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In this episode of "Full Tech Ahead," host Amanda Razani interviews John Edwards, SVP of Citius Healthcare Consulting at CitiusTech. They discuss the rapid acceleration of AI in the healthcare sector, shifting from simple proof-of-concepts to full-scale, operationalized enterprise solutions. 

Edwards highlights that the primary barriers to healthcare AI are not technical, but human and procedural. He notes that healthcare data is uniquely time-sensitive, and capturing the unwritten clinical context from a practitioner's head requires robust data quality and "human-in-the-loop" metrics. 

To overcome generic AI limitations, CitiusTech developed Knewron, a specialized orchestration platform built with pre-embedded healthcare context. Ultimately, Edwards argues that the success of healthcare AI relies on strict governance to filter competing priorities, comprehensive change management to overcome clinician inertia, and a deep understanding of the human workflow—such as solving doctor burnout and "pajama time"—rather than just engineering prowess.

Key Quotes

●       "While we do a lot of engineering work lately, a lot of data and AI work has been dominating what we're selling because that's what people are buying. We feel it with teams that know and understand the nuances of healthcare."

●       "The elusive return on investment only really occurs when you adopt AI... it requires you to think differently than just experimenting."

●       "The biggest mistake I see people making is automating a bad process."

●       "A perfect mousetrap that's never used won't catch any mice. You need to be able to get the human side of it engaged and excited."

Takeaways

●       Overcome Clinician Inertia: Historically, adopting tools like the stethoscope took decades because doctors trusted their traditional methods. AI faces the exact same cultural resistance. Organizations must realize that driving adoption requires shifting budgets heavily toward change management—potentially spending two dollars on adoption for every one dollar spent on the technology itself.

●       Never Automate a Bad Process: Traditional healthcare processes were designed around human limitations and legacy software. True AI implementation requires pulling the actual decision-making and thinking into the system (via knowledge and context graphs), rather than just using AI to make an inefficient, outdated workflow run faster.

●       Use Healthcare-Specific AI Foundations: General AI tools lack clinical context and require rebuilding foundations from scratch every time. Utilizing industry-specific accelerators (like CitiusTech's Knewron platform) allows organizations to safely manage time-sensitive medical data and deploy agentic workflows much faster.

●       Solve Real Workforce Friction Points: Clinicians readily embrace AI when it relieves systemic burdens like "pajama time" (the hours spent typing clinical documentation into EHRs at night). Ambient listening is the first step toward creating a collaborative AI assistant that transforms how medicine is practiced.

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