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Record W3043841497 · doi:10.1093/milmed/usaa184

MHS Genesis Implementation: Strategies in Support of Successful EHR Conversion

2020· article· en· W3043841497 on OpenAlexaboutno aff
Edward W Woody

Bibliographic record

VenueMilitary Medicine · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Inclusion (mineral)Leverage (statistics)Medical educationPublic relationsMedicinePsychologyPolitical scienceComputer scienceSociologySocial science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.000

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.

Opus teacher head0.398
GPT teacher head0.614
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2020
Admission routes1
Has abstractyes

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