Problem Definition: Queensland’s Compulsory Third-Party (CTP) Insurance Scheme provides a mechanism for persons injured as a result of a motor vehicle accident to receive compensation. Managing CTP claims involves multiple stakeholders with potentially conflicting interests. It is therefore pertinent to investigate whether ‘best practice’ for claims processing can be identified and measured so all claimants receive fair and equitable treatment. The project set out to test the applicability of a mixed-method approach to identify ‘best-practice’ using qualitative, process mining, and data mining techniques in an insurance claims processing domain. Relevance: Existing approaches typically identify ‘best practice’ from literature or surveys of practitioners. The study provides insights into an alternative, mixed-method approach to deriving best practice from historical data and domain knowledge. Methodology: The study is a reflective analysis of insights gained from a practical application of a mixed-method approach to determine ‘best practice’. Results: The mixed-method approach has a number of benefits over traditional approaches in uncovering best practice process behavior from historical data in the real-world context (i.e., can identify process behavior differences between high and low performing cases). The study also highlights a number of challenges with regards to the quality and detail of data that needs to be available to perform the analysis. Managerial Implications: The ‘lessons learned’ from this study will directly benefit others seeking to implement a data-driven approach to understand a ‘best-practice’ process in their own organization.

问题定义：昆士兰州的强制性第三方保险计划为因机动车事故而受伤的人提供了一种获得赔偿的机制。管理CTP索赔涉及多个利益攸关方，它们之间的利益可能存在冲突。因此，有必要调查是否可以确定和衡量索赔处理的“最佳实践”，以便所有索赔人都得到公正和公平的待遇。该项目着手测试在保险理赔处理领域中使用定性，过程挖掘和数据挖掘技术来识别“最佳实践”的混合方法的适用性。关联：现有方法通常从文献或从业人员调查中确定“最佳实践”。该研究为从历史数据和领域知识中得出最佳实践的另一种混合方法提供了见解。方法：该研究是对混合方法实际应用中确定“最佳实践”的见解的反思性分析。结果：与传统方法相比，混合方法在从真实环境中的历史数据中发现最佳实践过程行为方面具有许多优势（即，可以识别出高绩效案例与低绩效案例之间的过程行为差异）。该研究还强调了进行分析所需的数据质量和细节方面的许多挑战。对管理的影响：从这项研究中获得的“经验教训”将直接使寻求实施数据驱动方法以了解其组织中“最佳实践”流程的其他人员受益。