Bridging Global Frameworks and Local Practice: Quantitative Evaluation of Electronic Health Record Safety in Kuwait’s Public Hospitals
Notice bibliographique
Résumé
Background: Electronic health records (EHRs) play a critical role in today's health care by enhancing data management, improving workflows, and supporting clinical decision-making. However, EHR implementation introduces technical and clinical challenges that can compromise patient safety. The Safety Assurance Factors for Electronic Health Record Resilience guides, developed by the Office of the National Coordinator for Health Information Technology, provide a structured framework for evaluating and optimizing EHR safety practices. Despite extensive research on EHR safety in developed countries, little is known about its implementation in regions with differing health care systems, such as Kuwait. Objective: This study aims to examine the EHR safety across hospitals in the State of Kuwait via (1) conducting a proactive risk assessment examining current safety practices and (2) proposing recommendations to improve EHR safety practices. Methods: A quantitative approach was used to evaluate EHR safety practices in 6 public hospitals. Multidisciplinary teams completed the Safety Assurance Factors for Electronic Health Record Resilience self-assessment questionnaire, scoring their implementation status of 165 recommended practices as "fully," "partially," or "not" implemented across 9 Safety Assurance Factors for Electronic Health Record Resilience guides. Data were analyzed to calculate the percentage of "fully implemented" recommended practices for each hospital, guide, and EHR safety domain. Standard deviations were calculated to assess data variability, and comparative analysis was conducted to identify implementation patterns. Results: The findings revealed significant variability in the implementation of recommended safety practices, with an average of 53% rated as "fully implemented" across hospitals. Infrastructure-focused guides, such as system configuration (77%) and system interfaces (80%), had the highest implementation rates, while clinical process guides, such as clinician communication (25%), scored the lowest. Among the 9 guides, 16 recommended practices were unanimously rated as "fully implemented," while 8 were predominantly rated as "not implemented." The high-priority guide showed notable variability, with implementation rates ranging from 17% to 89% across hospitals. Hospitals with longer EHR adoption periods tended to perform better, though hospital size and implementation type showed inconsistent effects on safety practices scores. Conclusions: The study highlights variability in EHR safety practice implementation across Kuwait's public hospitals, with stronger performance in technical domains and gaps in clinical processes. By applying the Safety Assurance Factors for EHR Resilience guides in a non-US context, the study offers a foundational understanding of EHR safety implementation in Kuwait's public health care system. Given the study's limited scope and reliance on self-reported data, findings should be interpreted with caution. Future research should adopt broader sampling and mixed methods approaches to validate these results and inform the development of context-specific strategies to enhance EHR safety and patient outcomes.
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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 | 0,025 | 0,031 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,006 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».