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Record W2414461504 · doi:10.1213/ane.0000000000000801

The Challenge of Studying and Improving Perioperative Teamwork, and Yes, Another Checklist

2015· letter· en· W2414461504 on OpenAlexaboutno aff
Meghan B. Lane‐Fall, Jacob T. Gutsche

Bibliographic record

VenueAnesthesia & Analgesia · 2015
Typeletter
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsChecklistTeamworkPatient safetyHealth carePerioperativeMedicineNursingMedical emergencyPsychologySurgery

Abstract

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Within the past 20 years, a growing number of checklists have been introduced into perioperative clinical practice. The most widely used of these include the World Health Organization’s (WHO) Safe Surgery Saves Lives checklist1 and the central line insertion checklist.2 Additional checklists address handoffs in various settings, including during surgery,3,4 on transfer to the postanesthesia care unit,4 and on transfer to the intensive care unit.5 Irrespective of each tool’s details, checklists aim to improve the safety and the reliability of critical processes that are sensitive to human factors concerns such as fatigue and information overload. Although checklists are conceptually simple, creation and implementation of clinical checklists may be fraught with difficulty. Urbach et al.6 showed that adoption of a surgical safety checklist failed to improve outcomes in a large, multicenter Canadian study. In analyzing the outcomes from the Keystone central line insertion checklist study conducted in Michigan intensive care units, Bosk et al.7 noted that adoption of a checklist was a quite complicated process, requiring identification of champions, training, and adaptation of the tool to account for center-specific practices. Any evaluation of checklist efficacy, then, should not only address the details of the tool in question but also consider the implementation method used, whether the checklist is embraced, and whether the checklist promotes the improvements in teamwork and communication thought to be important in improving patient outcomes. In this issue of Anesthesia & Analgesia, Tscholl et al.8 conduct just such an evaluation of a novel checklist called anesthesia preinduction checklist (APIC) that is meant to standardize preinduction safety checks. The authors used a Delphi approach to develop a list of items to be addressed before the induction of anesthesia. This checklist includes the 5 anesthesia-related items from the WHO checklist1 with an additional 8 items that include a review of the anesthetic technique (including contraindications for regional anesthesia, if applicable), a check of monitors, and confirmation that anesthesia equipment is functioning properly. The APIC tool was then implemented in 4 of 7 operating room areas in a single center (the remaining 3 areas served as a control). The APIC study begs the question: Do we need another checklist? When asked about the phenomenon of checklist fatigue, physician safety expert Thomas Varghese opined, “I do think there is checklist fatigue and I also think everyone is sick to death of the comparison between health care and the aviation industry!”9 Although it is clear that checklists have the potential to ensure that important care processes are followed, this benefit should be balanced with the additional burden imposed by checklist implementation and regular use.10 Also, despite obvious parallels between aviation and medicine, standardizing every aspect of clinical care is both impractical and undesirable. It behooves us to be selective in choosing which processes to standardize with a checklist or a template. Despite our need to be wary of additional checklists, Tscholl et al. offer a persuasive argument for the development of the APIC tool: In the surgical arena, there is strong evidence for the efficacy of preprocedure checklists. However, the tool that is most widely used (the WHO checklist) omits a number of items relevant to safe anesthetic care and monitoring. By introducing the APIC tool, the authors sought to improve information exchange and knowledge of critical information as well as 3 other team-level outcomes: team members’ perception of safety, perception of teamwork, and actual clinical performance. Although patient outcomes were not assessed, the authors note that all 5 of their team-level outcomes have been shown to contribute to patient safety, which supports their importance. After implementing the APIC tool, the authors found that both information exchange (“hearing”) and knowledge of critical information (“listening”) improved with use of the APIC tool. Moreover, safety perception improved, and there was a trend toward a perception of improved teamwork. Actual clinical performance of 14 specific items (e.g., “CO2 wave visible,” “intended opioid available”) did not change appreciably, which is not surprising given that only 1 of the 14 items (“suction device checked”) was part of the APIC. An additional interesting finding from the APIC work is that just 88% adherence was achieved in the operating rooms in which the tool was introduced. Tscholl et al. consider lack of knowledge about tool use, lack of checklist acceptance, and reluctance to change as possible factors explaining incomplete uptake of the APIC tool. Thinking back to the work of Bosk et al.,7 adoption of checklists is not a simple affair; active, theory-based change management is needed to achieve organizational acceptance. End-user input into the process is also helpful, as this allows any given checklist to be adapted for fit in specific settings and may increase user buy-in. Indeed, in the Michigan central line checklist study, >100 versions of “the checklist” were used in the 103 study sites.7 We do not know what tactics were used to promote uptake and use of the APIC tool, but sociocultural considerations undoubtedly affected appropriate use of the tool. Incomplete adherence in the APIC study hints at the presence of an important story beneath the numbers. How did clinicians feel about the introduction of this tool? What steps were taken to introduce this tool into clinical practice? What were the concerns of skeptics who opted not to use the tool? These and related questions may be answered using qualitative or mixed methods research techniques.11 Notwithstanding concerns about the compatibility of qualitative and quantitative research techniques,12 talking to clinicians affected by health care interventions may yield findings that facilitate implementation and that can help with troubleshooting and promoting intervention sustainability. Mixed methods designs are not often used in perioperative research but are well suited to the study of social phenomena such as organizational change.13 The findings from the APIC study, including incomplete adherence to the checklist and slightly different outcomes in consultant-led versus resident-led teams, are ripe for qualitative exploration. The additional information garnered from such an approach could be of use to clinicians seeking to adapt APIC to their own settings. Several avenues of inquiry may follow from the work of Tscholl et al. First, although the APIC showed promising initial results, the durability of this tool over time will need to be assessed. Also needed is a detailed plan for evaluation of adherence to the tool, training of individuals new to the organization, and evaluation of efficacy in improving patient outcomes. Second, it is important to understand how tools such as APIC can be adapted to new settings integrating existing technology while retaining their efficacy. Third, the creation of an anesthesia-specific checklist raises the question of whether each perioperative discipline (i.e., anesthesia, surgery, and nursing) needs its own preprocedure checklist. Because the WHO tool was designed to be multidisciplinary, it does not capture the detailed tasks that must be performed by each type of clinician to ensure safe surgical practices. In the future, the balance between inclusiveness and specificity in any proposed perioperative checklist should be considered. Finally, although APIC did improve several team-level outcomes, we do not know whether it improved patient outcomes. More study is needed to elucidate the mechanisms through which process improvement leads to better patient outcomes in this study and other quality improvement research work. For now, it seems that we have another checklist to contend with, one that shows promise in improving preprocedure information exchange. Hopefully, tools such as APIC will help standardize the routine aspects of perioperative care. This will allow us to more effectively attend to those tasks demanding insight, judgment, and experience, which no checklist has yet been able to capture. E DISCLOSURES Name: Meghan B. Lane-Fall, MD, MSHP. Contribution: This author helped write the manuscript. Attestation: Meghan B. Lane-Fall approved the final manuscript. Name: Jacob T. Gutsche, MD. Contribution: This author helped write the manuscript. Attestation: Jacob T. Gutsche approved the final manuscript. This manuscript was handled by: Sorin J. Brull, MD, FCARCSI (Hon).

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)0.071
metaresearch head score (Gemma)0.199
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.929
Threshold uncertainty score0.374

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0710.199
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.005
Science and technology studies0.0060.011
Scholarly communication0.0130.020
Open science0.0050.009
Research integrity0.0070.017
Insufficient payload (model declined to judge)0.0040.002

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 head0.022
GPT teacher head0.256
Teacher spread0.234 · 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.

Study designNot applicable
DomainMethods
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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Citations4
Published2015
Admission routes1
Has abstractyes

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