Clinical pathways are specialized healthcare plans that model patient treatment procedures. They are developed to provide criteria-based progression and standardize patient treatment, thereby improving care, reducing resource use, and accelerating patient recovery. However, manual modeling of these pathways based on clinical guidelines and domain expertise is difficult and may not reflect the actual best practices for different variations or combinations of diseases. We propose a two-phase modeling method using process mining, which extends the knowledge base of clinical pathways by leveraging conformance checking diagnostics. In the first phase, historical data of a given disease is collected to capture treatment in the form of a process model. In the second phase, new data is compared against the reference model to verify conformance. Based on the conformance checking results, the knowledge base can be expanded with more specific models tailored to new variants or disease combinations. We demonstrate our approach using Synthea, a benchmark dataset simulating patient treatments for SARS-CoV-2 infections with varying COVID-19 complications. The results show that our method enables expanding the knowledge base of clinical pathways with sufficient precision, peaking to 95.62% AUC while maintaining an arc-degree simplicity of 67.11%.

翻译：临床路径是用于建模患者治疗流程的专项医疗方案，旨在提供基于标准的诊疗进展并规范患者治疗，从而改善护理质量、降低资源消耗并加快患者康复。然而，基于临床指南和领域知识手工构建这些路径存在困难，且可能无法反映不同疾病变异或合并症的实际最佳实践。我们提出一种基于过程挖掘的两阶段建模方法，通过合规性检查诊断技术扩展临床路径知识库。第一阶段，收集特定疾病的历史数据，以过程模型形式捕获治疗方案；第二阶段，将新数据与参考模型进行对比以验证合规性。根据合规性检查结果，知识库可扩展出针对新变异或合并症的更精细化模型。我们采用合成基准数据集Synthea进行验证，该数据集模拟了不同COVID-19并发症的SARS-CoV-2感染患者治疗方案。结果表明，本方法能以足够精度扩展临床路径知识库，在维持67.11%弧段简度的同时，AUC最高达95.62%。