Review of computerized physician handoff tools for improving the quality of patient care
Bibliographic record
Abstract
BACKGROUND: Computerized physician handoff tools (CHTs) are designed to allow distributed access and synchronous archiving of patient information via Internet protocols. However, their impact on the quality of physician handoff, patient care, and physician work efficiency have not been extensively analyzed. METHODS: We searched MEDLINE, PUBMED, EMBASE, CINAHL, the Cochrane database for systematic reviews, and the Cochrane central register for clinical trials, from January 1960 to December 2011. We selected all articles that reported randomized controlled trials, controlled clinical trials, controlled before-after studies, and quasi-experimental studies of the use of CHTs for physician handoff for hospitalized patients. Relevant studies were evaluated independently for their eligibility for inclusion by 2 individuals in a 2-stage process. RESULTS: The literature search identified 1026 citations of which 6 satisfied the inclusion criteria. One study was a randomized controlled trial, whereas 5 were controlled before-after studies. Two studies showed that using CHTs reduced adverse events and missing patients. Three studies demonstrated improved overall quality of handoff after CHT implementation. One study suggested that CHTs could potentially enhance work efficiency and continuity of care during physician handoff. Conflicting impacts on consistency of handoff were found in 2 studies. CONCLUSIONS: The evidence that CHTs improve physician handoff and quality of hospitalized patient care is limited. CHT may improve the efficiency of physician work, reduce adverse events, and increase the completeness of physician handoffs. However, further evaluation using rigorous study designs is needed.
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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 | 0.009 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".