Outcomes of an Advanced Epic Personalization Course on Clinician Efficiency through Use of Electronic Medical Records: Retrospective Study
Notice bibliographique
Résumé
Background: Since Singapore's first migration to Epic in 2022, we have been conducting an advanced Epic personalization course twice a year for health care professionals with at least 3 months of experience using the system. Electronic medical records education is an under-recognized pillar in reducing health information technology-related stress and clinician burnout. Objective: The intent of the course is to improve clinician efficiency through customization and personalization of Epic interfaces. We hypothesized that compared to their colleagues, trained clinicians would demonstrate significant quantitative improvements in use of the Epic system after our course. Methods: We performed a retrospective analysis from July 2022 to January 2024, including 17 clinicians among 77 individuals who attended our course. Recruitment was done through digital mailers sent out via the local hospital announcement channels. Interested clinicians were able to register for our course via the National University Of Singapore website. Our one-day course involves physical lessons and interprofessional, case-based discussions emphasizing a wide range of high-yield Epic functionalities with practice exercises, such as drafting referral letter templates. Three months of pre- and postcourse Epic usage statistics of the trained clinicians were retrieved based on aggregate data provided by Epic Singapore. Performance metrics included documentation length, time spent in Epic functionalities and use of SmartPhrases, order sets and preference lists. Results: At three months post-course, documentation length decreased by 45.8% (711.8 characters) compared to a 22.2% (126.4 characters) increase among controls. Trained clinicians demonstrated a 2.47-fold increase in use of order sets from 16.2% to 40%, and a 49.9% increase in orders from preference lists after the course, from 35.1% to 52.6%. In total, trained clinicians demonstrated a 1.8-fold increase in combined use of orders from preference lists or order sets after the course, from 51.4% to 92.6%. The number of SmartPhrases created by trained clinicians was 5.64 times higher than among controls, in addition to a 5.57-fold higher use of Quick Filters than controls. Moreover, time in the Chart Review section per day decreased by 29.3% (4.6 min) among trained clinicians versus an increase of 14.6% (2.8 min) among controls. Compared to controls, trained clinicians spent 36.7% (219.6 min) less time in the Epic system per day, 56.6% (29 min) less time on Notes per day, and 57.5% (10.7 min) less time on orders per day. Conclusions: Overall, trained clinicians demonstrated more efficiency in their use of Epic, with reduced time spent across various functionalities. Increased use of Smart Tools, including SmartPhrases and Quick Filters was also observed among trained clinicians, which indicated efficiency in reducing total usage time. These findings demonstrated broad improvements across multiple physician efficiency metrics among participating clinicians of our advanced Epic personalization course. These gains may contribute to improved mental health among health care professionals and enhanced productivity within health care systems.
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| 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) | 0,002 | 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 ».