Developing a standardized EMR workflow for medical students and preceptors. Issue 1 (March 2023)

• Record Type: Journal Article
• Authors: Viguera Altolaguirre, Cristina; Reddy, Ramya; Gamaldo, Charlene E.; Salas, Rachel Marie E.
• Is Part Of: Health policy and technology. Volume 12:Issue 1(2023)
• Publication Date: 2023-03
• Subjects: Education -- Methods of education -- Other education -- Electronic medical records; Medical policy -- Periodicals; Medical technology -- Periodicals; Medical policy; Medical technology; Health Policy -- Periodicals; Biomedical Technology -- Periodicals; Technology Assessment, Biomedical -- Periodicals; Periodicals; 362.105
• DOI: 10.1016/j.hlpt.2022.100696
• Languages: English
• ISSNs: 2211-8837

Abstract

Highlights: There is no standard teaching of the electronic medical record in medical education in the United States. We created an outpatient workflow that integrates the electronic medical record. The workflow model engages medical students in the electronic medical record. The workflow model improves productivity metrics, including documentation time. Abstract: Objectives: The electronic medical record (EMR) is an integral component of clinical encounters. However, there is no standardized curriculum for EMR education in medical school. This study aimed to create a standardized EMR workflow model for medical students in clinic. It was hypothesized that this workflow would be effectively implemented and increase physician productivity. Methods: An outpatient clinic workflow model was created to integrate students into clinical visits as active participants in EMR documentation. 240 third and fourth-year medical students on their Neurology clerkship at a U.S. medical institution and 34 outpatient neurologists were trained on the workflow (student EMR integration training; SEIT). A total of 733 Neurology outpatient clinic days were analyzed (473 pre-SEIT and 251 post-SEIT). Productivity metrics included average lag time, average Relative Value Units (RVUs) per day, and average invoices per day. Results: There was a statistically significant difference in documentation time, with a pre-SEIT lag time of 14.7 days (SD 13.1 days, 95% CI 9.4–20.0) compared to 10.3 days post-SEIT (SD 10.2 days, 95% CI 6.2–14.5; p-value = 0.001). The average RVUs per day were 18.5 pre-SEIT (SD 7.8, 95% CI 15.4–21.7) and 17.8 post-SEIT (SD 8.0, 95% CI 14.6–21.0; p-value = 0.7). The average invoices per day were 7.5 pre-SEIT (SD 2.6, 95% CI 6.5–8.6) and 7.3 post-SEIT (SD 3.0, 95% CI 6.1–8.5; p-value = 0.5). Conclusions: The workflow model utilizes medical student EMR documentation in the clinical note and decreases physician documentation time.