A Risk Management Strategy for Managing Critical Human Resource Changes in a Pediatric Heart Program
Bibliographic record
Abstract
BACKGROUND: Relocation, recruitment, or retirement of critical team members may lead to changes in the expertise pool that could threaten patient outcomes in a pediatric heart program. We developed a quality initiative aimed at risk management that uses risk-stratified case complexity and outcomes to guide a program during critical fluxes in the expert staff. The Ramp Down/Up protocol is a systematic, voluntary reduction in the complexity of cases performed, followed by a transparent and intentional escalation of case complexity. METHODS: Institutional Ethics Review Board approval for this quality initiative was obtained. Patient/caregiver consent for quality data collection is obtained at the time of hospital admission. Every surgical patient having their index cardiac surgical procedure at the Izaak Walton Killam (IWK) from January 1, 2003, to December 2015 is included. The Ramp Down/Up protocol evolved to have to 4 critical elements: (1) a trigger and a reduction in case complexity; (2) an external/objective expert observer; (3) an escalation in case complexity; and (4) data (qualitative and quantitative) collection and analysis. RESULTS: The Ramp Down/Up protocol was used 3 times over a 12-year period to address critical expert human resource challenges. The protocol was used for variable duration (3.5-9 months). Patient operative mortality was benchmarked to the Congenital Cardiac Surgery database, and outcomes were stable during and after protocol employment. CONCLUSIONS: A quality initiative aimed at risk management has allowed 1 pediatric heart team to ensure that patient outcomes were maintained during critical human resource changes.
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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 | 0.063 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".