Predictors of student use of an electronic record

Clin Teach. 2019 Apr;16(2):131-137. doi: 10.1111/tct.12785. Epub 2018 Mar 25.

Abstract

Background: Little is known on ambulatory clerkship students' use of an electronic medical record (EMR). We investigated students' use of recommended EMR tasks across different types of sites and studied the predictors of these recommended tasks.

Methods: Students documented how often they performed recommended EMR tasks and suggested improvements to enhance EMR use. We compared student performance of recommended tasks across different types of sites using χ2 tests and the Fisher's exact test. We performed regression analyses to investigate factors predicting students' performance of EMR tasks. Two faculty members read all of the suggested improvements and agreed on themes.

Results: From January 2014 to June 2015, 263 of 295 Family and Community Medicine Clerkship (FCMC) students (89.2%) were at sites that used an EMR. Of the 263 students, 68.4% typed their own note into the EMR, but only 31.2% entered orders and 27.8% entered prescriptions for their teacher to sign. Students' rating of the orientation to the EMR predicted their use of all EMR tasks. The number of years that the teaching site used an EMR predicted the students' use of some tasks. Suggested improvements included a better orientation to the EMR, more use of the EMR, and access to a computer and the EMR. Little is known on ambulatory clerkship students' use of an electronic medical record DISCUSSION: Many students did not perform recommended EMR tasks. To help more students learn EMR tasks, clinical teachers can offer students a detailed orientation to their EMR, provide them with access to a computer and the EMR, and give them the opportunity to perform recommended EMR tasks, including typing their own note and entering orders and prescriptions.

MeSH terms

  • Ambulatory Care Facilities / statistics & numerical data
  • Clinical Clerkship / statistics & numerical data*
  • Electronic Health Records / supply & distribution*
  • Humans
  • Learning
  • Students, Medical / statistics & numerical data*
  • Time Factors