Carayon, Pascale

Antibiotic use is often the target of interventions in health care organizations that aim to decrease healthcare-associated infections (HAI) such as Clostridioides difficile (CDI); this is particularly important for fluoroquinolones (FQ), which are frequently used in critical care settings. In this study, using a multiple case study research approach, we conduct an in-depth analysis of an intervention aimed at limiting ICU prescriber access to FQ in two ICUs of two hospitals. The data collection and analysis were guided by a human factors engineering approach based on the SEIPS (Systems Engineering Initiative for Patient Safety) model and evidence-based implementation principles. Our results show some differences in the implementation of the FQ intervention between the two ICUs, such as level and method of FQ restriction, and training and communication with physicians and pharmacists. In both ICUs, several organizational learning mechanisms helped to quickly identify problems with the intervention and ensure that changes were made in a just-in-time manner (e.g. just-in-time training, removal of FQ in order set for pneumonia). Despite their organizational differences, both sites developed strategies to successfully implement the FQ intervention.

The National Academies Board on Human Systems Integration (BOHSI) has organized this session exploring the state of the art and research and design frontiers relating to the application of Artificial Intelligence (AI) technologies to Healthcare. Advances in AI, machine learning, and robotics promise to revolutionize access to and quality of healthcare. At the same time lessons learned from earlier applications of AI technologies suggest a need to carefully consider the user and use context and the need for effective human-machine teaming design to avoid brittle systems that fail to be used or worse that negatively impact patient safety. The Panel brings together prominent researchers from both the Human Factors and Computer Science and AI communities to discuss promising applications, challenges and short-falls and ways forward for achieving the promise of enhanced delivery of health care through application of AI technologies.