Optimizing Physician Handover Through the Creation of a Comprehensive Minimum Data Set
Bibliographic record
Abstract
Handover is defined as the communication of information between individuals and teams of healthcare providers to support the transfer of patient care and maintain professional responsibility and accountability. Poor handovers are increasingly recognized as potentially dangerous for patient safety and are associated with adverse events. One suggested method to improve the timely and efficient exchange of clinical information at handover and to reduce discontinuities in care is through the use of a minimum data set (MDS). The objective of this study was to describe the process of developing a single comprehensive hospital-wide MDS, created through an analysis of current handover processes and customary information tools used to support physician handover (MDHO) at a large quaternary care pediatric academic health sciences centre. A 20-item questionnaire was administered in person to a senior resident or fellow on each of 49 services identified to objectively assess MDHO processes, including frequency, consistency, format, participants and duration, for each service. The presence, type, location, responsibility for updating and security characteristics of MDHO tools used to support MDHO were also analyzed. The MDHO tools currently in use were collected and analyzed to create a comprehensive cross-institutional MDS. The analysis indicates that MDHO is highly consistent in terms of frequency, processes, participants, duration and the use of written tools to guide information exchange across departments. However, many best practice recommendations for MDHO are not being followed. Further, many of the existing MDHO tools in use have a similar content structure and already contain a majority of the components of a comprehensive MDS. Current local consistency in practice will allow for improved acceptance and adoption of an MDHO tool that continues to meet the clinical and administrative needs of physicians but also addresses needs for data accuracy and security. These additional specifications can be met through the use of information communication technologies.
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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 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".