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      Factors associated with the use of cognitive aids in operating room crises: a cross-sectional study of US hospitals and ambulatory surgical centers

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          Abstract

          Background

          Operating room (OR) crises are high-acuity events requiring rapid, coordinated management. Medical judgment and decision-making can be compromised in stressful situations, and clinicians may not experience a crisis for many years. A cognitive aid (e.g., checklist) for the most common types of crises in the OR may improve management during unexpected and rare events. While implementation strategies for innovations such as cognitive aids for routine use are becoming better understood, cognitive aids that are rarely used are not yet well understood. We examined organizational context and implementation process factors influencing the use of cognitive aids for OR crises.

          Methods

          We conducted a cross-sectional study using a Web-based survey of individuals who had downloaded OR cognitive aids from the websites of Ariadne Labs or Stanford University between January 2013 and January 2016. In this paper, we report on the experience of 368 respondents from US hospitals and ambulatory surgical centers. We analyzed the relationship of more successful implementation (measured as reported regular cognitive aid use during applicable clinical events) with organizational context and with participation in a multi-step implementation process. We used multivariable logistic regression to identify significant predictors of reported, regular OR cognitive aid use during OR crises.

          Results

          In the multivariable logistic regression, small facility size was associated with a fourfold increase in the odds of a facility reporting more successful implementation ( p = 0.0092). Completing more implementation steps was also significantly associated with more successful implementation; each implementation step completed was associated with just over 50% higher odds of more successful implementation ( p ≤ 0.0001). More successful implementation was associated with leadership support ( p < 0.0001) and dedicated time to train staff ( p = 0.0189). Less successful implementation was associated with resistance among clinical providers to using cognitive aids ( p < 0.0001), absence of an implementation champion ( p = 0.0126), and unsatisfactory content or design of the cognitive aid ( p = 0.0112).

          Conclusions

          Successful implementation of cognitive aids in ORs was associated with a supportive organizational context and following a multi-step implementation process. Building strong organizational support and following a well-planned multi-step implementation process will likely increase the use of OR cognitive aids during intraoperative crises, which may improve patient outcomes.

          Electronic supplementary material

          The online version of this article (10.1186/s13012-018-0739-4) contains supplementary material, which is available to authorized users.

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          Most cited references 47

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          Diffusion of innovations in service organizations: systematic review and recommendations.

          This article summarizes an extensive literature review addressing the question, How can we spread and sustain innovations in health service delivery and organization? It considers both content (defining and measuring the diffusion of innovation in organizations) and process (reviewing the literature in a systematic and reproducible way). This article discusses (1) a parsimonious and evidence-based model for considering the diffusion of innovations in health service organizations, (2) clear knowledge gaps where further research should be focused, and (3) a robust and transferable methodology for systematically reviewing health service policy and management. Both the model and the method should be tested more widely in a range of contexts.
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            Disseminating innovations in health care.

            Health care is rich in evidence-based innovations, yet even when such innovations are implemented successfully in one location, they often disseminate slowly-if at all. Diffusion of innovations is a major challenge in all industries including health care. This article examines the theory and research on the dissemination of innovations and suggests applications of that theory to health care. It explores in detail 3 clusters of influence on the rate of diffusion of innovations within an organization: the perceptions of the innovation, the characteristics of the individuals who may adopt the change, and contextual and managerial factors within the organization. This theory makes plausible at least 7 recommendations for health care executives who want to accelerate the rate of diffusion of innovations within their organizations: find sound innovations, find and support "innovators," invest in "early adopters," make early adopter activity observable, trust and enable reinvention, create slack for change, and lead by example.
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              Disrupted Routines: Team Learning and New Technology Implementation in Hospitals

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                Author and article information

                Contributors
                (617) 990-7469 , shehnaz.alidina@mail.harvard.edu
                saragf@stanford.edu
                ahannenberg@bwh.harvard.edu
                dhepner@bwh.harvard.edu
                sara.singer@stanford.edu
                bneville@ariadnelabs.org
                jsachetta@ariadnelabs.org
                slipsitz@ariadnelabs.org
                wberry@ariadnelabs.org
                Journal
                Implement Sci
                Implement Sci
                Implementation Science : IS
                BioMed Central (London )
                1748-5908
                26 March 2018
                26 March 2018
                2018
                : 13
                Affiliations
                [1 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Health Policy and Management, , Harvard T.H. Chan School of Public Health, ; 677 Huntington Avenue, Boston, MA 02115 USA
                [2 ]ISNI 0000000419368956, GRID grid.168010.e, Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, , Stanford University, ; Stanford, CA USA
                [3 ]ISNI 000000041936754X, GRID grid.38142.3c, Ariadne Labs at Brigham and Women’s Hospital and the Harvard T.H. Chan School of Public Health, ; Boston, MA USA
                [4 ]ISNI 000000041936754X, GRID grid.38142.3c, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, , Harvard Medical School, ; Boston, MA USA
                Article
                739
                10.1186/s13012-018-0739-4
                5870083
                29580243
                © The Author(s). 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000133, Agency for Healthcare Research and Quality;
                Award ID: 5R18HS024235-02
                Award Recipient :
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                Research
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                © The Author(s) 2018

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