Variation in printed handoff documents: Results and recommendations from a multicenter needs assessment
Bibliographic record
Abstract
BACKGROUND: Handoffs of patient care are a leading root cause of medical errors. Standardized techniques exist to minimize miscommunications during verbal handoffs, but studies to guide standardization of printed handoff documents are lacking. OBJECTIVE: To determine whether variability exists in the content of printed handoff documents and to identify key data elements that should be uniformly included in these documents. SETTING: Pediatric hospitalist services at 9 institutions in the United States and Canada. METHODS: Sample handoff documents from each institution were reviewed, and structured group interviews were conducted to understand each institution's priorities for written handoffs. An expert panel reviewed all handoff documents and structured group-interview findings, and subsequently made consensus-based recommendations for data elements that were either essential or recommended, including best overall printed handoff practices. RESULTS: Nine sites completed structured group interviews and submitted data. We identified substantial variation in both the structure and content of printed handoff documents. Only 4 of 23 possible data elements (17%) were uniformly present in all sites' handoff documents. The expert panel recommended the following as essential for all printed handoffs: assessment of illness severity, patient summary, action items, situation awareness and contingency plans, allergies, medications, age, weight, date of admission, and patient and hospital service identifiers. Code status and several other elements were also recommended. CONCLUSIONS: Wide variation exists in the content of printed handoff documents. Standardizing printed handoff documents has the potential to decrease omissions of key data during patient care transitions, which may decrease the risk of downstream medical errors.
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 | 0.328 | 0.426 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".