both a geographical and medical speciality level, and the factors associated with DNAs have been shown to differ in terms of both importance and association. This research has demonstrated how machine learning techniques have real value in informing future intervention policies related to DNAs that can help reduce the burden on the NHS and improve patient care and well-being.