Evaluating Artificial Intelligence in Clinical Settings-Let Us Not Reinvent the Wheel.

Given the requirement to minimize the risks and maximize the benefits of technology applications in health care provision, there is an urgent need to incorporate theory-informed health IT (HIT) evaluation frameworks into existing and emerging guidelines for the evaluation of artificial intelligence (AI). Such frameworks can help developers, implementers, and strategic decision makers to build on experience and the existing empirical evidence base. We provide a pragmatic conceptual overview of selected concrete examples of how existing theory-informed HIT evaluation frameworks may be used to inform the safe development and implementation of AI in health care settings. The list is not exhaustive and is intended to illustrate applications in line with various stakeholder requirements. Existing HIT evaluation frameworks can help to inform AI-based development and implementation by supporting developers and strategic decision makers in considering relevant technology, user, and organizational dimensions. This can facilitate the design of technologies, their implementation in user and organizational settings, and the sustainability and scalability of technologies.

A Work-Sampling Approach for Assessing Adequate Availability of Computers on Hospital Wards.

A key barrier to the use of health information technology on hospital wards is an insufficient number of computers to support clinical information system access. This paper reports on the development and pilot testing of a novel approach, based on work-sampling principles, for assessing adequate availability of computers.

A Real-Time Dashboard Approach to Decision Support: An Exercise in Co-Design.

Laboratory tests play an integral role in the delivery of quality health care. However, evidence indicates variations in diagnostic testing, which can lead to patient safety risks. Electronic decision support systems are often identified as key to reducing diagnostic error. However, such tools are often introduced into a clinical setting with little understanding of clinician workflow and how tools are likely to impact this. This study reports a qualitative co-design methodology and results from the first phase in the design and development of an analytics-driven, dashboard approach to supporting clinician test ordering in the Emergency Department.