ISQUA17-2143MANAGING TOP RISKS IN HEALTHCARE THROUGH A SHARED INTEGRATED (ENTERPRISE) RISK MANAGEMENT APPROACH
Bibliographic record
Abstract
Many leaders of healthcare organizations have indicated that industry-related integrated risk management (IRM) programs are complex and not well-suited for healthcare. Healthcare organizations in Canada are working together to implement a IRM to track top risks utilizing shared online risk register to effeciently track and manage key organizational risks and to share knowledge and best practice recommendations across the healthcare system. IRM has been identified as an important requirement to monitor and improve quality and safety in the leadership and governance area by the national healthcare accreditation body. HIROC, together with IRM Steering Committee comprised of risk management experts from various healthcare organizations, developed a web-based IRM Risk Register program in 2014. The output of this initiative were comprised of 1) a comprehensive guide synthesising knowledge of IRM best practices; 2) the taxonomy of key risks in healthcare organizations; and 3) the shared Risk Register application. Five guiding principles influenced the development of this program: go with the evidence, focus risks to key organizational objectives, gear to board and senior leadership needs, recognize that it is an evolving area, and “keep it simple”. The program was successfully launched in January 2015 and the early results are promising.
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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 | 0.135 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.039 | 0.014 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.075 | 0.030 |
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".