Understanding Nursing Workflow for Inpatient Education Delivery: Time and Motion Study
Bibliographic record
Abstract
BACKGROUND: Diabetes self-management education and support improves diabetes-related outcomes, but many persons living with diabetes do not receive this. Adults with diabetes have high hospitalization rates, so hospital stays may present an opportunity for diabetes education. Nurses, supported by patient care technicians, are typically responsible for delivering patient education but often do not have time. Using technology to support education delivery in the hospital is one potentially important solution. OBJECTIVE: The aim of this study was to evaluate nurse and patient care technician workflow to identify opportunities for providing education. The results informed implementation of a diabetes education program on a tablet computer in the hospital setting within existing nursing workflow with existing staff. METHODS: We conducted a time and motion study of nurses and patient care technicians on three medical-surgical units of a large urban tertiary care hospital. Five trained observers conducted observations in 2-hour blocks. During each observation, a single observer observed a single nurse or patient care technician and recorded the tasks, locations, and their durations using a Web-based time and motion data collection tool. Percentage of time spent on a task and in a location and mean duration of task and location sessions were calculated. In addition, the number of tasks and locations per hour, number of patient rooms visited per hour, and mean time between visits to a given patient room were determined. RESULTS: =.001). Nurses averaged 16.2 tasks per hour, while patient care technicians averaged 18.2. The mean length of a direct patient care session was 3:42 minutes for nurses and 3:02 minutes for patient care technicians. For nurses, 56% of task durations were 2 minutes or less, and 38% were one minute or less. For patient care technicians, 62% were 2 minutes or less, and 44% were 1 minute or less. Nurses visited 5.3 and patient care technicians 9.4 patient rooms per hour. The mean time between visits to a given room was 37:15 minutes for nurses and 33:28 minutes for patient care technicians. CONCLUSIONS: The workflow of nurses and patient care technicians, constantly in and out of patient rooms, suggests an opportunity for delivering a tablet to the patient bedside. The average time between visits to a given room is consistent with bringing the tablet to a patient in one visit and retrieving it at the next. However, the relatively short duration of direct patient care sessions could potentially limit the ability of nurses and patient care technicians to spend much time with each patient on instruction in the technology platform or the content.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".