Users’ perspectives of key factors to implementing electronic health records in Canada: a Delphi study
Notice bibliographique
Résumé
BACKGROUND: Interoperable electronic health record (EHR) solutions are currently being implemented in Canada, as in many other countries. Understanding EHR users' perspectives is key to the success of EHR implementation projects. This Delphi study aimed to assess in the Canadian context the applicability, the importance, and the priority of pre-identified factors from a previous mixed-methods systematic review of international literature. METHODS: A three-round Delphi study was held with representatives of 4 Canadian EHR user groups defined as partners of the implementation process who use or are expected to use EHR in their everyday activity. These groups are: non-physician healthcare professionals, health information professionals, managers, and physicians. Four bilingual online questionnaire versions were developed from factors identified by the systematic review. Participants were asked to rate the applicability and the importance of each factor. The main outcome measures were consensus and priority. Consensus was defined a priori as strong (≥ 75%) or moderate (≥ 60-74%) according to user groups' level of agreement on applicability and importance, partial (≥ 60%) when participants agreed only on applicability or importance, or as no consensus (< 60%). Priority for decision-making was defined as factors with strong consensus with scores of 4 or 5 on a five-point Likert scale for applicability and importance. RESULTS: Three Delphi rounds were completed by 64 participants. Levels of consensus of 100%, 64%, 64%, and 44% were attained on factors submitted to non-physician healthcare professionals, health information professionals, managers, and physicians, respectively. While agreement between and within user groups varied, key factors were prioritized if they were classified as strong (≥ 75% from questionnaire answers of user groups), for decision-making concerning EHR implementation. The 10 factors that were prioritized are perceived usefulness, productivity, motivation, participation of end-users in the implementation strategy, patient and health professional interaction, lack of time and workload, resources availability, management, outcome expectancy, and interoperability. CONCLUSIONS: Amongst all factors influencing EHR implementation identified in a previous systematic review, ten were prioritized through this Delphi study. The varying levels of agreement between and within user groups could mean that users' perspectives of each factor are complex and that each user group has unique professional priorities and roles in the EHR implementation process. As more EHR implementations in Canada are completed it will be possible to corroborate this preliminary result with a larger population of EHR users.
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 | 0,033 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,015 | 0,004 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».