MHS Genesis Implementation: Strategies in Support of Successful EHR Conversion
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".