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Healthcare organizations face mounting pressure to improve patient outcomes, reduce costs, and address widespread workforce shortages. As care models shift from fee-for-service to value-based reimbursement, patient experience and clinical outcomes have become central performance metrics, driving demand for improved care coordination, data transparency, and effective communication.
Operational realities often fall short of these expectations. According to a time and motion study published in Health Affairs, physicians spend 49.2% of their clinic day on EHR and desk work, compared to just 27% on direct patient interaction, and an additional 1–2 hours after clinic hours on clerical tasks. The National Academy of Medicine confirms that clinicians spend one-half to two-thirds of their workday on EHR-related tasks, contributing to cognitive overload and burnout.
These inefficiencies take a direct toll on the workforce. The CDC’s Vital Signs report found that 45.6% of healthcare workers report feeling burnout often or very often, up from 31.9% in 2018. Meanwhile, healthcare systems are inundated with siloed, underutilized data.
This challenge is especially urgent given that 6 in 10 U.S. adults live with at least one chronic condition, and 4 in 10 have two or more, according to the Centers for Disease Control and Prevention. Scalable, coordinated care is no longer optional— it’s a strategic imperative.
Healthcare organizations are increasingly looking to AI not just for predictive insights, but for agentic capabilities that can take intelligent action within workflows. From surfacing clinical risks to automating follow-ups and eligibility checks, agentic AI platforms are positioned to help reduce administrative overhead, drive efficiency, and improve outcomes.
In a special series of the ‘AI in Business’ podcast sponsored by Productive Edge, Emerj Senior Editor Matthew DeMello recently sat down with experts in healthcare and innovation who carefully explain to the executive listening audience that making AI-driven systems robust enough for these outcomes requires careful integration, clinician trust, and measurable ROI.
These guests included Raheel Retiwalla, Chief Digital Officer at Productive Edge, and Brad Kennedy, Assistant Vice President of Strategic Innovation at Orlando Health, to explore how healthcare organizations can turn data into action by leveraging agentic AI and patient-centered trust strategies — ultimately improving outcomes while navigating regulatory complexity.
This article examines their insights into how healthcare leaders can reduce clinician burden and improve care outcomes by applying agentic AI to high-friction workflows, while also maintaining patient trust in a highly regulated, data-sensitive environment. We will highlight two key strategies for addressing the operational and ethical challenges of AI adoption in healthcare:
- Automation and orchestration for reduced burnout: By automating manual tasks and orchestrating end-to-end processes that integrate data from claims and outdated patient records, agentic AI helps care teams reclaim time and reduce burnout, particularly in high-volume workflows such as care plan creation.
- Building patient trust through responsible AI design: Healthcare organizations must align innovation with transparency, using de-identified data and patient-centered communication to ensure privacy, safety, and long-term engagement.
Automation and Orchestration for Reduced Burnout
Episode 1: Building Readiness for AI Agents in Healthcare Systems – with Raheel Retiwalla of Productive Edge
Guest: Raheel Retiwalla, Chief Strategy Officer at Productive Edge
Expertise: Agentic AI, Healthcare Automation, Care Management
Brief Recognition: Raheel has over 15 years of experience in digital transformation and data strategy. He previously led the digital transformation of global services at Motorola Solutions and served as CTO at Virtus IT Ltd. He is currently the CEO of Productive Edge, with a focus on AI solutions in healthcare.
Raheel explores how agentic AI fundamentally changes care management by automating labor-intensive tasks and orchestrating actions across multiple disconnected healthcare systems, enabling care teams to focus more on patients rather than paperwork.
The traditional care management workflow involves manually extracting data from claims, electronic health records, and other sources to create service plans — a process that can take hours daily for clinicians, limiting their time with patients. By embedding agentic AI, healthcare organizations can dramatically reduce these bottlenecks, accelerating care delivery without compromising quality.
“Think of agents as not just automation. They’re workflow transformers. At a large payer we built an agent that prepares service plans for high risk members. The agent reviews the member’s history, their claims, their assessments, their care plans, and then drafts a service plan that includes all the right services, the right frequency, the right duration, and the right authorization codes. What used to take 45 minutes per member can now be done in three to five minutes. It’s not just that time is saved, you know, whole lot of burnout that’s avoided, and throughput that’s double, right?””
— Raheel Retiwalla, Chief Strategy Officer at Productive Edge
This reduction in time spent on administrative tasks not only enhances operational efficiency but also addresses the growing problem of clinician burnout. By automating routine, repetitive work, agentic AI frees care managers to focus on higher-value activities such as patient engagement and individualized care planning.
As Brad Kennedy’s episode also attests, even simple automotive deployments can trigger transformation in workflows that directly contribute to better patient outcomes and satisfaction, key metrics in value-based care models. Raheel further emphasizes the comprehensive nature of this automation:
“Healthcare systems have many disconnected systems—claims, EHRs, care management platforms—and agents bring these data points together into one orchestrated flow that drives end-to-end actions. This is not just about recommendations; it’s about connecting signals to actions, automating everything from alerts to documentation and next steps. This orchestration transforms how care teams operate, reducing manual steps and improving patient engagement.”
— Raheel Retiwalla, Chief Strategy Officer at Productive Edge
By integrating data across silos, agentic AI enables a more holistic and timely view of the patient journey. The connectivity of data that drives agentic systems allows healthcare teams to proactively identify care gaps and intervene sooner, enhancing preventive care and reducing costly hospital readmissions.
Raheel’s insights highlight that agentic AI is less about replacing clinicians and more about amplifying their ability to deliver personalized, efficient care.
Overall, he emphasizes that agentic AI is a powerful enabler for healthcare organizations seeking to optimize workflows, enhance clinical accuracy, and alleviate the administrative burden that contributes to staff burnout. By shifting from static data analysis to dynamic action orchestration, care teams can reclaim valuable time and better meet both patient and organizational goals.
Building Patient Trust Through Responsible AI Design
Episode: Turning Healthcare Innovation into Real Patient Impact – with Brad Kennedy of Orlando Health
Guest: Brad Kennedy, Senior Director of Business Solutions Strategy at Orlando Health
Expertise: Healthcare Operations Transformation, Value-Based Care Strategy, Enterprise Technology Implementation
Brief Recognition: Brad has over 20 years of experience in healthcare strategy and operations. He currently serves as Vice President of Strategic Innovation at Orlando Health, where he leads enterprise transformation initiatives. He holds a Master of Healthcare Administration (MHA) from Texas A&M University.
Brad discusses how, beyond the technical capabilities of AI, healthcare organizations must prioritize trust and transparency to foster patient acceptance and ensure ethical use. With growing concerns about data privacy, patients expect clear communication about how their data is used, particularly in regulated environments such as healthcare. Responsible AI design is crucial for maintaining trust while enabling innovation that enhances care delivery and outcomes:
“Transparency is key to building patient trust in AI. Patients need to understand not just what data is being used, but how it is protected and applied to their care. When we design AI solutions, we focus heavily on using de-identified data and ensuring that privacy and safety are at the core of every decision. This way, patients feel confident that their information is secure and that the AI’s recommendations are designed with their best interests in mind.”
— Brad Kennedy, Senior Director of Business Solutions Strategy at Orlando Health
Brad emphasizes that responsible AI is closely tied to patient-centered communication strategies. Clear messaging about the benefits and limitations of AI-driven care helps manage expectations and fosters long-term engagement, which is critical in value-based care models where patient adherence and experience are tied to outcomes and reimbursements.
He notes that patients are more likely to abide by the nuanced details of care plans when they trust the technology behind them. Building that trust requires clear communications with patients from clinicians about how AI tools support their work, rather than replace their connection with the patient.
Patients should also be directly informed about how AI-driven systems are handling their data, Kennedy emphasizes, which is crucial for AI to support, rather than undermine, patient-clinician relationships.
“It’s really about that communication with the patient to build the trust — to a patient who may have a guard up about a new technology that you put in front of them, if you can explain to them why it benefits them, and also make sure that they are aware it’s being used safely. Your information is safe. And so, you know, without going into details on specific projects, that is something that we are always looking at and making sure that we’ve got our security and our risk team looking at these types of things, that any patient data is ever being transferred, especially to maybe a partner that we’re using in a technology space, that goes through a rigorous review process. And the thought process in designing these types of solutions is always with the mindset of the least amount of data that we need, the better.”
— Brad Kennedy, Senior Director of Business Solutions Strategy at Orlando Health
Focusing on transparency and ethics, especially in patient communications related to advanced technology, is not only a compliance imperative but also a strategic business advantage.
Brad’s insights demonstrate that healthcare providers who integrate responsible AI practices directly into their guidelines for providing patients with standard updates for their care are better positioned to meet regulatory expectations, mitigate risks, and enhance patient satisfaction metrics that drive contract success in value-based care arrangements.
His overall perspective underscores that effective AI adoption in healthcare depends as much on trust-building and ethical design as on technological innovation. Throughout his podcast appearance, Kennedy advises healthcare leaders that organizations that center patient privacy and communication in their AI strategies create a foundation for sustainable engagement, improved outcomes, and competitive advantage in a rapidly evolving industry landscape.
