EXCLUSIVE: “Scale Innovation Without Compromising Reliability or Safety”: eClinicalWorks CEO on Building Trusted AI Systems for Healthcare

For more than two decades, healthcare systems have focused on digitizing information, building extensive electronic health record infrastructures to capture and store patient data. Yet, for many providers, particularly smaller practices, this data has remained largely underleveraged, serving as a record-keeping system rather than a dynamic tool for clinical and operational decision-making.

In a recent conversation with Girish Navani, CEO and co-founder of eClinicalWorks, a clearer direction emerges. The shift is no longer about accumulating more data, but about activating the data that already exists within healthcare systems.

That equation is now beginning to change.

Artificial intelligence, as Navani outlines, is accelerating a transition in how healthcare systems engage with their data, transforming electronic health records from passive repositories into active decision support environments. The emphasis is moving beyond data capture toward data activation, where insights are surfaced in real time and embedded directly into clinical and administrative workflows.

Under his leadership, eClinicalWorks is positioning itself within this transition by advancing a workflow-centric approach to AI adoption. Instead of layering AI onto existing systems, the focus is on integrating intelligence into the core of everyday operations, spanning documentation, patient engagement, and revenue cycle processes.

A central pillar of this strategy is the AI API Workbench, which enables healthcare organizations to build and deploy AI agents tailored to their specific workflows. Importantly, Navani emphasizes that the focus is not on large-scale, high-risk transformation, but on targeted, incremental automation, an approach that is particularly relevant for small and mid-sized practices operating under tight financial and resource constraints.

In such environments, he highlights, the ability to implement focused, low-risk AI solutions without the need for extensive infrastructure or large IT teams can significantly accelerate adoption while delivering immediate, tangible value.

In this conversation with Digital Health News, Girish Navani shares how this shift is unfolding on the ground, from AI-assisted clinical decision making and front office automation to interoperability and the practical challenges of scaling AI across healthcare environments.

AI is now being positioned as a core layer within healthcare systems rather than a supporting tool. From what you are seeing across your network, how is this shift beginning to influence clinical decision-making and operational workflows on the ground?

AI acts as a co-pilot to support clinical decision-making; it helps process and analyze vast amounts of documents that would otherwise be time-consuming to review at the point of care. It surfaces relevant insights, flags risks, and provides evidence-based recommendations in real time for clinicians to review.

Operationally, AI transforms clinical documentation, appointment scheduling, patient communication, billing, and patient requests. Routine tasks that once required manual intervention are now automated, reducing delays and freeing staff to focus on higher-value work.

The shift is about re-architecting workflows, so intelligence is embedded directly into the system, allowing providers to get back to what they do best, caring for patients.

Your vision of autonomous AI spans both front office and back office functions. Which areas of healthcare practice are proving most ready for this level of autonomy, and where is resistance still strongest?

There are several administrative workflows across the practice that are well-positioned for automation, and we are already seeing success in both. Patient intake, appointment scheduling, documentation, eligibility checks, refill requests, and revenue cycle processes have clear rules, measurable outcomes, and low clinical risk, making them ideal for automation.

Physicians want transparency, explainability, and control, which is appropriate. Adoption accelerates when AI assists rather than dictates, and when clinicians can validate or override recommendations. Trust builds over time as systems prove reliable and consistent.

With solutions like healow Genie managing large parts of the patient journey, how do you ensure that automation enhances patient experience without making interactions feel transactional or impersonal?

healow Genie is designed to enhance patient engagement and improve patient satisfaction. It is a smart, conversational AI contact center solution that engages in two-way communication with patients. We focus on personalization, context awareness, and natural language engagement. Patients interact in their preferred language, at their own pace.

When a complex patient query requires clinical nuance, the system escalates it to a human. Technology fades into the background when the patient experience becomes smoother and more responsive. The goal is to handle repetitive tasks, answering questions, managing patient requests, coordinating care, so clinicians and staff have more time for meaningful patient interaction.

healow Genie users have reported that patients appreciate the timeliness of prescription refills, as they know some action is being taken. Another practice shared that patients previously had to wait on hold for a staff member who spoke their preferred language; now, they can interact with healow Genie immediately.

Interoperability frameworks like FHIR and networks like PRISMANet have expanded access to data, but access does not always translate into usability. How is eClinicalWorks addressing the gap between data availability and meaningful clinical insights?

We focus on turning data into actionable insights by embedding AI directly into existing workflows. AI models analyze vast patient records, claims, labs, and other operational data to surface what matters most for that patient and clinician.

Rather than presenting more dashboards, we provide contextual prompts, risks to address, care gaps to close, and actions to consider, all at the point of care. The shift is from passive data consumption to active, real-time clinical support. Also, integrating an AI Assistant into PRISMA provides faster and more concise highlights of the patient's record summary, further enhancing the user experience. Blue Bonnet Family Medicine reported saving an average of three minutes per patient encounter using eClinicalWorks AI assistant for PRISMA.

Similarly, AI Document Insights helps extract patient data from faxed documents.

There is a visible gap between AI announcements and real-world deployment across the industry. In practical terms, what challenges, technical, regulatory, or cultural, most often slow down AI adoption at scale?

AI adoption has accelerated in the past few years. Last year, we surveyed 887 healthcare professionals who are also eClinicalWorks users and found that 50 percent of practices used at least one AI solution. Culturally, healthcare workflows are deeply ingrained; people are hesitant to change, especially when tools like AI are new to them.

However, those who have embraced AI say they would not return to traditional methods. Technically, integrating AI and ensuring data quality can be complex, but we ensure our users have a seamless implementation experience. Regulatorily, the industry rightly demands strong policies around privacy, compliance, and accountability. Successful adoption happens when AI is introduced incrementally, by demonstrating measurable value, reduced burnout, better access, lower costs, and improved outcomes.

As you enable developers to build custom AI agents through the API Workbench, how do you maintain consistency, safety, and clinical reliability across a potentially diverse ecosystem of solutions?

We operate within defined guardrails around security, data access, clinical safety, and compliance. Our AI solutions are clinically vetted, and we apply clear role definitions, what AI can and cannot do.

Just as importantly, our APIs align with clinical workflows and regulatory requirements. Innovation is encouraged, but always within a framework that protects patients, supports clinicians, and maintains trust.

Our philosophy is simple: scale innovation without compromising reliability or safety.

Wrap up

What emerges clearly is a shift in how AI is being approached across healthcare, not as a broad, aspirational transformation, but as a series of focused, workflow-level interventions designed to deliver measurable impact.

For many providers, especially those operating with limited resources, this distinction is critical. The ability to adopt AI incrementally, embedding it within existing systems, validating its performance over time, and scaling it responsibly, may ultimately determine how effectively these technologies translate into real-world improvements.

As healthcare continues to navigate cost pressures, workforce challenges, and growing data complexity, the emphasis is likely to remain on practicality over promise, where success is defined not by the scale of innovation but by its relevance and reliability in everyday care delivery.

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