I-CATCH: A Novel Bundle to Improve Postcall Morning Handoffs
Bibliographic record
Abstract
ABSTRACT Background Conducting postcall morning handoffs using a resident handoff bundle such as I-PASS can prove challenging. This may delay recognizing and acting on clinically important patient issues that arose overnight. Objective We developed and implemented the I-CATCH morning handoff bundle and evaluated its impact on the proportion of overnight patient issues handed off from the on-call resident to the daytime team. Methods We evaluated the I-CATCH (Identify patient; Characterize situation; Action–what was done overnight?; To do for the team in the morning; Confirm the Handoff) handoff bundle from November 2015 to May 2016 on general internal medicine wards at 1 academic teaching hospital. The bundle entailed staff/resident training, structured communication, and dedicated handoff space and time. We compared handoffs of overnight on-call issues by evening resident to daytime medical team before and after implementation, and used statistical process control to analyze adherence to the mnemonic. Results We observed 435 handoffs (242 pre- and 193 postimplementation) over 63 days. There was no significant association between I-CATCH implementation and proportion of on-call overnight issues handed off (OR = 0.96; 95% confidence interval [CI] 0.52–1.47; P = .85). Running the list by going through patients one-by-one (OR = 1.74; 95% CI 1.1–2.77; P = .019), progress note documentation (OR = 3.80; 95% CI 2.19–6.60; P < .001), and direct handoff (OR = 4.84; 95% CI 1.43–16.42; P = .011) correlated with an increased likelihood of morning handoff. Conclusions Implementing the I-CATCH bundle did not improve handoff of overnight issues to the daytime team.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".