Using mobile devices for inpatient rounding and handoffs: an innovative application developed and rapidly adopted by clinicians in a pediatric hospital
Bibliographic record
Abstract
OBJECTIVE: To describe the usage of a novel application (The FLOW) that allows mobile devices to be used for rounding and handoffs. MATERIALS AND METHODS: The FLOW provides a view of patient data and the capacity to enter short notes via personal mobile devices. It was deployed using a "bring-your-own-device" model in 4 pilot units. Social network analysis (SNA) was applied to audit trails in order to visualize usage patterns. A questionnaire was used to describe user experience. RESULTS: Overall, 253 health professionals used The FLOW with their personal mobile devices from October 2013 to March 2015. In pediatric and neonatal intensive care units (ICUs), a median of 26-26.5 notes were entered per user per day. Visual network representation of app entries showed that usage patterns were different between the ICUs. In 127 questionnaires (50%), respondents reported using The FLOW most often to enter notes and for handoffs. The FLOW was perceived as having improved patient care by 57% of respondents, compared to usual care. Most respondents (86%) wished to continue using The FLOW. DISCUSSION: This study shows how a handoff and rounding tool was quickly adopted in pediatric and neonatal ICUs in a hospital setting where patient charts were still paper-based. Originally developed as a tool to support informal documentation using smartphones, it was adapted to local practices and expanded to print sign-out documents and import notes within the medicolegal record with desktop computers. Interestingly, even if not supported by the nursing administrative authorities, the level of use for data entry among nurses and doctors was similar in all units, indicating close collaboration in documentation practices in these 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".