Exploring Stakeholder Requirements to enable research and development of AI algorithms in a hospital based generic infrastructure: Results of a Multi-step mixed-methods Study (Preprint)

BACKGROUND Legal, controlled, and regulated access to high-quality data from academic hospitals currently poses a barrier to the development and testing of new 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 pre-existing Medical Data Integration Center. OBJECTIVE 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. METHODS We designed a multi-step mixed-methods approach. First, researchers and employees from stakeholder organizations were invited to participate in semi-structured interviews. In the following step, questionnaires were developed based on the participants’ answers and distributed among the stakeholders’ organizations. Additionally, patients and physicians were interviewed as well. RESULTS The identified requirements covered a wide range and were conflicting sometimes. Relevant patient requirements include 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 re-identifiable. Requirements AI researchers and developers encompassed contact with clinical users, an acceptable 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. CONCLUSIONS 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 RR2-42208