Electronic Clinical Safety Reporting System: A Benefits Evaluation
Bibliographic record
Abstract
BACKGROUND: Eastern Health, a large health care organization in Newfoundland and Labrador (NL), started a staged implementation of an electronic occurrence reporting system (used interchangeably with "clinical safety reporting system") in 2008, completing Phase One in 2009. The electronic clinical safety reporting system (CSRS) was designed to replace a paper-based system. The CSRS involves reporting on occurrences such as falls, safety/security issues, medication errors, treatment and procedural mishaps, medical equipment malfunctions, and close calls. The electronic system was purchased from a vendor in the United Kingdom that had implemented the system in the United Kingdom and other places, such as British Columbia. The main objective of the new system was to improve the reporting process with the goal of improving clinical safety. The project was funded jointly by Eastern Health and Canada Health Infoway. OBJECTIVE: The objectives of the evaluation were to: (1) assess the CSRS on achieving its stated objectives (particularly, the benefits realized and lessons learned), and (2) identify contributions, if any, that can be made to the emerging field of electronic clinical safety reporting. METHODS: The evaluation involved mixed methods, including extensive stakeholder participation, pre/post comparative study design, and triangulation of data where possible. The data were collected from several sources, such as project documentation, occurrence reporting records, stakeholder workshops, surveys, focus groups, and key informant interviews. RESULTS: The findings provided evidence that frontline staff and managers support the CSRS, identifying both benefits and areas for improvement. Many benefits were realized, such as increases in the number of occurrences reported, in occurrences reported within 48 hours, in occurrences reported by staff other than registered nurses, in close calls reported, and improved timelines for notification. There was also user satisfaction with the tool regarding ease of use, accessibility, and consistency. The implementation process encountered challenges related to customizing the software and the development of the classification system for coding occurrences. This impacted on the ability of the managers to close-out files in a timely fashion. The issues that were identified, and suggestions for improvements to the form itself, were shared with the Project Team as soon as they were noted. Changes were made to the system before the rollout. CONCLUSIONS: There were many benefits realized from the new system that can contribute to improved clinical safety. The participants preferred the electronic system over the paper-based system. The lessons learned during the implementation process resulted in recommendations that informed the rollout of the system in Eastern Health, and in other health care organizations in the province of Newfoundland and Labrador. This study also informed the evaluation of other health organizations in the province, which was completed in 2013.
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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 | 0.086 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".