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Record W2254870042 · doi:10.1097/bcr.0000000000000287

Is Real-Time Feedback of Burn-Specific Patient-Reported Outcome Measures in Clinical Settings Practical and Useful? A Pilot Study Implementing the Young Adult Burn Outcome Questionnaire

2015· article· en· W2254870042 on OpenAlexaff
Colleen M. Ryan, Austin F. Lee, Lewis E. Kazis, Gabriel D. Shapiro, Jeffrey C. Schneider, Jeremy Goverman, Shawn P. Fagan, Chao Wang, Julia Kim, Robert L. Sheridan, Ronald G. Tompkins

Bibliographic record

VenueJournal of Burn Care & Research · 2015
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsMcGill University
FundersU.S. Public Health Service
KeywordsMedicineBenchmarkingPromPatient-reported outcomePatient satisfactionPhysical therapyPopulationMedical emergencyQuality of life (healthcare)Nursing

Abstract

fetched live from OpenAlex

Long-term follow-up care of survivors after burn injuries can potentially be improved by the application of patient-reported outcome measures (PROMs). PROMs can inform clinical decision-making and foster communication between the patient and provider. There are no previous reports using real-time, burn-specific PROMs in clinical practice to track and benchmark burn recovery over time. This study examines the feasibility of a computerized, burn-specific PROM, the Young Adult Burn Outcome Questionnaire (YABOQ), with real-time benchmarking feedback in a burn outpatient practice. The YABOQ was redesigned for formatting and presentation purposes using images and transcribed to a computerized format. The redesigned questionnaire was administered to young adult burn survivors (ages 19-30 years, 1-24 months from injury) via an ipad platform in the office before outpatient visits. A report including recovery curves benchmarked to a nonburned relatively healthy age-matched population and to patients with similar injuries was produced for the domains of physical function and social function limited by appearance. A copy of the domain reports as well as a complete copy of the patient's responses to all domain questions was provided for use during the clinical visit. Patients and clinicians completed satisfaction surveys at the conclusion of the visit. Free-text responses, included in the satisfaction surveys, were treated as qualitative data adding contextual information about the assessment of feasibility. Eleven patients and their providers completed the study for 12 clinical visits. All patients found the ipad survey and report "easy" or "very easy" to use. In nine instances, patients "agreed" or "strongly agreed" that it helped them communicate their situation to their doctor/nurse practitioner. Patients "agreed" or "strongly agreed" that the report helped them understand their course of recovery in 10 visits. In 11 visits, the patients "agreed" or "strongly agreed" that they would recommend this feedback to others. Qualitative comments included: "it helped organize my thoughts of recovery," "it opened lines of communication with the doctor," "it showed me how far I have come, and how far I need to go," and "it raised questions I would not have thought of." Only four of 12 provider surveys agreed that it helped them understand a patient's condition; however, in two visits, the providers stated that it helped identify a pertinent clinical issue. During two visits, providers stated that a treatment plan was discussed or recommended based on the survey results. Separately, qualitative comments from the providers included "survey was not sensitive enough to identify that this patient needed surgery for their scars." This is the first report describing clinical use of a burn-specific patient reported outcome measure. Real-time feedback using the ipad YABOQ was well received for the most part by the clinicians and burn survivors in the outpatient clinic setting. The information provided by the reports can be tested in a future randomized controlled clinical study evaluating impacts on physician decisions.

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 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)0.018
metaresearch head score (Gemma)0.006
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 score0.064
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.268
GPT teacher head0.481
Teacher spread0.213 · 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 teacher head, 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

Citations36
Published2015
Admission routes1
Has abstractyes

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