Using mobile devices for inpatient rounding and handoffs: an innovative application developed and rapidly adopted by clinicians in a pediatric hospital
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it