Impacts of an Electronic Medical Records System on Work Practices and Quality of Care at a Specialty Acute Care Hospital: A Mixed Methods Study
Notice bibliographique
Résumé
Background: Despite the increasing shift towards implementing hospital information systems (HISs), there is limited evidence of their effects as perceived by physicians following the use of longstanding implemented systems in specialty acute care hospitals, notably in Canada. Additionally, due to its burden on work practices, studies which assessed the impacts of implementations have shown limited or mixed findings on its ability to improve quality of care (QoC) (Tajirian et al., 2020; Trout et al., 2022; Campanella et al., 2016; Upadhyay & Hu, 2022; Price et al., 2022). In 2019, the University of Ottawa Heart Institute (UOHI) and five other Ottawa hospitals launched their use of Epic Systems. An initial pre-post study assessed the impacts of the implementation (Jaana et al., under review). This follow-up study aims to investigate physicians' perceptions of system use effects on work practices and QoC at the UOHI. Methods: This study employed a sequential explanatory mixed methods design consisting of a survey distributed to physicians working at the UOHI, followed by semi-structured individual interviews with key physicians. Survey data were collected (n=31, response rate 42.5%) and analyzed to assess physicians' perceptions of system use, attributes and impacts on QoC, performing descriptive and bivariate analyses. 11 physicians were interviewed, and thematic analysis of transcripts identified six key aggregate dimensions. Results: Physician survey respondents expressed dissatisfaction with system attributes such as functionality, information quality and support. Nevertheless, many expressed Epic's beneficial impact on several items related to care coordination, clinical documentation and clinical workflow efficiency. Some differences in perspectives were observed among groups with different tenure, age, specialty and biological sex. System feature use and satisfaction with system attributes were moderately associated with perceived improvements in documentation, care coordination and guideline adherence. Interviews' codes were aggregated into six dimensions (i.e., Use Behaviours, System Quality, Information Quality, Support Quality, User Satisfaction and Driving Improvements in QoC). These results complemented the quantitative findings and highlighted the poor alignment between system design and physicians' needs, inconsistent documentation practices and a lack of support for using Epic data in QI and research projects on QoC. Conclusion: This mixed methods study provides a comprehensive assessment of how Epic system use influences QoC. Main findings reveal physicians' ability to navigate the system and access relevant and accurate patient information can affect the efficiency, effectiveness and safety of patient care. This study provides benchmarking of system use effects despite familiarity with Epic, informing future and ongoing implementation efforts in Canada. Improving system usability and providing transparent and sustained support to physician end users is critical. Furthermore, customization efforts need to be collaborative and tailored to subspecialty workflows, rather than relying on individual adaptations. This would promote standardized and complete data collection practices across the organization to further support physicians' medical decision-making capacity. Future studies should expand the scope of evaluation to include multiple sites using Epic and explore the experiences of various end-user groups.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».