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Record W4414908397 · doi:10.1093/intqhc/mzaf105

Sustaining perioperative patient safety improvement: the relevance of patient safety policies and contextual factors in European healthcare systems

2025· article· en· W4414908397 on OpenAlexaff
Kaja Kristensen, Sophie Wang, Daniel Arnal Velasco, Kaja Põlluste, Adam Žaludek, Paulo Sousa, Carola Orrego, Oliver Groene

Bibliographic record

VenueInternational Journal for Quality in Health Care · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsInstitute of Health Services and Policy ResearchUniversity of Toronto
FundersHORIZON EUROPE Framework ProgrammeEuropean Commission
KeywordsPatient safetyRelevance (law)PerioperativeHealthcare systemHealth careSustainability

Abstract

fetched live from OpenAlex

BACKGROUND: Perioperative patient safety aims to minimize risk and reduce adverse events throughout the surgical journey. Despite investments in national and international initiatives, sustaining these efforts remains a challenge. Contextual factors such as national policies and legal requirements play a key role in ensuring long-term success. This qualitative study examines the national patient safety policies and frameworks in five European countries and investigates contextual factors to understand how these policies may affect the implementation and sustainability of perioperative patient safety initiatives. METHODS: Semi-structured interviews were conducted with decision-makers from Ministries of Health, regulatory or accreditation bodies, professional medical or scientific societies, managerial hospital staff, and academic patient safety experts from Spain, the Netherlands, Portugal, Estonia, and the Czech Republic. A desktop search for relevant policy and regulatory frameworks around perioperative patient safety informed the development of the semi-structured interview guide. Generated data were coded using an a priori framework adapted from the updated Consolidated Framework for Implementation Research (CFIR) and a framework for assessing health systems' quality improvement and patient safety initiatives. Using content analysis, codes were thematically analysed to delineate and compare the perioperative patient safety landscapes of the five countries. RESULTS: In total, 28 high-level decision-makers were interviewed. Based on the insight from interviewees, a patient safety policy profile was generated for each of the five countries, capturing the key features of their frameworks and strategies. While all countries have developed policies to improve patient safety, the scope and structure of these frameworks vary widely. Some countries have established centralized systems with detailed national action plans and robust oversight mechanisms, whereas others rely on more fragmented approaches with responsibilities distributed across various organizations. Common challenges identified include the inconsistent integration of patient safety education into medical curricula and cultural barriers, such as a fear of blame that affects reporting practices. Interviewees provided several propositions how project-based patient safety initiatives could be embedded in national contexts. These propositions differed considerably between countries. CONCLUSION: This study highlights the diverse and evolving nature of patient safety policy landscapes across five European countries. The varying scope, structure, and implementation of patient safety frameworks emphasize the need for context-specific approaches to promote the sustainability of perioperative patient safety initiatives. As the field continues to advance, it is important to tailor approaches that aim to sustain patient safety initiatives.

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)0.027
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0050.010
Scholarly communication0.0130.007
Open science0.0010.007
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.468
Teacher spread0.407 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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