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Record W4410523375 · doi:10.1136/bmjoq-2025-qshu.242

242 Harm is harm: incorporating the patient experience of avoidable harm in the science of investigation

2025· article· en· W4410523375 on OpenAlexaff
Jane C. Ballantyne, Sun Drews, Krizia Tatangelo, Laura Danielle Pozzobon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsHarmDo no harmPsychologyPsychiatrySocial psychology

Abstract

fetched live from OpenAlex

Introduction There is a global call to learn from patient experiences of avoidable harm for healthcare improvement. However, a paradigm shift is required. Healthcare organizations must expand their understanding of avoidable harm beyond the historical focus on physical harms. At University Health Network (UHN) - a large multisite academic health science centre in Canada - a multi-disciplinary working group recognized the need for an expanded definition of avoidable patient harm where harm goes beyond physical harms and includes non-physical harms (NPH).We define NPH as: event(s) causing damaging effects to an individual’s dignity or their emotional, psychological, social, or spiritual health.Intervention Using quality improvement methods, a learning response framework to identify, analyse and learn from avoidable physical and non-physical patient harms was co-designed and integrated into existing patient safety reporting and learning process. Focus groups with patient partners, and leaders, were conducted to determine the required resources to learn from patient experiences of NPH. Results informed the development of a framework, including scoring matrix, and toolkit for implementation. Further, de-identified feedback cases were reviewed and screened using the proposed matrix, with level of agreement from 10 reviewers analysed to inform the iterative improvement of the matrix. Understanding of NPH by impacted leaders and teams was assessed using a pre/post knowledge survey. Additionally, the rate of NPH cases is tracked to monitor and inform improvement.Results Focus groups conducted with patent partners on processes for reporting and reviewing NPH revealed several potential patient priorities, including: the need for a patient-facing NPH reporting form, an anonymous reporting option, increased psychological safety in sharing experiences, and increased awareness/visibility of the reporting process and potential outcomes. Focus groups with leaders revealed several anticipated gaps, including: an accepted definition of NPH, a process to identify and review NPH incidents, role clarity in the review process, and communication strategies for team engagement with this new concept. Also articulated was the need for a leadership toolkit to support operationalization, and standardized education for reporting and review of NPH. Based on these focus groups, the NPH framework launched across the organization in April 2024, and included: a co-designed definition, screening matrix for consistent case identification; and analysis resources. A leadership toolkit was also launched, and included: NPH definition, supportive resources to discuss NPH with teams; and tools for identification and review of NPH cases. Pre/post knowledge survey conducted with impacted leaders and teams demonstrated an increase in NPH knowledge post framework deployment across all groups (leaders=13%, Patient Relations=13%, Patient Safety= 30%), with reasonable response rates (Leaders pre n=98, post n=54; Patient Relations: pre n=6, post n=5; Patient Safety: pre n=6, post n=6).Conclusion Incorporating a broad view of avoidable patient harm in incident reporting and learning adds to the science of incident investigation. The involvement of patient partners was critical in the development of the of the screening matrix, as well as processes for reporting NPH that were responsive to, and considered patient needs, including psychological safety in the reporting process. The lessons learned from NPH cases identified and reviewed since launch of the framework will continue to inform improvements reflective of what matters most to our patients. Additionally, this framework supports responding to patient experiences of care and their expressed need for improved healthcare outcomes.

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)0.147
metaresearch head score (Gemma)0.236
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.147
Threshold uncertainty score0.777

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1470.236
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.001
Science and technology studies0.0060.109
Scholarly communication0.0220.028
Open science0.0040.015
Research integrity0.0240.026
Insufficient payload (model declined to judge)0.0030.001

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 head0.378
GPT teacher head0.538
Teacher spread0.159 · 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 designTheoretical or conceptual
Domainnot available
GenreCommentary

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".

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Citations3
Published2025
Admission routes1
Has abstractno

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