A single-centre hospital-wide handoff standardisation report: what is so special about that?
Bibliographic record
Abstract
Healthcare leaders and scholars have articulated gaps in handoff quality across nearly all healthcare settings. A variety of drivers, including hospital accreditation, internal and external safety event analyses and medical education objectives, have given rise to a proliferation of imperatives to improve this situation. Healthcare leaders have developed a greater appreciation that handoff is a key component of a larger set of culture and teamwork strategies that are necessary to reduce harm. Researchers and medical educators have created handoff programmes, provided empirical evidence for their positive impact on safety and worked tirelessly to disseminate them.1 ,2 Quality improvers from a variety of disciplines have begun to adapt and apply standardised handoff in an increasingly diverse array of settings. In light of this, one might think it less than noteworthy to discover a report of a single institution's hospital-wide handoff standardisation programme.3 To the contrary, we find this report by Shahian et al 3 novel and rich with important messages. We agree with their assertion that this is the largest single-institution implementation of the I-PASS handoff system2 reported in a tertiary general hospital, in this case, Massachusetts General Hospital, which has 25 000 employees. Using a relatively low-cost approach, they managed to implement the system across 15 medical departments, as well as nursing, train nearly 6000 healthcare staff and collect observational data on process reliability at baseline and over 7 months of implementation. Our combined experience in multiple organisations has afforded us opportunities to understand and engage with the effort to improve handoff from multiple vantage points, including through participation as a site in the I-PASS …
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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 | 0.047 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".