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Record W4303446476 · doi:10.1136/bmjoq-2022-001945

Improving incident reporting among physicians at south health campus hospital

2022· article· en· W4303446476 on OpenAlexaffabout
Jennifer Ngo, Darren Lau, Jodi Ploquin, Tracey Receveur, Kobus Stassen, Colin Del Castilho

Bibliographic record

VenueBMJ Open Quality · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsUniversity of CalgaryUniversity of AlbertaAlberta Health Services
Fundersnot available
KeywordsIntervention (counseling)MedicineFamily medicineIncident reportPatient safetyNursingMedical emergencyHealth care

Abstract

fetched live from OpenAlex

Reports of adverse events and near-misses provide the opportunity to learn about latent (systems) errors. However, voluntary incident reporting systems are underused by physicians. While reports submitted by nursing staff relate to common hazards such as medication administration or falls, physicians have broader exposure to patients' entire hospital journey. Reports by physicians have the potential to uncover more serious errors that could span multiple departments and layers of personnel. Organisational safety culture thrives when all staff are represented and feel empowered to share safety concerns.At the South Health Campus (SHC) Hospital in Calgary, Alberta, Canada, the baseline proportion of physician-submitted reports within our site's Reporting and Learning System (RLS) from July 2013 to December 2016 was 1.12%. We implemented an intervention to double the proportion of physician-submitted RLS reports, using quality improvement methods.Focus groups identified lack of experience with the RLS system, lack of feedback or closure after an RLS submission, and apprehensions about disclosing the incident to the affected patient as barriers to physician submission. Accordingly, the intervention involved direct responses from physician leadership to each physician-submitted RLS report, multimedia demonstrations of efficient RLS submission to physician groups and medical learners, and linkage to materials on safe disclosures. Effectiveness was assessed using a controlled before-and-after design, comparing SHC with the rest of Calgary and with the rest of Alberta.Following the intervention, the proportion of RLS reports that were physician submitted increased to 2.65% (OR 2.42 [95% CI 1.96 to 3.02], p<0.001), sustained over the following 4 years. While an increase was observed for the rest of Calgary, it was smaller (OR 1.27 [1.15 to 1.40], p<0.001). A decrease in the odds of physician submission was observed for the rest of Alberta. Differences between sites were significant (p<0.001).Overall, we found that physician-submitted incident reports can be increased and sustained over time if submitters receive personalised feedback by a physician safety leader. At our site, reports submitted by physicians have been valuable in uncovering complex systems issues that may not have been readily apparent.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Reporting · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.032
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 score0.094
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.201
GPT teacher head0.535
Teacher spread0.334 · 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

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
DomainReporting
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

Citations7
Published2022
Admission routes2
Has abstractyes

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