Commure Up Close: Kaila Drives Measurable Growth Through Data-Driven PR and Marketing Programs

Denied claims are one of the most overlooked drivers of revenue loss in healthcare. During an on-site discovery session, Commure found that one healthcare organization lost $3.2 million annually, or 5% of its ARR, due to unpaid outstanding balances. This was simply the result of a lack of infrastructure in place to efficiently triage, correct, and resubmit denied claims.

This is a both a grim and common state of affairs for healthcare practices. A recent study of 280 hospitals across 23 states found that:

• Adjudication of claims cost hospitals over $25.7 billion in 2023, marking a 23% increase over what was reported the previous year.
• Nearly 70% of denials were ultimately overturned, but only after several costly rounds of review. This means that healthcare practices incurred roughly $18 billion in unnecessary expenses.

Clearly, the inefficiencies of current systems contribute heavily to this enormous amount of wasted time and resources.

In response to this issue, Commure built out a proprietary AI Denial Automation System that automates away over 80% of denied claim reprocessing. This system decreases labor costs on denial resubmissions, increases claim resubmission volume, and significantly improves denial re-approval rates.

Understanding Insurance Claim Denials

Here’s a quick refresher on the relevant part of the lifecycle of a claim, illustrating just how many steps are involved and, consequently, how many opportunities there are for human error and for a claim to get stuck in process.

Payers deny claims constantly for all kinds of reasons. Figure 1 depicts the general flow of the section of the claim submission process in which denials occur.

Payers then send their decisions and any payments in the form of remittances. If the claim was denied, either fully or partially, they indicate the reasons for the denial with Claim Adjustment Reason Codes (CARCs) and Remittance Advice Remark Codes (RARCs).

While there are overall trends as to which CARC/RARC combinations most commonly occur, different practices often see spikes in denials for CARCs and RARCs unique to their practice or field. This is why Commure tailors automatic denial tracking and resubmission to each practice, rather than taking a one-size-fits-all approach.

We can see this phenomenon occur in the following sample data from two sites during Q4 of 2024. Figure 2 shows that nearly half of all denials for this practice come from CARC 59, the code for apparently concurrent procedures that get billed as individual procedures.

Figure 3, on the other hand, reveals that nearly three quarters of this practice’s denials are due to CARC 104, which indicates that a provider sent inadequate or incomplete supporting documentation for the services they rendered.

The potential lost revenue exceeds $250,000 in one quarter just from that single CARC. For a practice with $6.7 million ARR, needlessly losing $1 million a year because of missing documentation is painful.

In all cases involving manual claim recon and denial resubmission, humans are both far slower and more prone to error than machines and AI proactively detecting potential errors and executing established rules and flows. The longer claims take to process, the more expensive they get for the practice.

Some of the revenue lost to unworked denials will be written off, damaging the practice’s financial health. Some will be pushed onto patients, who end up paying or going into debt over getting medical care for which they should not be held liable. When denials are accepted not worked and resubmitted, patients and practices both lose.

This is why cheap and effective automation of both the initial claim submission process and denial resubmission process is increasingly crucial for healthcare practices. This is also where Commure’s proprietary rules engines and AI solutions shine.

How Large Language Models (LLMs) Can Prevent Denials Before Submission

What are LLMs?

Large language models (LLMs) are deep learning models trained on huge amounts of text data that can then perform a variety of natural language processing (NLP) and analysis tasks, including translating, classifying, and generating text. Due to the sheer breadth of their knowledge base, they can provide reasonably accurate answers to queries, even in the absence of specifically labeled examples.

Data Ingestion & Analysis

At Commure, we train our LLMs on a massive corpus of historical claims, payer denial responses, and policy documents. Our AI agent can fetch EOBs, work denials, and complete other repetitive tasks. They automatically leverage the resources of supporting LLMs and engines. For example, we utilize an LLM that tests data we extract from EHRs, and another that empowers human staff with precise recommendations for coding denials and rejections (Figure 4). The agents parse through payer documents and surface these coding recommendations with a 95% QA pass rate.

We use these agents to ingest encounter-level data from EHRs, remittances, and insurance responses to build a knowledge graph that can:

• Surface payer-specific denial patterns.
• Run deep research across structured and unstructured payer documents to extract specific policy nuances.
• Recommend compliant CPT, ICD, and modifier codes based on past success rates.

Pre-submission Validation

Commure’s LLMs validate claims in real-time by identifying missing fields, incorrect codes, and misaligned documentation. Submissions are automatically reviewed against payer-specific rules and historical data, with our AI agent, Scout, flagging errors and proposing corrections before submission.

In one workflow, for example, Scout automates submission integrity QA by validating claim formatting and billing rules—tripling QA coverage and significantly improving first-pass acceptance rates.

Integration with Existing Systems

Commure embeds LLM-based validation directly within systems like our database, Normandy, and external EHRs such as Athena and AMD. These tools automatically pull and reconcile patient, payer, and encounter data, ensuring seamless pre-submission verification.

Automating Denial Management and Resubmission

Automated Denials Explanation Analysis

Commure’s LLMs extract denial reasons from insurer responses, mapping them to specific errors in the original claims. In order to stay a step ahead, we also scrape payer policies and preemptively create rules in the rule engine to prevent denials based on recent payer policy changes.

In real-world deployments, Scout uses standardized templates to generate concise denial notes (Figure 5) that are automatically added to claims—automating 40% of related tasks with over 2,000 AI-generated notes weekly.

Intelligent Claim Modification

Scout and Denial Copilot suggest fixes based on prior approvals of CARCs/RARCs and payer policy interpretation. Whether it’s a missing authorization or a modifier mismatch, AI tools generate corrected claims and route them for immediate resubmission.

The authorization debugging workflow, for example, identifies prior auth numbers within EHRs and auto-resubmits if found. In cases where prior auth cannot be found, it can initiate AI calls to validate whether prior auth is required for the billed procedures, if the payer has it on file, and then reprocess the claim if applicable. LLMs will also summarize the key points of the phone call.

Figure 6, below, shows daily automatic claim modifications and resubmissions for a particular practice over a one-month period.

Autonomous Resubmission Process

Commure integrates with clearinghouses and portals via APIs and robotic workflows. For Medicare appeals, Scout extracts medical records, generates the appeal package, and submits it via portals like Novitas—achieving $127K in billed charge automation with zero human error.

AI Agents: Automated Calls for EOB Procurement

When an ERA is not received within the predefined SLA, our AI agents initiate automated outbound calls to payers to retrieve the ERA and extract any associated denial codes. This integration minimizes manual intervention in the A/R follow-up process. As of this writing, the system executes approximately 1,500 calls daily—automating 80% of related workflows and delivering annualized savings of $195,000, with continued efficiency gains expected.

Deep Dive Into Automated Denial Management

Let’s take a closer look at Commure’s process for rules establishment and management automation for the tens of thousands of denials that pour in every day.

As mentioned above, LLMs can customize our engines to evaluate, categorize, and resolve claim denials through rule-based logic and insurance detection mechanisms. We do this for each of our partner practices. This section breaks down how denials are classified and how we use structured batch submissions, enriched patient data, and external eligibility checks to drive intelligent claim resubmissions.

Denial Categorization by RARC/CARC/Payer

Every claim that enters our resubmission pipeline is first evaluated to determine the type of denial it encountered and track which payers consistently send which kinds of denials:

• Coverage-related denials (identified by specific CARC and RARC codes) are prioritized for automated eligibility verification.
  • For example, (CARC) CO 22 indicates denial due to care that may be covered by another payer per coordination of benefits. All CO 22 denials will therefore enter this category and corresponding workflow.
• Other denial types may be resolved through standard workflows without invoking insurance detection.
  • Diagnosis and modifier code denials are good examples here.
  • Note that once eligibility and coverage are established, all other possible reasons for denial are analyzed and remedied at once. The claim is then sent through our engine again in a dry run prior to resubmission.

For coverage denials, our engine checks for updated patient insurance information (PII) and validates it with real-time eligibility checks. If active coverage is confirmed, a new claim submission is created. If not, the system prepares to engage insurance detection.

Change in Submission Payloads

When a denial progresses to resubmission—especially one influenced by insurance detection—the claim payload must adapt dynamically. Key components updated in the payload include:

• Insurance company ID (determined via match against historical claims or clearinghouse results)
• Subscriber/dependent information (pulled from eligibility response or existing PII)
• Coverage priority (inferred based on denial type, existing insurance, and returned coverage)

We leverage an [upsert_verify_and_submit_claim_correction] endpoint, passing a [fields_to_update_dict] to make direct, surgical updates to claims without requiring custom rules engine invocations. This approach mirrors how manual claims are currently corrected.

Listening for and Acting on Denial Batches

Our system processes denied claims in batches of 500, drawn via [_get_denied_claims_to_analyze]. These are inserted into a tracking table [resubmission_batches], where each claim is marked with [has_run_completed = False]. Claims are processed individually, and this flag is updated accordingly.

Once all claims in a batch are marked complete, we check—under DB-level locking—that no [has_run_completed = False] entries remain. Only then do we trigger insurance detection for that batch, ensuring batch integrity and avoiding duplicate processing.

Insurance Detection Flow

For claims reaching the insurance detection stage, the following workflow is used:

1. Tracking:
   • Unique patient/DOS combinations are logged in [insurance_detection_resubmission_tracking], preventing duplicate inquiries within a defined time frame.
   • If a matching entry exists but lacks a usable result, the system skips resubmission and awaits response.
2. Submission:
   • Claims eligible for detection (new or failed past attempts) are batched into a [.COV] file and sent to our clearinghouse via SFTP.
   • Each request is logged in [insurance_detection_batches], and individual inquiries are stored in [insurance_detection_checks], linked by [batch_id].
3. Ingestion and Matching:
   • Periodic jobs parse clearinghouse response files into [insurance_detection_check_details], using file name correlation for mapping.
   • These results determine next steps for each claim.

Automated Resubmission Logic

When eligibility results are returned, we assess whether a claim can be auto-resubmitted:

• Active coverage with a match score of [STRONG], [PROBABLE], or [PROBABLE_NO_ADDRESS] is required.
• We map clearinghouse payer data to our internal insurance company records, using:
  • Historical claims at the site
  • Encounter billing type (PROFESSIONAL, INSTITUTIONAL, WC)
  • Payer name and clearinghouse payer ID
• Priority logic distinguishes between primary and secondary claims based on denial reasons and available coverage.

If subscriber or dependent information is missing, fallback strategies pull PII from our database, provided the relationship is verifiable. If any required fields are missing and cannot be inferred confidently, we escalate the claim to manual review.

This streamlined, rule-driven approach allows our LLMs to intelligently manage denials, adapt submissions, and reduce manual overhead, while maintaining strong safeguards around data accuracy and claim validity.

Overcoming Implementation Challenges

Commure ensures full compliance with HIPAA, GDPR, and payer-specific mandates by implementing:

• End-to-end encryption
• Role-based access control
• Detailed audit logs

Data Security and Privacy Risks

All patient data processed by Scout is encrypted in transit and at rest. The system’s architecture follows zero-trust principles and ensures minimal human access to PHI.

Model Accuracy and Explainability

LLM-driven decisions are made transparent via audit trails, human-readable recommendation logs, and QA pipelines that validate every automated task. Denial Copilot and EOB Copilot include explainable AI features, with manual override options.

Adoption by Healthcare Providers and Insurers

To build trust, Commure delivers measured rollouts and demonstrates measurable cost savings—e.g., $100K+/year from eligibility detection automation alone
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The Future of AI in Insurance Claims Processing

As models improve, we anticipate:

• Real-time adjudication: AI-powered negotiation and approvals during patient encounters.
• Proactive coverage detection: Automated and highly accurate eligibility checks before services are rendered.
• Expansion beyond claims: Fraud detection, personalized billing plans, and real-time patient financial education.

The trajectory is clear: AI will soon be a copilot across all of RCM—not just denials.

Commure is reshaping medical claims processing with scalable, accurate, and autonomous AI agents. By preventing denials before submission, streamlining rework, and integrating deeply with existing systems, Commure reduces costs, enhances accuracy, and improves outcomes for providers and patients alike.

With 80% of RCM already automated and a roadmap to reach 95%, Commure invites industry stakeholders to explore what AI-driven claims processing can unlock for the future of healthcare.
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Explore AI-Driven RCM

Many thanks to all the engineers who lent their expertise for this blog post: Rithesh Shetty, Jasu Mandakh, Yash Wani, Jasdeep Grover, Jordan Chow, and Thuy Ngo. You all do incredible work.

The healthcare industry is experiencing a rapid shift in how clinical documentation is handled, driven by mounting pressure to reduce clinician burnout, close revenue gaps, and streamline operations at scale. Traditional approaches like manual note-taking and point-solution scribes no longer meet the demands of modern healthcare delivery.

AI-powered documentation tools have been gaining traction to help relieve that burden. But not all solutions are equal. Two terms frequently used in this space—AI Scribe and Ambient AI—describe fundamentally different approaches. One offers a tactical fix; the other, a platform-level upgrade.

This post breaks down the difference between the two, how each functions, and why more health systems are shifting to ambient AI that integrates deeply with EHRs, ties into revenue workflows, and delivers measurable impact across both the front line and back office.

What Is an AI Scribe?

An AI Scribe is a digital tool that uses speech recognition and natural language processing to generate clinical documentation from doctor-patient conversations. These tools are typically standalone apps that record the visit, transcribe the audio, and produce a draft note for the provider to review and sign.

Most AI Scribes are built to assist only during the visit. They don’t integrate deeply with EHRs, they don’t support pre-visit preparation, and they don’t automate downstream tasks like coding, quality reporting, or referrals. As a result, they operate outside the core clinical and financial systems, requiring providers to make manual edits to ensure accuracy or completeness.

For many health systems, AI Scribes serve as an entry point into ambient-style documentation. But because they are disconnected from broader workflows, they’re difficult to scale and often add parallel processes that limit enterprise-wide impact.

What Is Ambient AI in Healthcare?

Ambient AI in healthcare is technology that automatically generates clinical documentation and supports provider workflows before, during, and after the visit, without requiring manual note-taking.

Unlike AI Scribes, which focus solely on transcription, Ambient AI is designed to support the entire clinical workflow. It integrates directly with the EHR to enable real-time documentation, pre-visit context gathering, and post-visit automation like coding and referrals.

This level of integration turns Ambient AI into more than a documentation tool by drafting notes, surfacing relevant patient data, suggesting codes, and reducing manual overhead for providers. Across the industry, clinicians using ambient AI tools report time savings of anywhere from 25% in the case of A&A Women's Health to 41% for Dignity Health. Burnout levels are also decreasing, with one industry report noting a 60% drop in self-reported burnout after adoption (which correlates directly to increased retention).

Where AI Scribes offer a point solution, Ambient AI functions as an operational layer that can scale across departments, connect with downstream systems, and drive long-term efficiency across both clinical and financial workflows.

AI Scribe vs. Ambient AI: A Side-by-Side Comparison

Now that we've outlined what each tool does, here’s how they compare directly. The table below breaks down the major differences between AI Scribes and Ambient AI across common decision-making factors:

At Commure, we’ve developed a tiered approach to Ambient AI that gives health systems flexibility based on their needs, workflows, and desired level of support. The chart below outlines the differences between our three tiers so teams can see exactly what each level offers in terms of time savings, automation, and human-in-the-loop services.

Why Ambient AI Is the Right Tool for the Future

The documentation burden isn’t going away, nor is the pressure on health systems to improve efficiency, reduce burnout, and operate with leaner resources. While AI Scribes can help with note-taking, they don’t address the broader workflow challenges. Ambient AI goes further, streamlining documentation and tightly integrating with the systems clinicians already use.

A platform-powered approach to Ambient AI is what unlocks its full potential. When ambient tools are embedded within the EHR and connected to back-office functions like autonomous coding and revenue cycle workflows, they save time while helping to close the loop between documentation, billing, and reimbursement. This end-to-end visibility and automation is essential for health systems trying to scale care while maintaining financial stability.

If your organization is still relying on narrow scribe tools, it may be time to evaluate whether they’re built for where healthcare is headed. The next wave of documentation technology is already here—and it’s ambient.

Explore how Commure Ambient AI can help your team work more efficiently, reduce burnout, and deliver better care.
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See How Ambient AI is Different

For our inaugural healthcare leadership summit, Commure Nexus, connection, innovation, and collaboration were the main focus. By bringing together more than 50 healthcare leaders, clinicians, technologists, investors, and partners under one roof, we created a space to confront some of healthcare’s toughest challenges—together. 

“It’s been really great to see all of the top hospital systems that are here, who have already been working with Commure so we can see what all the possibilities are.” – Gian Varbaro, MD, MBA, Chief Medical Officer & VP, Ambulatory Services, Bergen New Bridge Medical Center

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Throughout the day, leaders heard from some of the most forward-thinking voices in healthcare and technology:

• Hemant Taneja, CEO and Managing Director of General Catalyst, and Tanay Tandon, CEO of Commure, kicked off the day with a visionary keynote on cutting-edge innovations in agentic AI, ambient AI, workflow automation, and enterprise intelligence technologies that are shaping the future of healthcare.
• Dr. Stephen Klasko shared a futurist’s forecast of AI’s role in healthcare technology in the next ten years, and the profound impacts that will have on how care is delivered. 
• Dr. Jamie Colbert, Chief Medical Officer of Commure led a panel discussion with Executives from HCA Healthcare, Bergen New Bridge Medical Center, and Compassus. They shared practical lessons and insights from partnering with Commure and deploying Ambient AI across EHR environments and care sites. 
• Murali Athuluri, Chief Information Officer of North East Medical Services, joined Dhruv Parthasarathy, Chief Information Officer of Commure, and Max Krueger, Head of Forward Deployed Engineering at Commure, to discuss how the Commure’s co-development model accelerates real-world impact.
• Dr. Ashish Atreja, a Professor of Medicine at UC Davis and Founding Chair of Valid AI, and Ian Shakil, Commure’s Chief Strategy Officer, explored how platform-based Ambient AI is streamlining documentation, coding, and clinical efficiency.
• Dr. Naqi Khan, Lead Physician Executive for Healthcare and Life Sciences Solutions at Amazon Web Services (AWS) closed the main sessions with a keynote on the power of AI, ML, and cloud infrastructure to transform healthcare at scale.

Commure Nexus was a chance to step out of the everyday, listen deeply, and build with intention and urgency.

“Commure Nexus is such a special event because it brings together Commure’s best and brightest builders with healthcare’s top executives—all in one place. We’re collaborating closely, going on a listening tour of the real-world problems these leaders face every day. And in the next 48 hours, our teams are shifting immediately into building mode. Speed-above-all-else for our customers—faster than anyone thought possible.” – Deepika Bodapati, COO, Commure

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When people come together, so do ideas, vision, and a shared drive for change. Commure Nexus is just the beginning of forging deeper, cross-industry partnerships—and we’re already looking forward to what’s next.

Watch the highlights below and stay tuned for deeper session recaps and speaker interviews over the next several weeks.

You likely had several options — why did you choose Commure?

Rishi Bhuwaneswara, Senior Manager, Strategic Growth (Electrical Engineering Computer Science & Economics ‘23): Interning at several large tech companies, I felt like most moved slowly and didn’t iterate fast enough to meet the needs of the customer. I was looking for an environment where I could make an impact in an industry I was passionate about and quickly take my ideas to production. The fast-paced culture and the ability to work with people who are super passionate about creating a better patient experience drew me to Commure. While big tech companies often offer stability, the upside and ability to make an impact is also limited. There is truly no limit to your growth at Commure. 

Ash Bhat, Head of R&D (Interdisciplinary Studies Field - Society & Technology ‘18): My company was acquired by Commure. At the time, I was evaluating an exit between a public data set company and Commure. Ultimately, I wanted to continue to learn and bet on an industry that I saw long-term growth in. No matter what happens, healthcare will always be an important part of critical infrastructure.

Colin FitzGerald, Software Engineer (Economics, minor in Data Science ‘22):  I chose Commure because I connected with Dhruv’s vision of building a refined operating system for healthcare. I’ve experienced firsthand the inefficiencies of the American health system. When you are presented with the opportunity to be a part of something that contributes to that solution, it is a no-brainer to say yes.

Michael Huang, Software Engineer (Computer Science & Economics ‘22): I was excited by the pace and impact of working in healthcare infrastructure. Commure sits at a unique intersection of fast-moving technical work and deeply meaningful outcomes. It felt like a rare opportunity to help solve real, complex problems in a space that hasn’t historically moved fast. Also, healthcare is one of the few spaces where technical leverage directly translates to real-world impact. 

How did UC Berkeley prepare you for working at Commure?

Rishi: Going to Berkeley was an amazing experience where I had the opportunity to learn alongside some of the smartest students in the country. The countless late nights in EECS lab and the plethora of extracurricular activities I participated in taught me that accomplishing hard things was possible. Commure has a similar environment where we’re constantly trying to solve large problems with employees who are super passionate about our mission.

Ash: UC Berkeley has a public campus culture where you interact with hundreds of people from many cultures throughout your academic career! Similarly, Commure has a large team and in many ways feels like a college community with many teams and cultures. The intense rigor of Berkeley’s curriculum and education also prepares you for the environment of supporting large health systems.

Colin: Berkeley is a tough academic environment, made even more difficult when balancing athletics alongside. For those who bring seriousness to both, it is a harrowing journey. If you persevere through this dual challenge and emerge on the other side, you'll find yourself well-equipped to handle any career obstacles that come your way.

Michael: Berkeley was a fast-paced, collaborative environment. Whether it was late-night project sprints in the CS labs or hashing through a problem set with friends in the Econ department, you learn how to think clearly under pressure and work well with others—skills that translate directly to how we build at Commure.

How have you grown at Commure?

Rishi: I’ve been at Commure for two years now and have really learned to believe in myself and in the well-researched ideas I have. At Commure, we make decisions quickly based on data, and I’ve realized that in most cases well-executed ideas tend to result in the desired outcome.

Ash: I've learned a lot about the inner workings of healthcare during my time at Commure. Along with learning how to operate within a large-scale company, I also got the opportunity to learn how to work with physicians and lead a forward-deployed team on the ground at a hospital.

Colin: I have learned to have more confidence in my intuition, attention to detail, and ability to think on my feet. Healthcare lends itself to many ambiguous problems that need quick solutions. At Commure, we are blessed with the freedom and trust to make both of the aforementioned happen.

Michael: I’ve leveled up my infrastructure skills significantly, especially when it comes to designing for scale, reliability, and long-term maintainability. Commure gives you real ownership over critical systems, which accelerates both technical and decision-making growth.

What is something you are really proud of accomplishing?

Rishi: A year ago, when we launched our Commure Ambient AI product, I was tasked with figuring out a GTM field sales motion. The goal was to bring our tool to as many providers’ workflows in the shortest amount of time. The opportunity to build this team from the ground up and see it become the backbone of our GTM motion is something that I’m really proud of.

Ash: Something I’m proud of accomplishing is building and launching the EHR (Air) product and scaling it to the first $1M in revenue. I’m also proud of leading a forward-deployed team and collaborating with Cincinnati Children’s Hospital to design and build out what the patient experience in a hospital of the future could look like.

Colin: I am proud of rebuilding the EHR Scribe backend to support the more specific workflows of EHR customers. The opportunity and resources to dissect the shortcomings of our initial infrastructure have led to immense growth for me as an engineer and redeemed our team’s initial shortcomings.

Michael: I’m proud of helping evolve our infrastructure to be more self-serve, configurable, and scalable. We’ve taken workflows that used to require manual coordination or one-off engineering efforts and turned them into systems that can handle higher throughput with much less friction. It’s rewarding to see the team move faster because the platform underneath has become more robust and flexible.

What’s something you love about your team that has nothing to do with work?

Rishi: I love how our team just feels like a group of friends that are trying to figure something out together. I keep our morning standups pretty fun, and I’ve also had the opportunity to travel to over 25 cities with most of my team over the last couple of quarters. I do believe that creating a fun culture within a team helps boost productivity.

Ash: Being with the team feels a bit like college again. We have a good group of folks coming from different backgrounds on a shared journey. 

Colin: I love all of the inside jokes we have on our team, the lightness and bond we have makes it easier on the tougher days. Humor helps create a nice balance.

Michael: We come from very diverse technical and professional backgrounds, and somehow it all meshes. There’s a “melting pot” vibe on our team, everyone brings a different perspective, and that makes working together both fun and high-impact.

As of May 27, 2025, Strongline has returned to market after signing a deal with QLog, a leading innovator in real-time location systems (RTLS) and Bluetooth Low Energy (BLE) technologies for healthcare. Read the details here.

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San Francisco, CA – May 21st, 2025 — Yesterday, the Ninth Circuit Court of Appeals declined to stay the recent preliminary injunction entered in the ongoing legal dispute between Commure and Canopy. The ruling maintains temporary restrictions on Commure’s ability to install Strongline Pro at new sites or sign new contracts for that product.

We respect the court’s decision and remain focused on supporting the caregivers and health systems that rely on us. Our appeal is ongoing and will be heard in the coming months. We remain confident that the injunction should not have been granted and will be fully reversed. In the meantime, we’re committed to delivering the highest possible level of service, support, and stability across our current customer base.

Commure Strongline is deployed across over 50 health systems, supporting more than 230,000 caregivers. This ruling does not impact any existing customers or their current deployments. It applies only to Strongline Pro at new sites. Our teams are in direct contact with customers whose expansion plans may be affected.

While disappointing, the injunction is merely a temporary setback in a long legal dispute. All other Commure solutions—including patient engagement, ambient clinical documentation, RCM, and PatientKeeper—are unaffected and continue without restriction.

We are committed to complete transparency with our Strongline partners as the appeal proceeds. 

Dan Warner
SVP and General Manager, Commure

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Healthcare call centers are facing mounting operational pressure. Surging call volumes, rising patient service expectations, staffing gaps, cost constraints, and outdated infrastructure are all limiting their ability to respond effectively.

The operational impact is measurable. Average hold times now exceed four minutes, well above the 50-second industry benchmark set by the HFMA. Roughly 30% of patients abandon the call if they wait longer than a minute. And when they do reach an agent, only half of the issues are resolved on the first attempt.

Staffing is also a persistent bottleneck to meeting patient needs. Many centers operate at just 60% of their necessary capacity, and are particularly under-resourced during evenings and weekends. Labor accounts for nearly half of total call center costs, and because call volume scales linearly with staffing needs, increasing headcount quickly becomes cost-prohibitive.

Agentic AI offers solutions to automate high-volume, rules-based tasks such as scheduling, insurance verification, and routine triage. This helps healthcare organizations handle more requests with fewer manual steps, respond more consistently to patients, and stay available at all hours without adding staff.

What Healthcare Call Centers Handle Today

Healthcare call centers are no longer just support functions, they’re a core part of how patients navigate and access care within a health system. On a typical day, human agents manage thousands of inbound requests covering a wide spectrum of operational and clinical needs, including:

• Appointment scheduling and rescheduling
  
  
• Prescription refill and prior authorization requests
  
  
• Insurance eligibility and benefits verification
  
  
• Billing inquiries and payment support
  
  
• Referral coordination and authorization follow-up
  
  
• Nurse triage routing based on symptom severity
  
  
• General service questions (e.g., location, hours, provider availability)
  

Typically, these patient requests outlined above are not one-question calls. A single patient interaction often spans multiple categories, for example, rescheduling an appointment, verifying insurance coverage, and requesting a medication refill. Handling these requests typically requires agents to navigate EHRs, payer portals, and internal communication systems. Each added layer increases average handle time and the likelihood of handoffs or callbacks.

Increasingly, patients expect real-time service and seamless digital experiences. What was once a back-office support function has evolved into a high-volume, front-line service channel that demands speed, consistency, and the ability to scale with demand. Luckily, many of the tasks driving that demand are repetitive and rules-based, making them ideal candidates for automation.

What Tasks AI Healthcare Call Centers Can Automate

AI agents are not traditional chatbots or Interactive Voice Response (IVR) systems; they’re workflow-oriented tools designed to execute discrete, high-volume tasks across multiple systems without human input. Unlike traditional LLM-based AI technologies, AI agents can retain context across conversations, interact with third-party applications and data sources, and respond dynamically to real-time requests.

In healthcare call centers, their highest-impact use cases fall into a few key operational categories:

• Appointment Management: AI agents can schedule, confirm, reschedule, and cancel appointments by integrating with an EHR’s scheduling module. They support both inbound and outbound workflows, enabling patients to make changes via voice or chat. By automating these common tasks, health systems can reduce average handle time and lower the volume of follow-up calls resulting from missed or unconfirmed appointments.
  
  
• Prescription Refill and Prior Authorization Intake: Rather than routing refill and authorization requests to nursing staff, AI agents can collect relevant medication details from patients, initiate the request via structured forms or EHR-integrated prompts, and push the case into existing pharmacy workflows or prior auth queues. This streamlines medication access, freeing clinical teams to focus on more complex cases.
  
  
• Insurance Verification: Agents can interface with payer portals and eligibility databases in real time to confirm active coverage, identify copays, and check for plan-specific limitations. Depending on their configuration, AI agents can either retrieve information to assist a human agent or fully automate the eligibility lookup process, eliminating back-and-forth calls and manual portal navigation.
  
  
• Triage Routing and Intent Detection: Using natural language processing, AI agents can capture patient intent (e.g., “I have chest pain” vs. “I need a referral”) and match it to predefined routing logic. For flagged clinical terms or symptom combinations, they can escalate directly to a triage nurse or refer them to the emergency room based on the urgency. For non-urgent requests, they can direct the patient to self-service tools or begin the intake workflow.
  
  
• Follow-Up and Post-Visit Outreach: AI agents can initiate proactive contact after a visit, sending discharge instructions, satisfaction surveys, or medication reminders via SMS, phone call, or patient portal messages. They can track responses and escalate any red flags (e.g., patient reports worsening symptoms) to the appropriate care team.
  

These agents interact with EHRs, CRM platforms, payer systems, and internal scheduling tools in real time, either pulling in data to inform conversations or pushing updates based on patient responses. By taking on these repeatable, structured tasks, AI agents reduce the burden on human staff, accelerate response times, and allow organizations to scale without increasing headcount.

Beyond the Call Center: Expanding the Role of AI Healthcare Agents

While healthcare call centers are a practical first area for health systems to apply this technology, the potential uses for AI agents extend much further. Commure offers a growing portfolio of agents designed to support operational workflows across patient engagement, revenue cycle management, and administrative coordination. Examples include:

• Denials Autopilot Agent: Supports denial management by identifying rejected claims, flagging likely causes, and preparing resubmission recommendations to accelerate appeal workflows.
  
  
• Claims Processing Agent: Automates repetitive aspects of the claims lifecycle, including status checks and reconciliation tasks, to reduce manual work and improve throughput.
  
  
• Payer Portal Agent: Retrieves documentation such as EOBs directly from payer portals, reducing the administrative burden on revenue cycle teams.
  
  
• Outbound Follow-Up Agent: Initiates post-visit check-ins to monitor recovery and adherence, with workflows that escalate issues to clinical teams when needed.
  
  
• Web-Based Benefits Lookup Agent: Gathers patient eligibility and benefits data from external systems, streamlining pre-visit verification and coverage checks.
  

Commure’s recent webinar with AWS showcased how AI agents can access real-time data, interact with external tools, and retain conversational context across interactions. These capabilities allow AI agents to coordinate tasks across disconnected systems, responding to real-time inputs rather than following static scripts.

Modernizing Healthcare Call Centers With AI

AI agents are reshaping how healthcare organizations deliver service across patient-facing and administrative workflows. Staffing shortages, rising service expectations, and increasingly complex payer and system interactions have exposed the limits of manual processes. Core functions such as scheduling, benefits verification, triage, and follow-up now demand levels of speed and consistency that are difficult to sustain with human agents alone.

By automating structured, repeatable tasks, agentic AI enables organizations to increase service availability, reduce operational overhead, and improve response time without expanding headcount. Agents maintain context across interactions, connect to external tools and data sources in real time, and coordinate workflows that span departments and systems. Rather than serving a single function, they provide a flexible operational layer that supports scale, accuracy, and resilience across health systems.

Commure supports this transformation with a forward-deployed engineering model, embedding technical experts directly within health systems to accelerate deployment and ensure alignment with existing workflows. This hands-on approach helps health systems move quickly from concept to production, minimizing integration friction and surfacing high-impact use cases early.

Ready to modernize patient operations with less overhead and more impact? Check out the Commure Agents product page to learn more or schedule a demo via the button below.
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See AI Healthcare Agents in Action

In 2024, coding-related denials surged by 126%, one of the largest increases in the past three years. At the same time, the U.S. healthcare system is facing a 30% shortage of medical coders, putting the financial performance of health systems at risk.

Medical coding is critical to healthcare organizations because it directly influences reimbursement accuracy, cash flow, and compliance with regulations—errors or delays in coding can quickly translate into significant financial disruption. Yet coding is increasingly complex due to frequent regulatory updates from CMS and commercial payers, expanding documentation requirements tied to value-based care, and detailed clinical data needed for accurate code selection.

This evolving complexity puts substantial pressure on clinicians to provide exhaustive documentation and on coders to interpret that documentation precisely. These demands lead to inefficiencies, inconsistent results, and increased administrative overhead, especially when coding workflows rely on manual processes (more than half of health systems are not utilizing AI tools) stretched beyond their limits.

Enter AI medical coding: a transformative technology that leverages natural language processing (NLP) and machine learning to automatically interpret clinical documentation, extract relevant clinical concepts, and map them to the most appropriate billing codes in real time; all while integrating directly with the EHRs via APIs. These tools can also proactively surface key details such as missing documentation, potential coding inaccuracies, or incomplete patient histories, presenting them to coders and clinicians through intuitive nudges and dashboards before, during, and after the patient encounter.

This article explores how AI medical coding is transforming clinical documentation and RCM, and what to look for in a next-generation solution.

The Increasing Complexity of Medical Coding

Medical coding has long served as the foundation of healthcare reimbursement, compliance, and reporting. Since the introduction of systems like the International Classification of Diseases (ICD), Current Procedural Terminology (CPT), and Healthcare Common Procedure Coding System (HCPCS), coders have been tasked with interpreting and coding clinical encounters based on documentation.

Clinical documentation has become increasingly complex due to factors such as the adoption of value-based care, which requires detailed reporting on patient outcomes, and quality initiatives that mandate more comprehensive clinical information. Additionally, payer requirements have become stricter, with insurers frequently updating coding policies and enforcing specific documentation criteria to justify reimbursement.

It’s no surprise that manual coding methods have struggled to keep pace as coders must continuously adapt to changing guidelines from entities like CMS and insurers, while navigating tight deadlines, ranging from 24 hours to just a few days after patient discharge, depending on payer contracts. A 2023 study found that 56% of medical coders failed coding accuracy audits, underscoring the widespread difficulty coders face in meeting these evolving requirements.

The transition to electronic health records was meant to streamline documentation and simplify coding workflows, but in many cases, it introduced new complexities. Instead of reducing the burden, EHRs often increase the volume of documentation and fragment the data needed for coding, creating more friction for both providers and revenue cycle teams.

These specific challenges have driven healthcare organizations to seek new approaches, including AI medical coding solutions, that can help coders and clinicians manage complexity, improve coding accuracy, and reduce reimbursement delays.

How AI is Revolutionizing Medical Coding

AI medical coding is about fundamentally rethinking how clinical data is interpreted, structured, and used to support accurate, timely reimbursement. That shift in perspective opens the door to entirely new capabilities that go far beyond code suggestion.

Improve Accuracy and Reduce Denials

AI tools can analyze clinical documentation, including physician notes, discharge summaries, lab results, and imaging reports, to identify key terms and clinical details. By automatically mapping these details to appropriate ICD-10 and CPT codes, AI significantly reduces the time coders spend manually searching for and assigning codes. This automated extraction helps reduce the selection and input of incorrect codes while simultaneously decreasing the overall time required to complete each chart, enabling coding teams to better manage high chart volumes.

Accurate coding is essential for reducing claim denials, and many denials can be traced back to issues like incomplete documentation, mismatched codes, or missing clinical details. AI helps address these issues by flagging potential gaps in documentation, verifying that selected codes align with payer policies, and prompting for clarification when needed. In fact, Commure Ambient AI customers have seen an average 25%+ reduction in denials. This kind of proactive support can reduce the risk of preventable denials and improve overall coding reliability.

Speed Up the Revenue Cycle

In many organizations, coding happens hours or even days after a patient visit, especially when teams rely on manual processes. Coders often need to review long notes, search for relevant clinical details, and double-check documentation before submitting claims. This review process takes time, and when coders are managing high volumes, it can lead to backlogs that delay reimbursement.

AI medical coding shortens that turnaround by analyzing clinical notes as they’re written and suggesting relevant codes directly within the workflow. Instead of waiting for a chart to be finalized and routed to a coder, the system extracts diagnoses and procedures in real time, flagging potential issues or missing information before the chart moves to billing.

This kind of support helps teams submit cleaner claims faster (Commure Ambient AI customers average 43 seconds to close a clinical note). It doesn’t eliminate the need for review, but it reduces the time spent on routine cases and minimizes errors that cause delays. As a result, coders can focus on complex encounters that still require human judgment, while more straightforward cases move through the process more efficiently.

Support the Full Encounter

Ambient AI tools extend the benefits of AI medical coding by assisting at every stage of the patient visit. Before the appointment, they pull relevant details of the patient’s record (such as prior diagnoses, recent test results, or care gaps) from the EHR and present them in a concise view to help clinicians prepare.

During the patient visit, ambient AI captures the conversation between clinician and patient, identifies key clinical elements like symptoms, assessments, and treatment plans, and structures that information in a draft note format. This reduces the need for manual note-taking and helps ensure important details aren’t missed.

After the visit, the system generates a draft of the clinical note and recommends billing codes based on the captured content. These recommendations are aligned with payer requirements and can be reviewed and edited by the clinician or coding staff before submission.

What sets ambient AI apart is its ability to tie together the entire encounter, from pre-visit data to real-time capture and post-visit coding, within a single, continuous workflow. This helps reduce duplication, supports consistency across documentation and coding, and gives teams a clearer picture of each visit from start to finish.

Key Considerations When Choosing an AI Medical Coding Provider

AI medical coding platforms vary widely in both capability and impact. Some are limited to code suggestions, while others offer deeper workflow integration, documentation support, and audit-ready reporting. When evaluating vendors, healthcare organziations should seek out tools  that not only automate tasks but also meaningfully reduce errors, improve throughput, and fit your team’s real-world workflows. Here are a few factors to prioritize:

• Coding accuracy and audit trail: Ask for validated accuracy metrics, ideally backed by real customer results or third-party testing. The platform should allow coders or auditors to see how each code was assigned and flag potential discrepancies.
  
  
• Integration with your existing systems: The solution should connect to your EHR and billing systems through established standards or custom APIs. Avoid platforms that require duplicate data entry or disrupt existing workflows.
  
  
• Capacity to scale with your organization: Whether you’re coding 100 charts a day or 10,000, the system should maintain performance under load. If you’re growing, make sure the vendor can scale infrastructure and support accordingly.
  
  
• User experience for coders and clinicians: Look for a system that fits naturally into existing workflows, minimizes disruption, and doesn’t add unnecessary steps. Features like in-line suggestions, simple editing tools, and onboarding guides can make adoption smoother.
  
  
• Implementation and long-term support: Evaluate what onboarding looks like including training, configuration, and timeline. Post-launch, confirm the vendor provides a clear support structure, regular updates, and responsiveness to your team’s evolving needs.
  
  
• Security and compliance: In addition to HIPAA compliance, look for industry-standard certifications like SOC 2 or HITRUST. The vendor should be transparent about how data is stored, accessed, and protected.

AI Medical Coding is a Must-Have in 2025

Increased documentation demands, understaffed coding teams, and stricter claim scrutiny by payers continue to make traditional medical coding a challenge for healthcare organizations. These pressures often lead to delays, denials, and added administrative overhead.

AI medical coding offers a practical way to reduce that burden. By supporting documentation and code selection throughout the entire clinical encounter, these tools can help teams submit cleaner claims faster, without overhauling existing workflows.

Leading health systems like Dignity Health are investing in solutions such as Commure Ambient AI that go beyond basic automation to focus on impact, integration, and usability. Ready to see what this looks like at your organization?

‍Discover Ambient AI

Orthopedic care isn’t just about surgery. It’s also about how well patients are supported throughout their journey—from preparing for treatment to healing afterward. As healthcare shifts to focus more on value-based care, hospitals and orthopedic practices are looking for better ways to keep patients informed, involved, and on track.

Thanks to new technology, digital tools powered by artificial intelligence (AI) are helping to make this possible. These tools, sometimes referred to as patient care journeys, guide patients before and after surgery, answer questions, send helpful reminders, and alert care teams when something might be wrong. This not only helps patients feel more confident, but it also leads to better health outcomes—like shorter hospital stays and fewer people returning to the hospital soon after discharge.

Why Patient Engagement Matters in Orthopedic Care

Orthopedic patients—whether they’re getting a joint replacement, healing from a sports injury, or managing long-term muscle or bone issues—often have a lot to keep track of. Preparing for surgery, recovering at home, and following rehab plans can feel overwhelming without the right support.

When patients don’t feel connected to their care team or don’t understand what to do next, problems can happen. They might miss appointments, skip important steps, or end up with complications that require another hospital visit. That’s why clear communication and ongoing support are so important.

How Digital Tools Help Keep Patients on Track

AI is changing the way patients experience care. Instead of feeling confused or forgotten, patients can now get personalized help throughout their journey. Here’s how these digital tools support orthopedic patients:

1. Helpful Guidance Before Surgery

AI-powered platforms like Commure Engage can send customized instructions, reminders, and educational materials to patients before their procedure. This helps patients feel more prepared and reduces the chance of last-minute cancellations.

2. Support During Recovery

Recovering from surgery is a critical time. These tools check in with patients automatically, collect feedback about how they’re feeling, and notify the care team if something seems off. This helps catch problems earlier and reduces readmissions.

3. Better Communication with Care Teams

Patients can message their care team, ask questions, or get updates through digital assistants or chatbots. This makes it easier to stay in touch without needing a clinic visit. Doctors and nurses can also use AI to make note-taking during visits faster and smoother, so they can focus more on the patient.

4. Smooth Coordination Between Providers

Orthopedic care often includes many people—surgeons, physical therapists, and home health aides. AI tools help make sure everyone stays informed and that patients get the right support at the right time.

Real Results from AI-Powered Engagement

Studies suggest that the power of digitally-enabled patient engagement is already here. For example, at Mount Sinai Hospital, Commure Engage helped improve orthopedic patient care in measurable ways for patients enrolled in the orthopedic digital care pathway:

• Patients left the hospital 1.5 days earlier on average.
  
  
• Fewer patients were readmitted within 30 days, which means better recovery and lower costs.
  

Commure Engage, our patient experience platform, is even more advanced with the integration of Memora Health’s technology and agentic AI, offering more intelligent automation, deeper personalization, and expanded support across the orthopedic patient journey.

What’s Next in Orthopedic Engagement

In the future, digital engagement in orthopedics will keep advancing and making a greater impact on patient care. We’ll see tools that:

• Use predictive insights to spot potential problems before they happen.
  
  
• Offer 24/7 conversational support for patients with orthopedic needs.
  
  
• Connect with electronic health records and wearable devices to track patient progress in real time.
  
  

Orthopedic care is evolving. It’s no longer just about what happens in the operating room—it’s also about how well we support patients every step of the way. By using AI-powered digital tools, providers can offer a better experience, build stronger relationships, and help patients heal faster.

‍Want to learn more?
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Read the full case study to see how Commure Engage is helped improve orthopedic patient care journeys at Mount Sinai.