Most telehealth companies sell a medication with a subscription wrapped around it. A questionnaire. A prescription. A box that shows up.
Mochi Health works differently. The company connects patients, providers, and independent pharmacies on one platform. It’s the structure of that connection, not any single medication moving through it, that Mochi is actually selling.
That distinction matters more than it sounds. A prescription pipeline can be copied by any competitor with a similar drug supply.
A working three-sided marketplace, where each side has a reason to keep showing up, is considerably harder to replicate, and it’s the reason Mochi frames its business this way rather than as a weight-loss app with a pharmacy attached.
The Prescription Pipeline Most Telehealth Runs On
The standard direct-to-consumer model is a straight line. A patient fills out a form. An algorithm or a rushed provider approves a prescription. A single contracted pharmacy ships it.
The patient never chooses the pharmacy and rarely keeps the same provider twice. This model works fine for a single, simple request. It breaks down the moment a patient needs anything ongoing.
The Three Sides, Explained
Mochi’s marketplace gives each of the three parties something a straight pipeline doesn’t. Patients choose their own provider from a roster of more than 400 board-certified physicians, nurse practitioners, and dietitians, and they keep that same provider as their care continues, rather than being routed to whoever’s available.
Getting started is a short eligibility survey, then a provider match, and from there unlimited ongoing access to that care team rather than a single one-off visit.
Providers get the other half of the arrangement. The ability to practice without being boxed into a single condition or a script written by someone else’s protocol, building an actual panel of patients they know over time.
And independent pharmacies plug into Mochi’s software as fulfillment partners that patients can see and compare, competing for that business on service and price rather than being handed volume by default. None of the three sides is just a vendor. Each one has a reason to stay.
Why the Structure Is the Product, Not the Medication
The medication itself is close to a commodity; multiple pharmacies can produce comparable versions of the same GLP-1 or dermatology treatment. What isn’t commoditized is the structure that connects a specific patient to a provider who knows them and a pharmacy that’s been vetted and is competing to serve them well.
Mochi has described its role plainly, as “a software-enabled marketplace that’s making pharmacies better at what they do, which results in better patient outcomes.” This is a description of infrastructure, not of a drug.
That framing is also why Mochi’s pricing looks the way it does. A flat membership starting at $39 for the first month and $79 a month after, with no hidden fees, and medication priced and shown separately rather than bundled into an opaque total.
What This Means Day to Day for a Patient
In practice, a Mochi member gets an in-app care team, messaging with their provider, and 24/7 support that persists across their relationship, plus access to more than 9 treatment areas, from weight management to skincare, mental health, and menopause, without switching platforms or providers each time a new need comes up.
The company reports members averaging meaningful, sustained progress over the course of a year of continued care, the kind of outcome a one-off prescription pipeline isn’t structured to produce, because it was never designed to keep anyone around long enough to get there.
The Marketplace Is the Product
Most of the category still markets itself around the medication: whichever drug is trending, whichever price point undercuts a competitor this quarter.
Mochi’s bet is that the medication is the least differentiated part of the business and the marketplace connecting patient, provider, and pharmacy is the part actually worth building.
More than 500,000 patients choosing their own provider and pharmacy inside that structure is the evidence that the bet is working, and it’s the reason Mochi Health describes itself first as a marketplace, and only second as anywhere you can get a prescription filled.
Healthcare organizations can no longer afford to treat revenue integrity as a problem to be addressed after a claim is denied. As reimbursement pressures intensify and both payers and providers turn to artificial intelligence to analyze claims, identify anomalies and automate processes, health systems need a more proactive approach to protecting revenue while maintaining compliance.
Ritesh Ramesh, CEO of MDaudit, believes that shift requires more than deploying new technology. It means bringing data, people and processes together across coding, compliance, auditing and revenue cycle operations, while using AI where it can deliver measurable business value.
Ramesh recently spoke with Electronic Health Reporter about the changing revenue integrity landscape, the growing “AI versus AI” dynamic between payers and providers, the importance of keeping human judgment in the loop, and what healthcare organizations should consider as they invest in AI-driven revenue cycle strategies.
Healthcare organizations have traditionally focused on managing denials after they occur. Why do you believe that approach is no longer sufficient in today’s reimbursement environment?
The reactive approach to denials management is no longer feasible, as the denial volumes, dollars per denial, and adjudication days per claim have grown incrementally over the years. According to our 2025 Benchmark Report, the average amount per coding-related denial increased 28% from 2023 to 2025. Health systems are under tremendous financial pressure due to a tighter reimbursement and policy environment, and a reactive approach will further stretch their already thin cash flow. Leveraging data, insights, and technology will help them proactively identify denial trends, fix issues before claims are paid, and stay two steps ahead of payers.
You’ve spoken about “Revenue Integrity Redefined.” What does that concept mean in practical terms for health systems, and why is now the right time to rethink traditional revenue integrity strategies?
Revenue integrity is the sustained alignment of three outcomes: reimbursement that reflects the care delivered, compliance that withstands payer and regulatory scrutiny, and operations efficient enough to hold both at enterprise scale. When you pursue any one of the three in isolation, the other two erode. With the advent of AI, now is the right time to transform the people and process dimensions so revenue integrity strategies can be successful. Technology can enable outcomes, but it cannot replace the people and process dimensions in the health system revenue cycle. Investments in technology should be balanced with process reengineering and upskilling people in new technologies.
Many healthcare organizations still treat revenue integrity as a department rather than an enterprise-wide strategy. What mindset shift needs to happen at the executive level?
Enterprises that drive successful revenue integrity strategies leverage a scalable technology platform; stand up a cross-functional program across billing compliance, coding, and revenue cycle; share insights and knowledge; track KPIs that matter; and make measurable progress toward the three outcomes: optimal reimbursement, compliance, and operational efficiency. They don’t see a successful revenue integrity program as a short-term, transactional approach to get claims paid today; they focus on fixing difficult, long-term processes and data issues across the revenue cycle continuum, so these issues don’t recur.
Some cross-functional programs need a strategic charter, actionable KPIs, change management, and business sponsorship to break departmental silos and office politics. This is where executives can drive their revenue integrity vision for the enterprise and align their respective teams to play together as one team.
AI and the Future of Revenue Cycle
Payers are increasingly using artificial intelligence to review claims and identify anomalies. How should providers respond to this changing landscape?
Providers must centralize their claim, payment, and clinical data and glean insights to identify issues and trends before payers report them. A continuous monitoring program and investments in technology platforms that can identify billing, coding, and payment anomalies in real time are the needs of the hour. This will allow them to respond to payers’ use of AI and their investments in this area in a timely and effective manner
AI has become one of the biggest topics in healthcare technology. Where do you see it delivering the greatest value in revenue cycle management today, and where do you think expectations may be getting ahead of reality?
AI has the highest potential to eliminate many manual administrative tasks and automate many parts of the healthcare revenue cycle. Many industry analysts see a world where the cost-to-collect metric will go down for health systems over the next decade because AI and agents help them keep more profits to invest in patient care. Lots of folks hear the word “autonomous” and think AI is a magic wand. I urge them to view AI like any other technology: it needs process and people to succeed. Many AI projects fail because of poor process design and a lack of talent upskilling.
Workforce shortages continue to challenge HIM, coding, and revenue cycle teams. How can AI and intelligent automation help organizations do more with limited resources while maintaining compliance and coding accuracy?
AI can really help with repetitive and mechanical tasks. It can scale exponentially across millions of rows of data and a voluminous number of tasks. When it goes properly, it’s beautiful to watch. When it fails, errors can be amplified, resulting in heavy financial losses. AI governance is critical. With a shortage of expertise in coding and revenue cycle, automation coupled with process and workflow redesign is critical to free those experts to focus on the parts of the process that require human judgment, including validating AI results and handling high-stakes areas.
Payers are increasingly using AI to identify potential claim issues, while providers are also turning to AI to improve audit readiness and revenue integrity. How is this accelerating “AI versus AI” dynamic changing the role of the human auditor, and why is keeping human judgment in the loop becoming more important rather than less?
AI can never be a substitute for human judgment and clinical expertise. It can be trained on large volumes of data sets to find patterns and help identify issues quickly so health systems can act. Providers must invest in AI to level the playing field and accelerate responses to payer queries and denials. Someone recently told me that AI will help them reduce denials. I replied that AI would help them defend the services provided, but it doesn’t expand the insurer’s risk premium pool, which can only pay a finite number of claims. It’s much more important for providers to defend the services they provided in days than for systems to integrate and analyze data for months. They will be left behind.
There has been a lot of discussion about AI adoption in healthcare, but less about proving measurable value. What does “Meaningful AI” mean in practice, and what should healthcare organizations look for to ensure AI investments deliver ROI, reduce operational friction, and strengthen decision-making rather than simply add another technology layer?
Meaningful AI is a framework with five components: data, models, workflow, security controls, and humans in the loop. The whole goal of this framework is to be pragmatic in leveraging AI in use cases with tangible business value. This can be reducing process friction, capturing revenue, or reducing risk. When designing an AI system, people should say, “This use case is not fit for AI because it does not produce any tangible benefits.” Meaningful AI brings that scrutiny and clarity to where AI is applied and what it produces for end users.
Leadership and Industry Outlook
Many healthcare organizations struggle with disconnected data across coding, CDI, compliance, auditing, and revenue cycle teams. How important is breaking down those silos to improving financial performance?
Organizations that achieve strong revenue integrity outcomes do so by combining a scalable technology platform with a coordinated, cross-functional program spanning billing compliance, coding, and revenue cycle operations. They foster knowledge sharing, monitor meaningful KPIs, and drive measurable progress toward three core objectives: optimal reimbursement, regulatory compliance, and operational efficiency.
Rather than treating revenue integrity as a short-term, transactional effort focused solely on getting claims paid, leading organizations take a broader view. They address the underlying process and data issues that create revenue leakage and compliance risks across the revenue cycle, ensuring those challenges are resolved permanently rather than repeatedly managed.
Effective cross-functional initiatives often require a clear strategic charter, actionable KPIs, strong change management, and executive sponsorship to overcome departmental silos and organizational politics. This is where leadership plays a critical role by establishing a unified revenue integrity vision and aligning teams across the enterprise to work collaboratively toward shared goals.
If you were advising a health system CFO making technology investments today, what capabilities would you consider essential for protecting both revenue and compliance?
I would advise CFOs to consider 1) deploying a data driven, proactive strategy to documentation, coding accuracy and denials management; 2) paying attention to payer behavior in real time to understand how best to adapt their RCM strategies; 3) keeping an open mind when reimagining your business processes and upskilling talent while deploying AI; and 4) investing in AI risk and governance as a mandatory function
Looking ahead five years, what do you think revenue integrity will look like, and what changes do you expect will have the biggest impact on healthcare organizations?
As healthcare organizations deploy AI over the next five years, there will be lots of learning from successes and losses. Every health system hopes AI will automate administrative tasks, eliminate waste, and lower cost-to-collect so they can reinvest profits in patient access and care. Various timelines and expectations must be met over the next decade. I am optimistic that there will be many great success stories at the end for others to scale. The current health system infrastructure has been built over decades; it’s unfair to expect AI to perform its magic quickly. I will leave it at that.
Finally, what excites you most about the future of healthcare revenue cycle management and MDaudit’s role in helping organizations navigate that future?
At MDaudit, we are pragmatic about the use of AI in healthcare RCM. We’ve spent a lot of time thinking through which workflows and end users we can impact by leveraging AI. For every idea we accept, we reject 5-10 others.
Our customers work with us as co-creators to develop real products with AI functionality that work in their operational setting. They are the first to tell us what we tried just didn’t work.
We have been embedding Meaningful AI into the MDaudit platform for nearly three years to generate real value. In the last 12 months alone, we’ve generated more than $400 million in value for our customers associated with revenue retention, risk mitigation, and labor productivity.
We see so many future opportunities on our roadmap to continue delivering AI-enabled business value. Our whole organization is both excited about the potential of AI and aware of its risks. Luckily, our customers keep us grounded.
By Patti Artley, DNP, RN, NEA-BC, President, WorldWide HealthStaff Solutions, and Chief Clinical Officer, Medical Solutions.
In my 35 years in nursing, I have seen how quickly a staffing gap can affect a patient’s care. Surgery may be scheduled, the care team may be ready, and the family may have rearranged work, travel and caregiving responsibilities. But everything can still come down to one question: Are enough experienced nurses available to care for the patient?
When perioperative staffing (which supports patients before, during and after surgery) falls short, the impact goes far beyond the operating room schedule. Patients may face delays, prolonged pain or worsening symptoms; families are left to adjust their plans again; and care teams carry the added pressure of trying to do more with fewer people.
That is why perioperative staffing is not just a recruitment issue. It is an access-to-care issue and a patient care issue.
The need for experienced perioperative nurses remains significant. The Association of periOperative Registered Nurses reported that vacant perioperative positions declined from 18% in 2022 to 8% in 2024. That progress is encouraging, but 53% of respondents in 2024 still said surgeries had been delayed or canceled because of short staffing. Staffing challenges continued to disrupt surgical care for more than half of respondents.
At the same time, perioperative nursing is not simply a matter of adding more people. These roles require specialized experience across pre-op, the operating room, circulating and scrubbing, post-anesthesia care, recovery, endoscopy and other procedural settings.
AORN’s 2026 position statement on safe perioperative staffing reinforces that point. Staffing decisions should reflect patient needs, procedural complexity, technology, professional competency and the overall skill mix of the team. Healthcare organizations need nurses whose experience fits the clinical environments in which they will practice.
Finding those nurses is becoming more difficult across the broader labor market. The U.S. Bureau of Labor Statistics projects about 189,100 registered nurse openings each year from 2024 through 2034. The Health Resources and Services Administration also projects an 8% national RN shortage in 2028 and a shortage of about 108,960 full-time-equivalent registered nurses in 2038.
Healthcare organizations cannot wait for the labor market to correct itself. They need a range of workforce strategies, including retaining experienced nurses, strengthening specialty education, improving work environments, and creating more pathways for experienced nurses to join their teams.
International direct hire recruitment is one of those pathways. It can help hospitals and surgical care providers connect with experienced nurses who have already worked in highly specialized clinical settings.
More than 1,000 have operating room and OR subspecialty experience. More than 200 have worked in women’s services, obstetric operating rooms and labor and delivery. More than 300 have experience in pre-op, post-op or post-anesthesia care.
On average, these nurses bring seven years of total nursing experience and 6.5 years in their specialty units. They are not new to nursing. They have worked in areas such as cardiovascular surgery, orthopedic surgery, general surgery, endoscopy, interventional radiology, and post-anesthesia care units.
That level of experience can help organizations strengthen surgical teams, reduce vacancy pressure and support more consistent patient care. But successful international recruitment requires more than filling an open position.
International nurses must complete licensing, credentialing and immigration requirements before they begin employment. Healthcare organizations also have an important role to play once nurses arrive. Strong clinical orientation, peer support, cultural integration and opportunities for continued development all contribute to long-term success.
Recruitment should not be treated as a transaction that ends when a nurse starts the job. It should be viewed as a long-term workforce partnership.
International recruitment is not a replacement for domestic nursing education, stronger retention efforts or healthier work environments. It will not solve the perioperative workforce shortage on its own. But when it is thoughtful and connected to a broader workforce strategy, international direct hire recruitment can help organizations bring experienced clinicians into areas where they are urgently needed.
After many years in nursing, I still believe that workforce planning is patient care planning. Every surgical schedule represents a patient who is counting on a qualified team to be ready.
Our responsibility as healthcare leaders is to build those teams with intention, support them well and create the stability patients and clinicians need. International nurse recruitment, when done thoughtfully and responsibly, can help make that possible.
In the years that have passed since the COVID-19 pandemic, hospital executives have been examining the promises of remote patient monitoring (RPM), telehealth, and hospital-at-home (HaH) services with more intense scrutiny. No longer under pressure to adopt these technologies at any cost, health leaders are looking for hard evidence that they improve ROI.
But health systems may be defining financial returns too narrowly. Many of their evaluations measure RPM against a narrow set of financial indicators: reduced admissions, fewer readmissions, and incremental reimbursement capture. But patient data from mature programs points to gains in areas hospitals rarely built their ROI models around in the first place: clinician time recovered, care escalation caught earlier, and patient adherence sustained past the point where in-person follow-up would have lapsed. If the measurement framework was built for 2020’s pilot programs, it will keep returning the wrong verdict on 2026’s operational reality.
The Other Side of ROI
The biggest disconnect between the metrics that dominate RPM evaluations and the way RPM actually improves ROI comes from focusing too much on counting admissions and reimbursement. RPMs are most useful for reducing the size, scale, and intensity of the resource allocation required per patient. Counting admissions doesn’t capture that data nor does it show where RPM is most effective.
Clinician time is the clearest example. A nurse monitoring 80 patients through a well-tuned RPM platform is not doing the same job as a nurse making 80 individual phone calls. The platform absorbs the routine checking, the vitals review, the flagging of normal readings as normal, so clinical staff spend their attention on patients who most need it. That time recovered rarely appears on a P&L, but it shows up in retention, in burnout rates, and in how many patients a care team can safely manage without adding headcount.
Care escalation timing tells a similar story. RPM programs built around this benefit are catching the early signals of disease in a weight trend, a blood pressure drift, or a symptom pattern before it becomes an emergency department visit or an inpatient stay. RPM can completely change the point of clinical intervention.
Adherence sustained over time is the least visible of the three, and possibly the most durable. Most in-person follow-up has a natural expiration date. Patients stop coming back, or spacing between visits widens, and engagement drops. Continuous monitoring extends that window, and programs with strong adherence data are seeing patients stay connected to their care team months past where a conventional follow-up schedule would have let them drift.
None of this shows up cleanly in a standard ROI model built around admissions and reimbursement. These are useful overall markers of a facility’s care volume and expense, but they don’t capture each patient’s complete care footprint.
Modernizing the Measurement Toolkit
To capture these hidden gains, health systems must look beyond billing data and standard claims registries. Shifting to a realistic evaluation framework starts with workflow and capacity analytics. Instead of simply measuring clinical hours worked, platform data can track time-per-patient-managed and panel size capacity. Comparing the time clinicians spend on exception-based monitoring versus traditional, reactive phone outreach quantifies labor savings, showing exactly how many more patients a single care team can safely manage without increasing headcount or accelerating burnout.
Measuring the true financial impact also requires shifting from short-term, 30-day readmission windows to longitudinal cost tracking. Total-cost-of-care models that follow patients over six to 12 months reveal the compounding value of remote monitoring, contrasting the low cost of proactive, early medication adjustments against the high price of averted emergency room crises. Furthermore, tracking long-term adherence helps systems quantify reduced patient leakage. When continuous engagement keeps individuals from dropping out of the care network to seek treatment elsewhere, the lifetime value of that retained patient directly offsets the initial technology investment.
To effectively measure RPM ROI, executive leadership should consider replacing P&L statements with a balanced value scorecard. A unified dashboard should weigh clinical stabilization and staff retention alongside traditional reimbursement. When evaluating program performance, operational resilience and workforce stability must carry the same financial weight as direct revenue generation to reveal the true return on investment.
MDaudit, an award-winning continuous risk-monitoring platform, today announced that it earned the No. 232 ranking among healthcare and medical companies and No. 2134 overall on the 2026 Inc. 5000 list of the fastest-growing private companies in America. MDaudit’s return to the Inc. 5000 highlights more than a decade of sustained growth, with its ranking improving from No. 4,364 in its previous appearance to No. 2134 this year — an advancement of 2230 positions over a 12-year period that puts the company in the top half of the Inc. 5000.
The most prestigious ranking of the nation’s most successful independent and entrepreneurial businesses, the Inc. 5000 recognizes companies that have achieved remarkable growth while driving innovation, creating jobs, and shaping the future of the economy. Past honorees include companies such as Microsoft, Meta, Chobani, Oracle, and Patagonia.
“Being back on the Inc. 5000 and climbing more than 2,200 spots since our last appearance is a direct reflection of the momentum we’ve built with our customers,” said MDaudit CEO Ritesh Ramesh. “Ranking No. 232 in the healthcare and medical industry is what happens when you stay focused on delivering continuous innovation that drives measurable value. This recognition belongs to every member of the MDaudit team and the customers who trust us with their revenue integrity.”
This year’s Inc. 5000 recognizes a new class of companies redefining what growth looks like. From AI and advanced manufacturing to healthcare, consumer products, and professional services, these businesses are expanding their impact, creating jobs and proving that entrepreneurial ambition continues to fuel the U.S. economy. Among the 5,000 companies on the list, the median three-year revenue growth rate was 130%, and those companies have collectively added more than 627,208 jobs to the U.S. economy over the past three years.
For the full Inc. 5000 list, honoree company profiles, and a searchable database by industry and location, please visit: www.inc.com/inc5000.
“Every company on the Inc. 5000 has a story of perseverance, smart decision making, and a refusal to sit still,” says Mike Hofman, editor-in-chief of Inc. “Their growth reflects more than strong financial performance–it reflects creativity, resilience, and the customer focus required to build companies that make a lasting impact. We congratulate all honorees on this significant achievement.”
Inc. will celebrate the honorees at the 2026 Inc. 5000 Conference & Gala, taking place October 14–16 in Dallas, Texas and the top 500 will be listed in the Fall issue of Inc. Magazine. Tickets are on sale now.
By Suhas Nair, Executive Director of Product Management, AGS Health.
For the past several years, the conversation around AI in medical coding has been dominated by a single question: How much of the coding process can actually be automated? It’s an understandable question. Health systems continue to face mounting pressure from workforce shortages, increasingly complex coding requirements, evolving payer expectations, and persistent financial challenges. AI has demonstrated that it can help address many of these issues, particularly by accelerating routine coding tasks and improving consistency.
But in my conversations with healthcare leaders across the country, I have noticed a shift in the conversation. The organizations making the most meaningful progress aren’t asking how to remove people from the process. Instead, they’re asking how to combine AI with the expertise of experienced coding professionals to improve accuracy, strengthen compliance, and create more resilient coding operations.
That’s an important distinction.
Medical coding has never been simply about assigning codes. It requires interpreting complex clinical documentation, applying evolving coding guidance, understanding payer expectations, and exercising judgment when documentation is unclear. Those responsibilities don’t disappear as AI becomes more capable; if anything, they become more important.
This is why I believe the future of coding is less about autonomous AI and more about orchestrating human and AI collaboration: thoughtfully combining the speed and scalability of automation with the experience and judgment that only skilled coding professionals can provide. That is the model that matters most.
Why the Conversation Is Changing
That shift isn’t simply changing how coding gets done. It’s also changing how healthcare organizations think about AI’s role.
The timing of this shift isn’t accidental. Coding has become significantly more complex over the past decade. Documentation requirements continue to expand. Regulatory expectations evolve. Payer scrutiny has intensified. At the same time, organizations are expected to code accurately, submit claims more quickly, improve financial performance, and do it all with limited staffing resources.
It’s precisely this confluence of pressures that has made AI such an attractive investment. It also revealed that automation delivers its greatest value when it’s designed to complement human expertise rather than operate independently.
That realization is reshaping how many healthcare organizations approach coding modernization.
Early AI initiatives often focused on whether technology could replicate the work of experienced coders. Today, the conversation is more nuanced. Healthcare leaders are recognizing that while AI excels at processing large volumes of information quickly and consistently, coding still depends on clinical context, regulatory interpretation, and professional judgment.
That isn’t a limitation of AI. It’s simply the nature of healthcare.
The strongest coding operations have always combined technology, expertise, and well-designed processes. AI doesn’t change that equation. It strengthens it.
What Successful AI Adoption Looks Like
One lesson has emerged consistently as healthcare organizations expand their use of AI in medical coding: technology alone rarely changes outcomes. Instead, the organizations seeing meaningful operational improvements are redesigning workflows alongside the technology, allowing automation and human expertise to complement one another rather than compete.
While implementation strategies vary, several common characteristics appear among organizations that are moving beyond pilot projects and realizing measurable gains.
One is intelligent case routing. Rather than treating every encounter the same, organizations are increasingly matching work to the resource best equipped to handle it. Straightforward, high-confidence cases can often move through automated workflows, while encounters involving complex documentation, multiple comorbidities, or nuanced payer requirements are escalated to experienced coding professionals. The objective is not to maximize automation for its own sake, but to apply it where it creates the greatest value.
Another is the recognition that AI improves through collaboration. Human review doesn’t simply identify errors. It also provides the feedback needed to refine models as documentation patterns, coding guidance, and reimbursement policies evolve. In that sense, experienced coders remain central to the system’s long-term performance, even as automation assumes a greater share of routine work.
Transparency is also becoming more important as AI-generated coding recommendations play a larger role in the revenue cycle. HIM leaders, compliance teams, and auditors need confidence that recommendations can be understood, validated, and traced back to supporting documentation. Explainability is no longer just a technical consideration. It’s becoming an operational requirement.
Finally, organizations are taking a broader view of success. Productivity metrics remain important, but they tell only part of the story. Coding quality, audit outcomes, denial prevention, reimbursement accuracy, turnaround times, and workforce satisfaction all contribute to determining whether an AI strategy is delivering meaningful value.
Viewed together, these patterns suggest that successful coding automation isn’t measured solely by how much work AI performs independently. Instead, it’s measured by how effectively technology and human expertise work together.
The Medical Coder’s Role Is Becoming More Strategic
Perhaps the biggest misconception surrounding coding automation is that it’s primarily a technology initiative. In practice, success typically has less to do with algorithms and more to do with workflow design, governance, and how organizations choose to leverage the expertise already on their teams.
As AI assumes more routine coding responsibilities, the role of experienced coding professionals naturally evolves. Their focus increasingly shifts toward resolving complex cases, auditing AI-generated recommendations, supporting quality initiatives, and providing the oversight needed to maintain coding integrity as regulatory requirements continue to change.
This shift also presents an opportunity to rethink how coding expertise is used. Rather than asking highly skilled professionals to spend much of their time on repetitive, lower-complexity work, organizations can redirect that expertise toward the situations where clinical judgment has the greatest impact. In an environment where experienced coders remain difficult to recruit and retain, making better use of existing talent may prove just as valuable as increasing productivity.
The Future Is Digital Workforce Orchestration
For many organizations, the challenge is no longer deciding whether AI belongs in the coding workflow. Instead, it is determining how people and technology can work together to strengthen both operational performance and coding quality.
From where I sit, that’s where the conversation becomes the most interesting. Healthcare has never been an industry that embraces technology simply because it’s new. It adopts technology when it solves real problems, fits within complex clinical and operational environments, and earns the trust of the people using it every day.
The conversation around medical coding is maturing beyond replacement and toward collaboration. Instead of asking whether AI can do the work of a coder, healthcare organizations are asking where automation creates the greatest value and where human judgment remains indispensable.
That distinction changes everything.
Rather than viewing AI as a substitute for expertise, organizations are beginning to see it as a force multiplier. It enables coding professionals to focus on cases that require the highest level of clinical interpretation while allowing automation to improve efficiency, consistency, and scalability where appropriate.
I believe that’s where the greatest opportunity lies.
The future of medical coding won’t be defined by how autonomous AI becomes. It will be defined by how thoughtfully healthcare organizations combine technology with human expertise to create coding operations that are more accurate, more resilient, and better prepared for what comes next.
By Baha Zeidan, Chief Executive Officer, Azalea Health.
Every year, we see more technology innovation in healthcare. The current focus is largely on what AI technology can do, and how clinicians experience technology. AI is rapidly changing what is possible in healthcare, from documentation assistance to clinical and billing support.
But as organizations look to modernize their workflows, success will depend on more than adding new capabilities. The goal should not be to add AI, but to implement it where it simplifies and complements the existing workflow. This means making technology feel more intuitive rather than more burdensome.
The questions healthcare organizations need to be asking are: does the technology make the work of delivering exceptional care easier? Can a new provider or staff member learn the technology without weeks of training? For ambulatory practices, this means technology that is specific to outpatient workflows, not systems that are better suited to large hospitals or require unnecessary complexity. It means technology that helps streamline clinical and billing processes.
Giving practices technology that teams will actually use and that fits their needs is essential. When adoption is easy, practices are more likely to understand the value of investment. Connected documentation, scheduling, and billing also give staff quick access to the information they need without unnecessary searching. Small improvements like reducing work help ease cognitive burden have a meaningful impact for the practice over time.
It’s easy to talk about implementing new technology in theory. The reality inside a busy practice is very different. Healthcare organizations face the challenge of introducing new technology while continuing to manage staffing shortages, patient volumes, and administrative demands. It is not realistic to pause operations while clinicians and staff spend valuable time learning new systems. For AI technology to deliver real value, it must be intuitive. A barrier to successful technology adoption is the time it takes to learn. The best systems are the ones that clinicians and staff can learn quickly without weeks of training or steep learning curves. Faster adoption allows teams to focus on high quality patient care instead of adjusting to new systems.
This is where intuitive technology with complementary AI can make a significant difference. The goal is to create a user experience that feels seamless. That means reducing clicks, bringing the patient journey into a single view, and making everyday tasks more efficient. Repetitive administrative work is time consuming, and advanced technologies should assist with workflows, easily surface relevant patient information, and streamline communication across the practice. By removing the small frictions that add up throughout the day, clinicians and staff can spend less time navigating technology and more time focused on patient care and supporting sustainable operations.
Patients don’t see the technology behind the shift to more intuitive systems, but they do experience its benefits. When technology isn’t a distraction, patients will notice that the provider’s focus is on them instead of a computer screen. These are the moments that build trust and create a more connected care journey for the patient. Experience is what makes the difference in healthcare for patients, providers, and staff, not sophisticated technology.
When we start asking the right questions, we start to see what really works for a practice. Sophisticated capabilities have no value if they add more hours and stress to the day. Meaningful innovation and effective healthcare technology is designed around the realities and needs of the clinical practice. In the end, AI technology is innovative when the ease of using it makes everyday lives better. When we make that possible, we see that modern healthcare is less about advanced technology and more about giving care teams the practical support they need to work efficiently and remain focused on patients.
By Ganesh Ramamoorthy, Senior Vice President, Onix
Today’s digital healthcare professionals face unprecedented complexity. The quality and accessibility of clinical data is vital to delivering the best possible patient outcomes. Yet clinicians often struggle to quickly find and retrieve relevant information.
In fact, the sheer volume of data is staggering. A single hospital can produce 137 terabytes of data every day, or roughly 50 petabytes of data per year. This data tsunami is only getting worse, due to rapid expansion of digital health tools, electronic health records and connected devices.
As a result, healthcare administrative costs continue to skyrocket. In fact, administrative spending is estimated to be between 25-30 percent of the nearly $5 trillion spent annually for U.S. healthcare expenditures. More importantly, failure to tame the data dilemma can substantially impact both regulatory compliance as well as patient outcomes.
The healthcare industry is in dire need of transformation, but change happens slowly. How can healthcare providers navigate this massive, complex system to streamline data management in order to reduce costs, grow revenues and increase efficiencies?
Empower Intelligent Insights
To address this issue, a growing number of healthcare leaders are leveraging the latest artificial intelligence (AI) advancements to transform their legacy data systems into a modern, scalable and agile data platform. In this way, healthcare chief information officers (CIOs) are able to take full advantage of augmented intelligence to unlock predictive data analytics and clinical insights, enabling measurable improvements without adding administrative burden.
Indeed, AI-powered data modernization enables organizations to realize substantial clinical and operational benefits, while improving return on investment (ROI). With the help of enterprise-grade agentic AI and generative AI (Gen AI) technologies, healthcare organizations can achieve measurable results such as 10-25 percent reduction in the cost of care, 15-20 percent drop in hospital readmissions, and substantial reduction in mortality rates.
Collaborative Compliance
It’s no secret that administrative friction in healthcare is a significant challenge, with nearly 25 percent of every dollar spent on paperwork. A primary driver of this cost is the prior authorization (PA) process, which typically requires a significant amount of time to conduct manual reviews, send faxes and make phone calls. This burden not only increases costs, but also delays patient care through a “missing information” loop, where simple administrative omissions trigger denials and appeals.
By leveraging the latest agentic and Gen AI, healthcare professionals can transform their workflow from “Reject and Appeal” to “Detect and Clarify” to greatly improve the speed, precision and outcomes of the PA process. The system works by ingesting unstructured clinical notes and matching them against insurance policies, enabling AI agents to perform a real-time gap analysis.
When information is missing, the AI agent flags the issue and drafts a clarification for the provider in less than a minute, ensuring valid claims are approved on the first pass. This not only streamlines billing, it also allows nurses and doctors to focus on patient outcomes rather than paperwork.
Privacy Protections
Of course, when dealing with sensitive patient data, it’s paramount that hospitals and healthcare organizations have access to reliable, secure data they can trust to ensure regulatory and HIPAA compliance. This means that a key aspect of selecting the best AI solution is to ensure it is an enterprise-grade offering that prioritizes a high level of security, data governance and compliance.
In fact, advanced AI capabilities enable additional privacy innovations as well. For example, with the help of GenAI, hospitals can generate millions of records of synthetic data, allowing them to train and test new AI models without exposing sensitive protected health information (PHI). Plus, by compressing processes that otherwise take hours or weeks into minutes, AI agents return valuable time to medical practitioners.
It’s important to note, however, that healthcare CIOs need to implement robust governance policies when taking advantage of AI technology. As the number of AI agents making autonomous decisions increases throughout the healthcare industry, responsible AI practices will become a mandatory business requirement with decisions being driven by trust and transparency.
Healthcare Transformation Success
Today’s healthcare industry is poised for progress, and responsible AI deployments will be an integral part of this transformation – from building a new level of personalized patient experiences, to realizing substantial gains in productivity for improved patient outcomes.
Armed with the right tools, intelligence and insights, healthcare leaders are empowered to realize this transformation and build a brighter future for their patients. The true differentiator for successful healthcare enterprises will not be if they use AI, but rather how they responsibly manage and fully integrate AI into established processes.
MDaudit
By Suhas Nair, Executive Director of Product Management, 
