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

医院异常值：作为缓解策略对住院时间和长链的影响

• 批准号: 2413895
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2413895
• 关键词: Hospital; outliers; Impact; length; stay

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.

医院病房专门针对特定类型的患者（例如骨科、血液科、老年科）。然而，每个专科病房每天可以治疗的患者数量受到床位数量的限制。由于入院人数随着时间的推移而波动，通常会发生患者必须被分配到不专门满足患者临床需求的病房的情况，这些患者被称为异常值。据估计，英国所有住院患者中异常值的患病率在 5% 到 10% 之间。论文将由两部分组成：在第 1 部分中，我们将进行彻底的计量经济学分析，以调查异常值住院时间较长的说法。我们的分析将基于阿登布鲁克医院和 83 家德国医院的历史健康事件数据。在第二部分中，我们将应用长链理论（一种经典的运营管理概念）来解决异常值问题。我们将描述用于管理异常值的长链理论的实施，并实证评估其有效性。 第 1 部分：估计异常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，人们普遍认为异常值得到的护理较差：边远病房的护士与病人满员的家庭病房的医务人员之间的沟通经常存在问题；不适当病房的护士往往不具备护理异常值的专业知识；不合适的病房环境可能不适合边远患者的需求。较差的护理可能会对患者的康复及其住院时间产生不利影响。现有的实证研究并没有对异常值是否具有更长的停留时间提供令人信服的答案。估计离群值影响很复杂：首先，离群值可能不是随机选择的。相反，如果病房太忙而无法接收新患者，医生很可能会选择更健康的患者作为异常值。这将导致低估外围效应。其次，在整个住院期间属于异常值的患者相对较少。患者可能会在家庭病房和离群病房之间移动。这就提出了一个问题，如何才能充分衡量患者随时间的偏离程度？为了解决这些问题，我们建议将生存模型与工具变量方法相结合，以在成为异常值和住院时间之间建立因果关系。第 2 部分：探索长链理论以更好地管理异常值 医院可以被视为一个系统，拥有 n 个专门的服务器（病房），负责照顾 n 个不同类型的客户（患者）。长链是运营管理中的一种流行理论，用于有效协调那些由于该服务器繁忙而无法由最适合他们的服务器满足的客户流，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）减少异常值的数量，以及（ii）将临床专科的异常值集中在一个其他病房（“副服务器”）上，从而促进这些异常值护理的规模经济效应。文献综述表明，不存在现成的长链设计可以轻松适用于异常值管理。因此，我们将考虑异常值问题的两个关键特征，开发合适的长链设计：(i) 在知道当天晚些时候将有多少其他不同类型的患者到达之前，必须做出接收新患者的病房的决定；(ii) 强烈建议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。在描述了长链理论对异常值的应用之后，我们将使用以下方法凭经验评估我们的长链设计的有效性： 我们的大型医院事件数据集。我们的文献综述表明，明显缺乏对长链理论的实证评估研究。