Process Improvement Before Artificial Intelligence and Automation: Building Trust With the...

Area of Science:

• Healthcare innovation
• Medical artificial intelligence (AI)
• Clinical workflow integration

Background:

• AI and automation are transforming healthcare, but integration faces technical, ethical, and organizational hurdles.
• Lack of trust from both patients and providers is a central barrier to AI adoption in clinical settings.
• Concerns include safety, transparency, the physician-patient relationship, algorithmic opacity, data quality, and legal ambiguity.

Purpose of the Study:

• To introduce the Understand, Transform, and Sustain (UTS) framework as a behavior-based, systems-level approach for AI deployment in healthcare.
• To address the challenges hindering the integration of AI into clinical workflows.
• To promote responsible AI innovation aligned with clinical priorities.

Main Methods:

• The UTS framework integrates process improvement principles across three phases: Understand, Transform, and Sustain.
• Emphasis on stakeholder engagement, transparency, and patient safety throughout the AI lifecycle.
• Methods include workflow mapping, data collection, bias evaluation, opacity risk assessment, outcome monitoring, and data audits.

Main Results:

• The Understand phase identifies inefficiencies and priorities for AI tool development.
• The Transform phase focuses on designing, implementing, and testing interventions with bias evaluation and transparency checks.
• The Sustain phase ensures long-term value through outcome monitoring, standardization, and data fidelity for scalability.

Conclusions:

• The UTS framework provides a systematic approach to overcome barriers in AI healthcare integration.
• It prioritizes human intelligence and stakeholder engagement for responsible AI innovation.
• UTS aims to align technological advancements with clinical needs, fostering sustainable transformation in healthcare.