Improving Providers' Decision-Making and Reducing Information Overload Using Information Visualization in EHRs

Critical Care, Electronic Health Records, Information Seeking Behavior, Usability

Visualization

This study aims to test the efficacy of an information visualization dashboard on decision-making using a randomized controlled trial with crossover. This study aims to test the efficacy of using an information visualization dashboard on ICU providers' decision-making, efficiency, and performance compared to their institutional EHR through a randomized control trial with crossover.

Electronic Health Records (EHRs) are a major source of data for ICU clinicians. Synthesizing complex, electronic patient data is key to effective care delivery. EHRs contain both a record of past medical data and present continuous flows of new clinical data from various sources such as physiological inputs, laboratory results, imaging studies, and clinician notes. This complex, continuous stream of patient data can contribute to information overload, which can create barriers to the key cognitive tasks of data identification, extraction, and interpretation. Intensive Care Unit (ICU) providers must quickly synthesize data from more than 200 variables during critical care rounds, with critically ill patients generating a median of 1348 individual data points per day. Information overload has been identified as a key factor in the misinterpretation of data, leading to medical errors such as misdiagnoses. Improving our understanding of information overload-and investigations into new efforts to minimize it-can improve clinician workflow and productivity as well as patient safety. The objective of this study is to explore the impact of current data representation in the EHR on ICU clinicians' cognitive workload, performance, and satisfaction. The research design uses a mixed methods approach, including eye-tracking assessment and surveys, to assess the efficacy of current EHR interfaces for ICU clinicians in live and simulated environments. The investigators will randomize consented participants into two groups: the control (EHR) group and the intervention (AWARE) group. During the Randomized Controlled Trial (RCT) crossover, providers in each group will review the same patient records and will perform the same tasks, and complete the same survey instruments. Providers in the control groups will review two patient cases in their institution EHR (Epic or Cerner) first and then, two new cases in AWARE, and providers in the intervention group will review two patient cases in AWARE and then two new cases in the EHR. Cases will be randomized to eliminate order bias and selection bias. For the cases in the EHR, participants will navigate through the EHR as per their usual routine in the ICU, with no added training sessions before the study. For the cases in AWARE, participants will receive a short training presentation by the study team, explaining the functionality and design of AWARE. Also, a day before each session, the RA will send an email to the provider with a short demonstration video of AWARE to become familiar with the tool. The study will be conducted in simulation or Biobehavioral labs at each site. The PI, or research assistant, will explain the study procedure and obtain consent; participants will be asked to use Tobii Pro Fusion during the session, participants will not need to wear anything, the eye-tracker will be mounted to the monitor. During the session, participants will review 2 patient cases in their institutional EHR and two in AWARE. After the participant completes the patient record review, the research assistant (RA) will ask the participant a series of decision-based Q&A activities requiring verbal responses or task completion in the EHR or AWARE. The provider may use the EHR or AWARE to complete the Q&A activity. Cases will be in random order for each participant to avoid selection or order bias. After usability testing, the investigators will ask the participant to fill out the NASA-TLX survey and the System Usability Scale (SUS). The NASA-TLX measures the perceived workload of using the EHR, and the SUS measures the level of satisfaction as a result of using the EHR. All sessions will be recorded.

United States