Patient Communication Software Has Outgrown the Appointment Reminder

Hospitals lose about 3% to 5% of net revenue every year to revenue leakage, according to HFMA. Set that against the margin most systems actually operate on (the aggregate hospital operating margin is 5.2%), and the leak is roughly the size of the entire margin.

Most of that lost revenue gets chased after the fact. Retrospective chart audits, appeals on denied claims, quarterly reconciliation reviews. The work happens weeks or months after care was delivered, when the documentation has gone cold, and the filing window may already be closed.

Revenue integrity is the discipline built to stop that. It's the accuracy-and-compliance layer of the revenue cycle, and AI is moving it upstream: correctness enforced at the point of care, on every encounter, as it happens. That shift, from audit to assurance, is the story of this piece.

What is revenue integrity?

Revenue integrity is the practice of making sure what a health system documents, codes, and bills accurately reflects the care it actually delivered. The goal is to capture every dollar earned while staying compliant with payer and regulatory rules.

That means guarding accuracy in both directions. Under-capture costs you revenue you rightfully earned. Over-capture, like upcoding or billing for services that don't match the documentation, invites audits, recoupments, and compliance penalties.

In practice, healthcare revenue integrity spans several connected functions: clinical documentation, coding accuracy, charge capture, denial prevention, and underpayment recovery. A failure in any one of them surfaces downstream as lost or non-compliant revenue.

A revenue integrity function sits across clinical operations, coding, and billing, connecting teams that often work in silos. It ties the exam room to the balance sheet, which is where errors begin and where they get expensive.

Revenue integrity vs revenue cycle management

Revenue cycle management is the full pipeline: patient access, eligibility, charge capture, coding, claim submission, denials, and payment posting. It carries a service from delivery to payment.

Revenue integrity is the quality-control layer inside that pipeline. RCM asks whether a claim got paid. Revenue integrity asks whether it was right: coded to the correct level, supported by documentation, compliant, and complete.

You can run a fast revenue cycle that still leaks. Clean-looking claims get paid at the wrong amount, miss charges entirely, or trigger audits down the line. Revenue integrity is the check that keeps speed from turning into risk.

The documentation and coding accuracy pillar

Accurate reimbursement starts with accurate documentation. When a note fails to capture the full complexity of a visit, coders can't support the correct billing level, and the claim comes in low or wrong. The same gap distorts quality and risk-adjustment scores, since the codes that drive reimbursement also feed severity and outcomes reporting.

The scale of the documentation problem shows up in federal data. In FY2025, the Medicare fee-for-service improper payment rate was 6.55%, or $28.83 billion, and CMS is explicit that most of those improper payments trace to insufficient documentation rather than fraud.

This is where clinical documentation improvement and precise coding do the heavy lifting. Commure customers using a prevention-first approach report a 97% first-pass rate, and Autonomous Coding generated 85% of CPT and ICD-10 codes at Ob Hospitalist Group, with every code traceable back to its source documentation.

The charge integrity pillar

A service delivered but never charged never becomes a denial. It just disappears. That makes charge integrity the quietest form of revenue leakage and one of the hardest to catch.

Charges leak through lag, chargemaster mismatches, and manual processes that depend on someone remembering to enter a code. HFMA estimates as much as 1% of net charges are lost to charge integrity leakage, and its benchmarks call for capturing charges within 3 to 5 days and holding late charges under 2% of the total.

Charge capture closes that gap when it runs automatically. A New York health system increased monthly charges by 20% and cut late-filing denials roughly in half using Charge Note Reconciliation in Commure Pro, and Commure customers have seen a 32% reduction in billing omission errors.

The denial prevention pillar

Denials are the visible symptom of upstream integrity failures. A denied claim usually points back to a documentation gap, a coding error, or an eligibility miss that happened well before the claim went out.

HFMA puts standard denial rates at 5% to 10%, with 2% to 3% considered a strong result. Every point carries weight: as one health system leader has noted, moving denials from 5% to 2.5% has a fundamental financial impact.

Commure customers using denial management tools with a pre-bill approach have cut pre-bill denials by 16% and reduced error rates by 35%, catching problems before claims are ever submitted.

How AI moves revenue integrity from audit to assurance

The traditional model is retrospective by design. Coders and revenue integrity teams review charts after discharge, auditors sample claims after payment, and appeals teams fight denials after the money's already been withheld. Each step catches some errors and misses others, and all of them cost time and staff.

AI changes the timing of that check, moving it to the moment care is delivered. During the visit, Ambient AI captures complete documentation while Autonomous Coding assigns accurate codes within seconds, each with its supporting rationale attached. On every encounter, Charge Note Reconciliation surfaces services that were documented but never billed, and denial tooling flags at-risk claims before they reach the payer.

Brought together on one platform, these steps make integrity continuous. Correctness gets built in as care is documented and coded, so fewer errors reach the claim and far fewer surface months later as denials or audit findings. It also means problems get caught while there's still time to fix them, before a filing window closes or a wrong claim goes out.

What a modern revenue integrity program looks like

A strong program is cross-functional by design, with clinical operations, coding, and billing sharing data and accountability across departments. It monitors continuously, flagging issues as they happen rather than collecting them for a quarterly cleanup.

Track the KPIs that expose leakage early: net collection rate (95% to 99% is the target), denial rate, charge lag, and avoidable write-offs held to 2% to 5% of net patient revenue, per HFMA. These numbers tell you where people, process, or technology are underperforming.

HFMA describes a dedicated revenue integrity function as the connective tissue between clinical operations and billing, one that pays off in stronger revenue capture and lower compliance risk.

The technology layer, often called revenue integrity software, is what makes continuous checking practical at scale. The programs that get results treat revenue integrity as a revenue system in its own right, with the staffing, data, and executive attention that implies.

The bottom line

Revenue leakage runs about the size of a typical hospital's operating margin, and most of it still gets chased retrospectively. Revenue integrity closes that gap by keeping documentation, coding, and charges accurate from the start.

The move from audit to assurance is the real opportunity. AI makes correctness continuous, built into every encounter, so revenue integrity protects revenue in real time.

See how Commure RCM turns revenue integrity into a continuous process, from the exam room to the balance sheet.
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Frequently Asked Questions

What causes revenue leakage in healthcare?

Revenue leakage happens when care delivered doesn't fully convert into accurate, paid claims. The most common causes are incomplete clinical documentation, coding errors, missed or late charges, chargemaster mismatches, eligibility and authorization gaps, and denials that never get worked. HFMA estimates hospitals lose about 3% to 5% of net revenue to leakage each year, much of it invisible until a retrospective audit catches it.

What are revenue integrity KPIs?

Revenue integrity KPIs are the metrics that show where accuracy breaks down and revenue slips away. The core set includes net collection rate (95% to 99% is the target), initial and final denial rates, clean claim rate, first-pass rate, charge lag (capture within 3 to 5 days), late charges (under 2% of the total), and avoidable write-offs (held to 2% to 5% of net patient revenue). Tracking them continuously, rather than reviewing them quarterly, is what lets teams catch problems while there's still time to fix them.

Why is revenue integrity important in healthcare?

Revenue integrity matters because the money at risk is roughly the size of a hospital's entire operating margin. Hospitals lose an estimated 3% to 5% of net revenue to leakage each year, while aggregate operating margins run around 5.2% and close to 4 in 10 hospitals operate in the red. Getting documentation, coding, and charges right protects the revenue a health system earned and keeps it compliant, which guards against both lost income and payer audits.

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Tell us a little bit about yourself - what do you like to do outside of work?

I'm happiest when I'm discovering something new. That curiosity has taken me through 10+ national parks, over 5 half marathons in different cities, and onto a snowboard for the first time this past winter. Exploring is my default mode, be it a new trail, a new neighborhood, or even a new country. When I'm not out adventuring, I'm mostly at a coffee shop with my lovely girlfriend or catching up with friends and family. I love long conversations that meander through every imaginable topic.

As a kid, what did you want to be when you grew up?

For a stretch, I was convinced I'd be an actor - I trained in drama and dance, and loved the idea of portraying different characters. While that dream faded, the instinct behind it continues to remain in that I love wearing different hats. As an engineer, that means moving between system design, team leadership, product strategy, and hands-on coding - sometimes all in the same day.

Describe a day in the life of your role.

No two days look the same, which is exactly how I like it. I usually start with mapping out my focus areas - whether that's writing a design document, reviewing PRs, shipping a feature, or sharpening one of my Claude skills for better accuracy.

Leading a team of four means regular syncs to stay aligned, but the most energizing moments are the unscripted ones: a cross-team whiteboard session on a tangled healthcare problem, or a breakthrough on something we'd been stuck on.

What made you decide to join Commure?

It was an unprecedented opportunity that turned out to be an excellent fit. Healthcare had always felt like meaningful territory to me, but I hadn't fully appreciated how transformative technology could be for the industry. Commure sits right at that intersection - where the mission is real and the technical challenges are genuinely hard. That combination was incredibly compelling.

How would you describe the Commure company culture?

Three focal points: ownership, speed, and versatility. People here own outcomes end-to-end, not just individual pieces of the work. We ship fast and iterate without ego. Everyone is encouraged to step outside their usual lane when solving problems that don’t fall neatly within their areas of expertise. It keeps things exciting.

What advice would you give someone on their first day at Commure?

On your first day at Commure, be curious and proactive. Ask questions early, seek feedback, and don’t wait for clarity to come to you. Talk to people whose work you admire - you’ll often learn more in one conversation than weeks of observation.

What has been your greatest accomplishment so far at Commure?

Leading the redesign of our Encounter Ingestion System - the pipeline that processes patient visit data at the core of our billing infrastructure.

As we scaled, the cracks in the old system became impossible to ignore: traceability gaps, duplicate encounters, tangled rule logic, operational blind spots. Instead of continuing to patch it, we tore it down and rebuilt it from first principles.

What we shipped was a platform built on three pillars: an Encounter Data Abstraction layer as a reliable source of truth for EHR data, the Genesis Rule Engine to unify transformations and validations in a flexible, scalable way, and end-to-end Auditability & Error Tracking so teams can see exactly how encounters are created - and exactly where things go wrong.

Watching that system roll into production and become the foundation for how we process encounters going forward is, without question, the work I'm most proud of at Commure.

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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.

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Ambient scribing gets most of the attention when clinicians talk about AI in the exam room. And for good reason, it's one of the most tangible, immediately impactful technologies to ever enter clinical practice. But there's a quieter, equally significant problem that AI is now starting to solve: what happens before a provider ever walks into the room.

Pre-charting. And for most clinicians, it's a significant, largely invisible drain on their day.

What Is Pre-Charting, and Why Does It Matter?

Before a provider sees a patient, the provider prepares by reviewing previous visit notes, checking recent labs, scanning the problem list, and looking through active medications. The goal is to walk into the encounter fully informed, understanding what's changed, what needs follow-up, and what the priorities are for this patient.

Done well, pre-charting enables better care. A physician who's done their homework before stepping into the room can be more present with the patient, ask more targeted questions, and make more confident clinical decisions. The preparation is the foundation for everything that follows.

The problem is that it takes time. A lot of it.

In a typical outpatient setting, pre-charting for even simple patient encounters can take 10 minutes or more. For a panel of 20 or 30 patients in a day, those minutes can compound into hours. In more complex care settings (oncology, for instance), pre-charting a single patient can take up to 30 minutes! Patients may have long and complex histories, extensive labs, imaging across multiple time points, and notes from multiple specialists. Getting up to speed on where a patient stands before a visit requires synthesizing an enormous amount of information scattered across a chart.

And just like documentation at the end of the day, pre-charting often spills outside of clinical hours. Physicians are doing it at home the night before and reviewing charts in the early morning before their first patient. The same "pajama time" problem that ambient scribing addresses on the documentation side exists on the pre-charting side too, it's just less visible, and less discussed.

The Limits of the EHR for Pre-Charting

Part of what makes pre-charting so time-consuming is the nature of the tools clinicians are using to do it.

Electronic health records were designed primarily as documentation and data storage systems. They were not built to synthesize clinical information on demand. A provider trying to quickly understand a patient's current clinical status has to navigate across multiple sections (notes, labs, imaging, problem lists, medications) and do the synthesis themselves. The EHR surfaces the data. The clinician does all the interpretive work.

This is a structurally inefficient workflow. It puts the burden of information aggregation on the clinician, whose strength is clinical judgment, not data retrieval. It's the same fundamental problem that makes documentation itself so burdensome: technology that forces providers to operate at the bottom of their license rather than the top.

What a Pre-Visit Summary Actually Does

A pre-visit summary is automated pre-charting. Rather than requiring a provider to navigate through a chart and assemble the clinical picture manually, the technology pulls together the relevant information and surfaces it in a structured, readable format.

Commure’s pre-visit summary functionality is made possible by our Patient360 platform, the clinical data foundation that makes AI useful at the point of care. Patient360 unifies a patient's full EHR history into a structured, queryable data object, surfacing specialty-tailored information, source-cited AI Q&A, and longitudinal patient context at the moment it matters most.

At its best, a pre-visit summary gives a provider (before they ever enter the room) a coherent view of:

• Patient at a glance: why this patient is being seen, what makes them clinically distinctive, what the key active issues are
• Last visit summary: what happened at the previous encounter, what was assessed and planned, what follow-up was ordered
• Today's focus: relevant recent developments, outstanding labs or results, issues flagged for this visit
• Active diagnoses and medications: current problem list and medication list in a reviewable format, including when medications were started
• Social history and relevant context: information that shapes clinical decision-making but often takes time to locate in a standard chart view

This is the information a clinician is hunting for during pre-charting. A pre-visit summary delivers it in one place, automatically, before the visit begins.

The Hallucination Problem and Why It Has to Be Solved

Any clinician hearing about AI-generated clinical summaries has an immediate and legitimate concern: what if the system hallucinates a part of the history?

In clinical medicine, a hallucinated finding or fabricated lab result is a patient safety and medicolegal issue. A provider who sees inaccurate information in a pre-visit summary and relies on it in clinical decision-making is in a worse position than a provider who had to do pre-charting manually.

This is why any serious pre-visit summary technology has to solve the attribution problem, not just the synthesis problem. It's not enough to generate a summary that reads well. Every clinical claim in that summary needs to be traceable to a specific source in the patient's chart so the provider can verify the information, understand its provenance, and calibrate their confidence accordingly.

Expandable citations that link directly to the source documentation (the specific note, the specific lab result, and the specific imaging report) are what make pre-visit summaries trustworthy. The AI does the synthesis. The clinician retains the ability to verify.

Turning Pre-Visit Summaries Into Clinical Intelligence

For patients with complex, longitudinal histories (chronic illness, oncology, or multi-system disease to name a few), even a well-constructed summary may leave questions unanswered. A provider might want to know what happened to a specific lab value over the past year, or which specialist last addressed a particular issue, or whether a medication change in the last visit was associated with any documented side effects.

When a pre-visit summary is built on top of a comprehensive patient data framework, providers can ask those questions directly. Natural language queries against the patient's chart, surfacing abnormal labs since the last visit, flagging documented changes in condition, and answering specific clinical questions. All of this becomes possible without leaving the workflow.

This is a meaningful shift in how providers interact with clinical information. Instead of navigating a chart and hoping they don't miss something, they can ask specific questions and get specific answers, with the underlying documentation available for review.

Connecting Pre-Visit Preparation to Documentation

Pre-visit summaries and ambient documentation aren't separate products with separate value propositions. They're two components of a continuous clinical workflow, one that starts before the patient arrives and ends with a complete note after they leave.

A provider who has reviewed an AI-generated pre-visit summary before the encounter walks in prepared, already thinking about the clinical priorities for this visit. When the encounter begins, and ambient documentation starts capturing the conversation, the provider is already operating at a higher level, asking better questions, engaging more directly with the patient's concerns, and making more informed decisions in real time.

The downstream effect is documentation quality. The clinical picture that the ambient AI captures is richer because the encounter itself was richer. Diagnoses are more specific. The assessment and plan reflect more precise clinical reasoning. Coding suggestions that emerge from the documentation are more accurate because the documentation more faithfully reflects the complexity of the care that was actually delivered.

What This Means for Providers

The conversation about AI in healthcare has spent a lot of time on what AI can do after the clinical encounter (documentation, coding, billing). But it represents only part of the opportunity.

The administrative and cognitive burden on physicians doesn't begin when the visit ends. It begins when the workday starts. Pre-charting is where providers spend time that they can't easily account for, that doesn't show up in RVU counts, and that no one has historically built technology to address.

Pre-visit summaries change that. They return time to providers at the front of the clinical day, not just the back. They reduce the cognitive load that accumulates before a provider has even greeted their first patient. And they create the conditions for a better clinical encounter, which is ultimately what both providers and patients are there for.

The technology to automate this work exists now. Providers who are already experiencing two hours of pajama time at the end of the day shouldn't also be spending significant time on manual pre-charting at the start of it. That's a problem with a solution, and AI is ready to deliver it.
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Interested in learning more about how Commure's Patient360 platform and pre-visit summaries can work for your organization? Contact Dr. Jean-Luc Neptune, Clinical Commercial Leader at Commure, at jeanluc.neptune@commure.com.

Most revenue cycle conversations start in claims and billing. The money usually goes missing much earlier than that.

The Healthcare Financial Management Association (HFMA) estimates that as much as 1% of a health system's net patient revenue is lost to charge capture leakage. On a $500 million system, that's roughly $5 million a year in care that was delivered but never billed.

What makes charge capture errors so dangerous is that they never show up as a denial. There's no rejection to appeal, no error message, no line on a dashboard. The charge simply never gets created, and by the time anyone notices, the timely filing windows have closed.

So before you tune your denials process or renegotiate a payer contract, look at the part of the revenue cycle where the leakage actually starts.

What charge capture is

Charge capture is the process of recording every billable service, supply, and procedure from a patient encounter so it can be coded and billed.

It happens at or right after the point of care, during charting, or immediately after the patient leaves. It's the handoff between the care a clinician delivers and the claim your billing team builds.

Two distinctions matter. Charge capture and coding are separate steps: coding translates the captured service into CPT and ICD-10 codes after the charge exists. Charge capture also depends on clinical documentation. If a service isn't documented, it usually won't be charged, meaning it won't be coded, billed, or paid.

Why revenue leaks at the charge

Charge capture breaks in quiet ways.

A clinician finishes a procedure and moves to the next patient before the charge is entered. An incision is made and an implant is placed in the OR, but no corresponding charge is recorded. An E&M visit gets documented at a lower complexity than the care actually delivered. Supplies used at the bedside never make it onto the bill.

Each miss is small. Across thousands of encounters a month, the small misses compound into real money.

Charge lag adds to the problem. The longer the gap between the service and the charge, the more detail gets forgotten, and the closer the claim drifts toward its filing deadline. Chargemaster mismatches make it worse: clinical practice changes faster than the codes and pricing logic behind it, so the charge that does get recorded may not reflect the full value of what was delivered.

The root cause is usually a manual process. When charge entry depends on memory, spreadsheets, and retrospective chart review, coverage is thin and slow. Most manual reconciliation touches a fraction of charts, and it happens after claims have already gone out.

To make things worse, these missed charges are permanent. You can rework a denied claim. You cannot bill for a service you never recorded once the filing deadline passes.

Hospital charge capture is harder at scale

The bigger the organization, the more places a charge can fall through.

Hospitals bill both facility and professional fees, and the two don't always line up. High-volume, high-acuity settings like the emergency department make real-time capture difficult because clinicians are focused on patients, not charge tickets.

Procedure-heavy specialties carry the most risk. Cardiology, orthopedics, radiology, and surgical services all involve dense documentation and high-dollar items that are easy to miss or undercount.

When charge capture depends on people remembering to act, every open seat on a revenue cycle team is a gap in coverage.

Charge capture in the revenue cycle

Charge capture sits upstream of everything else in revenue cycle management.

Eligibility, coding, claim submission, denials, and posting all depend on a charge existing in the first place. A clean claims rate can look healthy right up until you realize it can't recover a charge that was never created.

This is why charge capture deserves attention as a strategic control. It's the first point where clinical activity turns into revenue, and the one point where a loss can't be recovered.

How AI is changing charge capture software

For years, charge capture software meant a mobile app that let clinicians enter charges faster. Useful, but still dependent on a human remembering to enter the charge.

AI changes the model. Rather than waiting for someone to log a charge, the system reads the clinical note, compares what was documented against what was billed, and flags services that were documented but never charged.

This is the difference between charge capture and charge note reconciliation. Charge capture depends on someone remembering to act. Charge note reconciliation runs automatically on every encounter, whether or not anyone did.

That shift in coverage is the whole point. Manual review samples a slice of charts after the fact; automated reconciliation reads all of them before the claim goes out, which is when a missed charge can still be fixed.

Pair that with autonomous coding, which generates CPT and ICD-10 codes directly from the documentation, and the path from encounter to clean claim gets shorter and more accurate. The same approach turns charge capture solutions from a faster way to type into a safety net that runs automatically.

What this looks like in practice

The proof is in what happens when health systems close the gap.

HFMA documented Novant Health, a 14-hospital system with $4.3 billion in net patient revenue, that uncovered $7.5 million in recoverable revenue within 15 months of overhauling its charge capture process. That was revenue from services that had been delivered, documented, and then missed.

Commure built charge capture and charge note reconciliation into Commure Pro, the clinical intelligence platform clinicians use at the point of care, and connected it to the broader RCM platform so a captured charge flows straight toward the claim.

One New York health system deployed it and saw a 20% increase in monthly charges with late-filing denials cut in half, driven by charge note reconciliation paired with autonomous coding.

Getting charge capture right

Charge capture is the front end of getting paid for care you've already delivered.

When it runs on memory and retrospective review, revenue leaks quietly and permanently. When the clinical note, the charge, and the code connect automatically, the leak closes and the revenue shows up where it should.

If you're rethinking where your revenue cycle loses money, start at the charge. It's the cheapest dollar to recover, because it's a dollar you already earned.

See how Commure connects charge capture to the rest of the revenue cycle.

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Tell us a little bit about yourself—what do you like to do outside of work?

Outside of work, I spend most of my time with my family. I also love trying new restaurants and finding great local spots. When I finally get some downtime, you'll usually find me watching reality TV, which I consider an unofficial study in human behavior.

As a kid, what did you want to be when you grew up?

My dream was to play in the WNBA. Becoming an Operations Leader at a healthcare technology company was a close second. Unfortunately, I lacked both the height and basketball skills to make the first one happen.

Describe a day in the life of your role.

No two days are the same. My role is a mix of solving operational issues, improving processes, supporting clients, and partnering with teams across the organization to keep things moving. A typical day might include troubleshooting escalations, digging into data to identify trends, aligning with cross-functional teams on priorities, and removing blockers for both internal teams and clients. I usually start the day with a plan, but things shift quickly, so staying flexible and focused on impact is a big part of the job.

What made you decide to join Commure?

I was introduced to Commure by a former manager I had worked with previously. I respected their judgment and was intrigued by the opportunity to work on meaningful, complex problems in healthcare. When the role came up, it felt like the right next step. Two and a half years later, we're both still here, which says a lot about the people and the work.

How would you describe the Commure company culture?

.Gritty, driven, and fast-moving. People here care deeply about the mission and are willing to do what it takes to solve hard, often ambiguous problems. There is a strong bias toward action and ownership, and expectations are high in a good way. I've never worked with a group of people more committed to actually getting to the right outcome, not just talking about it.

What advice would you give someone on their first day at Commure?

Trust that you know more than you think you do. The learning curve can be steep, but one day you'll realize that what once felt chaotic now feels routine, and you'll recognize the role you played in making it better.

What has been your greatest accomplishment so far at Commure?

While I am proud of the operational improvements and results we have achieved, my greatest accomplishment has been the relationships I have built with my clients. Many of them have been with me since my very first day at Commure, and we have grown together through challenges, successes, and significant change. Looking back on how far we have come as partners, and knowing the trust we have built along the way, has been one of the most rewarding parts of my journey here. In many ways, their continued partnership is the accomplishment I am most proud of.
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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.

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Patients now shop for care the way they shop for everything else. They compare, they read reviews, and they leave when the experience frustrates them. A 2024 Deloitte survey of US health care consumers found that about 24% are willing to switch doctors just to get access to virtual care options.

That pressure has a name in healthcare strategy: the digital front door. And the market behind it is growing fast. Mordor Intelligence projects the digital front door market will expand from about $31.66 billion in 2026 to $82.25 billion by 2031.

For health system leaders, the question is how to build a digital front door that works. This guide covers what a digital front door is, the four components that make it up, why your patients expect one, and how to evaluate a platform.

What is a digital front door in healthcare?

A digital front door is the set of connected digital tools a health system uses to guide patients from their first interaction through scheduling, intake, communication, and ongoing care. It gives patients one consistent way to find care, book it, prepare for it, and stay connected after.

Think of it as the digital equivalent of walking up to the front desk, except it spans every step before, during, and after a visit. A patient might search for a provider, book online, complete intake on their phone, get reminders by text, and message the care team once they're home.

Done well, a digital front door connects these moments so patients don't have to repeat themselves or chase down information.

The four components of a digital front door

A digital front door is made up of four capabilities that work together. When health systems buy them as separate point solutions, they tend to create friction rather than remove it.

Patient scheduling and access

This is the most visible component, and often the first thing patients judge you on. Online self-scheduling lets patients book, reschedule, and cancel without calling a line that may sit on hold for several minutes. The advent of AI voice agents is also helping to ease the load on hospital staff while giving patients who prefer to speak to somebody an avenue to schedule care.

Scheduling sets the tone. A patient who can book in 90 seconds at 11 p.m. has a very different first impression than one stuck in a phone tree.

Patient intake

Once an appointment is booked, intake collects the information clinicians need before the patient arrives. Digital intake moves registration, insurance details, and clinical history off the clipboard and onto the patient's phone.

This is one of the highest-ROI components, because it cuts front-desk data entry and shortens waiting room time. We covered the shift in depth in our look at how AI is replacing the clipboard at patient intake.

Patient communication

Communication runs across the entire journey: appointment reminders, prep instructions, billing notices, and two-way messaging with the care team. Patients expect this on the channels they already use, mostly text and email.

Omnichannel, HIPAA-compliant messaging keeps patients informed and cuts inbound call volume.

Care navigation

Care navigation guides patients to the right next step, whether that's a specialist referral, a follow-up, or a post-discharge check-in. This is where a digital front door earns its keep over time.

Automated care pathways and reminders keep patients on track between visits, which is where most no-shows and care gaps happen. Our piece on AI-powered care journeys and no-show reduction walks through how this works in practice.

Why health systems need a digital front door

Three forces are pushing this from nice-to-have to expected.

First, patient expectations. Banking and retail have trained patients to expect 24/7 self-service, and they apply that standard to healthcare. Not to mention, when a patient is seeking care, they are likely in a vulnerable position and need help right away.

Second, competition for patients. Retail clinics, virtual-first providers, and large systems with mature digital tools are all chasing the same patients. A clunky access experience sends them elsewhere.

Third, operational pressure. Staffing shortages and rising costs mean phone and paper-based access doesn't scale. Digital tools (such as AI call center agents) absorb routine work so staff can focus on patients who need a human.

No-shows, abandoned appointment requests, and leakage to competitors all cost revenue. A working digital front door recovers some of that by making it easy for patients to start and stay in care with you.

How to evaluate a digital front door platform

Once you decide to invest, the build-versus-buy and single-platform-versus-point-solution questions matter more than any feature checklist. Four criteria separate platforms that work from those that create new silos.

EHR integration. The platform has to write back to your system of record. If scheduling, intake, and messaging data don't flow into the EHR, you've added work instead of removing it.

A unified platform. Patients feel it when scheduling, intake, and communication come from three different vendors. One platform covering all four components gives patients a consistent experience and gives you one place to manage it.

Omnichannel, compliant communication. Look for SMS, voice, and email in one place, with HIPAA-compliant messaging built in rather than bolted on.

Measurable outcomes. Ask vendors for proof: no-show reduction, response rates, staff time saved. Yale New Haven Health, for example, cut no-show and same-day cancellation rates by 54% in a breast imaging program built on this kind of outreach.

Frequently asked questions

Is a digital front door the same as a patient portal?

A patient portal is one piece of a digital front door, usually focused on records and messaging for patients who are already established. A digital front door is broader. It covers how new and returning patients find and book care in the first place.

What's the first component to prioritize?

Most systems start with scheduling and intake, since those touch every patient and show fast results in reduced phone volume and front-desk work. Communication and care navigation build on that foundation.

Who should own the digital front door in a health system?

Ownership usually sits across several teams, which is part of why these projects stall. Marketing cares about patient acquisition, operations cares about access and staffing, and IT owns the EHR. The systems that succeed name one owner or steering group with authority across all three, so the experience doesn't fragment by department.

Building your digital front door

Patients form an opinion about your health system before they ever meet a clinician. The digital front door is where that opinion starts, and it runs through every step that follows: how they book, how they check in, how they hear from you, and how they find their way to the next visit.

Commure Engage brings scheduling, intake, communication, and care navigation into one platform connected to your EHR, so patients get one front door instead of four. See how it fits your access strategy.

Explore Commure Engage

Every clinician knows the feeling. Packed schedule, patient about to walk in, and you're scrambling through charts trying to piece together context. All of this quietly contributes to burnout before the day has even started.

That pre-visit scramble is exactly what I set out to solve.

Patient 360 and the Pre-Visit Summary

At Commure, we've been building toward Patient 360 (a a knowledge engine  that unifies all of a patient's data into a single clinical picture: active problems, current treatments, care history, and open follow-ups), and surfaces them at the moment they matter most.

Pre-Visit Summary is the first major feature built on top of Patient 360. A few weeks ago, we released it to over a thousand providers across multiple specialties. The response has been overwhelming.

A primary care physician put it simply: "Patient 360 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."

And from an orthopedic surgeon, right before a busy clinic: "It gave me exactly the information I needed and saved me several minutes of going into the charts. Providers who normally wouldn't use AI tools are now gravitating towards this."

Built by Clinicians, for Clinicians

Leaning on my experience as a physician, I led the clinical strategy for this project.

Drawing on my own clinical judgment, I consulted physicians across specialties, validated the design against real workflows, and iterated with our Commure  Clinical Council, Commure’s internal body of clinicians. All of this reinforced the central principle of our work: understanding a patient's key care considerations is foundational to establishing a therapeutic alliance. That's why the summary surfaces not only information about labs, medications, and other aspects of the current treatment, but also interactions with other providers and other significant life contexts. A provider walking into a room should know what else is going on in that patient's world.

Our goal for providers was simple: be able to glance through patient information in two to three minutes and walk in fully prepared, including what's still open, what needs follow-up, and where there are gaps that need reconciliation.

A Good AI Product Rests on a Good Data Strategy

We believe clinicians should drive data strategy because they know which information matters most and where it comes from. So physicians and clinicians at Commure led the work of defining a data hierarchy framework to determine what gets surfaced, in what order, and why.

That hierarchy had to solve for three things simultaneously: relevance (what an orthopedic surgeon needs differs from what a cardiologist needs), recency (information had to reflect the patient's current state), and longitudinal coherence (surfacing how a condition has evolved over time, not just a snapshot).

‍This wasn't a technical exercise with clinical input bolted on at the end. It was clinical thinking embedded into the architecture of the product itself.

This Is What Clinician-Led AI Looks Like

There's a version of AI in healthcare that automates tasks. And then there's a version that understands clinical workflow, anticipates what a provider needs, and surfaces it at exactly the right moment.

Pre-Visit Summary is the latter. But beyond reducing burden, what excites me about this project is its potential for patient impact. When providers are well-prepared, continuity of care improves. Nothing falls through the cracks and they can deliver better care while taking better care of themselves.

Anthropic released its first generally available Mythos-class model, Fable 5, a new tier of AI that sits above every previous frontier model. For the first time, the most advanced and capable class of AI models ever built was designed with clinical and operational healthcare work in mind.

Commure has done the security, compliance, and architecture work to deploy Mythos-class models within our healthcare-grade environment, and we now have the ability to apply them across the Commure platform.

What this class of model does for healthcare workflows

The defining trait of Mythos-class AI models is endurance. They sustain long, complex, tasks far beyond what previous generations could hold together, and they are measurably more reliable across the multi-step workflows healthcare operations run on. 

Voice and call center agents. A patient call is a long-horizon reasoning problem: understand an unwell or frustrated caller, identify intent, navigate scheduling within the EHR, recover when the conversation jumps tracks, and know when a human should take over. Each underlying model generation improves the weakest link in that chain, which is sustained multi-turn reasoning under ambiguity. Mythos-class models are the largest single improvement to that link yet.

‍Patient intake. Intake agents gather history, reconcile it against the chart, follow clinically validated pathways, and hand the clinician a summary worth reading. High-fidelity synthesis of patient records and care histories is precisely where this model class excels, and the longer and messier the intake, the bigger its advantage.

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‍Autonomous coding and revenue cycle. Producing correct CPT, ICD-10, and modifiers from documentation is dense, rules-heavy reasoning where small errors compound into denials. The frontier labs now consider this work important enough to benchmark: the newest evaluations in the model's own system card include prior authorization, denials and appeals, and DME orders, executed across simulated EHR and payer-portal environments. Cleaner coding upstream means fewer denials downstream and lower cost-to-collect.

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Measured on healthcare work

Mythos-class models are state-of-the-art on nearly all tested benchmarks of AI capability, spanning software engineering, knowledge work, vision, and scientific research. Healthcare has rigorous benchmarks of its own, and the new model class was evaluated on them directly.

HealthBench Professional tests real provider workflows. It is built from 525 conversations authored by practicing physicians, spanning clinical consults, documentation, and research tasks, each graded against physician-written criteria. A model's score reflects whether its clinical reasoning and documentation would hold up in front of the clinician who wrote the task.

HealthBench tests how a model handles patients. It grades 5,000 multi-turn patient conversations across 26 medical specialties against more than 48,000 expert-written rubric items, measuring safety, accuracy, and communication in realistic healthcare contexts. This is the benchmark closest to what a patient-facing agent does all day.

In physician-authored clinical work, Fable 5 Mythos-class scores nine points above the previous flagship (Opus 4.8) and fourteen above GPT-5.5. 

The Next Frontier of Agentic AI for Healthcare

These gains represent a fundamental shift in what agents can handle on their own, enabling more complex, multistep use cases that previously required human intervention at every turn. With Commure's deep healthcare expertise, data integration, and infrastructure, we can deploy these models in clinical settings at an even higher level of performance.

Apply the latest frontier models today

Mythos-class capability is now part of the Commure platform. If you are a current customer, you are already set as you keep the same deployment, workflows, and integrations. If you are not yet a customer, this is a good week to see what an LLM-native platform can do. 

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