Deviations and variations are the norm rather than the exception in medical diagnosis and treatment processes. Physicians must leverage their knowledge and experience to choose an appropriate variation for each patient. However, this knowledge and experience is often tacit. Process modeling offers a way to convert tacit to explicit knowledge. Many process mining techniques have been developed due to the difficulty of doing this manually, yet, they often neglect the decisions themselves, and these proposed techniques are just one piece of a comprehensive process discovery method. In this paper, we use the Action Design Research methodology to develop a method for process and decision discovery of medical diagnosis and treatment processes. The method was iteratively improved and validated by applying it to a practical setting, which was the emergency medicine department of a hospital. An analysis of the resulting model shows that previously tacit knowledge was successfully made explicit.

在医疗诊断和治疗过程中，偏差和变异是正常现象，而不是例外情况。医生必须利用他们的知识和经验为每位患者选择合适的变异。但是，这种知识和经验通常是默认的。过程建模提供了一种将隐性知识转换为显性知识的方法。由于手工操作的困难，已经开发了许多过程挖掘技术，但是，它们常常忽略了决策本身，这些提议的技术只是一种全面的过程发现方法中的一种。在本文中，我们使用“行动设计研究”方法论来开发医学诊断和治疗过程的过程和决策发现方法。通过将该方法应用于实际环境，对该方法进行了迭代改进和验证，那是医院的急诊科 对结果模型的分析表明，以前的隐性知识已被成功地明确了。