The Cancer Registry of Norway (CRN) is a public body responsible for capturing and curating cancer patient data histories to provide a unified access to research data and statistics for doctors, patients, and policymakers. For this purpose, CRN develops and operates a complex, constantly-evolving, and socio-technical software system. Recently, machine learning (ML) algorithms have been introduced into this system to augment the manual decisions made by humans with automated decision support from learned models. To ensure that the system is correct and robust and cancer patients' data are properly handled and do not violate privacy concerns, automated testing solutions are being developed. In this paper, we share the challenges that we identified when developing automated testing solutions at CRN. Such testing potentially impacts the quality of cancer data for years to come, which is also used by the system's stakeholders to make critical decisions. The challenges identified are not specific to CRN but are also valid in the context of other healthcare registries. We also provide some details on initial solutions that we are investigating to solve the identified challenges.

翻译：挪威癌症登记中心（CRN）作为公共机构，负责采集并整理癌症患者病史数据，为医生、患者及政策制定者提供统一的研究数据与统计信息访问。为此，CRN开发并运维着一套复杂且持续演进的社会技术型软件系统。近年来，该系统引入了机器学习（ML）算法，通过训练模型生成的自动决策支持来增强人工决策。为确保系统正确性与鲁棒性，并保障癌症患者数据得到妥善处理且不违反隐私规定，自动化测试方案正在开发中。本文分享了我们在CRN开发自动化测试方案时识别出的挑战。这些测试可能对长期使用的癌症数据质量产生影响，而相关数据被系统利益相关者用于关键决策。所识别的挑战并非CRN特有，在其他医疗登记机构场景中同样具有适用性。我们同时提供了针对这些挑战的初步解决方案的探索细节。