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Record W2039885558 · doi:10.1186/1472-6920-14-37

A real-time locating system observes physician time-motion patterns during walk-rounds: a pilot study

2014· article· en· W2039885558 on OpenAlexaff
David Ward, William A. Ghali, Alecia Graham, Jane B Lemaire

Bibliographic record

VenueBMC Medical Education · 2014
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsReal-time locating systemWorkflowSession (web analytics)Scale (ratio)Duration (music)Medical educationMedicineMedical emergencyComputer scienceReal-time computing

Abstract

fetched live from OpenAlex

BACKGROUND: Walk-rounds, a common component of medical education, usually consist of a combination of teaching outside the patient room as well as in the presence of the patient, known as bedside teaching. The proportion of time dedicated to bedside teaching has been declining despite research demonstrating its benefits. Increasing complexities of patient care and perceived impediments to workflow are cited as reasons for this declining use. Research using real-time locating systems (RTLS) has been purported to improve workflow through monitoring of patients and equipment. We used RTLS technology to observe and track patterns of movement of attending physicians during a mandatory once-weekly medical teaching team patient care rounding session endorsed as a walk-rounds format. METHODS: During a project to assess the efficacy of RTLS technology to track equipment and patients in a clinical setting, we conducted a small-scale pilot study to observe attending physician walk-round patterns during a mandatory once-weekly team rounding session. A consecutive sample of attending physicians on the unit was targeted, eight agreed to participate. Data collected using the RTLS were pictorially represented as linked points overlaying a floor plan of the unit to represent each physician's motion through time. Visual analysis of time-motion was independently performed by two researchers and disagreement resolved through consensus. Rounding events were described as a sequence of approximate proportions of time engaged within or outside patient rooms. RESULTS: The patient care rounds varied in duration from 60 to 425 minutes. Median duration of rounds within patient rooms was approximately 33% of total time (range approximately 20-50%). Three general time-motion rounding patterns were observed: a first pattern that predominantly involved rounding in ward hallways and little time in patient rooms; a second pattern that predominantly involved time in a ward conference room; and a third balanced pattern characterized by equal proportions of time in patient rooms and in ward hallways. CONCLUSIONS: Observation using RTLS technology identified distinct time-motion rounding patterns that hint at differing rounding styles across physicians. Future studies using this technology could examine how the division of time during walk-rounds impacts outcomes such as patient satisfaction, learner satisfaction, and physician workflow.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.019
GPT teacher head0.300
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2014
Admission routes1
Has abstractyes

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