Sessions

Learn about Vulcan, an HL7 FHIR Accelerator that is developing interoperability standards in clinical research. During the workshop, we'll provide an overview of HL7, HL7 FHIR, the Vulcan Program, recent EuroVulcan event in Paris and HL7 FHIR activities in Europe. Workshop participants will discuss how to continue to develop & implement clinical research standards in Europe.

This presentation concerns the results of pilot study conducted to provide directions for prescription of mHealth in Denmark.

physical activity social media chatbot

The shortage of skilled nursing personnel is – among other reasons – due to the low attractiveness of the profession, comprising high workloads and atypical working hours. Studies show that speech-based documentation systems increase documentation efficiency and satisfaction of physicians. This paper describes the development process of a speech-based application to support nurses, according to the user-centered design approach. User requirements were collected based on interviews (n=6) as well as observations (n=6) in three institutions and were evaluated by means of qualitative content analysis. A prototype of the derived system architecture was implemented. Based on a usability test (n=3), further potentials for improvement were determined. The resulting application enables nurses to dictate personal notes, share them with colleagues and transmit notes to the existing documentation system. We conclude that the user-centered approach ensures the extensive consideration of the nursing staff’s requirements and shall be continued for further development.

Ai in its current form and function is unrealistic unsupportable and for some unattainable economically speaking. A vision of democratizing and commoditizing Ai while running at scale never seen. In addition how can this new user not human never takes a break be managed without overwhelming the clinical staff.Presented will be a challenge to all vendors in the industry and the future. It’s time for a change.Presented will be an idea for a platform that leverages existing application as we manage human/clinical driven workflows vs data/machine learning workflows going forward.

Remarkable progress in artificial intelligence (AI) algorithms performance, and the fast growth in “real world” data available in electronic form generate high hopes for healthcare quality, efficiency, and accessibility improvements. But this game changing progress also causes growing concerns about the effects of growing AI use and its unintended, unanticipated, or even intentionally unethical consequences. Numerous issues and limitations of the algorithms and data used in healthcare and beyond have become more visible, and several organizations and researchers have proposed advice and guidelines to help address these concerns, issues, and limitations. Principles of Responsible AI are now promoted by several important organizations and stakeholders in the AI industry, but there is a need to move these principles towards practical realization and application in real-world scenarios. This panel will address several key aspects of responsible AI in health: explainability and interpretability; bias and fairness; reliability, reusability, and efficiency; privacy and confidentiality protection.