Strategies in Electronic Medical Record Downtime Planning: A Scoping Study.
Bibliographic record
Abstract
PURPOSE: This review will identify dominant themes, common to published articles that discuss downtime planning in a clinical setting. These common themes will represent key considerations for healthcare organizations' comprehensive downtime plans. METHOD: A scoping study was performed using search results from PubMed, CINAHL and Medline. The 4 articles meeting the inclusion criteria were analyzed for common themes and findings. RESULTS: Four common themes were found in the included articles: 1) Communications plans, 2) Procedure review and revision, 3) Managing system availability and 4) Preparing staff for handling incidents. CONCLUSION: Organizations must have comprehensive downtime plans available to ensure continuity of patient care during the periods of limited availability. A comprehensive downtime plan that includes these four strategies can become the framework for a set of organizational procedures that ensures the best possible access to vital patient information before, during, and after a downtime event.
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 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 | 0.079 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.028 | 0.033 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".