In 2025, healthcare revenue cycle management (RCM) saw major advances in AI-driven automation, cloud-based partnerships, and new tools for denial prevention, patient engagement, and financial integrity. It shifted from incremental automation to AI-first strategies that blended cloud, automation, and predictive analytics to reduce denials, accelerate cash flow, and improve patient trust. We asked our experts what progress they think we will see in 2026 for RCM in healthcare. Here is what they had to say.
And check out all our prediction posts looking to 2026.
Alicia Arrick, Chief Growth Officer, P-n-T Data Corp.
LinkedIn: Alicia Arrick
As more patient check-in tasks become more automated, including eligibility checks, benefits validation, and prior auth, the opportunity for AI to support these functions is huge. But so is the risk of exposing sensitive information. In 2026, we expect to see smarter, AI-driven tools that not only speed up insurance verification and reduce claim denials but also eliminate unnecessary retention of PHI and PII. The best systems will quietly do the heavy lifting, verifying coverage, checking for prior auth requirements, and confirming COB, while keeping patient data out of harm’s way.
Carol Berry, CEO, Health Care Administrators Association (HCAA)
LinkedIn: Carol Berry, CSFS
As TPAs support more dynamic and data-heavy self-funded plans, AI will become a critical tool for driving accuracy and efficiency across the revenue cycle. In 2026, the greatest gains will come from automating the front-end steps that routinely slow everyone down, patient intake, eligibility checks, and preauthorization. AI can validate information instantly, reduce back-and-forth, and prevent incomplete submissions. On the backend, AI will help produce cleaner claims and reduce denials by identifying patterns and payer-specific risks earlier. The goal is to give TPAs and providers clearer insight, faster turnaround, and a more predictable financial workflow for self-funded employers.
Daniel Blumenthal, Vice President of Strategy, MDClone
LinkedIn: Daniel Blumenthal
AI is transforming and simplifying the patient financial journey by personalizing estimates, easing self-service tasks, and tailoring outreach based on individual payment likelihood and preferences. When patients understand their obligations early, satisfaction increases and collection rates improve. It’s a win for transparency, access, financial performance, and patient satisfaction.
Noah Breslow, CEO, Revecore
LinkedIn: Noah Breslow
I see AI fundamentally reshaping the revenue cycle by finally giving hospitals the leverage they’ve been missing as complexity keeps outpacing capacity. At a recent industry event, one healthcare leader told me, ‘Every dollar we lose to denials is a dollar we can’t put back into patient care,’ and that urgency is exactly what’s pushing AI from ‘cautious experimentation’ to ‘practical action’. AI won’t eliminate all the friction, but it will reduce administrative burden, address workforce shortages, and materially improve margins by exposing underpayments faster, preventing denials earlier, and surfacing the patterns teams simply can’t catch on their own. The organizations that lean into this shift won’t just improve their financial resilience, they’ll redefine what success looks like.
Fawad Butt, CEO and Co-founder, Penguin Ai
LinkedIn: Fawad Butt
For years, revenue cycle teams have fought against pre-authorization bottlenecks, coding errors, and denials that never seem to end. In the year ahead, intelligent systems that leverage AI will learn to make decisions on their own, finding ways to improve these manual processes with greater speed and accuracy.
Gautam Char, Chief Strategy Officer, Omega Healthcare
LinkedIn: Gautam Char
In 2026 and beyond, healthcare organizations will face even greater pressures as regulatory demands intensify, and margins continue to tighten. RCM leaders stand at the forefront of healthcare’s next transformation, forging strategic partnerships that will help drive technology innovation, powered by advances in AI, to help maximize financial outcomes and enhance the patient experience. The key is to strategically infuse AI with humans in the loop at the right moments. Those who can embrace this convergence will be well positioned to lead the next wave of healthcare innovation.
Andy De, Chief Marketing Officer, Lightbeam Health Solutions
LinkedIn: Andy De
Ambient listening powered by GenAI and LLMs, along with Agentic AI, is rapidly expanding beyond capturing and documenting physician-patient encounters. These capabilities are now automating Revenue Cycle Management (RCM) tasks, streamlining workflows, and enhancing patient self-service—from check-ins and insurance verification to prior authorization. We will also see AI drive greater accuracy in billing, patient collections, coding, and claims filing, significantly reducing denials that cost healthcare organizations billions in lost revenue each year.
Tony DiGiorgio, Chief Architect, symplr
LinkedIn: Tony DiGiorgio
While much of the industry’s attention to AI has been trained on the clinical domain, from documentation to clinical decision support, the next real leap comes from pairing AI with deep automation across the entire healthcare enterprise. Hospitals and health systems won’t just see meaningful gains from operational use cases; they’ll depend on them. AI-driven automation will become a lifeline for healthcare professionals by attacking the administrative burden that fuels burnout across frontline teams and management alike.
End-to-end workflow agents, hands-free scheduling, auto-orchestrated revenue cycle tasks, and intelligent supply chain automations can return hours back to staff for patient care while stabilizing financially strained organizations. And these aren’t just “nice-to-haves”; our 2025 Compass Survey shows clinicians are losing nearly 90 minutes a day to administrative tasks. That’s daylight we simply cannot waste. Operational AI combined with automation at every layer, from back-office operations to clinical logistics, will be essential in 2026 for improving the livelihoods of the healthcare workforce, unifying disjointed systems, and creating a more resilient, modernized health system.
Michelle Durbin, Manager of Solutions Management for Ventus, Altera Digital Health
LinkedIn: Michelle Durbin
In 2026, AI and robotic process automation (RPA) will transform revenue cycle management by automating repetitive tasks and, in turn, reducing manual work and alleviating staff burnout. These technologies will streamline the reimbursement process by enabling healthcare organizations to predict and proactively address claim denial patterns. Ultimately, these automation-driven efficiencies will enhance financial stability, allowing for greater investment in clinical care.
Mike Esworthy, Chief Strategy Officer, EnableComp
LinkedIn: Mike Esworthy
In 2026, it will be essential to apply AI in proven, practical ways in Revenue Cycle Management as hospitals face increasing labor shortages, evolving payer rules, and more complex claims that cannot be handled reliably through standard EHR workflows. Providers will need to rely more on AI-enabled technology to automate fundamental tasks like check-in, eligibility verification, prior authorization, coding support, and payer compliance, so staff can focus on the exceptions that truly require their expertise. The most successful systems in 2026 will integrate strategic partners and AI into their processes to allow for exception-driven workflows. This combination will allow for the delivery of payer-specific insights – reducing preventable denials, accelerating reimbursement, and creating a smoother financial experience for patients and their care teams.
Jeff Fallon, General Manager, eVideon, a TigerConnect company
LinkedIn: Jeff Fallon
Smart rooms are emerging as core hospital infrastructure to improve throughput and workload manageability
Smart hospital rooms were recently featured on the front page of Modern Healthcare, and for good reason: they’re no longer an amenity. In 2026, they’ll become mission-critical infrastructure for hospitals seeking stronger margins and more efficient care delivery. Smart rooms reduce non-clinical work that typically falls on nurses, everything from environmental requests to status updates, and redirect that time back to direct clinical care. They also improve patient engagement in ways that directly influence operational performance, such as faster throughput and fewer delays caused by miscommunication. The next wave of smart room impact will come from faster, simpler integrations that don’t require a software engineer. Orchestration layers that make it easy to connect patient-facing tools with the clinical workflows that power nurse efficiency are essential for consistently coordinated bedside-to-backend communication. When both patients and clinicians can interact with the system seamlessly, hospitals see a level of ROI that pure patient experience solutions have never been able to deliver on their own.
Bob Farrell, CEO, mPulse
LinkedIn: Robert Farrell
AI and LLM regulation shaping 2026: In 2026, we will see large health plans shift away from “no AI” policies to embracing AI and machine learning for efficiency and navigation support as more state and federal regulations bring a sense of certainty to the industry, especially for health plans that have been under scrutiny for how and when AI is being used. I expect LLMs trained on de-identified cross-plan data to unlock better member insights, while trust in how AI is used in the process, not the technology itself, will be key to truly getting member buy-in and adoption. Ultimately, heath plans who don’t embrace AI and LLMs for improving claims and prior authorization processes and unlocking deeper member insights to influence next steps – like getting a screening for a certain disease or getting a flu shot – risk being left behind.
Carol Howard, Vice President of Innovation and Adoption, Janus Health
LinkedIn: Carol Howard
AI is already transforming revenue cycle management by automating repetitive tasks like insurance verification, preauthorization, and claims follow-up, helping staff focus on exceptions that require human judgment. Today, it’s improving accuracy, speeding approvals, and reducing denials, while enhancing the patient financial experience. Moving forward, AI will help teams become proactive at preventing denials, guiding staff in real-time, and continuously optimizing the end-to-end revenue cycle for both financial and operational outcomes.
David Lareau, President and CEO, Medicomp Systems
LinkedIn: David Lareau
AI-driven automation is already reshaping revenue cycle management, expediting eligibility checks, pre-authorizations, and billing accuracy. Yet true progress depends on systems that understand the clinical context behind each claim. In 2026, healthcare organizations will prioritize comprehensive, interoperable data foundations that allow AI to deliver measurable efficiency and financial integrity.
Itzik Levy, CEO, vcita
LinkedIn: Itzik Levy
AI is reshaping revenue cycle management by removing friction from the entire patient financial experience. Considering healthcare billing has long been defined by delays, manual tasks, and patient confusion, I believe AI’s biggest impact is in patient payments and collections. It offers healthcare professionals intelligent reminders, personalized payment plans, and automated follow-ups, creating a more compassionate and consistent process. This level of AI-driven precision, something staff rarely have time to maintain manually, not only improves financial performance, helping providers get paid faster, but also restores trust and transparency for patients navigating care costs.
Laxmi Patel, Chief Strategy Officer, Savista
LinkedIn: Laxmi Patel
AI and Automation will Drive Uneven ROI
While nearly every healthcare organization will increase investment in AI and automation by 2026, not all will see equal returns. The cost of implementation, including technology integration, data management, and workflow redesign, will often outpace short-term savings. The greatest value will emerge where automation directly addresses complexity and manual effort, particularly in coding, denials management, and documentation integrity. Other areas will see slower financial returns as systems, data, and teams adapt to new technology over time.
More Organizations Will Outsource Low-Balance Payer Accounts
By 2026, an increasing number of healthcare organizations will turn to specialized RCM partners to manage smaller, low-balance payer accounts. While the majority of revenue continues to come from high-value claims, collectively these low-balance accounts represent a meaningful opportunity that is often inefficient to manage in-house. Outsourcing these accounts will streamline collections, reduce internal workload, and ensure that predictable revenue is captured, allowing internal teams to focus on higher-value activities and complex claims.
Denials Management Evolves from Reactive to Predictive
By 2026, healthcare organizations will shift from addressing denials after claims are rejected to preventing them before submission. First-pass denial rates currently average around 5–10% for in-network claims and up to 22% for non-contracted claims, and increasing payer scrutiny, complex coding requirements, and prior authorization mandates will continue to drive these numbers higher. AI-powered analytics and automation will help identify high-risk claims, correct errors in real time, and streamline appeals workflows. Organizations that successfully implement predictive denial management will reduce revenue leakage, shorten cash cycles, and improve overall revenue integrity, while those relying on reactive processes risk significant financial and operational pressure as denials continue to grow.
Nicole Rogas, President, RevSpring
LinkedIn: Nicole Rogas
In 2026, AI will become the ultimate empathy engine across industries, not just in healthcare. As customers grow more cost-conscious and automation fatigue sets in, the organizations that succeed won’t be those that merely automate but those that sense when a person needs compassion, clarity, or human intervention. In tighter economic conditions, people scrutinize every touchpoint more sharply. The most trusted brands will use intelligent automation to preempt stress, tailor tone and timing based on behavior, and offer seamless escalation to human support when needed. In sectors pressed by budget constraints, this “automated empathy” becomes a differentiator: not just a convenience, but a vital trust-builder. Successful adoption will hinge on balancing efficiency with emotional intelligence, especially in regulated, sensitive domains.
Andrea Romero, Senior Healthcare Executive, TruBridge
LinkedIn: Andrea Romero
AI gives rural and community facilities a practical way to stay ahead of rising denials. With clearer visibility into historical patterns and payer behavior, organizations can shift from reactive cleanup to proactive prevention. Leveraging AI and machine learning to reduce denials and enhance revenue cycle efficiency produces fewer billing touches, less burnout, and more operational stability. In 2026, solutions that allow timely insights to proactively manage and prevent denials should be the norm, not the exception. Beyond leveraging technology to assist in the latter parts of the revenue cycle, organizations can gain efficiencies by concentrating on the front-end with solutions that address insurance eligibility and bring missed coverages to light.
Tanya Sanderson, RN, MBA, MHA, CHFP, CRCR, Senior Director of Revenue Integrity, Xsolis
LinkedIn: Tanya Sanderson, RN, MBA, MHA, CHFP, CRCR
In 2026, we’ll see the revenue cycle evolve from reactive to truly proactive. AI and advanced analytics will give healthcare organizations the objective data and predictive insights to prevent unnecessary denials from happening in the first place, not just clean them up after the fact. The key will be collaboration: payers, providers, clinicians, and their revenue cycle teams working from the same data, speaking the same language, and aligning on care decisions earlier during the patient’s journey. When we use technology to anticipate friction instead of respond to it, we don’t just protect revenue, we protect relationships, efficiency, and the integrity of care itself.
Scott R. Schell, MD, PhD, MBA, Chief Medical Officer, Cognizant
LinkedIn: Scott Schell
The frontier is a learning system that unites discovery, delivery, and reimbursement into a single, continuous cycle. Imagine a discharge workflow that compiles a summary, reconciles medications, verifies payer rules, and produces a clean claim before the patient leaves. Imagine that same data stream feeding real-world evidence studies and refining future treatment protocols. The goal is not automation for its own sake but an auditable loop where insights travel as quickly as data itself. Pull Quote: A learning system should bring discovery and delivery into the same conversation.
Vikram Singh, Chief Revenue Officer, Infinite Computer Solutions
LinkedIn: Vikram Singh
Healthcare Finds Its Balance Between Cost and Care
By 2026, the healthcare industry’s greatest challenge, delivering high-quality care while containing costs, will intensify. With reimbursements tightening and Medicaid programs under pressure, providers and payers will lean heavily on technology to achieve efficiency without sacrificing care quality. The next year will mark a shift from chasing every innovation to investing strategically in tools that deliver measurable impact. Application rationalization, retiring underused or duplicative systems, will free up funds for AI-powered automation, telehealth, remote patient monitoring (RPM), and predictive analytics that drive both savings and better outcomes.
Telehealth will remain a core part of care delivery, extending access to underserved regions and helping offset physician shortages. Virtual visits will help close workforce gaps and extend care into underserved regions, offsetting a looming physician shortage. Remote monitoring will expand its footprint, offering continuous data on patient health and enabling earlier, lower-cost interventions. Behind the scenes, AI and automation will streamline administrative workflows, revenue cycle management, and call centers, reducing costs while improving accuracy and staff efficiency. Predictive analytics will give organizations the foresight to anticipate demand, allocate resources effectively, and personalize care for at-risk populations.
In 2026, success in healthcare will hinge on balancing financial sustainability with clinical excellence. Organizations that align technology investments with strategic goals, prioritizing efficiency, interoperability, and patient outcomes, will emerge as leaders in a system where cost control and compassionate care can finally coexist.
Stephanie Smith, MD, VP, Clinical Intelligence, Accuity
As AI becomes deeply embedded across the revenue cycle, the priority will evolve from speed to precision and trust. Healthcare operates in one of the most high-risk, high-accountability environments, and we stand at a pivotal moment: to either amplify risk or responsibly harness AI’s full potential. The future belongs to clinically governed AI, technology guided by medical expertise and contextual intelligence. This approach will empower hospitals to code, bill, and reconcile with unprecedented accuracy, reducing denials, strengthening compliance, and ensuring every claim reflects the true clinical complexity of care.
In 2024 and 2025, the race was to collect more data and add more compute. In 2026, the real value shifts to verifiable truth, systems that can demonstrate what they know and why. The differentiator won’t be who has the most data or the largest model, but who can prove integrity, lineage, and logic from source to signal. Data is no longer the new currency, truth is.
Jim Szyperski, CEO, Acuity Behavioral Health
LinkedIn: Jim Szyperski
Behavioral healthcare, and inpatient psychiatry specifically, suffers from a poor per diem reimbursement rate that continues to negatively impact the ability of an incredibly dedicated and talented workforce of nurses and clinical staff to properly deliver patient care. In fairness to payors, this is largely an industry self-inflicted wound as the high degree of variability in clinical operations from site to site, and the inability to quantify improvement or regression in patients, makes it difficult for payors to determine who and how higher reimbursement for the acute services provided should be reimbursed. This has to change as the current (downward) path is IMO not sustainable. The current state of inpatient psychiatry is simply not sustainable. It requires the same level of AI data-driven change that is sweeping across healthcare. The lack of acuity measurement isn’t just a clinical blind spot, it’s a systemic liability. Without the data, there is no visibility into acuity. Without acuity models, hospitals cannot manage staffing levels, predict surges in care needs to manage them, or secure appropriate reimbursement.
Jaideep Tandon, Chairman & CEO, Infinx Healthcare
LinkedIn: Jaideep Tandon
In 2026, AI adoption in the revenue cycle will make proactive denial prevention a reality. Rather than waiting for payer rejections, AI systems will flag claims with a high likelihood of denial before they’re ever submitted, giving providers a chance to correct issues upstream and protect cash flow. At the same time, AI will meaningfully elevate the patient financial experience by streamlining financial clearance — from real-time eligibility validation to prior authorization, self-pay workflows, and clearer price transparency. These advancements will reduce administrative friction on both sides of the encounter, strengthening operational efficiency while improving how patients navigate their financial responsibilities.
Malinka Walaliyadde, EO and Co-founder, AKASA
LinkedIn: Malinka Walaliyadde
There’s an entire ecosystem of “quantified self” companies emerging where you can order an annual full-body MRI, a complete lab panel, and a longitudinal record of your data. Historically, that volume of data was useless because humans couldn’t interpret it all. But now, with AI, you can have an assistant that understands your health history, synthesizes across modalities, and gives you personalized guidance. The same shift is coming to RCM: AI systems that can interpret every datapoint from the patient’s record and automate the work that slows health systems today.
Johnathan Welch, Chief Product Officer, TrustCommerce
LinkedIn: Johnathan Welch
AI has the potential to bring genuine clarity to healthcare payments by translating insurance benefits, deductibles, and co-pays into straightforward guidance at each stage of the patient journey. By aligning services rendered with EOBs, invoices, and payments already made, it can show patients precisely how their financial responsibility relates to their healthcare plan and where they stand against their out-of-pocket limits. It also helps advise when additional payments are likely to be due, replacing guesswork with timely, comprehensible information. The result is a shift from an opaque, reactive process to one that feels predictable, transparent, and far easier for both patients and providers to manage.