Holistic Human-Serving Digitization of Health Care Needs Integrated Automated System-Level Assessment Tools.

Area of Science:

• Digital Health
• Health Informatics
• Human Factors Engineering

Background:

• Digital health tools and AI-based clinical decision support systems are increasingly integrated into healthcare.
• Successful deployment hinges on functional integration into clinical workflows, requiring interoperability and intuitive user interface design.
• Current evaluation methods for digital health tools are often ad hoc and non-standardized, focusing on individual products rather than system-level effects.

Purpose of the Study:

• To propose a standard framework for system-level, holistic evaluation of interacting digital health systems.
• To enable systematic and standardized postmarket surveillance and technology assessment across multiple products.
• To facilitate the collection of system-level data from real-world use cases for quality improvement.

Main Methods:

• Development of a standardized evaluation framework for digital health systems.
• Integration of the framework into a system accessible via an application programming interface.
• Leveraging human factors research and purposeful design for workflow integration.

Main Results:

• The proposed framework enables systematic, standardized, and system-wide evaluation of digital health technologies.
• It facilitates the collection of real-world data on performance and user experience.
• This approach addresses the limitations of current ad hoc evaluation methods.

Conclusions:

• A standardized, system-level evaluation framework is crucial for the safe and effective integration of digital health tools.
• Such a framework, potentially mandated by regulatory bodies, can improve postmarket surveillance and technology assessment.
• Prioritizing usability and workflow integration alongside algorithm quality is essential for optimizing digital health implementation.