What question did this study set out to answer?

This research aims to address challenges in integrating artificial intelligence and automation into healthcare workflows, focusing on building trust.

May 9, 2026Journal of Clinical and Translational Science0 citationsOpen Access

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

AGAlejandro Fabrega Gerbaud AGAlejando Fabrega Gerbaud MGMarta Berguido de la Guardia

Key Points

This research aims to address challenges in integrating artificial intelligence and automation into healthcare workflows, focusing on building trust.
Developed the Understand, Transform, Sustain (UTS) framework to guide AI deployment.
Engaged stakeholders to identify inefficiencies, implement interventions, and monitor outcomes.
Used data audits and automated extraction tools for process improvement and scalability.
The Understand phase identified workflow inefficiencies, allowing prioritization of improvements.
Transform phase interventions were designed and tested, ensuring algorithm data quality and transparency.
Sustain phase established monitoring practices to standardize outcomes, enhancing long-term value.

Abstract

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.

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