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

错过他们的预约的患者，被称为“不显示”或“没有参加”（DNA），对医疗保健提供性能具有不利影响。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Covid 19大流行的情况下，这一点更为重要，因为等待名单中的患者数量增加，并且可能减少可用的医疗保健人员数量。电子医疗记录使得能够分析历史患者的入院数据以预测每个患者的不出席。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，以及（2）如何使用这些信息来使患者的时间表更具弹性和效率。我们将与考文垂和沃里克郡大学医院合作。他们将参与（1）数据收集，（2）讨论可能有助于识别DNA患者的现实因素以及他们目前的调度实践，以减轻DNA的影响，以及（3）评估开发的决策支持工具。过去的研究发现，根据设施和实践的类型[1，2]，患者的不出席预约可能会影响生产力，消耗资源，延长检查的等待时间并降低客户满意度。拟议研究的新颖性包括-调查最先进的数据分析工具，用于预测不出现的患者。理想情况下，这些不仅可以预测DNA的概率，还可以提供置信度得分。- 与患者调度系统紧密集成，该系统利用预测并为患者推荐时间段和超额预订限制，从而实现弹性时间表。明确考虑多个目标，如病人的预约时间，病人在病房的等待时间，医生的损失时间，医生的加班时间，和未使用的时隙。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，包括处理大多数DNA数据集显示的不平衡（大多数患者都会出现）。使用元启发式搜索等技术，调查并缩小与未就诊患者预测相关的因素。可能的因素包括患者的年龄，条件和感知的紧迫性，就业，交货时间，交通/停车设施等一组合适的因素降低模型的复杂性和训练时间，并避免过拟合。然后将识别的因素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.建立仿真模型。这将需要证明所开发的方法的有效性，但也为基于模拟的优化。4.制定一种基于优先级规则的调度方法，从调度弹性的角度建议最合适的时间段。例如，可以为“有风险”的患者提供时间段，使得他们分布在一天和一周中，并且可能不在一天的早些时候。将以类似的方式安排有迟到历史记录的患者的时间表。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，圣玛丽亚N，克莱顿L.为什么门诊病人未能出席他们的预约：一个前瞻性的比较之间的差异出席和不出席。Aust Health Rev. 2003;26：52-63. 2.摩尔CG，威尔逊-威瑟斯彭P，普罗布斯特JC.时间和金钱：家庭实习医师诊所不出现的影响。家庭医学2001;33：522-7

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