Examining nursing vital signs documentation workflow: barriers and opportunities in general internal medicine units
Bibliographic record
Abstract
AIMS: To characterise the nursing practices of vital signs collection and documentation in a general internal medicine environment to inform strategies for improving workflow design. BACKGROUND: Clinical workflow analysis is critical to identify barriers and opportunities in current processes. Analysis can guide the design and development of novel technological solutions to produce greater efficiencies and effectiveness in healthcare delivery. Research surrounding vital signs documentation workflow in general internal medicine environments has received very little attention making it difficult to compare the effectiveness of new technologies. DESIGN: Qualitative ethnographic analyses and quantitative time-motion study were conducted. METHODS: Workflows of 24 nurses at three hospitals in five general internal medicine environments were captured, and timeliness of vital signs assessment and documentation was measured. RESULTS: Clinical assessment of vital signs was consistent, but the documentation process was highly variable within groups and between hospitals. Two themes characterised workflow barriers surrounding point-of-care documentation. First, a lack of standardised documentation methods for vital signs resulted in higher rates of transcription, increasing not only the likelihood of errors but delays in recording and accessibility of information. Second, despite advancements in electronic documentation systems, the observed system was not conducive to point-of-care documentation. Average electronic documentation was significantly longer than paper documentation. Nurses developed ad hoc workarounds that were inefficient and undermined the intent of electronic documentation. CONCLUSION: We have identified barriers and opportunities to improve the efficiency of nursing vital signs documentation. Changes in technology, workflows and environmental design allow for significant improvements and deserve further exploration. RELEVANCE TO CLINICAL PRACTICE: Attention to clinical practice and environments can improve the workflow of prompt vital signs documentation and increase clinical productivity and timeliness of information for clinical decisions, as well as minimising transcription errors leading to safer patient care.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".