Commure Up Close: Brad Forges Partnerships That Drive Growth

In healthcare, what gets documented gets paid for. What doesn't, doesn't.

That simple equation is why clinical documentation improvement (CDI, also known as clinical documentation integrity) has become one of the most consequential disciplines inside modern health systems. CDI sits at the intersection of clinical care, coding accuracy, regulatory compliance, and revenue. When it works, hospitals get paid appropriately for the care they deliver. Quality scores reflect reality. Clinicians spend less time chasing queries.

When it doesn't, the costs compound, resulting in denied claims, underpayments, distorted quality metrics, and frustrated providers.

The discipline isn't new. But the way leading health systems approach it is changing fast. The traditional CDI playbook was built around manual chart reviews and retrospective physician queries, designed for an era of paper records and fee-for-service billing. AI is rewriting that playbook.

Here's what CDI is, why it matters more than ever, and how AI is changing the way health systems approach documentation.

What is clinical documentation improvement?

Clinical documentation improvement is the practice of making sure a patient's medical record accurately reflects their clinical condition, the care they received, and the medical decision-making behind it. Done well, CDI bridges the gap between clinical reality and what eventually shows up on a claim form.

A traditional CDI program runs on certified specialists, usually nurses or coders with additional credentials. They review patient charts, identify gaps or ambiguities, and submit queries to physicians for clarification. They work alongside medical coders to make sure diagnoses are documented with the specificity required for accurate ICD-10 and CPT coding, that complications and comorbidities (CCs and MCCs) are captured, and that the final claim reflects the true acuity of the patient.

AHIMA's guidance for CDI programs frames the discipline as foundational to accurate reporting under CMS quality measures, value-based purchasing, and DRG-based payment.

Documentation determines whether a chart reflects the full acuity of a patient, and that distinction often drives thousands of dollars per case in reimbursement. A patient documented with a major complication or comorbidity (MCC) can generate more than double the payment of an otherwise identical chart without one. The clinical reality is the same. The documentation is what changes the number.

A peer-reviewed study in the Journal of Vascular Surgery found that a physician-led CDI initiative significantly increased the case mix index and contribution margin. That finding has been replicated across specialties and care settings.

This is also why CDI is foundational to revenue cycle management. Coding, claim submission, denials prevention, reimbursement: they all depend on documentation that meets payer and regulatory standards, with CDI sitting upstream of all of it.

Why clinical documentation improvement matters

CDI isn't a back-office function. Its impact shows up in three places every health system executive cares about: revenue, quality, and clinician experience.

Financial impact. Strong CDI programs have a direct line to the bottom line. AHIMA's Clinical Documentation Improvement Toolkit identifies CDI as one of the most effective levers a hospital has for reducing denials, capturing severity of illness and risk of mortality, and securing appropriate reimbursement. Health systems using modern CDI tooling embedded in EHR workflows have seen revenue gains of up to 3% when documentation gaps are flagged in real time. For an enterprise health system, 3 points of net patient revenue could be the difference between hitting and missing a year.

Clinical quality and risk adjustment. Documentation determines how the outside world sees a hospital's quality. Mortality indices, publicly reported outcomes, case mix index (CMI), and risk-adjusted payment under value-based contracts all flow from what gets documented. CMS continues to expand value-based care arrangements, which makes precise documentation a revenue, quality, and contractual question all at once.

Regulatory complexity. The documentation bar keeps rising. The WHO's ICD-11 update introduced roughly 17,000 new diagnostic categories, and the 2025 Medicare Physician Fee Schedule ties more of reimbursement to documentation precision. The global CDI market is projected to grow from $4.88 billion in 2024 to $10.44 billion by 2034, a clear signal that health systems recognize the cost of getting documentation wrong.

The pressure is increasing right when CDI capacity is shrinking. Two-thirds of HIM leaders cite staffing shortfalls that directly impact revenue integrity, and the workforce of qualified CDI specialists isn't expanding fast enough to close the gap.

The problem with the traditional CDI model

The old CDI playbook is running out of road for one fundamental reason. It relies on humans reviewing charts after the fact, and there aren't enough humans to do it.

Clinicians are already drowning in documentation. Research from Annals of Family Medicine and cited by the AMA shows that physicians spend 5.9 hours of an 11.4-hour workday in the EHR. An AJMC analysis found 76.1% of office-based physicians spend more than an hour per day documenting outside clinic hours, averaging nearly 2 hours of "pajama time" daily. The AMA identifies clinical documentation as one of the leading contributors to physician burnout.

Layering more retrospective queries onto already-burned-out physicians is a tax. And it's a tax that traditional CDI programs depend on to function.

How AI is reshaping clinical documentation improvement

The real shift in CDI isn't faster query workflows or better dashboards. It's that AI moves documentation improvement from a retrospective discipline to a real-time one.

Ambient AI captures the clinical picture as it happens. A clinician used to dictate a stripped-down note after the visit, then a CDI specialist would query for missing details days later. With ambient AI listening to the encounter, structuring the note in real time, and surfacing CareCues during the visit itself, the documentation gap closes before it opens.

AI-assisted CDI expands specialist capacity. According to MedLearn Publishing's analysis of AI in clinical documentation integrity, CDI specialists augmented by intelligent systems can manage 35–45% larger chart volumes, which lets organizations expand CDI coverage without proportional staffing increases. The same analysis reports 12–18% denial reductions for complex inpatient stays and 20–25% reductions on prior authorization submissions when AI-assisted CDI is deployed at scale.

Autonomous coding closes the loop from documentation to claim. When documentation is captured cleanly in real time, AI can generate accurate CPT codes, ICD-10 diagnoses, and modifiers directly from the clinical note. In a deployment with Ob Hospitalist Group, Commure's Autonomous Coding generated 85% of CPT and ICD-10 codes and reduced charge entry time by 83%. At a leading NYC health system, the combination of ambient documentation, charge note reconciliation, and autonomous coding drove a 20% increase in monthly charges and cut late-filing denials in half.

These gains reflect a different operating model for CDI, one where documentation improvement is built into the workflow itself instead of bolted on afterward.

What to look for in a modern CDI approach

For health system leaders evaluating where CDI is headed, four questions matter:

1. Is documentation improved in real time or retrospectively? Real-time intervention prevents errors. Retrospective intervention chases them.
2. Is the system EHR-integrated or bolted on? CDI that requires clinicians to switch contexts or duplicate work won't get adopted.
3. Does it connect documentation to coding to claims? The biggest gains come from treating the documentation-to-reimbursement pipeline as one continuous workflow instead of three disconnected ones.
4. Is it built for enterprise scale? Pilots are easy. Sustained CDI improvement across hundreds of providers and dozens of specialties is the real test.

The health systems pulling ahead on documentation today are rebuilding CDI around AI that works at the point of care and connects clinical documentation directly to the revenue cycle

The U.S. healthcare industry could save $20 billion a year by replacing manual administrative workflows with electronic ones, according to the latest CAQH Index. The same shift saves 70 minutes per patient visit.

That's the cost of the clipboard era. And for the first time, the technology to end it actually exists.

For two decades, "patient intake software" meant the same thing: take the paper forms, put them on a tablet. The clipboard got a screen, but patients still answered the same questions. Staff still re-keyed the same data, but eligibility still got checked somewhere downstream, usually by phone.

The clipboard didn't go away. It just got a software wrapper.

AI changes the assumption underneath all of it by replacing the workflows the form was built to support.

The clipboard era costs more than most health systems realize

Patient intake is one of the most measured workflows in healthcare and one of the least scrutinized. Most enterprise health systems track no-show rates, registration time, and front-end denials as separate metrics, but they rarely add them up.

When they do, the picture is consistent. The manual pre-visit experience bleeds revenue, staff hours, and patient trust at every step.

CAQH's 70-minutes-per-visit figure is the cumulative cost of staff calling patients to confirm appointments, re-verifying insurance by phone, fielding follow-up questions about pre-visit prep, and re-entering the same data into multiple systems. Multiply that across a health system's daily patient volume, and those minutes turn into a full-time staffing burden that grows with every new clinic and every new payer contract.

The patient side is worse. Patients show up to fill out the same forms they already completed online. They wait while staff verifies what should have been verified the week before. They leave with the impression that the most technologically advanced industry in the world can't remember their address from one visit to the next.

This is the gap modern patient intake software has to close.

What AI patient intake actually does

The shift is a redefinition of what patient intake software is responsible for.

In the clipboard era, intake software's job was to collect information at the front desk. In the AI era, the job is to manage the entire pre-visit experience. From the moment an appointment is scheduled to when the patient walks in already verified, prepared, and registered.

That broader definition matters because it changes what health systems are actually buying. A traditional intake product replaces the form. An AI-powered patient engagement platform replaces the workflow. The first reduces clicks. The second reduces phone calls, no-shows, denied claims, and the staff hours required to chase each one of them down.

In practice, AI-driven patient intake covers four overlapping capabilities:

• Automated, conversational outreach that confirms the appointment, gathers pre-visit information, and answers patient questions in plain language over SMS or voice. No portal, no app, no login.
  
  
• Intelligent referral intake that captures and tracks incoming referrals so patients don't fall through the cracks between schedule and visit.
  
  
• Personalized pre-procedure preparation that delivers the right instructions at the right time and flags patients who haven't completed them.
  
  
• Bi-directional messaging with intelligent triage that escalates urgent issues to a human and resolves the routine ones automatically.

Each of these used to be a separate vendor conversation. Now they're table stakes for a single platform.

Why the AI layer changes the economics

The most underappreciated thing about AI-driven patient intake is that in addition to reducing manual work, it changes which work is worth doing at all.

When pre-visit confirmation, prep, and information capture happen automatically through conversational AI, the marginal cost of engaging each patient drops close to zero. That unlocks workflows that health systems couldn't justify before. Multi-touchpoint reminders for complex procedures. Proactive outreach that meaningfully reduces no-shows. Bowel prep follow-ups for colonoscopies. Pre-op education for orthopedic patients. Behavioral health check-ins before the first visit.

The AI layer also reaches places the clipboard never could. Voice-based agents can capture patient information during inbound and outbound calls (which is still the channel where most patients engage with their health system). The same AI that confirms an appointment can spot a patient who needs to reschedule, triage a clinical question, or hand off to a human when escalation is warranted. Modern call center automation extends intake into a 24/7 capability.

The financial picture changes with it. The clipboard era measured intake as a cost center. The AI era measures it as a lever for access, throughput, denial prevention, and retention. The systems already operating this way are seeing the math play out. At Yale New Haven Health, pre-appointment AI texting drove a 54% reduction in no-show and cancellation rates for breast cancer screening alone.

What enterprise buyers should look for in 2026

The patient intake software market is in the middle of a category shift. The products that win the next decade won't be the products that won the last one.

A few principles separate the AI-era platforms from the legacy ones.

• Platform breadth over form depth. The right question is no longer how good the digital forms are. It's how much of the pre-visit experience the platform actually owns. A vendor that handles intake but not referral management, or messaging but not scheduling coordination, is selling only a piece of the workflow when health systems need the whole thing.
  
  
• Retrieval-based AI, not generative guessing. In healthcare, an AI that hallucinates a medication or a pre-op instruction is a safety event. Enterprise-grade patient intake AI has to be grounded in clinically validated content and deterministic logic.
  
  
• Deep EHR integration as table stakes. Patient data that enters the platform should land in the EHR in the right fields, without staff re-keying. Bi-directional integration with the major EHRs separates platforms that look modern from platforms that actually reduce work.
  
  
• Omnichannel by patient preference. SMS, RCS, voice, and email. Meeting patients on the channel they actually use, not the channel the vendor built first.

Health systems making this transition are replacing the entire pre-visit stack with one platform built for the AI era.

How Commure Engage approaches patient intake

Commure Engage is built for the shift from clipboard to AI. It combines automated patient outreach, conversational AI, referral and intake management, and personalized care pathways into one platform that owns the pre-visit experience end to end.

Engage uses retrieval-based AI grounded in over 500 clinically validated care pathways, integrates bi-directionally with all major EHRs, and reaches patients through SMS, RCS, voice, and email without requiring an app or portal login. Health systems using Engage resolve 80%+ of patient inquiries automatically through conversational workflows, increase outreach capacity by 6.5x, and cut no-shows by 54%.

The clipboard era is ending. The platforms replacing it are already in production.

See how Commure Engage is rebuilding patient intake for the AI era

We're excited to announce that Commure Ambient AI offers complete, end-to-end integration with Cerner, bringing ambient documentation directly into the workflows clinicians already rely on, with no context switching, no copy-paste, and no duplicate charting.

Documentation that meets clinicians where they work

Clinicians have spent too long absorbing the cost of fragmented documentation tools, toggling between systems, retyping notes, and losing precious face time with patients to administrative work. Commure Ambient closes that gap inside Cerner.

With this integration, patient context flows automatically from Cerner into Commure Ambient, and finished notes sync back exactly where they belong (into DynDoc components or PowerNote). Clinicians can then review, finalize, and sign directly in PowerChart.

A connected workflow, from waiting room to signed note

The integration is built around the way clinicians actually work:

• Select Patient — Choose from ambulatory appointments, the patient list, or walk-ins.
• Pre-Chart — View patient context and pre-visit summaries inside a DynDoc template or the Commure app.
• Record Visit — Ambient capture is tied to the specific encounter, with real-time transcription.
• Review Note — AI-generated, structured notes ready for provider review and edits.
• EHR Sync — Draft sections flow automatically into DynDoc components or PowerNote.
• Sign in Cerner — Finalize and sign directly in PowerChart, with no duplicate entry.

Personalized for every clinician, optimized for every care setting

Commure Ambient adapts to how each provider documents, at both the speciality and individual provider level. Teams can configure output by care setting, save edited notes as reusable templates, and control how prior notes carry forward across orthopaedics, psychiatry, internal medicine, GI, OB/GYN, cardiology, and more.

Whether you're documenting in inpatient and hospitalist settings, the emergency department, or ambulatory, Commure Ambient is purpose-built for the full hospital environment.

Trusted at scale

Commure Ambient is already in use at more than 2,000 sites of care and 135 of the largest health systems in the country, including HCA and Tenet. This Cerner integration extends that reach, bringing complete ambient AI documentation to even more clinicians, with the depth of integration that real clinical workflows demand.

Ready to see Commure Ambient in your Cerner environment?

Ask any nurse what they spend their shift doing, and documentation will land somewhere near the top of the list. A 2025 study published in JMIR Nursing found that nurses spend an average of 31% of a 12-hour shift documenting in flowsheets alone, and that's before accounting for narrative notes, handoffs, or the charting that spills over after a shift ends.

That number tells us something important: nursing documentation isn't a smaller version of physician documentation. It's a fundamentally different workflow challenge. And as ambient AI adoption moves from providers to nurses, the vendors, health systems, and clinical leaders who recognize that difference are the ones who will thrive.

We sat down with Kelum Fecht, Enterprise Sales Director at Commure and a nurse by training, to talk about what makes nursing documentation distinct, where ambient AI can move the needle, and what it actually takes to build a solution that works.

Documentation doesn't clock in and out

When a physician sees a patient, the documentation work is concentrated around a 20- to 30-minute encounter with some pre- and post-charting on either side. Nursing doesn't work that way.

"In a 12-hour shift, that burden is there constantly," Kelum told us. A nurse might be caring for up to nine patients at once, returning to each one repeatedly, fielding interruptions, and capturing information across hundreds of small interactions. The frequency is higher, the conversations are more varied, and the documentation lives in flowsheets, which Kelum describes as "an Excel spreadsheet with dropdowns, radial buttons, and free text", rather than narrative notes.

That structural difference is why bolting nursing onto an existing physician ambient AI tool doesn't work. And the cost of getting it wrong shows up in places that don't always make it into the ROI deck.

"When a nurse should be checking out at 7 p.m. after a 12-hour shift, if they're not done with documentation, they're often spending another 30 minutes to an hour after handoff going back in to make sure everything's charted, and that overtime is real dollars for healthcare organizations."

This documentation bottleneck quickly becomes a retention, safety, and margin problem all at once.

Why ambient AI for nursing and why now

Health systems have tried to solve nursing documentation before. Workstations on wheels, bedside computers, mobile devices; each one promised to bring documentation closer to the point of care, but none of them addressed the underlying tension.

"Patient comes first, patient first always. So even the nurses that are most routine at being able to pull up their computer, their workstation on wheels, or whatever technology platform they have, there is still always that burden of how do I document while I'm taking care of a patient."

Ambient AI is the first technology with a credible answer: capture the conversation, generate the documentation, and let the nurse keep their eyes on the patient. The biggest immediate wins, Kelum believes, are in shift handoffs (where critical context routinely gets lost between nurses) and in admission and shift assessments (where the structure of the interaction maps cleanly to what Ambient AI can capture).

There's also a second-order benefit that mirrors what we've seen on the physician side: when clinicians narrate their care out loud, patients get more information, and nurses surface details that would otherwise stay locked in their heads.

When we asked Kelum how ambient AI changes what it means to be a nurse over the next three to five years, his answer reframed the question:

"It doesn't change anything because it makes nursing what it's always meant to be. What we as nurses always learn is to care for the patient, understand the patient, and be an advisor to the patient. Ambient AI can only strengthen that because enabling nurses to spend more one-on-one time having conversations and having eye contact during their care, that's what nursing was a hundred years ago, before EMRs and all this technology was thrown in front of us."

The integration problem most vendors haven't solved

Generating a note from a recorded conversation has become table stakes. What hasn't is generating the right documentation in the right place inside the EHR, especially for nursing.

"Generating a note is easy. Generating a high-quality note is challenging."

Physician notes can often be written back through a handful of API fields. Nursing flowsheets are a different beast: structured grids, discrete data elements, and EHR-specific quirks. Any vendor serious about nursing ambient AI has to engineer for all of them and engineer it deeply enough that the integration holds up across shift changes, edge cases, and the realities of a 12-hour day.

That's why we're co-developing Commure's ambient AI for nursing alongside health systems rather than shipping a finished product and asking nurses to adapt. The workflows that matter are best designed with nurses at the table, not after the fact.

Beyond documentation

The same ambient layer that captures a conversation can power smart prompts, what we at Commure call CareCues (such as a reminder to follow up on a wound check), surface relevant orders a nurse hasn't seen yet (such as an MRI scheduled for a patient she's caring for), and automate communication between care team members who today rely on chasing each other down.

The throughline is the same one that drew Kelum to this work in the first place: give nurses back the time and attention that documentation has been quietly taking from them, and let them do the job they trained for.

Want to see what ambient AI for nursing looks like in your environment? Book a demo with the Commure Ambient AI team.

Clinical documentation is changing quickly, and Ambient AI is a big reason why. It is helping providers reduce manual work, ease documentation burden, and move through visits more efficiently. But even as that shift accelerates, one reality remains true: a sizable percentage of providers still rely on dictation, and for many of them, that workflow is deeply ingrained in how they practice medicine. 

Documentation should support how providers actually work

This is why I think the future of documentation has to account for how providers actually work today. Some clinicians are ready to fully embrace ambient workflows. Others want to dictate. Many will want both, depending on the visit, the setting, or simply personal preference. A modern documentation platform should support that full range.

The real opportunity is not choosing between dictation and ambient but bringing them together in a way that creates a more comprehensive experience. A provider might record a patient visit and let Ambient AI generate the first version of the note, then use dictation to add findings, make edits, or refine details for that encounter. In another case, a provider may skip recording altogether and dictate the note from start to finish. Both workflows should be supported in a comprehensive platform.

Having this flexibility matters because adoption does not happen in a vacuum. For some providers, moving from long-standing dictation habits to fully ambient documentation feels natural. For others, it feels like too much change all at once. Giving them access to dictation within the same broader platform lowers that barrier and lets organizations support more clinicians without forcing a one-size-fits-all transition.

AI can make dictation more valuable

This is also where AI starts to make documentation meaningfully better, even within dictation workflows. The value goes well beyond transcription. While a provider is dictating, AI can help choose the appropriate note template, automatically pull in relevant patient demographics, medical history, and lab data, reducing the amount of manual work needed to build a complete note. That saves time and helps the provider focus on the clinical thinking behind the encounter rather than the mechanics of documentation.

Over time, AI can help improve what is being documented, correct mistakes in real time, and connect providers to deeper intelligence while they work. For instance, a clinician could pause during dictation and ask an AI assistant a question about the patient’s history, get reminded of key details from a complex treatment course, or surface information that might otherwise require digging through the chart. In the right workflow, AI can also support clinical decision-making by helping providers quickly access evidence-based treatment recommendations in real time. The system could also help catch missing elements, identify documentation gaps, or prompt providers to include details that affect compliance, coding, and reimbursement.

 When dictation sits inside a connected AI platform, it becomes part of a smarter documentation ecosystem that can work alongside ambient capture, AI assistants, and downstream workflows in a way that standalone tools cannot.

The goal is a more complete path forward

I do think the industry will continue moving toward broader AI adoption in documentation. Over time, more providers will shift toward ambient workflows because they open the door to much more advanced capabilities. But that future will come faster if we acknowledge where clinicians are today and give them the tools that fit into real practice patterns right now.

The goal should be simple: give every provider a documentation experience that works for their specialty, their setting, and their workflow. When you do that, you are building a more complete path forward for clinical documentation. This is exactly what we offer at Commure by rolling out dictation directly inside our Ambient AI solution.

Oncology documentation is uniquely complex compared to other ambulatory specialties. Every visit draws on months or years of prior treatments, imaging, pathology, lab results, and evolving clinical decisions. Oncologists must synthesize dense patient histories, manage long treatment arcs, and coordinate across multidisciplinary teams, all while maintaining meaningful patient connections.

As oncology practices look to reduce the administrative burden of clinical documentation without sacrificing clinical rigor, Ambient AI is emerging as a critical foundation for modern cancer care. However, off-the-shelf AI documentation solutions aren’t set up to support the complexities of oncology.

Commure’s Ambient AI is already being used across a growing set of oncology environments, from community clinics to national enterprises, including practices within the US Oncology Network, Cardinal Health, City of Hope, and dozens of multi-specialty IDNs such as HCA Healthcare and TriHealth. 

Meeting the Complexity of Oncology Documentation

Ambient AI solutions have rapidly gained traction to help alleviate the administrative documentation burden facing oncologists, but not all solutions are ready for the complex oncology environment. Generic templates miss oncology-specific nuance and struggle to provide the same accuracy and conciseness of classic dictation.

Commure Ambient AI is built specifically for the realities of oncology care, where documentation depends on longitudinal context, precision, and preparation. It captures the full clinical dialogue, translating it into structured, consistent documentation that supports continuity across the entire cancer care journey.

The platform integrates with major EHRs and connects oncology data with Ontada McKesson and Onco EMR, enabling intelligent documentation that provides a seamless provider and patient experience.

Key oncology capabilities include:

• Flexible automation for all levels of complexity: Supports both fully autonomous Ambient AI and the human-assisted Ambient experience, allowing practices to match documentation support to visit complexity and clinician preference.
  
  
• Sub-specialty specific templates: Designed for medical, hematology, radiation, and surgical oncology workflows, these templates capture staging, treatment planning, toxicity assessments, response evaluations, and survivorship care.
  
  
• Ambient CareCues grounded in oncology best practices: Context-aware prompts that surface documentation gaps, lab interpretations, and follow-up needs at the point of care, grounded in clinical guidelines and shown only when relevant.
  
  
• Previsit Summaries: Generates concise, oncology-specific summaries ahead of the encounter, surfacing relevant history, treatment milestones, recent labs and imaging, and other key clinical context to help clinicians prepare more efficiently.
  
  
• Clinical Intelligence layer: Maintains a working memory of the patient’s oncology history, enabling Ambient AI to generate more informed, consistent documentation as care evolves.
  
  
• Autonomous Coding (in development): Automatically translates oncology documentation into accurate, compliant codes by accounting for staging, treatment complexity, toxicity grading, and longitudinal care context.

Together, these capabilities support clinicians before the visit with assembled patient context, during the visit with captured conversations, and after the visit with complete, consistent notes that carry forward across the patient’s care journey.

Proven Momentum Across Oncology

As oncology practices continue to evolve, Ambient AI is becoming a foundational layer that helps clinicians focus less on documentation and more on delivering high-quality, compassionate cancer care. It underscores a broader shift within oncology toward intelligent automation that supports clinicians without disrupting care delivery.

Ready to see what Commure Ambient AI would look like in your oncology workflows?
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Schedule a Demo

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

Outside of the office, I’m an avid traveler with a goal to visit every state in the US (I’ve checked 10 states off the list so far!). I’m also a big motorsports fan and a self-proclaimed foodie who loves exploring new restaurants.

Photography is another passion of mine. I love capturing the essence of the places I visit. My ultimate goal is to curate a "travel wall" in my home, filled with physical prints of the moments and landscapes I’ve encountered on the road. It keeps me inspired and reminds me how big the world really is.

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

I’ve always been fascinated by how technology can be a lever to improve lives. I was definitely the "tech kid"; I actually built my first website back in 2012 using an early website builder. Even then, I knew I wanted to build tools that solved problems, though I didn’t yet realize those tools would eventually help clinicians and patients.

Describe a day in the life of your role.

As a Tech Lead, my role is a blend of high-level strategy and deep technical execution. I lead product roadmaps for major features, but I’m still very much "in the trenches" with my team.

On any given day, I might be "heads-down" tackling a complex feature or architecting base frameworks that allow other engineers to scale their work. I also spend a significant amount of time reviewing code and collaborating on technical designs with other teams. The goal is always the same: build cleaner, faster, and more intuitive software so that our users can spend less time on software and more time doing what they do best, providing care.

What made you decide to join Commure?

Commure sits at the perfect intersection of cutting-edge tech and meaningful impact. The realization that the code we write directly improves a patient’s experience is a massive daily motivator.

I was also drawn to the culture of extreme ownership. At Commure, engineers don’t just "write code"—they own the entire feature. We collaborate directly with Product, Operations, and Design to ensure we are building the best possible product for the healthcare industry.

How would you describe the Commure company culture?

Commure moves at an incredible pace. We are focused on getting things done the right way, as quickly as possible. What makes that work is our blameless culture. In a high-velocity environment, mistakes can happen, but at Commure, those moments are treated as collective learning opportunities rather than failures.

It’s also an incredible place for career growth. The hands-on experience here is unparalleled. I’m constantly learning, not just from our brilliant engineering team, but from leaders across the company who are absolute experts in their respective fields.

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

Just soak it in! Healthcare is an incredibly complex domain with layers of regulation and intricate workflows. My best advice is to never be afraid to ask "why." No question is too small, and curiosity is the fastest way to get up to speed in an environment that moves as fast as ours.

What has been your greatest accomplishment so far at Commure?

Watching the evolution of Athelas Air has been incredibly rewarding. We’ve scaled from a single customer to over 75 practices across 20+ states.

I’m also particularly proud of our work on the Commure Prescriptions engine. Many thought it was impossible to go live so quickly, given the regulatory hurdles, especially for controlled substances. However, by deeply immersing ourselves in the compliance requirements and leveraging our existing internal systems, we built a robust, successful product in record time. Seeing it live and in the hands of clinicians today is a huge win for the team.
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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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At the HIMSS session, “Expanding Patient Access and Reducing Costs with Interoperable AI Agents,” featuring Optimus Health Care and Commure, one theme emerged clearly: healthcare organizations are reaching a breaking point with administrative capacity.

Patient demand continues to rise, but staffing levels for call centers have struggled to keep pace, with a 35% annual turnover rate. The result is familiar across the industry: missed calls, long hold times, and administrative teams overwhelmed by routine requests.

The session explored how a new generation of AI-powered agents can scale call center capacity and improve access to care. Rather than simply routing calls or providing static chatbot responses, Commure’s Call Center Agents, part of Commure Engage, are designed to actively manage patient interactions, resolve common requests, and escalate complex situations when human attention is required. The goal is to improve patient access and experience without expanding staff.

Call Center Agents in Action

The session opened with a live demonstration of how Call Center Agents manage inbound patient calls in real time. The AI agent answers immediately, identifies the patient’s need, and guides the interaction to resolution—handling appointment scheduling, patient intake, department routing, and common questions through natural, conversational responses.

Rather than replacing staff, the agent resolves routine requests upfront, allowing teams to focus on cases that require human judgment and coordination. It can also respond to emotional cues, acknowledging frustration or discomfort, and is configurable to reflect each organization’s workflows and preferences.

The result is straightforward: calls are answered instantly, routine needs are handled without delay, and staff remain available for more complex patient interactions.

Optimus Health Care’s Perspective

For Optimus, improving patient access and operational efficiency has been a major priority. Gary Avery, Senior Director at Optimus, described the challenges many organizations face in managing high volumes of inbound patient communication.

“If patients are unhappy with long wait times and high abandonment rates, it pushes them to find another service provider.”

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Research data supports this, with 60% of patients abandoning their call after more than one minute on hold, meaning answers regarding their health or next steps on care get delayed. Often, patients must make multiple calls to address a single issue.

As Gary explained, traditional call center models often struggle to keep up with peak demand. When call volume spikes, staff members are forced into a reactive posture, handling repetitive inquiries while more complex patient needs compete for attention.

AI agents offer a different model. By automating routine interactions, organizations can maintain responsiveness even during peak demand.

While earlier on in their deployment of Agents, Optimus will measure success by looking at improvements in:

• Patient satisfaction scores and feedback
• Call abandonment rates
• Scheduling and fill rates

“In order for us to continue our mission as an FQHC, we absolutely have to have patients come through our doors.”

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Gary emphasized that successful AI deployment requires early involvement from a broad group of stakeholders—including front office registrars, practice managers, clinical teams, IT teams, and operational leadership—to ensure the solution reflects real workflows, incorporates staff needs, and minimizes implementation issues.

The Impact of Call Center Agents

The operational impact of AI agents becomes particularly clear when applied at scale.

Across customer deployments using Commure Agents, organizations have observed significant improvements in how patient calls are handled. Agents provide 24/7 coverage, ensuring calls are answered even outside normal office hours. 

For healthcare organizations managing large call volumes, this can translate into meaningful operational efficiency. Observed customer performance indicate:

• 800 labor hours saved per 10,000 calls handled
• $240,000 in annual labor savings at same volume
• 30-80% of inbound calls can be resolved without staff intervention, depending on workflow design 

A New Layer of Healthcare Operations

The session underscored an emerging shift in how healthcare organizations think about AI. For years, much of the focus has been on clinical documentation or analytics. But AI agents represent something different: operational intelligence applied directly to the front door of care delivery. By answering calls, resolving routine requests, and routing patients efficiently, agents can extend the capacity of administrative teams while improving the patient experience.

For organizations struggling with patient access and administrative workload, the emergence of AI agents may represent one of the most practical tools available today to solve one of healthcare’s most persistent operational challenges.