An Investigation of Radiation Treatment Learning Opportunities in Relation to the Radiation Oncology Electronic Medical Record: A Single Institution Experience

Abstract

PurposeA modern radiation oncology electronic medical record (RO-EMR) system represents a sophisticated human-computer interface with the potential to reduce human driven errors and improve patient safety. As the RO-EMR becomes an integral part of clinical processes, it may be advantageous to analyze learning opportunities (LO) based on their relationship with the RO-EMR. This work reviews one institution's documented LO to: (1) study their relationship with the RO-EMR workflow, (2) identify best opportunities to improve RO-EMR workflow design, and (3) identify current RO-EMR workflow challenges. Methods and MaterialsInternal LO reports for an 11-year contiguous period were categorized by their relationship to the RO-EMR. We also identify the specific components of the RO-EMR used or involved in each LO. Additionally, contributing factor categories from the ASTRO/AAPM sponsored Radiation Oncology Incident Learning System's (RO-ILS) nomenclature was used to characterize LO directly linked to the RO-EMR. ResultsA total of 163 LO from the 11-year period were reviewed and analyzed. Most (77.2%) LO involved the RO-EMR in some way. The majority of the LO were the results of human/manual operations. The most common RO-EMR components involved in the studied LO were documentation related to patient setup, treatment session schedule functionality, RO-EMR used as a communication/note-delivery tool, and issues with treatment accessories. Most of the LO had staff lack of attention and policy not followed as 2 of the highest occurring contributing factors. ConclusionsWe found that the majority of LO were related to RO-EMR workflow processes. The high-risk areas were related to manual data entry or manual treatment execution. An evaluation of LO as a function of their relationship with the RO-EMR allowed for opportunities for improvement. In addition to regular radiation oncology quality improvement review and policy update, automated functions in RO-EMR remain highly desirable.