Weinert Lina, Klass Maximilian, Schneider Gerd, Heinze Oliver
Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany.
Section for Translational Health Economics, Department for Conservative Dentistry, Heidelberg University Hospital, Heidelberg, Germany.
JMIR Form Res. 2023 Apr 18;7:e43958. doi: 10.2196/43958.
Legal, controlled, and regulated access to high-quality data from academic hospitals currently poses a barrier to the development and testing of new artificial intelligence (AI) algorithms. To overcome this barrier, the German Federal Ministry of Health supports the "pAItient" (Protected Artificial Intelligence Innovation Environment for Patient Oriented Digital Health Solutions for developing, testing and evidence-based evaluation of clinical value) project, with the goal to establish an AI Innovation Environment at the Heidelberg University Hospital, Germany. It is designed as a proof-of-concept extension to the preexisting Medical Data Integration Center.
The first part of the pAItient project aims to explore stakeholders' requirements for developing AI in partnership with an academic hospital and granting AI experts access to anonymized personal health data.
We designed a multistep mixed methods approach. First, researchers and employees from stakeholder organizations were invited to participate in semistructured interviews. In the following step, questionnaires were developed based on the participants' answers and distributed among the stakeholders' organizations. In addition, patients and physicians were interviewed.
The identified requirements covered a wide range and were conflicting sometimes. Relevant patient requirements included adequate provision of necessary information for data use, clear medical objective of the research and development activities, trustworthiness of the organization collecting the patient data, and data should not be reidentifiable. Requirements of AI researchers and developers encompassed contact with clinical users, an acceptable user interface (UI) for shared data platforms, stable connection to the planned infrastructure, relevant use cases, and assistance in dealing with data privacy regulations. In a next step, a requirements model was developed, which depicts the identified requirements in different layers. This developed model will be used to communicate stakeholder requirements within the pAItient project consortium.
The study led to the identification of necessary requirements for the development, testing, and validation of AI applications within a hospital-based generic infrastructure. A requirements model was developed, which will inform the next steps in the development of an AI innovation environment at our institution. Results from our study replicate previous findings from other contexts and will add to the emerging discussion on the use of routine medical data for the development of AI applications.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/42208.
目前，从学术医院获取合法、受控且规范的高质量数据，对新型人工智能（AI）算法的开发与测试构成了障碍。为克服这一障碍，德国联邦卫生部支持“pAItient”（面向患者的数字健康解决方案的受保护人工智能创新环境，用于临床价值的开发、测试和循证评估）项目，目标是在德国海德堡大学医院建立一个人工智能创新环境。它被设计为对现有医学数据整合中心的概念验证扩展。
“pAItient”项目的第一部分旨在探索利益相关者在与学术医院合作开发人工智能以及允许人工智能专家访问匿名个人健康数据方面的需求。
我们设计了一种多步骤混合方法。首先，邀请利益相关者组织的研究人员和员工参加半结构化访谈。接下来，根据参与者的回答制定问卷，并在利益相关者组织中分发。此外，还对患者和医生进行了访谈。
确定的需求范围广泛，有时相互冲突。相关的患者需求包括为数据使用充分提供必要信息、研发活动明确的医学目标、收集患者数据的组织的可信度以及数据不应可重新识别。人工智能研究人员和开发人员的需求包括与临床用户的联系、共享数据平台可接受的用户界面（UI）、与计划基础设施的稳定连接、相关用例以及处理数据隐私法规方面的协助。下一步，开发了一个需求模型，该模型在不同层次描述了确定的需求。这个开发的模型将用于在“pAItient”项目联盟内传达利益相关者的需求。
该研究确定了在基于医院的通用基础设施内开发、测试和验证人工智能应用的必要需求。开发了一个需求模型，这将为我们机构人工智能创新环境的下一步发展提供信息。我们研究的结果重复了其他背景下的先前发现，并将为关于使用常规医疗数据开发人工智能应用的新兴讨论增添内容。
国际注册报告识别号（IRRID）：RR2 - 10.2196/42208。
