Validation of the Work Observation Method By Activity Timing (WOMBAT) method of conducting time-motion observations in critical care settings: an observational study
Bibliographic record
Abstract
BACKGROUND: Electronic documentation handling may facilitate information flows in health care settings to support better coordination of care among Health Care Providers (HCPs), but evidence is limited. Methods that accurately depict changes to the workflows of HCPs are needed to assess whether the introduction of a Critical Care clinical Information System (CCIS) to two Intensive Care Units (ICUs) represents a positive step for patient care. To evaluate a previously described method of quantifying amounts of time spent and interruptions encountered by HCPs working in two ICUs. METHODS: Observers used PDAs running the Work Observation Method By Activity Timing (WOMBAT) software to record the tasks performed by HCPs in advance of the introduction of a Critical Care clinical Information System (CCIS) to quantify amounts of time spent on tasks and interruptions encountered by HCPs in ICUs. RESULTS: We report the percentages of time spent on each task category, and the rates of interruptions observed for physicians, nurses, respiratory therapists, and unit clerks. Compared with previously published data from Australian hospital wards, interdisciplinary information sharing and communication in ICUs explain higher proportions of time spent on professional communication and documentation by nurses and physicians, as well as more frequent interruptions which are often followed by professional communication tasks. CONCLUSIONS: Critical care workloads include requirements for timely information sharing and communication and explain the differences we observed between the two datasets. The data presented here further validate the WOMBAT method, and support plans to compare workflows before and after the introduction of electronic documentation methods in ICUs.
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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 | 0.067 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".