The Use of a Mobile Application to Track Process Workflow in Perioperative Services
Bibliographic record
Abstract
This article discusses the data collection tool developed to investigate how patient flow is affected by the delivery of different types of care within Perioperative Services. To better understand the Perioperative Services processes, this study tracked staff members as they perform their activities. A challenging aspect of documenting the processes observed while tracking the Perioperative Services staff is to record the specific times and order in which the activities took place. The Perioperative Services is a fast-paced, dynamic environment where the staff members often perform multiple tasks that may also be interrupted, and each staff member may perform these tasks in their own sequence. To meet the needs of accurate data gathering, an iPhone/iPod Touch application was developed. It provides several advantages over the traditional paper/pencil method: (1) time stamps are instantaneous and consistent among the data collectors, (2) activities are entered via swipe-and-click capability, (3) multiple active tasks and interruptions can be tracked, and (4) collected data can be output to Microsoft Excel or Access for analysis. The "app" has proven to be useful in capturing data for our study. This technology can be customized and applied to similar settings at other hospitals.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".