Intelligent Data Engineering and Automated Learning - IDEAL 2019 - 20th International Conference, Manchester, UK, November 14-16, 2019, Proceedings, Part II
Intelligent Data Engineering and Automated Learning - IDEAL 2019 - 20th International Conference, Manchester, UK, November 14-16, 2019, Proceedings, Part II
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智能数据工程和自动化学习 - IDEAL 2019 - 第 20 届国际会议，英国曼彻斯特，2019 年 11 月 14-16 日，会议记录，第二部分
DOI：
10.1007/978-3-030-33617-2_14
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Onah C
中科院分区：
文献类型：
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作者：
Onah C
患者病例组合是定义患者组的系统。出于报销目的，这些群体应该具有临床意义，并且在住院期间共享相似的资源使用情况。在英国国家医疗服务体系 (NHS) 中，这些群体被称为健康资源群体 (HRG)，主要根据专家建议得出，并随后检查其同质性，通常使用住院时间 (LOS) 来评估资源消耗的相似性。 LOS 并未完全捕获患者的实际资源使用情况，因此很难保证 HRG 作为支付费率推导基础的准确性。此外，对于复杂的患者群体，例如烧伤护理中遇到的患者群体，专家的建议通常仅反映普通患者的情况，因此无法反映许多患者受伤情况的复杂性和严重程度。石斑鱼的数据驱动开发可以支持特征和片段的识别，更准确地考虑患者的复杂性和资源使用。在本文中，我们描述了使用现有的降维和聚类分析技术开发这种石斑鱼。我们认为数据驱动的方法可以最大限度地减少特征选择中的偏差。通过对来自英格兰和威尔士 23 个烧伤服务机构的患者进行登记，我们证明与原始病例组合相比，已确定组的集群内成本变化有所减少。
Patient casemix is a system of defining groups of patients. For reimbursement purposes, these groups should be clinically meaningful and share similar resource usage during their hospital stay. In the UK National Health Service (NHS) these groups are known as health resource groups (HRGs), and are predominantly derived based on expert advice and checked for homogeneity afterwards, typically using length of stay (LOS) to assess similarity in resource consumption. LOS does not fully capture the actual resource usage of patients, and assurances on the accuracy of HRG as a basis of payment rate derivation are therefore difficult to give. Also, with complex patient groups such as those encountered in burn care, expert advice will often reflect average patients only, therefore not capturing the complexity and severity of many patients’ injury profile. The data-driven development of a grouper may support the identification of features and segments that more accurately account for patient complexity and resource use. In this paper, we describe the development of such a grouper using established techniques for dimensionality reduction and cluster analysis. We argue that a data-driven approach minimises bias in feature selection. Using a registry of patients from 23 burn services in England and Wales, we demonstrate a reduction of within cluster cost-variation in the identified groups, when compared to the original casemix.