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SYNERGEN Health’s Sunil Konda Explores AI’s True Role in Revenue Cycle Strategy

July 15, 2025
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Sunil Konda, Chief Product Officer, SYNERGEN Health

As health systems recalibrate financial operations to weather reimbursement volatility and labor shortages, the imperative for intelligent automation in revenue cycle management (RCM) has never been stronger. But for every institution pursuing transformation, many still grapple with fragmented technology deployments, unclear ROI, and hype-laden AI narratives that obscure operational reality.

Sunil Konda, Chief Product Officer at SYNERGEN Health, brings grounded clarity to this evolving landscape. With more than 16 years of experience spanning product strategy, population health, interoperability, and analytics, Konda has emerged as one of the sector’s most practically minded RCM voices. In this exclusive Q&A with HIT Leaders & News, he offers a measured, metrics-driven perspective on how automation should be implemented, what it should accomplish, and where its limits reside.

Konda challenges the notion that generative AI is a universal solution, instead advocating for focused automation in high-volume, rules-based processes like eligibility verification and denial triage. He also emphasizes the emergence of strategic forecasting as the next phase of RCM maturity, where predictive analytics inform staffing models, contract strategy, and patient financial engagement.

Most notably, Konda introduces a forward-leaning set of performance indicators—including denial prevention rates and one-touch resolution—that shift the automation conversation from task efficiency to enterprise value. For health system CFOs and clinical IT leaders, this is not a vendor pitch. It is a high-resolution map of what intelligent RCM investment must look like to be sustainable, measurable, and aligned with future financial integrity.

AI is making waves across healthcare, but in RCM specifically, where is it gaining real traction versus just generating buzz?

We’ve seen AI experience a remarkable journey from buzzword to business value in RCM. Where we once saw high levels of hesitation with questions around the possibility of these AI-powered technologies, today, we see a large cohort that are now asking questions around evaluation and how it can strengthen their operations. The American Medical Association (AMA) recently found that three in five physicians (61%) report that they’re concerned that payors’ use of AI is increasing prior authorization denials—underscoring the need to adopt AI to level the playing field and protect patient access to care. Many are using it today for more than just routine tasks and are using it to help identify trends, detect errors, and streamline claim processing. That being said, it’s important for leaders to be able to separate the promise from the practical. Not every challenge requires generative AI. Success is hinged on applying the right technology where it can make the most meaningful impact, not forcing it into workflows where it doesn’t belong.

Many organizations are looking for quick wins with automation—what areas of the revenue cycle are proving most ripe for meaningful, measurable impact?

Off the top of my head, the biggest quick wins we’re seeing are in areas where staff time is being eaten up by high volume, rules-based work like insurance eligibility checks, patient cost estimations, and denials mitigation. Automation is especially great for catching errors on the front end where accurate and complete information collection can prevent issues downstream. Catching things proactively before claims submission is also where we’re seeing strong ROI from AI-powered denial prediction. This not only reduces rework but also helps protect margins and plays a role in the patient financial experience. It’s always important to balance the desire for quick wins with a strategy for long term gains as well because those wins, while they may take longer, can be the most valuable.

How are organizations leveraging predictive analytics and machine learning not just for automation, but for strategic forecasting in revenue cycle performance?

With predictive analytics leaders are able to move from reactive problem solving to a place of proactive strategy where they can forecast the headwinds and tailwinds they’re looking at. We can now identify patterns that may indicate future bottlenecks for teams whether it be a payor with shifting policies, a coding trend that results in delays, or patient segments with higher likelihood of default. These kinds of insights provide departments with the real time data that helps fine tune things like staffing, contract negotiations, and patient engagement strategies. After you’ve automated workflows, predictive analytics and machine learning are the tools shaping tomorrow’s business outcomes with data you already have.

What metrics are emerging as the new gold standard for evaluating the success of automation in the revenue cycle, beyond days in A/R and clean claim rate?

Days in A/R and clean claim rate remain foundational metrics but there is a push for more nuanced performance indicators. Metrics like denial prevention rate, cost-to-collect reduction, one touch fixes and staff bandwidth utilization are becoming essential for measuring automation’s value because we need to know that it’s not only improving operations but not overburdening teams. Some more patient centric metrics we’re paying closer attention to are payment plan adoption and time to bill resolutions. Fundamentally, automation should be improving the organizations bottom line, freeing staff up for more high value tasks and help improve the patient experience. The best automation and AI investments don’t just make things fast they make things more sustainable with the human resources they have.

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