Hospital outliers: Impact on length of stay and long chains as mitigation strategy

医院的病房专门收治特殊类型的病人（如整形外科、血液科、老年病科）。然而，每个专科病房在任何一天可以治疗的病人数量都受到其床位数量的限制。由于入院人数随时间波动，经常发生的情况是，病人不得不被分配到不专门照顾病人临床需要的病房--这些病人被称为离群者。估计的患病率之间的5%和10%的所有医院patientsintheUK.The论文将包括两个部分：在第一部分，我们将进行一个彻底的计量经济学分析，调查声称，离群值有一个较长的住院时间。我们的分析将基于Addenbrooke医院和83家德国医院的历史健康事件数据。在第2部分中，我们将应用长链理论（一种经典的运营管理概念）来解决离群值问题。我们将描述长链理论的实施来管理离群值，并实证评估其有效性。第一部分：估计离群值对住院时间的影响正如最近的一项定性研究所强调的那样，人们普遍认为离群值得到较差的护理：偏远病房的护士和病人家中病房的医务人员之间的沟通往往是有问题的;不适当病房的护士通常没有适当的专业知识来照顾离群者;不适当病房的环境可能不适合离群患者的需求。住院治疗可能对病人的康复和住院时间产生不利影响。现有的实证研究并没有提供一个令人信服的答案是否离群有更长的逗留时间。估计离群值效应是复杂的：首先，离群值可能不是随机选择的。相反，如果病房因为忙碌而无法接收新病人，医生很可能会选择更健康的病人作为离群值。这将导致低估外围效应。其次，相对较少的患者在整个住院期间都是离群值。病人可能会在他们的家庭病房和离群病房之间移动。这就提出了一个问题，如何能够充分衡量随着时间的推移，病人的离群程度？为了解决这些问题，我们建议将联合收割机与工具变量方法结合起来，建立一个成为离群值和住院时间之间的因果关系。第二部分：探索长链理论以更好地管理离群点医院可以被视为一个系统，该系统具有n个专门的服务器（病房），负责照顾n种不同类型的客户（患者）。长链是运营管理中的一种流行理论，可以有效地协调那些无法由最适合他们的服务器提供服务的客户的流量，因为该服务器忙碌，因此必须由另一台服务器处理。应用长链理论可能有助于（i）减少离群值的数量和（ii）将临床专业的离群值集中在单个其他病房（“代理服务器”），从而促进这些离群值护理的规模经济效应。文献综述表明，目前还没有现成的长链设计可适用于离群值管理。因此，我们将考虑离群值问题的两个关键特征来开发合适的长链设计：（i）在决定入住哪间病房之前，必须先知道当天稍后会有多少其他不同类别的病人抵达;及（ii）强烈优选的是将患者分配到最合适的病房而不是副病房或任何其他病房。链理论的离群值，我们将经验性地评估我们的长链设计的有效性，使用我们的大型医院事件数据集。我们的文献回顾表明，明显缺乏长链理论的实证评估研究。

Hospital wards specialise on particular types of patients (e.g. orthopaedics, haematology, geriatrics). However, the number of patients each specialised ward can treat on any given day is limited by its number of beds. With the number of hospital admissions fluctuating over time, it routinely happens that patients have to be allocated to wards which are not specialised on the patients' clinical needs - those patients are called outliers. Estimates of the prevalence of outlying vary between 5% and 10% of all hospital patients in the UK.The dissertation will consist of two parts: In Part 1, we will carry out a thorough econometric analysis to investigate the claim that outliers have a longer hospital length of stay. Our analysis will be based on historic health episode data from Addenbrooke's Hospital and 83 German hospitals. In Part 2, we will apply long chain theory, a classic operations management concept, to the outlier problem. We will describe the implementation of the long chain theory to manage outliers and empirically evaluate its effectiveness.PART 1: Estimate the effect of being an outlier on length of stayAs highlighted by a recent qualitative study, it is widely believed that outliers receive poorer care: the communication between nurses on the outlying wards and medical staff on the fully occupied home ward of the patient is often problematic; nurses on inappropriate wards have often not the right expertise to care for outliers; and the environment on inappropriate wards may be unsuitable for outlying patients' needs. Poorer care may have an adverse effect on patients' recovery and therewith their length of stay. Existing empirical research does not provide a convincing answer on whether outliers have a longer length of stay. Estimating outlier effects is complicated: First, outliers may not be randomly chosen. Instead, doctors may well choose healthier patients as outliers if the ward is too busy to receive new patients. This would lead to an underestimation of the outlying effect. Second, relatively few patients are outliers during their entire stay in the hospital. Patients may move between their home ward and outlier wards. This poses the question, how one can adequately measure the degree of outlying for patients over time? To account for these problems, we propose to combine a survival model with an instrumental variable approach to establish a causal link between becoming an outlier and length of stay. PART 2: Exploring long chain theory to better manage outliersA hospital can be viewed as a system with n specialised servers (wards) caring for n different types of customers (patients). Long chains is a popular theory in operations management to efficiently coordinate the flow of those customers who cannot be catered for by the server most appropriate to them because this server is busy, and therefore have to be dealt with by another server. Applying long chain theory is likely to help (i) reduce the number of outliers and (ii) concentrate a clinical speciality's outliers on a single other ward ("deputy server") fostering economics-of-scale effects for those outliers' care.A literature review indicated that there exists no off-the-shelf long chain design readily applicable to outlier management. We will therefore develop a suitable long chain design taking into account the two key features of the outlier problem: (i) the decision on what ward to admit a new patient must be made before knowing how many other patients of the different types will arrive later during the day and (ii) it is strongly preferred to assign patients to the most appropriate ward rather than the deputy ward or any other ward.After describing the application of the long chain theory to outliers, we will empirically evaluate the effectiveness of our long chain design using our large hospital episode dataset. Our literature review indicates a clear lack of empirical evaluation studies for long chain theory.