Accepted for/Published in: JMIR Research Protocols
Date Submitted: Aug 26, 2022
Open Peer Review Period: Aug 26, 2022 - Sep 5, 2022
Date Accepted: Oct 18, 2022
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Exploring Stakeholder Requirements to enable research and development of AI algorithms in a hospital based generic infrastructure: Research Protocol for a multi-step mixed-methods study
ABSTRACT
Background:
In recent years, research and developments in advancing Artificial Intelligence (AI) in healthcare and medicine have increased. High expectations surround the use of AI technologies, such as improvements for diagnosis and increases in quality of care while lowering health care costs. The successful development and testing of new AI algorithms requires large amounts of high-quality data. Academic hospitals could provide the data needed for AI development, but legal, controlled, and regulated access for developers and researchers to this data is difficult. Therefore, 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, aiming to install an AI Innovation Environment at the Heidelberg University Hospital, Germany. The AI Innovation Environment is designed as a proof-of-concept extension of the already existing Medical Data Integration Center. It will establish a process to support every step from development to testing of AI based technologies.
Objective:
The first part of the pAItient project as presented in this research protocol 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 planned a multi-step mixed-methods approach. In a first step, 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. In addition, patients and physicians were interviewed as well. No survey questionnaires were developed for this second group of participants.
Results:
The results of this study will help in shaping the AI Innovation Environment at our academic hospital according to stakeholder requirements. With this approach, in turn, we aim to create an AI infrastructure that is both effective and deemed acceptable by all parties.
Conclusions:
The study was approved by the Ethics Committee of the Heidelberg University Hospital (S-241/2021). To date, we successfully concluded data collection.
Citation
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