Creating a Practical Transformational Change Management Model for Novel Artificial Intelligence–Enabled Technology Implementation in the Operating Room

Smith, Tianqi Gao; Norasi, Hamid; Herbst, Kelly M.; Kendrick, Michael L.; Curry, Timothy B.; Grantcharov, Teodor P.; Palter, Vanessa N.; Hallbeck, M. Susan; Cleary, Sean P.

doi:10.1016/j.mayocpiqo.2022.09.004

Cited by 10 publications

(11 citation statements)

References 30 publications

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“…The collection of data in future will validate and provide richer insights into the framework which was out of scope for this paper. Smith et al, 2022;Yusif et al, 2019;Musungwini & Mono, 2019;Mogogole & Jokonya,2018;Ziemba & Oblak, 2015 5…”

Section: Discussionmentioning

confidence: 99%

“…Rohmah and Subriadi (2022) have endeavoured to identify the change management factors that are relevant to the implementation phase therefore identification of the change management factors in the post-implementation phase such as effective use remain a gap that the higher education institutions must still consider (Adam et al, 2017). Smith et al (2022) study identified change management strategies for novel artificial intelligence implementations and advise that the future studies may customize the findings to address other project needs. Abatan and Maharaj (2018) also established the key perceptual factors relating to change management in IS implementation phase.…”

Section: Theoretical and Conceptual Backgroundmentioning

confidence: 99%

“…Rohmah & Subriadi, 2022;Smith et al, 2022;Yusif et al, 2019; Abatan & Maharaj, 2018;Kapupu & Mignerat, 2015; Ziemba & Oblak, 2015 …”

mentioning

confidence: 99%

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Conceptual change management framework for effective use of examination administration system: a systemic review

Mphahlele,

Kekwaletswe,

Seaba

2024

IJRBS

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This paper systematically reviews literature with the aim of conceptualizing a change management framework for the effective use of the Examination Administration System (EAS). Information systems (IS) change management factors in the implementation and the post-implementation phases are systematically reviewed to identify 37 reports between 2015 and 2023 and content was analyzed. Post-acceptance phase poses many challenges that often result in resistance from the user community thus relinquishing the expected gains. This paper explains change management factors in the post-implementation phase of information systems as they are not adequately addressed in existing literature. The conceptualization of the change management framework draws on the integration of the Unified Theory of Acceptance, Use of Technology and Effective Use Theory and nine change management factors as facilitating conditions. The paper concludes that effective use of EAS is realized through change management factors (i.e., user training, top management support activities, information flow, planning EAS as a change, change shared vision, performance measurement, user involvement and change recognition, user satisfaction and effective communication). This study further adds the indirect mechanisms (i.e., technology use and IS adaptive use) through which the change management factors can impact effective use. The study provides a useful guide to the practitioners and researchers to explain the change management strategies for the effective use of information systems and improve it. This paper also proposes that future studies adapt and/or empirically test conceptual change management apparatus for effective use to ascertain its validity within various contexts.

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Section: Discussionmentioning

confidence: 99%

Section: Theoretical and Conceptual Backgroundmentioning

confidence: 99%

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Conceptual change management framework for effective use of examination administration system: a systemic review

Mphahlele,

Kekwaletswe,

Seaba

2024

IJRBS

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“…Data can be stratified into short durations for objective data-driven IAE review and OR teamwork and efficiency studies. (Smith et al, 2022). Video is collected via four cameras: four wide-angle cameras recording surgical team activity and either an operative light fixture, lendoscope or robotic intracorporeal camera.…”

Section: Introductionmentioning

confidence: 99%

“…The system can synchronize all data and encrypt it on a server where Proceedings of the Human Factors and Ergonomics Society Annual Meeting 67 (1) specialized algorithms and trained analysts can explore intraoperative variables of interest (Møller et al, 2022). For privacy and confidentiality purposes, the system's AI algorithms de-identify faces, skin tone, body shape and identifiers like personalized surgical caps and redacts any personal information mentioned during surgery (Smith et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-enabled operating room data analysis: Current and future prospects for enhancing surgical safety

Tetteh,

Wang,

Norasi

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Despite advances in medical technology, intraoperative adverse events (IAE) continue to occur in hospital’s complex systems. Current approaches to understanding and mitigating IAEs rely on retrospective data analyses to recognize the series of events leading to an IAE. This paper discusses current challenges and highlights the potential of the Operating Room Blackbox (ORBB) system use to enhance patient safety.

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Artificial Intelligence in Operating Room Management

Bellini,

Russo,

Domenichetti

et al. 2024

J Med Syst

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This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence’s transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes.

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Creating a Practical Transformational Change Management Model for Novel Artificial Intelligence–Enabled Technology Implementation in the Operating Room

Cited by 10 publications

References 30 publications

Conceptual change management framework for effective use of examination administration system: a systemic review

Conceptual change management framework for effective use of examination administration system: a systemic review

Artificial intelligence-enabled operating room data analysis: Current and future prospects for enhancing surgical safety

Artificial Intelligence in Operating Room Management

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