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Record W4399324449 · doi:10.7759/cureus.61675

Effect of Departmental and Physician-Selected Interventions on Point-of-Care Ultrasound Documentation Completion

2024· article· en· W4399324449 on OpenAlexaboutno aff
Marc I Blatt, Jordan Rupp, Matthew Lipton, Tyler W. Barrett, Jeremy S. Boyd, Michael J. Ward

Bibliographic record

VenueCureus · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsnot available
Fundersnot available
KeywordsDocumentationPoint of care ultrasoundPsychological interventionPoint (geometry)MedicineCompletion (oil and gas wells)Medical physicsMedical emergencyNursingRadiologyComputer scienceUltrasoundEngineeringMathematics

Abstract

fetched live from OpenAlex

Background Point-of-care ultrasound (POCUS) has been disruptive to many experienced emergency physicians as it requires competence in a new physical skill, real-time image interpretation, and navigation of novel software for submission to the electronic health record (EHR). Incomplete documentation of a performed POCUS study used for clinical decision-making represents a potential medicolegal liability, may expose the patient to repetitive or potentially unnecessary imaging, and is a missed opportunity for reimbursement. Identifying effective facilitators of ED POCUS documentation completion requires additional investigation. Methods In the first part of this mixed-methods study, eligible attending physicians were stratified into levels of use ("high"/"low"/"never") based on recent POCUS documentation performance. Semi-structured interviews were conducted with high and low utilizers to explore their perceptions of the POCUS submission workflow and their receptivity to various proposed interventions. Qualitative data were analyzed using a thematic analysis that explored perceived usefulness and usability. The second part of the study consisted of two intervention phases. First, physicians achieving minimum POCUS documentation numbers were rewarded with additional shift scheduling flexibility. In the second phase, the intervention that garnered the most interview support, daily documentation reminder emails, was implemented. The primary outcome was the individual POCUS documentation rates calculated as all studies submitted divided by all studies performed (submitted plus unsubmitted) per month. Provider-level monthly data was aggregated into a departmental rate. Results Interviews were conducted with 12 physicians, six from the highest and six from the lowest documentation quartiles. Both groups supported the same two proposed interventions: reminder emails ranked first, then monetary rewards ranked second. High utilizers emphasized the clinical utility of POCUS, whereas low utilizers expressed concerns over "double billing" and exposure to medicolegal liability with uncertain scan interpretations. For low utilizers, a documentation decision could be dependent on the performing resident physician's displayed confidence. Both groups voiced frustration with the need to use a separate program, Qpath (Telexy Healthcare, Inc, Maple Ridge, British Columbia, Canada), for POCUS documentation. During intervention phase one, the aggregate departmental documentation rate increased from 44.6% to 60.1% with the introduction of the schedule request incentive. This improvement was seen across all documentation quartiles. The departmental rate remained stable and did not improve further following the addition of the daily documentation reminder emails in intervention phase two. When reminder emails ceased yet the day-off request incentive continued, the departmental rate did not drop. Conclusions The implementation of a non-financial shift scheduling incentive correlated with the largest increase in departmental POCUS documentation rate. Interviewees incorrectly predicted that email reminders would be the most influential intervention highlighting a mismatch between physician perception and effective drivers of behavior change. Further investigation may focus on determining the size and longevity of the isolated impact of a schedule request incentive, as one might expect diminishing marginal utility.

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)0.008
metaresearch head score (Gemma)0.061
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.061
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.015
GPT teacher head0.375
Teacher spread0.360 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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