OBJECTIVES: Although health information systems are constantly evolving, organizations continue to maintain their data in silos resulting in additional workflows and unnecessary delays that have potential to impact timely care. One major healthcare vendor is developing an oncology decision support cockpit that analyses and organizes patient data into one complete view and provides advanced AI/ML tools that enable care teams to accelerate care and more effectively treat patients. The objective of this study was to provide quantitative and qualitative evidence to assist with further product development and frame regulatory claims.

METHODS: The study design leveraged a comparative simulation using real-world cancer patient data located at a large cancer treatment center in the UK. Eight oncologic patient’s entire history of EMR data was collected and anonymized for use in this study and the evaluation was completed by 12 oncologists with experience ranging from 4 to 45 years. Each oncologist was presented with a simulated treatment task that required them to use either their existing EMR or the healthcare vendor’s new record system. Observational research captured aspects such as time & motion, task correctness, and behaviors of the participant. After each task, the participant was asked to complete a survey that contained both rating scales and open-ended questions to assess their perception.

RESULTS: Time savings were able to be quantified on each task ranging from a 69% loss of time through a 95% time savings. Additionally, through the time & motion data collection, a task complexity score was created, demonstrating how complex each task was when completed on the oncologists’ current EMR compared to the vendor’s new system.

CONCLUSIONS: The findings from this research provided the vendor with quantified data pertaining to the use of their product, user perception of the data, and recommendations for future usability/UX changes.