Academic Electronic Health Records in Undergraduate Nursing Education: Mixed Methods Pilot Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,013 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».