Reducing the impact of no-shows in healthcare by data-driven patient scheduling

通过数据驱动的患者安排减少缺席对医疗保健的影响

• 批准号: 2720588
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2720588
• 关键词: Reducing; impact; no; shows; healthcare

Patients who miss their appointments, referred to as "no-shows" or "did not attend" (DNA), have detrimental effect on healthcare delivery performance. They waste valuable scarce resources, thus affecting the efficiency of the healthcare provider. This is even more important in the current Covid19 pandemic with a higher number of patients in the waiting lists, and potentially reduced number of available healthcare staff. Electronic healthcare records enable the analysis of historical patients' admission data to predict no attendance of each patient. In this project Key research questions are (1) How can state-of-the-art data analytics be used to predict the probability of a patient attending the appointment and(2) How can this information be used to make the patient schedule more resilient and efficient. We will collaborate with the University Hospital of Coventry and Warwickshire. They will be involved in (1) data collection, (2) discussion about the real-world factors potentially useful for identification of DNA patients and about their current scheduling practice to mitigate the effect of DNA, and (3) the evaluation of the developed decision support tool. Past research identified depending on the type of facility and practice [1, 2], a patient's non-attendance to scheduled appointments may affect productivity, consume resources, prolong the waiting time for an examination and reduce customer satisfaction.The novelties of the proposed research include - the investigation of state-of-the-art data analytics tools for predicting no-show patients. Ideally, these would not only predict the probability of DNA, but additionally provide a confidence score. - a tight integration with the patient scheduling system that exploits the predictions and recommends time slots for patients and overbooking limits that lead to a resilient schedule.- the explicit consideration of multiple objectives such as a patient's time to get an appointment, a patient's waiting time at the ward, lost doctor's time, doctor's overtime, and unused time slots. The PhD project will include the following main steps. 1. Collect historical admission data and prepare them for data mining, including handling of imbalance, which most DNA datasets exhibit (majority of patients show up).2. Investigate and narrow down factors relevant to the prediction of no-show patients, using techniques such as meta-heuristic search. Possible factors include patient's age, condition and perceived urgency, employment, lead time, transport/parking facilities, etc. A suitable set of factors decreases model complexity and training time, and avoids overfitting. The identified factors will then be used in state-of-the-art machine learning techniques such as Random Forest, Support-Vector Machine, Artificial Neural-Network to predict a patient's DNA probability. 3. Build a simulation model. This will be needed for demonstrating the effectiveness of the developed methodology, but also for simulation-based optimisation.4. Develop a scheduling approach based on priority rules, which would suggest the most suitable time slot from a schedule resilience perspective. For example, "risky" patients could be offered time slots such that they are spread across the day and across the week, and perhaps not early on the day. The scheduling of patients with historical record of being late will be scheduled in a similar manner. 5. Develop a prototype tool that assists a healthcare manager in scheduling appointments. .References:1. Collins J, Santamaria N, Clayton L. Why outpatients fail to attend their scheduled appointments: a prospective comparison of differences between attenders and non-attenders. Aust Health Rev. 2003;26:52-63. 2. Moore CG, Wilson-Witherspoon P, Probst JC. Time and money: effects of no-shows at a family practice residency clinic. Fam Med. 2001;33:522-7

错过预约的患者，被称为“no-show”或“didn ' t attend”（DNA），对医疗保健服务的绩效产生不利影响。它们浪费了宝贵的稀缺资源，从而影响了医疗保健提供者的效率。在当前的covid - 19大流行中，这一点更为重要，因为等待名单上的患者数量更多，可用的医护人员数量可能减少。电子医疗记录支持对历史患者入院数据进行分析，以预测每个患者的不出勤情况。在这个项目中，关键的研究问题是：(1)如何使用最先进的数据分析来预测患者参加预约的概率；(2)如何使用这些信息来使患者的日程安排更具弹性和效率。我们将与考文垂大学医院和沃里克郡合作。他们将参与(1)数据收集，(2)讨论可能对识别DNA患者有用的现实因素，以及他们当前的调度实践，以减轻DNA的影响，以及(3)评估开发的决策支持工具。过去的研究表明，根据设施和实践的类型[1,2]，患者不出席预定的预约可能会影响生产力，消耗资源，延长等待检查的时间并降低客户满意度。提出的研究的新颖之处包括对最先进的数据分析工具的调查，用于预测未就诊的患者。理想情况下，这些不仅可以预测DNA的概率，还可以提供一个置信度评分。-与患者日程安排系统紧密集成，利用预测并为患者推荐时间段和超额预订限制，从而实现弹性日程安排。-明确考虑多个目标，例如患者预约的时间，患者在病房的等待时间，失去的医生时间，医生加班时间和未使用的时间段。博士项目将包括以下主要步骤。1. 收集历史入院数据，并为数据挖掘做好准备，包括处理不平衡，这是大多数DNA数据集所显示的（大多数患者出现）。使用元启发式搜索等技术，调查和缩小与预测缺勤患者相关的因素。可能的因素包括患者的年龄、病情和紧急程度、就业、交货时间、交通/停车设施等。一组合适的因子可以降低模型的复杂度和训练时间，避免过拟合。确定的因素将被用于最先进的机器学习技术，如随机森林、支持向量机、人工神经网络，以预测患者的DNA概率。3. 建立一个仿真模型。这将需要证明所开发方法的有效性，但也需要基于模拟的优化。开发基于优先级规则的调度方法，这将从调度弹性的角度建议最合适的时间段。例如，可以为“高风险”患者提供时间段，这样他们就可以在一天和一周内分散，也许不是在当天的早些时候。有迟到历史记录的患者的日程安排将以类似的方式安排。5. 开发一个原型工具，帮助医疗保健经理安排预约。参考文献：1。柯林斯J，桑塔玛利亚N，克莱顿L.为什么门诊病人不能按时赴约：门诊病人和非门诊病人差异的前瞻性比较。中华卫生杂志，2003;26:52-63。2. Moore CG, Wilson-Witherspoon P, Probst JC。时间和金钱：不去家庭实习诊所的影响。Fam Med. 2001；33:522-7