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Examining nursing vital signs documentation workflow: barriers and opportunities in general internal medicine units

2012· article· en· W2120849755 on OpenAlex
Melanie Yeung, Stephen E. Lapinsky, John Granton, Diane Doran, Joseph A Cafazzo

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Clinical Nursing · 2012
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Health and Long-Term Care
KeywordsDocumentationWorkflowWorkaroundVital signsMedicineHealth careNursingComputer scienceSurgeryDatabase

Abstract

fetched live from OpenAlex

AIMS: To characterise the nursing practices of vital signs collection and documentation in a general internal medicine environment to inform strategies for improving workflow design. BACKGROUND: Clinical workflow analysis is critical to identify barriers and opportunities in current processes. Analysis can guide the design and development of novel technological solutions to produce greater efficiencies and effectiveness in healthcare delivery. Research surrounding vital signs documentation workflow in general internal medicine environments has received very little attention making it difficult to compare the effectiveness of new technologies. DESIGN: Qualitative ethnographic analyses and quantitative time-motion study were conducted. METHODS: Workflows of 24 nurses at three hospitals in five general internal medicine environments were captured, and timeliness of vital signs assessment and documentation was measured. RESULTS: Clinical assessment of vital signs was consistent, but the documentation process was highly variable within groups and between hospitals. Two themes characterised workflow barriers surrounding point-of-care documentation. First, a lack of standardised documentation methods for vital signs resulted in higher rates of transcription, increasing not only the likelihood of errors but delays in recording and accessibility of information. Second, despite advancements in electronic documentation systems, the observed system was not conducive to point-of-care documentation. Average electronic documentation was significantly longer than paper documentation. Nurses developed ad hoc workarounds that were inefficient and undermined the intent of electronic documentation. CONCLUSION: We have identified barriers and opportunities to improve the efficiency of nursing vital signs documentation. Changes in technology, workflows and environmental design allow for significant improvements and deserve further exploration. RELEVANCE TO CLINICAL PRACTICE: Attention to clinical practice and environments can improve the workflow of prompt vital signs documentation and increase clinical productivity and timeliness of information for clinical decisions, as well as minimising transcription errors leading to safer patient care.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.383
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.305
GPT teacher head0.552
Teacher spread0.248 · 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