Academic Electronic Health Records in Undergraduate Nursing Education: Mixed Methods Pilot Study
Bibliographic record
Abstract
Background Teaching students about electronic health records presents challenges for most nursing programs, primarily because of the limited training opportunities within clinical practice settings. A simulated electronic health record is an experiential, learner-centered strategy that enables students to acquire and apply the informatics knowledge needed for working with electronic records in a safe learning environment before the students have encounters with real patients. Objective The aim of this study is to provide a preliminary evaluation of the Lippincott DocuCare simulated electronic health record and determine the feasibility issues associated with its implementation. Methods We used one-group pretest-posttest, surveys, and focus group interviews with students and instructors to pilot the DocuCare simulated electronic health record within an undergraduate nursing program in Western Canada. Volunteering students worked through 4 case scenarios during a 1-month pilot. Self-reported informatics knowledge and attitudes toward the electronic health record, accuracy of computerized documentation, satisfaction, and students’ and educators’ experiences were examined. Demographic and general information regarding informatics learning was also collected. Results Although 23 students participated in this study, only 13 completed surveys were included in the analysis. Almost two-thirds of the students indicated their overall understanding of nursing informatics as being fair or inadequate. The two-tailed paired samples t test used to evaluate the impact of DocuCare on students’ self-reported informatics knowledge and attitudes toward the electronic health record revealed a statistically significant difference in the mean score of knowledge before and after using DocuCare (before: mean 2.95, SD 0.58; after: mean 3.83, SD 0.39; t12=5.80, two-tailed; P<.001). There was no statistically significant difference in the mean scores of attitudes toward the electronic health record before and after using DocuCare (before: mean 3.75, SD 0.40; after: mean 3.70, SD 0.34; t12=0.39, two-tailed; P=.70). Students’ documentation scores varied from somewhat accurate to completely accurate; however, performance improved for the majority of students as they progressed from case scenarios 1 to 4. Both the faculty and students were highly satisfied with DocuCare and highly recommended its integration. Focus groups with 7 students and 3 educators revealed multiple themes. The participants shared suggestions regarding the DocuCare product customization and strategies for potential integration in undergraduate nursing programs. Conclusions This study demonstrated the feasibility and suitability of the DocuCare program as a tool to enhance students’ learning about informatics and computerized documentation in electronic health records. Recommendations will be made to academic leadership in undergraduate programs on the basis of this study. Furthermore, a controlled evaluation study will be conducted in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".