Client News Coverage

In the complex ecosystem of modern healthcare, the battle is no longer fought only in operating rooms and patient wards. A second front has opened in the back office, a high-stakes conflict over revenue, compliance, and data. Here, healthcare organizations face a relentless barrage of payer audits, complex coding denials, and shifting regulations that threaten their financial stability. In this environment, a new class of defensive technology is emerging, and recent data suggests it’s having a decisive impact.

MDaudit, a company specializing in AI-powered risk monitoring, recently received an overall performance score of 94 out of 100 in a “First Look” report from KLAS Research, a firm renowned for its independent, provider-funded evaluations of healthcare technology. This score not only surpasses the Best in KLAS software average by nearly 13 points but also comes with A+ ratings from customers for partnership, innovation, and likelihood to recommend. While impressive on its own, this validation points to a much larger trend: the critical role of AI in building more resilient financial systems within our most essential institutions.

Brief Glance Intelligence»

At first glance, the April 1, 2026, ICD 10 update from CMS and NCHS looks deceptively quiet. ICD 10 CM adds no new diagnosis codes, retires none, and leaves the Official Coding Guidelines untouched. It reads like a maintenance cycle, but that surface-level stability masks a more meaningful shift.

The 2026 update is less about what codes exist and more about how coders are expected to use them. It subtly changes the internal logic of classification, shifting decision-making away from structured hierarchies embedded in tabular instructions and toward greater reliance on clinical judgment and encounter context.

As CEO of the company, Ritesh focuses on driving growth and profitability for MDaudit with a customer-centric vision, strong team culture, and platform innovation. Ritesh has spent his entire career at the intersection of data, analytics and emerging technologies, transforming business models across various retail and consumer focused industries, including healthcare. Ritesh has 22+ years of experience working for leading professional services firms including PricewaterhouseCoopers(PwC), Hewlett-Packard(HP) and Knightsbridge Solutions before joining MDaudit.

The assessment comes from a HealthcareITToday interview with Ritesh Ramesh, CEO of MDaudit, an AI-powered continuous risk monitoring platform built for healthcare compliance and revenue integrity teams.

Ramesh described a compliance landscape in rapid transition — where the traditional model of periodic, internal audits is no longer adequate against a government enforcement posture using AI at scale.

Healthcare organizations operate in an increasingly complex financial environment where accuracy, speed and compliance directly impact profitability. As reimbursement models evolve and administrative burdens increase, hospitals and provider groups are turning to automation and artificial intelligence to optimize financial performance across the entire revenue cycle.

AGS Health unveils InnovationWorks, a new centre of excellence dedicated to transforming revenue cycle artificial intelligence from pilots into operational tools, supported by Amazon Web Services and tailored for complex healthcare environments.

AGS Health has launched InnovationWorks, a dedicated centre of excellence aimed at turning revenue cycle AI from a promising concept into tools that can be used in live hospital operations. The company says the initiative is designed to produce practical, return-on-investment-led applications for providers facing rising denial rates, labour shortages and mounting pressure on cash flow.

Noah News»

On June 8, AGS Health announced InnovationWorks™ — a dedicated center of excellence designed to take the hardest, most persistent unsolved problems in revenue cycle and build deployable, ROI-accountable solutions for health systems. Not pilots. Not proofs-of-concept. Solutions that ship.

The model is straightforward: InnovationWorks identifies the operational gaps where AI consistently fails to deliver, assembles internal expertise with external partners, and produces tools providers can actually run without a 12-month transformation program. The emphasis on "no complex transformation programs required" is deliberate — that's a direct shot at the implementation failure rate that's been quietly embarrassing enterprise RCM AI vendors for the past two years.

The financial infrastructure supporting the American healthcare provider network is currently weathering an acute operational storm. Hospital margins are under intense pressure due to skyrocketing clinical labor shortages, expanding uncompensated care liabilities, and an aggressive surge in commercial payer claim denials. To protect capital integrity and maintain cash flow, health systems have turned en masse to digital health applications and automation software.

However, a frustrating pattern has taken hold across the healthcare IT landscape: up to 80% of digital health and automated revenue cycle pilots fail to scale past an initial controlled sandbox. Most revenue cycle management (RCM) platforms are developed under the assumption that a general-purpose model can be easily integrated into any billing department. In reality, these point solutions collapse when forced to navigate a hospital’s chaotic, real-world environment.

HIT Consultant»

Market intelligence firm Black Book Research has released its “2026 State of Health & Hospital Systems Revenue Cycle Management Technology & Services report,” naming the top client-rated vendors across 49 hospital RCM categories.

The report profiles 420 vendors serving hospitals, health systems and inpatient provider organizations, according to a June 8 news release. Rankings are based on surveys of more than 1,300 validated provider-side participants conducted between October 2025 and June 2026, evaluating vendors across 18 qualitative KPIs per category and 720 total measures.

The latest ICD-10 update may look insignificant to many healthcare organizations. There are no sweeping diagnosis code additions, no major guideline rewrites, and no dramatic restructuring of the classification system at first glance.

That perception could become a costly mistake.

One of the biggest challenges facing healthcare organizations today has to do with revenue integrity and healthcare compliance.  Every organization is focusing on improving its revenue while still ensuring compliance.  Plus, this is becoming even more challenging as the government is leveraging AI to increase the number of audits and holding payments from many providers. This crackdown means provider organizations operating in Medicare or Medicaid programs will feel the impact across their entire compliance and revenue cycle footprint.

What’s interesting about this is that MDaudit was built for exactly this situation.  With that in mind, I had a chance to chat with Ritesh Ramesh, CEO at MDaudit, to learn more about what he’s seeing in the market and some of the ways MDaudit uses its AI-powered continuous risk monitoring platform to help hospitals and health systems.

Healthcare IT Today»

Picture a patient admitted for heart failure, treated appropriately, discharged in stable condition, then denied. Not because the care was wrong, but because the documentation didn’t clearly articulate why inpatient admission was medically necessary. By the time the denial lands on a reviewer’s desk, the attending physician has moved on to dozens of other patients, the appeal window is narrowing, and the revenue cycle team is already buried in last month’s backlog.

This scenario plays out countless times every day in U.S. health systems, and it is almost entirely preventable. Clinical denial rates have climbed more than 20% over the past five years, driven by payer automation that has industrialized the scrutiny once reserved for only the most complex claims. The administrative cost of fighting those battles, in staff time, delayed reimbursement, and clinician frustration, can come close to rivaling the cost of the denials themselves.

It is a well-known and well-discussed fact that all of the best decisions are backed by data. In the payer-provider ecosystem, the big areas that use a lot of data-backed decisions are claims management, utilization review, and care coordination. To get a better understanding of the specifics on data analytics’ role, we reached out to our talented Healthcare IT Today Community to ask — how are organizations using data analytics to improve claims management, utilization review, and care coordination across payer-provider ecosystems? The following are their answers.

Technology is a vast umbrella term that covers so many aspects in healthcare, ranging from the smaller side, like healthcare apps and personal wearable devices, to the larger side, like artificial intelligence and precision medications. This makes it very difficult to be aware of all the technology in healthcare and how it’s useful in all of the many aspects of healthcare. So today, we are going to narrow this wide field down by focusing our attention on the technologies that are helpful in aligning payer and provider goals.

We reached out to our incredible Healthcare IT Today Community to ask — what role does technology play in aligning payer and provider goals around value-based care and quality measurement? Below are their responses.

Healthcare revenue cycle management is bending under the strain of evolving payer policies, complex documentation, and higher demands for accuracy and cost control. Exacerbating these challenges are rising coding assignment volumes, which are forcing HIM departments to address continued workforce shortages.

These problems are interconnected and systemic. They cannot be solved by simply adding staff or deploying standalone automation tools. Instead, a more structured approach is emerging: hybrid intelligence, which blends AI-powered coding tools with globally sourced human-in-the-loop expertise to optimize performance while maintaining compliance.