Reid's Path to Ownership as a Forward Deployed Engineer at Commure

In a January 2026 MGMA Stat poll, 48% of medical group leaders named denials and appeals as their biggest revenue cycle leak, and another 23% pointed to front-end problems like eligibility and registration errors. Billing and collections, the work medical billing software was originally built to do, came in at just 14%.

That distribution reads like a product roadmap. Legacy billing systems concentrate on the stage of the cycle that leaks least, while the expensive failures happen upstream, before a claim ever exists. AI-powered medical billing software closes those upstream leaks, and it's changing what health systems should expect from the entire category.

What medical billing software does (and where it stops)

Medical billing software converts clinical encounters into paid claims. It captures charges, codes services, checks claims against payer rules, submits them through a clearinghouse, posts remittances, and manages what patients owe.

Health systems typically run two billing streams through medical billing software at once. Professional billing covers clinician services on the CMS-1500 claim form, and institutional billing covers facility services on the UB-04, each with its own payer rules and edits.

Traditional platforms handle all of this as rule engines. They validate what staff enter and submit what passes validation, which works until the input is wrong. A claim built on an expired authorization, a missed charge, or thin documentation clears every edit and comes back weeks later as a denial someone has to work by hand. Denial management then becomes its own department, cleaning up failures the software waved through.

Payers spent the last several years automating their side of the table. Rule-based systems on the provider side can't keep pace with edits that change faster than staff can learn them, and that asymmetry is what the AI generation of medical billing software was built to correct.

What AI changes in the billing workflow

AI moves billing software from processing claims to preventing failures.

Before the visit, AI agents verify eligibility, confirm coverage details, and check authorization requirements against current payer policy. Those front-end errors ranked second among revenue leaks in MGMA's poll, and they're the cheapest failures to prevent because nothing has been billed yet.

Between the encounter and the claim, documentation quality decides what medical billing and coding software has to work with. Platforms like Commure Pro connect ambient documentation and charge capture at the point of care, so the billing record is built during the visit and coding starts from a complete note.

Before submission, AI compares the claim against the clinical record to catch charge capture gaps, then scores the claim against payer behavior to predict which claims will be denied while there's still time to fix them. A rules engine checks whether a claim is well formed, and a model trained on payer behavior checks whether it will actually get paid.

After submission, AI posts remittances, drafts appeals with payer-specific language, and ranks work queues by dollars at risk so your team spends its hours where the money is.

Some of this runs as a copilot, with a person approving each action, while the routine, high-volume tasks can run autonomously. Commure RCM applies that model as end-to-end medical billing automation, from eligibility through payment posting, with AI working each stage of the cycle in one system.

Medical billing software vs. RCM software

Medical billing software handles the middle of the revenue cycle. It creates claims, submits them, and posts payments. Revenue cycle management (RCM) software covers the whole arc, adding front-end functions like scheduling, eligibility, and prior authorization along with back-end denial management and analytics.

The distinction used to drive purchasing decisions, since practices bought billing tools while health systems assembled RCM stacks from point solutions. AI is collapsing it. A platform that predicts denials needs front-end eligibility data, and a model that drafts appeals needs the clinical documentation behind the claim, so AI-era platforms work the entire cycle as one system. When vendors use the two terms today, read them as a signal of scope, and evaluate any platform against the full cycle it claims to cover.

How to evaluate AI medical billing software

Searching for the best medical billing software mostly returns lists built for small practices (not enterprise health systems), and nearly every vendor on those lists now claims to be utilizing AI. What separates platforms that move revenue metrics from a renamed rules engine comes down to 5 questions.

Does the AI act, or only flag?

Dashboards that surface problems still leave the work to your team. Ask what the system completes on its own, from correcting a charge to filing an appeal, and ask which metrics moved for existing customers. First-pass yield, denial rate, and cost to collect are the numbers that prove a platform protects revenue integrity upstream instead of reporting on losses downstream.

What does it cost all-in, per claim?

Subscription price is a fraction of the real number. Per-claim fees, clearinghouse fees, implementation, and the staff hours the system still requires all belong in the comparison, because administrative overhead is where billing gets expensive. In a July 2026 Health Affairs article, researchers put US administrative spending at 25% of all healthcare costs, with billing for a simple office visit costing more than $20 per bill.

How deep is the EHR integration?

An AI platform that works inside clinical workflows can fix problems at the source, while one that ingests exports can only react to them. Also, be sure to ask how the platform behaves across multiple EHRs, because most enterprises run more than one.

Can the vendor show named results?

Marketing claims are cheap; reference customers aren't. One New York City health system lifted revenue by 20% and cut timely-filing denials by 53% with Commure RCM's charge reconciliation. Whatever platform you evaluate, ask for real-world results and the baseline they started from.

Where does the AI run, and what does it learn from?

Billing data is protected health information, so the model architecture is a compliance concern. Ask where models are hosted, whether your data trains anyone else's models, how outputs are audited, and how the vendor documents HIPAA compliance for its AI components specifically.

Medical billing software at health system scale

A system processing millions of claims a year turns a 1-point change in denial rate into millions of dollars, in either direction, so the gap between flagging and acting compounds with every claim.

Scale also multiplies complexity. Billing software for healthcare enterprises has to absorb multiple facilities, multiple EHRs, and both professional and institutional billing without fragmenting into siloed workflows. That's why billing is usually the highest-yield place to start a broader healthcare automation program, since the return shows up directly in cash.

Judge platforms on outcomes

Feature checklists made sense when every billing platform did roughly the same things. AI broke that equivalence, and the way to compare platforms now is by the revenue outcomes they can prove, measured in first-pass yield, denial rate, and cost to collect.

Hold every vendor to that standard, including us. See how Commure RCM automates the revenue cycle from eligibility through payment posting.

Discover Commure RCM
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Frequently asked questions

What is AI medical billing software?

AI medical billing software uses machine learning and AI agents to automate the work of getting claims paid, including eligibility verification, coding, charge reconciliation, denial prediction, and appeals. Traditional billing software validates and submits claims that staff must still prepare. AI platforms prepare, correct, and defend claims themselves, with people reviewing the cases that need judgment.

How long does AI medical billing software take to implement?

Implementation depends on EHR integration depth, claim volume, and how many facilities are migrating, and enterprise deployments typically run from a few weeks to several months. Ask vendors for a phased plan that starts with high-volume, routine claim types, since early autonomous wins build the case for the longer integrations.

Can AI fully automate medical billing?

Not yet, and vendors claiming otherwise deserve scrutiny. AI reliably automates routine, high-volume work like eligibility checks, coding for common encounter types, charge reconciliation, and payment posting. Complex cases, clinical judgment calls, and payer disputes still need people. The practical model runs routine claims autonomously and routes exceptions to human review queues.

How much does AI medical billing software cost?

Enterprise platforms price on claim volume, modules, and integration scope, so quotes are custom. Compare total cost across subscription, per-claim and clearinghouse fees, implementation, and the staff hours the system still requires, then weigh that against measurable movement in denial rate, first-pass yield, and cost to collect.

In CMS's April 2026 HCAHPS public report, covering nearly 4,000 hospitals, only 71% of patients said they would definitely recommend the hospital that treated them. Nearly 3 in 10 finished an inpatient stay not fully willing to recommend it.

Patient experience in healthcare is the accumulated result of every interaction a person has with your organization, from the first scheduling call to the last bill. These scores tell us those interactions still fall short.

This guide covers what patient experience is, how it differs from patient satisfaction, how it's measured, where it breaks down across the care journey, and what actually improves it.

What is patient experience in healthcare?

Patient experience in healthcare is the sum of every interaction a patient has with a health system across the full course of their care. It spans finding a provider, scheduling, intake, the visit itself, discharge, follow-up, and billing, and it includes every person and process the patient touches along the way.

Every interaction counts. Patients don't separate clinical care from everything wrapped around it. The hold time, the parking, the intake clipboard, and the clarity of the bill all land on the same ledger as the physician conversation.

Perception is the unit of measurement. Patient experience lives in what the patient noticed, and what your organization intended matters only as far as it showed. A discharge plan the care team considers thorough still fails if the patient gets home confused.

It runs the full course of care. Experience starts when someone first searches for help and continues long after the visit ends. Health systems that trace it stage by stage tend to find their biggest gaps between encounters rather than during them, which is why experience work overlaps so heavily with the patient journey in healthcare.

Patient experience vs patient satisfaction

Patient experience and patient satisfaction measure different things.

Patient experience asks whether something happened, and how often. Did your doctor explain things in a way you could understand? Did staff respond when you pressed the call button? These are observable events, which makes experience data comparable across patients and hospitals.

Patient satisfaction asks whether care met the patient's expectations. Two patients can receive identical care and report different levels of satisfaction because they walked in expecting different things. A procedure that felt rushed to one patient may have felt efficient to another.

Experience data tells you what to fix, while satisfaction data tells you how people felt about it. You need both, but improvement work runs on experience data because it points to specific, repeatable behaviors.

Patient experience is also distinct from the patient journey. The journey is the sequence of stages a patient moves through; experience is how those stages land. You improve the experience by fixing the journey stage where it breaks.

Why patient experience matters

Patient experience predicts outcomes, drives revenue, and shapes loyalty.

Start with what patients themselves say. In the Beryl Institute-Ipsos PX Pulse survey published in February 2025, 92% of U.S. consumers said patient experience is important to them, and most pointed to their own health and wellbeing as the reason. Patients treat experience as part of care quality.

The clinical evidence backs them up. A 2026 review in the Journal of Brown Hospital Medicine found that patient experience influences adherence to treatment, trust in providers, and clinical outcomes, with communication quality standing out as the single most influential factor across the hospital stay.

There's a hard financial link too. Since 2012, CMS has tied a portion of hospital reimbursement to HCAHPS performance through the Hospital Value-Based Purchasing program.

And when experience fails, patients act on it. In KFF polling from May 2025, 36% of adults said they skipped or postponed needed care in the past 12 months because of cost, and 18% said their health got worse as a result. Cost is an experience problem as much as a price problem. Confusing bills, surprise charges, and the absence of upfront estimates all push patients toward delay. Delayed care produces sicker patients and lost volume, which means a poor experience compounds into both a clinical and a financial liability.

How to measure patient experience

Patient experience is measured through standardized surveys, and in U.S. hospitals the standard is HCAHPS, the CMS survey that asks discharged patients about specific, observable aspects of their stay.

HCAHPS covers communication with nurses and doctors, responsiveness of staff, communication about medicines, cleanliness and restfulness of the environment, discharge information, care coordination, an overall rating, and whether the patient would recommend the hospital. Results are publicly reported on Care Compare, and because every participating hospital asks the same questions the same way, the scores support real comparison.

HCAHPS has a blind spot in that it only measures the inpatient stay, and it reaches patients days or weeks after discharge, relying on recall and fixed response options. Meanwhile, most of a patient's interactions now happen before and after the hospital, in scheduling, intake, messaging, telehealth, and billing, none of which HCAHPS asks about.

That's why measurement is expanding beyond the annual survey mindset. Real-time feedback after visits, analysis of patient messages and calls, and structured listening programs catch problems while they're still fixable for the patient who reported them. Treat HCAHPS as the benchmark and floor, and build faster feedback loops on top of it.

Where patient experience breaks down across the journey

Patient experience fails at predictable points, and most of them sit between clinical encounters rather than inside them.

Access and scheduling. The experience starts before anyone provides care, with whether patients can find you, book you, and reach you. Long hold times and phone-only scheduling lose patients at the front door; health systems are countering with self-scheduling and AI agents that expand call center capacity and patient access. This front layer of tools is what health systems are increasingly calling the digital front door in healthcare.

Intake and administrative work. Patients repeat the same information on paper forms, then wait while staff key it in. Administrative friction reads as disrespect for the patient's time, and it's one of the most fixable stages. Modern patient intake software moves registration, history, and consent to the patient's phone before arrival.

Communication before and between visits. One-way appointment reminders leave patient questions unanswered, and unanswered questions become no-shows and unprepared arrivals. Two-way patient communication software closes that loop, and AI-powered care journeys that guide preparation and answer questions have been shown to reduce no-shows while improving how prepared patients feel.

The visit itself. Communication dominates here. The 2026 Journal of Brown Hospital Medicine review found that empathy, active listening, and structured bedside communication consistently improve how patients perceive both the competence and compassion of their care team.

Discharge and follow-up. A rushed discharge can undo an excellent stay. Patients need clear instructions, medication reconciliation, and a follow-up plan, and the handoff between settings is where information gets dropped; well-designed transitions of care close those gaps. Post-discharge outreach matters more than most systems assume, since patients forget much of what they were told and questions inevitably surface at home.

Billing. The bill is usually the last interaction, so it colors the memory of everything before it. Upfront estimates, plain-language statements, and a human to call about a charge all belong in the experience.

How to improve patient experience

Improving patient experience means fixing the journey stage where your data says patients struggle, then verifying the fix moved the numbers. Programs that start from a specific gap outperform broad culture campaigns.‍

1. ‍Find your lowest-scoring touchpoint. Pull your HCAHPS results and whatever operational data you have on hold times, no-show rates, intake duration, and portal usage. Rank the gaps by patient volume affected.
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2. ‍Fix the mechanics before the messaging. If patients can't get through on the phone, scripting a warmer greeting won't help. Capacity, self-service options, and response times come first.
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3. ‍Automate the routine so staff can handle the human. Reminders, prep instructions, intake forms, and common questions can run automatically. That returns staff time to the conversations that genuinely need a person.
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4. ‍Close the loop with patients. Ask for feedback close to the moment, act on it, and show patients what changed. Feedback that disappears into a dashboard teaches patients to stop giving it.
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5. Measure again. Rerun the same numbers you started with so you can prove the fix worked. Tying experience gains to a metric leadership already watches, like no-show rate, HCAHPS composite, or call abandonment, is what turns a one-time project into funded, ongoing work.

The evidence that this works is concrete. Yale New Haven Health used Commure Engage to send bilingual pre-appointment text outreach for breast imaging, with reminders, prep information, and wayfinding down to parking directions. No-show and same-day cancellation rates fell 54%, from 13% to 6%. Patients kept appointments because the experience finally gave them what they needed to show up ready.

Pick a stage, remove the friction, and let the results argue for the next stage.

Patient experience is the service

Health systems compete on clinical quality, but patients can rarely observe clinical quality directly. What they can observe is the experience. How easy you were to reach, how prepared they felt, how clearly people spoke to them, how respectfully their time was treated. These observables drive their ratings, their loyalty, and increasingly their choice of provider.

Commure Engage gives health systems the front-cycle infrastructure for that layer, including scheduling, intake, two-way communication, and care navigation all on one platform. This helps to ensure every stage of the journey works the way patients expect. If patient experience is on your roadmap this year, start where your patients already told you it hurts.
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Discover Commure Engage

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Frequently asked questions

What is patient experience in healthcare?

Patient experience in healthcare is the full range of interactions a patient has with the healthcare system. It measures whether specific aspects of care actually happened, such as clear communication and timely access, across the entire continuum of care.

What is the difference between patient experience and patient satisfaction?

Patient experience measures whether specific interactions happened and how often, such as whether providers explained things clearly. Patient satisfaction measures whether care met the patient's personal expectations. Two patients can receive identical care and report different satisfaction, which is why experience data drives improvement work while satisfaction data captures sentiment.

How is patient experience measured?

Patient experience is measured primarily through standardized surveys. In U.S. hospitals, the CMS HCAHPS survey asks discharged patients about communication, responsiveness, cleanliness, restfulness, medications, and discharge information, and results are publicly reported. Many organizations add real-time feedback tools, post-visit surveys, and analysis of patient messages to catch issues sooner than annual survey cycles allow.

Why is patient experience important?

Patient experience is linked to treatment adherence, trust, and clinical outcomes, and it carries direct financial weight. CMS ties a portion of hospital Medicare reimbursement to HCAHPS scores through Value-Based Purchasing. In a 2025 Beryl Institute-Ipsos survey, 92% of consumers said experience matters to them, citing their own health as the reason.

How can health systems improve patient experience?

Health systems can improve patient experience by finding the journey stage where their data shows patients struggling, fixing the underlying mechanics, and remeasuring. High-impact levers include self-scheduling, digital intake, two-way communication with reminders and prep guidance, structured discharge follow-up, and transparent billing.

For a decade, the best engineers in tech went to consumer, cloud, or autonomy. Now they're going into healthcare due to the sheer size of opportunity to make an impact. A growing share of them are landing at Commure.

We track every hire back to where they came from, and the map has gotten dense fast, with big tech, big healthcare, defense, finance, and semiconductors, all pointing the same direction.

In the last 12 months, we've seen a wave of incredible engineering talent join from a broad range of world-class companies: Meta, Google, Uber, Tesla, Apple, Carta, Epic, Verkada, Scale, Palantir, SpaceX, Stanford, MIT, Berkeley, and more.

Healthcare Is the Hardest, Highest-Leverage Problem Left

Engineers and top talent in any domain go where the leverage is highest. 

U.S. healthcare is a $4.5T industry that hasn't moved on productivity in two decades. Physicians work 57.8-hour weeks, and 7.3 of those hours go to prior auth, insurance forms, and admin. The 2025 CAQH Index put administrative savings from electronic transactions at $258B in 2024, with another $21B sitting on the table, not automated. Denials are climbing. Documentation eats a quarter of a clinician's day. Charge capture leakage costs the average health system millions a year in care delivered and never billed.

Every one of those numbers is a rate-limiting bottleneck software can remove. Healthcare is the last industry this size still waiting to be rebuilt, and Commure is building the AI-native platform to do it: ambient documentation, autonomous coding, AI call center agents, staff safety, and end-to-end RCM, running across 60+ EHR systems. One well-designed model saves an hour of a clinician's day. One well-run agent recovers millions in leakage. One well-instrumented workflow reaches 100 million patients. That's the pitch. It's landing.

Talent Cohorts

Talent moves in patterns. Here are a few we've noticed in the last few months:

Meta. Multiple engineering hires this year, most last quarter. They built ranking and inference at a trillion-event scale. Here they get the same rigor pointed at hours returned to a clinician's day instead of engagement metrics.

Uber. Wrote the playbook on real-time ops and marketplace dynamics inside a regulated industry. Now leading central ops for AI deployments across 130+ health systems.

Tesla / SpaceX. Full-stack thinking, vertical integration, comfort with the fact that the hard part is everything around the model.‍

Founders. A dozen-plus acqui-hired startups. They hit distribution walls solo; Commure gave them scale. Ex-founders bring some of the best talent in the company. 

Why Talent Stays

Scale. Tens of millions of U.S. healthcare appointments now run on Commure. HCA and Tenet, two of the largest for-profit systems in the country, partner with us. 100M+ patient interactions a year. 

Speed. Five acquisitions, the business has doubled growth every year for the last 3 years, and we still move with our “speed of all else” mantra. Ownership sits with individuals, not committees. Prototype to production inside a sprint. "I want to move faster" is the single most common reason big-tech hires give us.

Distribution. We're already inside 130+, with 60+ EHR integrations live and 350,000 clinicians on the platform. Shipping is only fun and impactful when someone uses what you built.

Stakes. $25B in annual claims processed. Every transaction is care delivered to a real patient. Get it right, and clinicians spend more time with patients, health systems stay funded, and doors stay open.

What's Next

Healthcare AI consolidates around a handful of companies with the platform breadth, distribution, and talent density to actually move care delivery. Commure intends to lead that consolidation.

We're hiring across engineering, product, ML, sales, customer success, and ops.
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See open roles →

When Cem Torun started on what would become Commure's Ambient AI product, he spent the first 4 months tasked with figuring out whether the idea was worth building at all. There were no users and no team, just Cem and a rough concept that Tanay, Commure's CEO, and Hersh, its CBO, had pointed him toward before stepping back to let him run with it.

Four years later, he's the VP of Engineering for Ambient AI and leads a team of more than 40.

When he first moved to the Bay Area after college, he skipped the apartment search and lived on a retired pharmacist's boat in the Redwood City harbor for 2 weeks, commuting to the office by Uber and turning up most mornings covered in mosquito bites. The owner had spent years in pharma, so their evening conversations about the industry ended up teaching Cem a fair amount about the field he'd just joined.

His route into healthcare took a few turns along the way. As a kid, he wanted to be a YouTuber, talking his teachers into letting him turn writing assignments into short films instead of essays. His parents wanted a doctor in the family, so he spent his first 2 years of college in pre-med, building on the time he'd spent volunteering at a palliative oncology center in high school.

That volunteer work showed him how many problems healthcare had, but fixing them one patient at a time felt too small for the scale he wanted, so he taught himself to code and figured out he could build technology for medicine instead of practicing it.

Being able to work on the first iteration of Commure’s Ambient AI kept rewarding his willingness to take on new problems and think outside of the box. After 4 months of iterating, he had proven the idea was worth pursuing and was told to build a team around it. When that team grew to 6 engineers and still had no product manager, he covered the role himself until the company hired one. He did the same with design, building it to a solid standard until a full-time designer came on board.

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Cem had his biggest test at Commure with the first major Ambient AI enterprise deal. Getting a large health system live was a big implementation, so Cem flew out and worked alongside the team in person for 4 to 6 weeks straight to make sure everything was specifically tailored for their unique workflows.

When Cem talks about why he's stayed 4 years, he says it's uncommon for someone early in their career to be handed stakes this high, and he's watched Commure choose to grow its own leaders instead of bringing in a 20-year veteran every time a hard problem surfaces.

He tells candidates their impact won't be capped by title or tenure, and that no one waits around for an assignment. If you see a problem worth solving, you go solve it. He's honest about the trade-off too, that people who measure their work in weeks tend to miss the results, while those who stay on a hard problem for a couple of years see what that kind of focus produces.

He points to his work on Ambient AI as the clearest example of this. The product supports some of the country's leading oncology and drug-discovery organizations, so the code his team writes reaches cancer research and patient care directly. He admits he undervalued that impact early on, since it was his first full-time job and he assumed most companies operated at those high stakes.

Cem now runs his own team on the same logic, giving people work that sits a little past what they've done before and puts enough structure around it so no one falls too far. As they show they can move quickly, he hands them more.

That approach is how a solo, 4-month experiment turned into a 40-person engineering org in less than 3 years, and how the person who started it went from proving out an idea by himself to leading the team that scaled it.

The same drive holds outside the office, where Cem is training for his first triathlon. He took it on because he wanted something genuinely hard, then talked 6 friends into doing it with him. He booked the race in Berlin because a domestic one sounded boring.

Nine months ago he couldn't swim, and the first time he got in a pool he couldn't make it a single length without stopping for air, which he says made him feel like a kid again. Since then he's learned to swim, bought his first real road bike, and picked up enough about gear sets, shifters, and fixing a flat to give a TED talk on it.

It's the same move he's made his whole career, going after the thing he doesn't know how to do yet and working at it to make it happen.

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Interested in a career building the next generation of healthcare technology powered by AI? We are always looking for talented people across our departments.

Explore Career Opportunities

In a report released in 2025, Premier found that providers spent an average of $57.23 to fight a single denied claim in 2023, up from $43.84 the year before, and nearly three-quarters of physicians now report that prior-authorization denials have climbed over the past five years, so denials are getting more expensive and more common at the same time.

Most of what drives denials is knowable and fixable, from the front-desk data that starts a claim to the coding and documentation behind it, which is why a structured denial management program pays for itself.

What is denial management?

Denial management is the systematic process of identifying denied claims, finding out why a payer refused them, correcting and resubmitting or appealing them, and fixing the upstream problems so the same denials stop recurring. Done well, it protects cash flow and surfaces the process gaps that cost an organization revenue.

A rejection and a denial aren't the same thing, and the distinction changes how you work each one. A rejection happens before the payer fully processes a claim, usually because of missing or malformed data, so you can correct and resubmit it quickly. A denial happens after the payer adjudicates the claim and decides it won't pay, which means you either correct and resubmit or file a formal appeal.

In medical billing, denial management runs alongside eligibility, coding, and collections, and it depends on all of them, since a claim denied for a coding error or a missing authorization started going wrong long before it reached the payer.

How common are denials, and why are they rising?

Denials are common and costly. On the plans where insurers are required to report their numbers, HealthCare.gov insurers denied 19% of in-network claims in 2024, close to one in five, and 37% of out-of-network claims, the most recent year the federal marketplace data covers. In a national survey of hospitals, initial denial rates ran around 15% on 2023 claims.

Prior authorization is a large and growing piece of the problem. Physicians and their staff now complete about 39 prior authorization requests per physician each week and spend roughly 13 hours on them, and nearly one in three physicians say those requests are often or always denied.

Regulators are responding to the pressure. Under the CMS Interoperability and Prior Authorization Final Rule, impacted payers must, as of January 2026, decide urgent prior authorizations within 72 hours and standard ones within 7 calendar days, give a specific reason for each denial, and publicly report their prior authorization metrics. Those rules should add transparency over time, though the denial volume providers face today is still climbing.

What causes claim denials?

Most denials trace back to a short list of recurring causes across the front end, coding, and documentation. In KFF's analysis of marketplace claims, the single most common reason was an unhelpful "other" category, followed by administrative reasons and excluded services, with lack of prior authorization or referral at 9% and only 6% tied to medical necessity.

In day-to-day billing, the causes that show up most often are:

• Eligibility and registration errors, where coverage is inactive or the patient's information is wrong on the date of service.
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• Missing or incorrect data, from a misspelled name to a transposed member ID.
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• Missing prior authorization, when a service that needed advance approval didn't get it.
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• Coding errors, including wrong CPT, ICD-10, or modifier choices, and duplicate claims. Getting this right is where AI medical coding is increasingly earning its keep.
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• Medical necessity, when the documentation doesn't support the service billed, which ties denial rates directly to clinical documentation improvement.
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• Timely filing, when a claim lands after the payer's submission window and becomes almost impossible to recover.
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• Non-covered services and coordination-of-benefits issues, common when a patient carries more than one plan.

Denials also fall into categories that shape how you work them. Soft denials are temporary and can be corrected and resubmitted, while hard denials require a formal appeal or turn into written-off revenue. Administrative denials come from process errors like eligibility or missing data, and clinical denials question medical necessity or coding and need clinical evidence to overturn.

The denial management process, step by step

The denial management process is a repeatable cycle that starts when a payer refuses a claim and ends only when the claim is resolved and the reason behind it is fixed. It breaks into five stages:

1. Identify and log the denial. Pull every denial from remittance advice, explanation-of-benefits statements, and your payer work queues, and log it right away so nothing slips past the timely-filing deadline.
   
   
2. Categorize the denial. Sort each one by reason code, payer, dollar value, and age, which turns a pile of individual problems into patterns you can act on.
   
   
3. Investigate the root cause. Read the Claim Adjustment Reason Codes and Remittance Advice Remark Codes to understand what actually went wrong, rather than what the denial appears to say on the surface.
   
   
4. Correct and resubmit, or appeal. Fix and resend the claim when it's a correctable error, or build a formal appeal when the payer got it wrong, knowing an appeal can run several rounds of review before it resolves.
   
   
5. Prevent the next one. Feed what you learned back upstream so the same denial stops recurring, which is the stage that compounds over time.

Because about 70% of denials are ultimately overturned once providers pursue them, prioritizing by dollar value, claim age, and likelihood of success is how a team recovers the most revenue with the staff it has.

How to prevent denials

The most effective denial management programs stop denials before submission by tightening the front end, documentation, and coding. Prevention is really about revenue integrity: making sure what's documented, coded, and billed matches the care delivered.

A few practices move the needle most:

• Verify eligibility and benefits at registration, so coverage problems surface before the visit rather than after the claim.
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• Handle prior authorization proactively, tracking which services need it and securing approval early.
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• Strengthen documentation and coding, since accurate notes and codes are what hold up under payer review. Autonomous coding helps here by generating codes with supporting rationale straight from the documentation.
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• Capture charges completely, because missed or late charges never surface as denials and become permanent losses, which is why charge capture belongs in any prevention plan.
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• Track denial trends with analytics, so a spike from one payer or one service line gets caught early.
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• Build cross-department accountability, matching each denial type to the team that can prevent it, from front desk to coding to billing.

Platforms like Commure Pro pull coding, charge capture, and documentation into one layer so these controls run on every encounter instead of depending on someone remembering to check.

Metrics that matter in denial management

A handful of KPIs tell you whether your denial management is working. The core four are your denial rate (the share of claims denied), your appeal overturn rate (how often you win the denials you fight), your days to resolution (how long a denial takes to clear), and your write-off rate (the revenue you give up).

There's no universal target, but recent MGMA benchmarking put the aggregate first-pass denial rate near 8%, and many organizations run well above that. Two other numbers frame the opportunity: denials get overturned roughly 70% of the time when pursued, and each one costs about $57.23 to work, so a lower denial rate and a higher first-pass yield translate directly into recovered margin.

How AI is changing denial management

AI is shifting denial management from an after-the-fact cleanup job toward prevention, flagging at-risk claims before they're submitted, and automating the appeals and resubmissions that used to consume staff hours. The same models can categorize denials by reason code, group similar claims for bulk resubmission, and draft appeals with the supporting documentation attached.

That's the approach behind Commure's AI work on denials, which pairs accurate coding and charge reconciliation on the front end with automated denial categorization and resubmission on the back end. One New York health system cut its timely-filing denials by 53% and raised monthly charges by 20% (from $7.5M to $9.4M) after deploying automated charge note reconciliation, detailed in the NYC health system case study.

Denials are one piece of a broader move toward healthcare automation across the revenue cycle, where prevention on every claim beats recovery on the ones that slip through.

Turning denial management into prevention

Denial management works best as part of a connected revenue cycle, where clean documentation, accurate coding, and complete charge capture combine into a claim that's right the first time. That's what revenue cycle management built on AI is meant to deliver.

See how Commure RCM turns denial management from a recovery scramble into a prevention system.
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Discover Commure RCM

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Frequently asked questions

What is the difference between a claim denial and a claim rejection?

A claim rejection happens before the payer processes the claim, usually because of missing or invalid data, so you can correct and resubmit it quickly. A denial happens after the payer adjudicates the claim and refuses to pay, which means you either fix and resubmit it or file a formal appeal. Rejections clear faster; denials take more work.

What is a good claim denial rate?

There's no official target, but recent MGMA benchmarking put the aggregate first-pass denial rate near 8%, while marketplace insurers denied 19% of in-network claims in 2024. A realistic goal depends heavily on your payer mix and service lines, so track your own trend rather than chasing a single industry number.

What is denial management in medical billing?

In medical billing, denial management is the workflow billing teams use to catch, correct, appeal, and prevent denied claims. It covers reading payer reason codes, resubmitting fixable errors, appealing wrongful denials before filing deadlines close, and feeding root causes back to registration, coding, and documentation so the same denials stop recurring.

What are denial management services?

Denial management services are the people, software, or outsourced partners that handle denied claims for a provider. They range from in-house billing teams to third-party RCM vendors and AI-driven tools that flag at-risk claims before submission, categorize denials by reason code, and automate resubmissions and appeals to recover revenue faster.

What is the difference between denial management and revenue cycle management?

Denial management is one part of revenue cycle management, which covers the full financial path of a claim from registration and eligibility through coding, billing, and payment posting. Denial management focuses specifically on the claims a payer refuses, resolving them and preventing the next ones, so it only works as well as the revenue cycle around it.

If you've spent any time in home health care operations, you already know the math: the agency that responds first wins the patient. It doesn't matter how good your clinical team is or how strong your outcomes are, if you're still processing referrals through fax queues and manual spreadsheets, you're losing business to the agency that picked up faster.

That's the reality Commure Orchestrator for Home Health was built to address.

The Problem Is Bigger Than It Looks

Referral intake is the front door to your entire business, and right now, for most agencies, that front door is held together with fax paper. 75% of home health organizations still rely on fax as their primary referral channel. Home health industry estimates 10 minutes per referral, scaling to five to eight hours of administrative time weekly at 30-50 referrals per week. Despite that investment, only 50% of referrals convert on average

Payers, hospitals, and discharge teams aren't waiting around. They're sending the same referral to a dozen agencies simultaneously, and they're moving on quickly. When your intake team is manually keying demographics, chasing payer verification, and managing status updates across phone calls and portals, you're leaving revenue on the table.

The administrative burden doesn't just cost agencies with missed referrals. It costs staff. The volume of manual, repetitive work in referral intake is one of the leading drivers of burnout and turnover on intake teams, a problem that compounds itself as agencies struggle to backfill.

Introducing Commure Orchestrator for Home Health

Commure Orchestrator is an AI-native referral management platform that automates the entire intake workflow, from the moment a referral arrives to the moment a patient is admitted. No manual handoffs, dropped follow-ups, or data entry.

The platform operates across five automated steps:

• Ingest. Referrals are captured from every source the moment they arrive (fax, portal, EHR, email) and pulled into a single unified system. Your team stops chasing down incoming referrals across channels and starts working them.
• Extract. AI automatically structures every referral: patient demographics, clinical notes, payer information, diagnosis codes. The data is clean, complete, and ready to act on, without anyone retyping it.
• Validate. Every business rule your agency runs, including service area, acuity, and payer eligibility, executes automatically before anyone touches the referral. Manual checklist work disappears.
• Coordinate. One queue for every team. Communication is centralized so there are no emails, spreadsheets, or dropped handoffs between intake, clinical, and scheduling staff.
• Close. Accepted referrals write directly back into the EHR. The audit loop closes automatically. Zero manual data entry at the back end.

The result is faster response times, higher acceptance rates, and a meaningful reduction in the administrative load your team carries every day.

One Workflow. Impact Across the Organization.

The effects of a faster, more automated intake process compound across the business:

• Revenue: Reduce missed referrals with higher acceptance rates.
• Operations: Increase staff throughput without adding headcount.
• Patient experience: Reduce time-to-care by matching patients to available capacity faster.
• Compliance: Maintain audit-ready, real-time visibility across your entire referral pipeline.

Commure Orchestrator integrates with AI Call Center Agents and digital patient intake, so automation doesn't stop at intake approval. Once a referral is accepted, patients can be scheduled immediately, and pre-visit intake can be completed via SMS or email before the first visit, connecting the full referral-to-visit handoff without gaps.

Orchestrator is already processing hundreds of thousands of tasks fully autonomously across home health, ambulatory, and specialty care settings.

Ready to See It in Action?

If your intake team is still managing referrals manually, we'd like to show you what a fully automated workflow looks like in practice.

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For the third year running, more than 60 executives from organizations like MEDITECH, HCA Healthcare, Willis Knighton Health, Lifepoint Health, WellNow Urgent Care, Summit Behavioral Healthcare, and Nathan Littauer Hospital gathered at Commure Nexus for a full day of sessions, roundtables, and candid conversations. The discussions spanned the front door to final payment, and every point of administrative friction in between.

Commure CEO Tanay Tandon opened the day by highlighting a core thesis at Commure: Healthcare has a work tax problem. Providers spend 15-30 minutes before every visit manually building the clinical picture they need, and another 15-30 minutes after on clinical documentation, for every patient. Layer on the back-end: every prior auth chased, every claim resubmitted, every administrative hour spent on billing complexity instead of care. That friction carries a price.

At the industry level, it adds up to an estimated $1 trillion in administrative costs annually, with providers absorbing a 10-15% cost to transact. The rest of the modern economy runs at roughly 3%. That gap is a structural failure, and that was the center of conversation among healthcare leaders who gathered at Commure Nexus in Boston.

A Tribute to Jim Merlin

This year's Nexus opened with a keynote called “Never Stop Inventing” alongside MEDITECH, and we want to honor the man who made that conversation what it was. Jim Merlin, 40-year veteran of MEDITECH and one of the most respected voices in health IT, passed away shortly after Nexus. Jim brought the kind of earned conviction to this work that only comes from decades in the field. We are grateful he was with us in Boston, and we hold his memory with tremendous respect. 

Jim shared the story of how MEDITECH pioneered the health IT category and modern day EHR, how they continue to help lead the industry. That spirit of innovation set the stage for the conversations that followed.

From Missed Calls to Admitted Patients: How Summit Behavioral Health Transformed Its Call Center

Every missed call is a missed patient. Across healthcare, front-desk capacity constraints, including hold times, after-hours gaps, and overwhelmed staff, mean that patients who can't get through often don't try again. In behavioral health, where that first call may be the hardest one a patient ever makes, the stakes are even higher.

Commure COO Deepika Bodapati opened with a live demo of Commure's AI call center agent, which offers 24/7 coverage, intelligent call routing, scheduling, and intake, handled without staff involvement. Then Caleb Rich, SVP of Patient Acquisition and Admissions for Summit Behavioral Health, walked through how the organization is transforming their call center experience with an AI agent.

Summit BHC is one of the fastest-growing behavioral health networks in the country, with 38 facilities nationwide, serving a highly diverse patient population across its nationwide network of psychiatric hospitals and addiction treatment centers. The administrative burden at the front door, including missed calls, delayed intake, lost admissions, is acute. With Commure's AI agent, Bella, live across their network, Summit achieved more inquiries handled and higher admissions conversion within the first three months of deployment. That's more people getting into treatment when they need it most, not lost in a queue.

After the Note: Where Ambient AI Goes Next

Documentation is both a burnout problem and revenue problem. If it isn't documented, it didn't happen; not for the patient record, and not for the claim. Leaders from Lifepoint Health, Willis Knighton Health, and WellNow Urgent Care shared ground-level perspectives on deploying AI-powered clinical documentation across their organizations. 

The session pushed further than ambient clinical documentation. The real value lives in what comes before and after the note: pre-visit summaries, pre-charting, real-time patient record queries, charge-note reconciliation.

The compounding advantage belongs to health systems that connect the note to the full clinical and financial workflow, not those who stopped at transcription. Documentation feeds coding. Coding feeds claims. Claims feed intelligence that improves the next encounter. That loop, running continuously on one data model, is where the structural advantage builds.

All In on AI: Building an End-to-End Platform Strategy Across the Patient Journey

Point solutions address individual moments of friction. A platform closes the loop. That was the argument at the center of the day's final panel, featuring two health systems at different points on the same path.

Ross Rigdon, COO at Raleigh Orthopaedic Clinic, brought the RCM story to life, unearthing the “work tax” with back end revenue cycle workflows. Raleigh Orthopaedic has built one of the most comprehensive full-platform Commure Athelas deployments in the ambulatory space, handing off the complete revenue cycle to an AI-native platform and capturing gains across charge acuity, denial rates, and time-to-payment. The bet was strategic: when documentation, coding, and claims run on a shared data model and continuously learn from one another, every encounter makes the next one more accurate and more efficient. 

James Wellman, VP & CIO at Nathan Littauer Hospital, laid out their own roadmap to full-platform, making the case that integrated AI infrastructure across the patient journey isn't a long-term aspiration. It's a decision health systems are making right now. Nathan Littauer's strategy spans four interconnected pillars: ambient clinical documentation, AI call center agents, Patient360 real-time chart querying, and full-cycle revenue cycle management. The vision is a connected set of rails: one intelligent data model, running from the patient's first call to final payment, where each layer of the platform makes every other layer smarter.

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Compounding Intelligence: The Commure Platform Future

Tanay opened the day with a clear goal: reduce the cost to transact in healthcare. Max Krueger, Head of Forward Deployed Engineering, closed it with a look at what comes next.

The platform already spans the full revenue cycle (front, mid, and back) on one intelligent data model. The roadmap compounds what's working: documentation feeds coding, coding informs charge reconciliation, claims data trains denial prevention upstream, and voice agents capture information at the front door that flows cleanly through every step that follows.

The destination: health organizations paid in 1-2 days, not 30+. Near real-time settlements. Staff focused on care, not chasing payments. A financial foundation stable enough to grow from.

That future requires infrastructure, not a collection of tools: the same kind of shared rails that made credit card networks transformative. Health systems that build on this platform accumulate a data and intelligence advantage that point-solution buyers simply can't close.

The Work Tax Has a Fix

The name Nexus has always meant something to us: a point where things converge. This year, what converged was a shared conviction that the work tax crushing American healthcare is not an immovable structural reality but a solvable engineering problem. And the health systems in that room are proving it, one deployment at a time.

We are grateful to Jim Merlin for helping open this conversation with the wisdom of someone who has seen what it actually takes to change how medicine is practiced. His voice will stay with this work.

We'll see you at the next Nexus.

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Before we built Patient360, my team and I spent weeks shadowing providers across hospitals and ambulatory clinics. The pattern was hard to miss: providers were spending 10 to 20 minutes prepping for a patient they'd see for 5 or 10 minutes.

And that prep time wasn't spent thinking about the patient. It was clicking through the EHR, pulling up labs and imaging one screen at a time, and calling around to track down documents that never made it into the chart.

Tens of thousands of providers now use it to walk into visits already caught up; this post is about what they've taught us so far.

The medical note was only half the problem

Commure Ambient AI solved clinical documentation. Providers stopped working on documentation at 10 pm and are now finishing their notes within a few minutes.

But when we asked providers where their remaining time was going, they pointed to everything that happens before the visit. Documentation had gotten faster while prep was still as slow as it had always been.

Providers didn't go to medical school to click through 20 screens collecting information. They went to make judgments and care for patients. Our job at Commure is to synthesize everything and hand it to them so they can do exactly that.

So we started building beyond the note. Patient360 is a complete clinical picture of the patient, assembled continuously from EHR data: labs, imaging, medications, allergies, signed notes, and past appointments.

That picture powers two things providers touch every day. The first is an AI pre-visit summary that gets a provider current on a patient in about 60 seconds, tailored to their specialty. The second is an AI assistant that answers natural-language questions about the chart, like "any abnormal labs since the last visit?"

Specialty tailoring was a day-one decision. A cardiologist and an orthopedic surgeon need different labs, different baselines, and different acronyms, so a generic summary gets you 60% of the way, which still leaves the provider digging through the EHR to close the gap. With insights from specialists working in the field every day, we created specialty-specific summaries for cardiology, orthopedics, oncology, OBGYN, and more.

"Patient360 gave me exactly what I needed to prep for the visit — the right level of patient-centered information, surfaced at the right time. The reminders on incomplete tests were especially valuable. We've been extremely pleased with the pre-visit summary." — Kansas Heart

What providers have taught us so far

The most common feedback in our early rollouts was a request for more. Providers who had the summary and assistant before the visit asked if they could have access to the tools during and after the visit as well. 

The second lesson was how dramatically prep timing shifts once the work is done for you. One clinical practice told us they used to prep charts a full week ahead because the process took so long. Now their providers review the summary 2 minutes before walking in.

"Patient360 has been extremely helpful. Yesterday, with a busy clinic schedule and an eval about to start, I used it to quickly get the key context for the visit. It gave me exactly the information I needed and saved me several minutes of going into the chart to find the information."  — Athens Orthopedics

The third surprised me. Some care teams included Patient360 as part of their daily huddle, pulling it up so everyone's current on a patient in seconds before discussing the case. We built it for individual prep, and providers turned it into a collaboration tool.

And the fourth is my favorite. Providers who had been hesitant about recording visits at all loved Patient360 once they saw what it gave them in return.

"Seeing providers who are typically slower to adopt new technology quickly embrace Commure, and specifically Patient360, has been a huge win. They're using it daily to prepare for visits faster and review patient information more efficiently. Now we want to see it rolled out across the entire site." — Mountainlands Community Health Center

Trust is everything

Every enterprise conversation I have starts with some version of the same question: how do I know the data is correct and safe?

It's the right question. A summary that's wrong, or that omits something like a prior heart attack, can be nearly as harmful as the EHR chaos it replaced. Trust in healthcare AI has to be earned in the product itself, so we built it with trust as the leading factor from the start.

Every insight in Patient360 links back to its source in the chart. If a provider wants to verify where a statement came from, the citation is one click away.

And when the chart conflicts with itself (which happens constantly when different providers document different things), instead of picking a winner, we surface the conflict on the summary so the clinician can make the call with full information.

What providers consistently asked us

Across these first rollouts, providers and health system leaders kept pressing us on the same three questions. They're the right ones to ask of any tool in this space.

Is this built for a provider like me? Generic summaries create noise as often as they save time. Providers wanted to know the summary was tailored to their specialty, because different clinical workflows genuinely need different things.

Can I trust the data? They asked how we handle conflicting information, whether every insight is cited to its source, and how we think about what's missing, since an absent heart attack is nearly as dangerous as an invented one.

Does it fit the workflow I already have? The worst outcome is a tool providers have to learn. Patient360 was built alongside clinicians from the first design review, and our bar is zero-second training: you open it, and you're ready.

Where Patient360 goes next

Patient360 started as the picture a provider sees before the visit. It's becoming the insights layer underneath the whole encounter.

Pre-charting is next: Patient360 context will flow directly into the medical note, so relevant history, labs, and the HPI foundation are already in place before the provider enters the room and starts documenting the encounter. We're also ingesting the documents and detailed medical history that live as PDFs in the chart, like scanned imaging results and referral packets, so nothing gets left out of the picture.

After that comes in-visit intelligence: care suggestions that surface in real time as the conversation unfolds, always as options the provider can accept or dismiss, never decisions made on their behalf.

Our goal is the same one we started with. Providers should spend their training and judgment on patients, and Patient360 exists to clear away everything standing in between.

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If your team is still prepping charts a week in advance, come see what a 2-minute prep workflow actually looks like. Email us at solutions@commure.com or book a demo via the button below.

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