Electronic health record system implementation processes at critical access hospitals
Bibliographic record
Abstract
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The US government allocated $30 billion to implement electronic health records (EHRs) in hospitals and provider practices through policy addressing Meaningful Use (MU). Most small, rural hospitals, particularly those designated as Critical Access Hospitals (CAHs), comprising nearly a quarter of US hospitals, had not implemented EHRs before. Little is known about implementation in this setting. Socio-technical factors differ between larger hospitals and CAHs, which continue to lag behind other hospitals in EHR adoption. Qualitative methods employing Glaserian Grounded Theory were used to develop question protocols and conduct 69 interviews and eight focus groups onsite at four CAHs in Arkansas (1), Kansas (2), and Tennessee (1) where staff were undertaking EHR implementation. In addition, 41 phone interviews were conducted with a spectrum of implementation experts, including newly-minted peer-experts from 10 additional CAHs, who had completed EHR implementations. Twenty-eight themes emerged from coding and analysis. Key barriers and facilitators for EHR implementation at CAHs were identified, and a prospective implementation framework for hospitals for these and similar, small rural hospitals was developed, with additional recommendations for ehealth policy makers and other stakeholders.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".