System Configuration Evaluation for a Province-Wide Clinical Information System Using the eSafety Checklist
Bibliographic record
Abstract
BACKGROUND: According to Digital Health Canada 2013 eSafety Guidelines, an estimated one-third of patient safety incidents following implementation of clinical information systems (CISs) are technology-related. An eSafety checklist was previously developed to improve CIS safety by providing a comprehensive listing of system-agnostic, evidence-based configuration recommendations. OBJECTIVES: We sought to use the checklist to support safe initial configuration of a provincial system-wide CIS (Alberta, Canada), referred to as Connect Care. METHODS: The checklist was applied to 13 Connect Care modules in three successive phases. First, the checklist was adapted to an abbreviated high-priority version. Second, demonstrations of each module were recorded. Finally, independent evaluation of each recording was conducted by two eSafety evaluators using the abbreviated eSafety checklist. RESULTS: All modules achieved greater than 72% compliance, with an average of 84%. Overall, 273 opportunities for improvement were identified, with four major areas or themes emerging: (1) inconsistent date and time, (2) unclear patient identification, (3) ineffective alert system, and (4) insufficient decision support. These opportunities were forwarded to the appropriate build teams for review and implementation. CONCLUSION: This work is the first to utilize the eSafety checklist in a real-world CIS, which will become one of the largest in Canada. The checklist has shown clinical applicability in identifying gaps in CIS configuration and should be considered for use in future and pre-existing CISs.
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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 | 0.118 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".