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Modifications of the World Health Organization’s Surgical Safety Checklist—Ways Forward to Ensure Sustainable Implementation

2023· letter· en· W4379599005 on OpenAlexaboutno aff
Arvid Steinar Haugen

Bibliographic record

VenueJAMA Network Open · 2023
Typeletter
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsnot available
Fundersnot available
KeywordsChecklistProcess managementBusinessRisk analysis (engineering)Operations managementEngineeringPsychology

Abstract

fetched live from OpenAlex

The study by Brindle and colleagues 1 elsewhere in JAMA Network Open provides novel international perspectives on modifications of the World Health Organization's (WHO) Surgical Safety Checklist (SSC).The article describes opportunities for increasing team members' involvement and ownership through modifications of the checklist.In their qualitative study, semistructured interviews of 51 clinicians and hospital administrators were conducted across 5 high-income countries: Australia, Canada, New Zealand, the United States, and the United Kingdom.Five themes emerged from the data: awareness and involvement in SSC modifications; reasons for modifications; types of modifications; the impact of modifications; and perceived barriers to SSC modifications.Importantly, these findings address contemporary issues in anesthesia and surgery and may hopefully contribute to reinvigorating the SSC as a dynamic and relevant tool for surgical patients' safety.Since the WHO globally introduced the SSC in 2009 as part of the Safe Surgery Saves Lives campaign, 2 it has become mainstream surgical practice to use the checklist.In recent reviews of research literature, there is evidence of SSCs' impact on patient outcomes, 3 reducing both morbidity and mortality when implemented well. 4As the fields of surgery and anesthesia evolve, so does the need to develop the SSC and to adapt it to fit different types of surgery.One example of such development is a consensus statement of stakeholders and experts, where Pilkington and colleagues 5 suggest modifying the SSC to be applied with an existing enhanced recovery after surgery (ERAS) guideline for major surgery, combining the SSC within an ERAS protocol.From the outset, the SSC was intended not to be comprehensive.The WHO encouraged modifications to make it fit local practices, and any alterations made to the checklist should be carried out with care and must involve clinicians, such as surgeons, anesthetists, and nurses, in the modification process. 2 The checklist must focus on the most critical issues, be brief, fit the local flow of care, be actionable on every specific item, promote verbal interaction among team members, support collaboration, and encourage sharing of critical information in the team, according to the WHO implementation guidelines. 2 Brindle and colleagues 1 affirm the emerging evidence on implementation of the checklist, highlighting the modification experiences that clinicians and hospital administrators have had since it was introduced.One of the emerging themes was reasons for specific modifications, which were enacted based on contextual demands and after adverse events occurred.Different types of modifications were identified to make the items fit the flow of care, eg, moving elements between the 3 parts of the checklist or adding an item to a preoperative team huddle (eg, blood loss requirements). 1 The authors also identified that one outcome of modifications was that they improved team members' ownership and engagement in the SSC's use.Finally, they identified some barriers to considering modifications of existing checklists, including institutional barriers to customization, ie, imposing it on team members who were told to adhere, and practicalities around customization of the SSC in electronic systems.This study's findings underline the importance of following the WHO's advice for adaptations of the checklist. 1,2The WHO implementation guidelines emphasize testing changes prior to rolling them out and using local data feedback, simulation, and training as strong drivers for the implementation. 2Sharing of critical information within the team is one of the key points of the checklist.Findings in the study from Brindle and colleagues 1 suggest that one should not remove from the checklist points that empower team communication, briefings, and debriefings.

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)0.013
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.005
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0030.003
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.382
GPT teacher head0.525
Teacher spread0.144 · 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.

Study designNot applicable
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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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