Ratwani, Raj M.

Abstract Objective This study aimed to evaluate how differences in case complexity affect information-processing burden, as measured by eye-tracking, as well as the efficiency of electronic health record (EHR) use among healthcare providers in the intensive care unit. Materials and Methods This cross-sectional study recruited providers from 4 U.S. medical centers that use 2 prominent EHR systems (Epic and Oracle). After reporting demographic information, participants reviewed 2 complex cases and 2 standard cases in their institution’s EHR system and then responded to 5 questions about each case, yielding a performance score. Information-processing burden was assessed by measuring the number of eye fixations via eye-tracking software. The efficiency of EHR use was assessed by measuring the task completion time, number of mouse clicks per minute, number of EHR screens viewed, and performance score. Results Eighty-one providers were included for analysis. Providers exhibited significantly more eye fixations (P < .001) and longer task completion times (P < .001) for complex cases than for standard cases. There were also significantly fewer mouse clicks per minute during complex cases (P < .001). Reviewing a complex case first led to significantly more eye fixations (P = .015) and longer task completion times (P < .01) than when a standard case was presented first. Case complexity did not significantly affect performance scores or the number of EHR screens viewed. Discussion Higher case complexity was shown to be associated with greater information-processing burden and less efficient EHR use. These findings have implications for enhancing the efficiency of EHR use, thereby leading to improved clinical decision-making and patient safety. Furthermore, reviewing complex cases first led to a greater information-processing burden, suggesting that providers could benefit from “warming up” with standard cases before reviewing complex cases.

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.