Turning Healthcare Innovation into Real Patient Impact - with Brad Kennedy of Orlando Health
🎯 Summary
Podcast Episode Summary: Turning Healthcare Innovation into Real Patient Impact - with Brad Kennedy of Orlando Health
This episode of the AI and Business Podcast, featuring Brad Kennedy, Senior Director of Business Solution Strategy at Orlando Health, focused on the critical intersection of patient experience, technology adoption (particularly AI/automation), and strategic investment within the highly regulated healthcare environment. The core narrative moved from the foundational importance of patient feedback to the practical challenges and strategic requirements for deploying emerging technologies like agenteic AI in clinical settings.
1. Focus Area
The discussion centered on Healthcare Technology Adoption and Strategy, specifically examining how patient experience metrics drive innovation investment, the challenges of integrating new technologies (like robotics and AI) into clinical workflows, and the strategic positioning of emerging AI capabilities, such as clinical decision support and future agenteic systems.
2. Key Technical Insights
- AI as Clinical Decision Support (CDS): The most promising current use of AI in healthcare is not replacing physician decisions but acting as a CDS tool. Effective AI synthesizes numerous patient factors into quick recommendations (e.g., flagging a potential diagnosis or surgical candidacy) that the physician then validates, maintaining human agency and accountability.
- Distinction in AI Applications: The conversation highlighted that AI technologies vary widely—from administrative uses (like billing transparency) to highly complex systems (like robotic surgery guidance)—and must be evaluated based on their specific substance, not just the underlying machine learning.
- Agentic AI Headwinds: While agenteic AI is on the horizon, the primary challenge for healthcare professionals will be ensuring these systems enhance workflows without compromising the provider’s decision-making authority or introducing new forms of friction or accountability gaps.
3. Business/Investment Angle
- Patient Experience as a Financial Driver: Patient experience metrics, formalized by CMS through surveys like HCAHPS and associated star ratings, are no longer just quality measures; they are increasingly tied to revenue under the shift from fee-for-service to value-based care contracts.
- Quantifiable ROI is Mandatory for Buy-in: Any proposed innovation, especially AI, must be supported by objective, quantifiable outcomes related to improving experience, enhancing outcomes, or driving efficiency (impacting the bottom line). Proposals lacking specific metrics are “dead in the water.”
- Stakeholder Alignment is Crucial: Securing wholesale buy-in across legal, IT, and clinical teams requires answering the “what’s in it for me” question for each group, often through early, collaborative development (e.g., involving nurses when designing a new unit-based solution).
4. Notable Companies/People
- Brad Kennedy (Orlando Health): Senior Director of Business Solution Strategy, providing the perspective of a large, not-for-profit health network navigating innovation strategy.
- CMS (Center for Medicare & Medicaid Services): The key regulatory body driving accountability for patient experience through the HCAHPS survey system.
- Productive Edge: The episode sponsor.
5. Future Implications
The industry is moving toward a model where patient engagement and experience are inseparable from clinical outcomes, necessitating technology investments that bridge communication gaps (e.g., virtual care). The next wave of AI adoption will require health systems to carefully vet agenteic systems to ensure they serve as powerful assistants rather than autonomous decision-makers, thereby safeguarding the Hippocratic oath and maintaining provider trust.
6. Target Audience
This episode is highly valuable for Healthcare Executives, Innovation Leaders, Strategy Officers, and Technology Vendors focused on enterprise deployment within regulated clinical environments. It is relevant for professionals interested in the practical realities of AI adoption, stakeholder management, and linking technology investment directly to patient-centric business outcomes.
🏢 Companies Mentioned
💬 Key Insights
"Second, clinical decision support tools need to enhance physician workflows, not replace them. The most effective use cases are those that save time and improve outcomes without compromising agency or accountability."
"if there's a program or a pilot, a new innovation or solution that we're looking to implement on the nursing unit, we better bring nursing to the table and ask them, "This is what we're thinking. How does this apply to your day? How does this work with your current processes? And let's build it together.""
"I think AI is really being leveraged as a clinical support tool, but not actually making the decisions, and that's where you have to be very, very careful."
"What it should do is perhaps speed up the process of diagnosing or providing lots of different key indicators together in one model that shows a potential for XYZ that then the physician could say, 'Okay, this algorithm or this solution is pointing towards this. Let me validate,' and yes, I agree. Now it's me, the physician, making that decision."
"you have to be careful with AI in healthcare, and that is, are you producing something that is going to replace the physician's decision? And ultimately, that answer should be no."
"Third, getting cross-functional buy-in requires measurable impact, whether it's clinical, operational, or financial. AI projects need clearly defined goals and stakeholder input early in the process to succeed."