Hostname: page-component-7b7567c8c-fvjsw Total loading time: 0 Render date: 2026-11-15T04:54:25.317Z Has data issue: false hasContentIssue false

68 Process improvement before artificial intelligence and automation – building trust with the Understand-Transform-Sustain framework

Published online by Cambridge University Press:  08 May 2026

Alejandro Fabrega Gerbaud
Affiliation:
Mayo Clinic Florida
Alejando Fabrega Gerbaud
Affiliation:
Acute Care Research Consortium, Mayo Clinic Florida; Department of Critical Care Medicine, Mayo Clinic Florida
Marta Berguido de la Guardia
Affiliation:
Acute Care Research Consortium, Mayo Clinic Florida; Department of Critical Care Medicine, Mayo Clinic Florida
Sandra Booth
Affiliation:
Quality Academy, Mayo Clinic Florida
LaRissa Adams
Affiliation:
Quality Academy, Mayo Clinic Florida
Matt Cox
Affiliation:
Quality Academy, Mayo Clinic Rochester
Kim Hunsinger
Affiliation:
Quality Academy, Mayo Clinic
Arizona Nelly Tan
Affiliation:
Quality Academy, Mayo Clinic Arizona; Department of Radiology, Mayo Clinic Arizona
Melissa Gulden
Affiliation:
Quality Academy, Mayo Clinic Rochester
Terri Menser
Affiliation:
Center for the Science of Healthcare Delivery, Mayo Clinic Rochester; Acute Care Research Consortium Mayo Clinic Florida
Robert D.
Affiliation:
Center for the Science of Healthcare Delivery, Mayo Clinic Rochester; Acute Care Research Consortium Mayo Clinic FloridaCenter for the Science of Healthcare Delivery, Mayo Clinic Rochester; Division of Gynecologic Oncology, Mayo Clinic Rochester
Patricia E. Kern
Affiliation:
Center for the Science of Healthcare Delivery, Mayo Clinic Rochester; Acute Care Research Consortium Mayo Clinic FloridaCenter for the Science of Healthcare Delivery, Mayo Clinic Rochester; Division of Gynecologic Oncology, Mayo Clinic Rochester
Ross Reichard
Affiliation:
Quality Academy, Mayo Clinic Rochester; Department of Laboratory Medicine and Pathology, Mayo Clinic Rochester
Sean C. Dowdy
Affiliation:
Center for the Science of Healthcare Delivery, Mayo Clinic Rochester; Division of Gynecologic Oncology, Mayo Clinic Rochester
Pablo Moreno Franco
Affiliation:
Acute Care Research Consortium, Mayo Clinic Florida; Quality Academy, Mayo Clinic Florida; Department of Critical Care Medicine, Mayo Clinic Florida; Department of Transplantation, Mayo Clinic Florida
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

Objectives/Goals: Artificial Intelligence(AI)and automation are rapidly transforming healthcare, yet their integration into clinical workflows often falls short due to technical, ethical, and organizational challenges. A standardized, quality-driven approach can aid the AI implementation process and ensure value delivery. Methods/Study Population: Trust emerges as the central hurdle, encompassing both patient and provider confidence in AI systems. Patients raise concerns over safety, transparency, and the physician-patient relationship, while providers express apprehension towards algorithmic opacity, data quality, and legal ambiguity. To address these concerns, the Understand, Transform, Sustain (UTS) framework offers a behavior-based, systems-level approach to AI deployment. Developed by Mayo Clinic’s Quality Academy, UTS integrates process improvement (PI) principles across three phases, emphasizing stakeholder engagement, transparency, and patient safety throughout the AI lifecycle. Results/Anticipated Results: The Understand phase identifies inefficiencies by mapping workflows, collecting data, and recognizing areas for improvement, ensuring appropriate priorities are addressed. In the Transform phase, interventions are designed, implemented, and tested through improvement cycles and feedback loops. Data to build algorithms are carefully evaluated to avoid biases, and AI output is assessed for opacity risk to maintain transparency and explain ability. The Sustain phase monitors outcomes and standardizes practices for long-term value. Data audits and automated extraction tools are applied for fidelity and harmonization, promoting scalability and collaboration among organizations. Discussion/Significance of Impact: While keeping human intelligence central to AI integration, UTS ensures sustained efficiency and trust – critical for successful healthcare transformation. This framework positions itself as a catalyst for responsible innovation, aligning technological advancement with clinical priorities and patient protection.

Information

Type
AI and Data Science
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2026. The Association for Clinical and Translational Science