Department of Health Care Management, Berlin Centre of Health Economics Research (BerlinHECOR), Technische Universität Berlin, Berlin, Germany.
Department of Health Economics and Health Management, Bielefeld University, Bielefeld, Germany.
Eur J Public Health. 2018 Apr 1;28(2):214-219. doi: 10.1093/eurpub/ckx169.
Care pathways are a widely used mean to ensure well-coordinated and high quality care by defining the optimal timing and interval of health services for a specific indication. However, evidence on common sequences of services actually followed by patients has rarely been quantified. This study aims to explore whether sequence clustering techniques can be used to empirically identify typical treatment sequences in ambulatory care for heart failure (HF) patients and compare their effectiveness.
Routine data of HF patients were provided by a large statutory sickness fund in Germany from 2009 until 2011. Events were categorized by either (i) the specialty of the physician, (ii) the type of service/procedure provided and (iii) the medication prescribed. Similarities between sequences were measured using the 'longest common subsequence' (LCS). The k-medoids clustering algorithm was applied to identify distinct subgroups of sequences. We used logistic regression to identify the most effective sequences for avoiding hospitalizations.
Treatment data of 982 incident HF patients were analyzed to identify typical treatment sequences. The cluster analysis revealed three distinct clusters of specialty sequences, four clusters of procedure sequences and four clusters of prescription sequences. Clusters differed in terms of timing and interval of physician visits, procedures and drug prescriptions as well as comorbidities and HF hospitalization rates. We found no significant association between cluster membership and HF hospitalization.
Sequence clustering techniques can be used as an explorative tool to systematically extract, describe compare and analyze treatment sequences and associated characteristics.
护理路径是一种广泛使用的方法，通过为特定病症定义卫生服务的最佳时机和间隔，确保协调一致和高质量的护理。然而，很少有证据量化患者实际遵循的常见服务序列。本研究旨在探讨序列聚类技术是否可用于实证识别门诊心力衰竭 (HF) 患者治疗的典型序列，并比较其效果。
德国一家大型法定疾病基金提供了 2009 年至 2011 年 HF 患者的常规数据。事件通过（i）医生的专业、（ii）提供的服务/程序类型和（iii）开具的药物进行分类。使用“最长公共子序列”（LCS）测量序列之间的相似性。应用 k-medoids 聚类算法识别不同的序列子组。我们使用逻辑回归来识别避免住院的最有效序列。
对 982 例新发 HF 患者的治疗数据进行了分析，以确定典型的治疗序列。聚类分析显示，专科序列有三个明显的集群，程序序列有四个集群，处方序列有四个集群。集群在就诊时间和间隔、程序和药物处方以及合并症和 HF 住院率方面存在差异。我们没有发现集群成员与 HF 住院之间存在显著关联。
序列聚类技术可用作探索性工具，系统地提取、描述、比较和分析治疗序列以及相关特征。
