dc.description.abstract | Background: The widespread adoption of electronic medical records (EMR) has created oppor-tunities for automating clinical checklists. Previously, when determining whether to administer tPA (tissue plasminogen activator) to patients with acute ischemic stroke, clinicians faced signif-icant time pressure and effort to verify if the patient had any contraindications. Additionally, the technical barrier for creating medical rules is substantial, often necessitating the help of technical personnel, which results in high maintenance costs for clinical decision support system (CDSS).
Objective: To develop an EMR-based CDSS utilizing natural language processing (NLP) tech-niques aimed at reducing the workload for clinicians reviewing checklists, and to improve both time efficiency and accuracy.
Materials and Methods: Our system utilizes MetaMapLite to extract medical concepts from un-structured EMR notes, integrating these with structured laboratory data to evaluate the comple-tion of checklist criteria. Additionally, this study introduced a Blockly-based visual program-ming interface to facilitate rule creation by clinicians, reducing the need for technical expertise and training costs. Twelve clinicians were enlisted by us to perform simulated tasks comparing our system with a traditional EMR interface. The testers reviewed tPA and Factor XI (plasma thromboplastin antecedent) checklists for four patients, during which completion time, checklist accuracy, usability (via the System Usability Scale, SUS), and cognitive workload (via the NASA-Task Load Index, NASA-TLX) were measured.
Results: The proposed system demonstrated statistically significant improvements over the tradi-tional EMR interface. Checklist accuracy increased from 0.89 to 0.97 (p = 0.046), and the time to complete checklists decreased by 23%. Additionally, our system’s SUS score is better than the traditional EMR interface (67.08 vs. 45.62, p = 0.005), and the NASA-TLX workload score is lower than the traditional EMR interface (38.65 vs. 56.42, p = 0.046).
Discussion and Conclusion: This study successfully integrated a block-based visual design in-terface to facilitate independent rule design by clinical specialists, significantly reducing both training costs and the reliance on technical personnel. Although the current implementation can-not fully interpret the context of clinical notes, it significantly reduced checklist errors and re-view time compared to traditional EMR interface. | en_US |