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Record W2805254439 · doi:10.1177/2374373518774399

Developing a Strategy for the Improvement in Patient Experience in a Canadian Academic Department of Surgery

2018· article· en· W2805254439 on OpenAlexaffabout
Lindy Luo, Alan J. Forster, Kathleen Gartke, John Trickett, Fraser D. Rubens

Bibliographic record

VenueJournal of Patient Experience · 2018
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsQuality managementMultidisciplinary approachMedicineNursingEmergency departmentQuality (philosophy)CurriculumPatient experienceHealth careMedical educationMedical emergencyPsychologyOperations managementManagement system

Abstract

fetched live from OpenAlex

Patient experience (PE) is recognized as a key component in the quality of health-care delivery. Public reporting of hospital, division, and physician-specific PE results has added to the momentum of adopting strategies to augment this metric of care. The Ottawa Hospital embarked on a journey to improve PE as a pillar of its quality improvement plan. This article demonstrates the efforts of a single surgery department from one large urban center to improve in-hospital PE in the rapidly changing environment of medicine and surgery. A multidisciplinary group within the department and a focus group of previous surgical inpatients were organized to address immediate challenges related to inpatient PE issues. We identified concrete strategies to optimize pain control, perceptions of patient respect and dignity, perceptions of surgeon availability, discharge medication understanding, and overall experience. Also, we identified a need in our department for timely patient feedback, improved communication styles in our staff and trainees, and an internal curriculum offering additional training for our staff and residents. We anticipate that the current results would be of significant interest to other departments wishing to optimize their PE profile as part of the ongoing quality improvement process at hospitals across North America.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.122
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.221
GPT teacher head0.478
Teacher spread0.257 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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