MHS Genesis Implementation: Strategies in Support of Successful EHR Conversion
Notice bibliographique
Résumé
INTRODUCTION: The Military Health System (MHS) is implementing a new electronic health record (EHR) which will impact 9.5 million Department of Defense (DoD) beneficiaries and over 205,000 MHS employees globally. The scale and scope of this EHR rollout is unprecedented; however, lessons learned from previous rollouts across smaller contexts in tandem with Kurt Lewin's Change Theory provide insights into critical success factors (CSFs) and critical barriers to implementation (CBIs) in which leadership may leverage to streamline future go-live efforts. MATERIALS AND METHODS: The researcher conducted a narrative literature review to identify breadth of knowledge currently available surrounding EHR implementation and change management. A Boolean search of UMGC OneSearch was conducted utilizing the search string "electronic health record* OR EHR* AND change* AND implement*" which resulted in 7,084 results. Additional inclusion criteria and limiters were then applied to these results which included full-text, scholarly, and published journal articles, written in English from January 2009 to November 2019, from Europe, the United States, and Canada, in health and medicine, military history and science, and social science and humanities disciplines. 758 articles were identified through database searching. A cursory review of titles and abstracts for goodness of fit eliminated an additional 696 articles leaving 62 for full review. 18 of these articles were used for the final literature review. Through snowballing as well as Google Scholar, eight additional articles were identified and included. Finally, as a result of MHS Genesis being a new, government-backed EHR, the researcher also utilized three pieces of gray literature and non-peer-reviewed articles from professional websites, and three articles for background regarding Lewin's Theory of Change bringing the total references to 32. RESULTS: The manuscript uncovered two main themes regarding organizational change and EHR implementation. The first theme, coined CSF, includes factors associated with positive outcomes in implementing EHRs. The three CSFs are Process Change Champions, Training, and Feedback, and definitions can be found in Table I. The second theme identified, coined CBI, includes factors associated with hindering EHR implementation. The three CBIs are Technophobia, Resistance from Leaders/Providers, and Insufficient Communication, and definitions can be found in Table II. CONCLUSIONS: By operationalizing pre-identified CSFs and CBIs, leaders of the MHS are able to streamline future waves of MHS Genesis rollouts utilizing Kurt Lewin's Change Theory and the newly crafted Conceptual Framework of MHS Genesis Implementation presented in Figure 1. Through full acceptance and use of CSFs, adapting to feedback and barriers, and dynamically adjusting strategies, the challenges associated with a large-scale phased EHR implementation can be minimized. The results and implications of this literature review are significant as the MHS Genesis rollout is still in its infancy and evidence-based best practices can still be executed. MHS Genesis continues to be phase implemented and currently only the Pacific Northwest and parts of California have gone operational. Increasing efficiency in this process provides a benefit to stakeholders at all levels: health care providers, patients, leadership, and taxpayers.
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 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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 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 ».