基于状态序列分析的患者诊疗路径挖掘与评价
Mining and evaluation of patients' diagnostic therapeutic paths through state sequences analysis
摘要：
护理路径的概念越来越多地被用于提高护理质量和优化卫生保健资源的使用。然而，关于治疗顺序的建议大多基于共识决定，因为缺乏有效治疗顺序的证据。在现实世界中，经典的统计工具导致不足以充分考虑具有如此高的可变性的现象，并且必须与适合在复杂数据结构中识别模式的新型数据挖掘技术相结合。数据驱动的技术可以通过从常规收集的数据中提取有效护理序列来潜在地支持有效护理序列的经验识别。本研究的目的是进行序列分析，以确定不同的治疗模式，并评估最有效的预防不良事件。这种方法的研究动机的临床应用涉及在心理健康领域提供的护理质量中经常遇到的几个问题。特别是，我们分析了伦巴第大区提供的与2015年至2018年意大利北部伦巴第地区诊断为精神分裂症的所有国民保健服务受益人相关的行政数据。该方法将患者的治疗路径视为一个概念单元，即，一个序列，由一系列不同的状态组成，可以描述纵向患者的状态。这类信息，例如使我们能够对患者进行风险分析的常见护理模式，可以为卫生政策制定者提供机会，通过分配适当的资源，分析人口健康状况的趋势，并找到可以利用的风险因素来规划最佳和个性化的患者护理。
英文摘要：
The concept of care pathways is increasingly being used to enhance the quality of care and to optimize the use of resources for health care. Nevertheless, recommendations regarding the sequence of care are mostly based on consensus-based decisions as there is a lack of evidence on effective treatment sequences. In a real-world setting, classical statistical tools resulted to be insufficient to adequately consider a phenomenon with such high variability and has to be integrated with novel data mining techniques suitable of identifying patterns in complex data structures. Data-driven techniques can potentially support the empirical identification of effective care sequences by extracting them from data collected routinely. The purpose of this study is to perform sequence analysis to identify different patterns of treatment and to assess the most efficient in preventing adverse events. The clinical application that motivated the study of this method concerns the several problems frequently encountered in the quality of care provided in the mental health field. In particular, we analyzed administrative data provided by Regione Lombardia related to all the beneficiaries of the National Health Service with a diagnosis of schizophrenia from 2015 to 2018 resident in Lombardy, a region of northern Italy. This methodology considers the patient's therapeutic path as a conceptual unit, i.e., a sequence, composed of a succession of different states that can describe longitudinal patient's status. This kind of information, such as common patterns of care that allowed us to risk profile patients, can provide health policymakers an opportunity to plan optimum and individualized patient care by allocating appropriate resources, analyzing trends in the health status of a population, and finding the risk factors that can be leveraged to prevent the decline of mental health status at the population level.
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