Background In recent years, research and developments in advancing artificial intelligence (AI) in health care and medicine have increased. High expectations surround the use of AI technologies, such as improvements for diagnosis and increases in the quality of care with reductions in health care costs. The successful development and testing of new AI algorithms require large amounts of high-quality data. Academic hospitals could provide the data needed for AI development, but granting legal, controlled, and regulated access to these data for developers and researchers is difficult. Therefore, the German Federal Ministry of Health supports the Protected Artificial Intelligence Innovation Environment for Patient-Oriented Digital Health Solutions for Developing, Testing, and Evidence-Based Evaluation of Clinical Value (pAItient) project, aiming to install the AI Innovation Environment at the Heidelberg University Hospital in Germany. The AI Innovation Environment was designed as a proof-of-concept extension of the already existing Medical Data Integration Center. It will establish a process to support every step of developing and testing 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 multistep mixed methods approach. In the first step, 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 to quantify qualitative findings and discover important aspects that were not mentioned by the interviewees. The questionnaires will be analyzed descriptively. In addition, patients and physicians were interviewed as well. No survey questionnaires were developed for this second group of participants. The study was approved by the Ethics Committee of the Heidelberg University Hospital (approval number: S-241/2021). Results Data collection concluded in summer 2022. Data analysis is planned to start in fall 2022. We plan to publish the results in winter 2022 to 2023. Conclusions The results of our 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 shape an AI environment that is effective and is deemed acceptable by all parties. International Registered Report Identifier (IRRID) DERR1-10.2196/42208
Background 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. 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 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. Results 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. 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 Identifier (IRRID) RR2-10.2196/42208
While the automotive industry is currently facing a contest among different communication technologies and paradigms about predominance in the connected vehicles sector, the diversity of the various application requirements makes it unlikely that a single technology will be able to fulfill all given demands. Instead, the joint usage of multiple communication technologies seems to be a promising candidate that allows benefiting from characteristical strengths (e.g., using low latency direct communication for safety-related messaging). Consequently, dynamic network interface selection has become a field of scientific interest. In this paper, we present a cross-layer approach for context-aware transmission of vehicular sensor data that exploits mobility control knowledge for scheduling the transmission time with respect to the anticipated channel conditions for the corresponding communication technology. The proposed multi-interface transmission scheme is evaluated in a comprehensive simulation study, where it is able to achieve significant improvements in data rate and reliability.
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
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.
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