Commure Up Close: Ashley Connects Talent with Opportunity

Interest in healthcare AI is growing fast. NVIDIA’s 2025 State of AI in Healthcare and Life Sciences survey found that 63% of respondents (more than 600 healthcare professionals spanning medical technology/tools, digital healthcare, pharmaceutical, payers, and providers) are actively using AI and 81% say it has helped increase revenue at their organization.

As innovative health systems and practices across the country are implementing AI solutions to streamline their workflows, some patients and care teams remain hesitant. The caution makes sense. Questions about safety, transparency, and reliability are valid in an industry where there’s little margin for error.

It’s up to healthcare leaders and technology providers to bridge that gap. Building trust in AI means understanding the concerns behind the skepticism and addressing them directly.

Common AI-Related Concerns Among Patients and Providers
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• Privacy and security: Patients and providers worry about data misuse or breaches. In one survey, 63% of patients and 87% of physicians flagged privacy as a top concern.
  
  
• Transparency: AI systems often lack clarity around how decisions are made, raising fears of hidden bias or errors.
  
  
• Accuracy: Misinterpretations can have serious consequences, especially when AI is used in diagnosis or treatment planning.
  
  
• Job security: Some clinicians see AI as a threat to their roles, which can slow adoption and fuel skepticism.
  
  
• Education gaps: Many users don’t fully understand what AI can and can’t do, making trust harder to build.

Turning AI Skepticism into Confidence

Patients and providers have valid concerns about AI in healthcare. But when implemented responsibly, AI can free up clinical time, reduce administrative burden, and improve the consistency of patient communication. For healthcare leaders, building trust starts with selecting the right solutions and creating clear strategies to earn buy-in across the organization.

‍Choose AI Tools With Safety at the Core

AI in healthcare takes many forms. Some tools generate responses from training data, while others rely on preset clinical content. Large language models are now capable of generating responses to a wide range of inputs and can even perform tasks such as data entry. However, AI models can also provide inaccurate responses. Grounding these models in clinical data, clinical guidelines, and scientific evidence helps to ensure that answers will be on-target. Furthermore, some AI systems also include safeguards that include continuous monitoring of model performance to minimize that risk. 

For health systems, trust begins with choosing technology that’s designed for safety. Leaders should evaluate how each system works, what information it relies on, and how it supports clinical oversight. Those decisions help ensure the tool strengthens care while minimizing risk.

Know the Source of Your AI’s Data

AI is only as good as the data behind it. When inputs are flawed, outputs follow suit. Inaccuracies, hallucinations, and outdated information have already cost organizations across industries. In healthcare, these risks are even more pronounced as they carry both a financial risk and are tied to patient trust. One Medscape survey found 88% of physicians worry about generative AI delivering inaccurate health guidance. Meanwhile, a Salesforce survey found 63% of patients share concerns about compromised or misleading information.

That’s why healthcare leaders must evaluate where an AI platform’s information comes from and how it’s maintained. Commure’s patient experience solution, Commure Engage is built on a clinician-curated content database developed with ongoing input from health systems. This ensures guidance reflects current practice and stays within defined clinical boundaries, giving patients reliable support and keeping care teams in control.

Equip Your Teams With Clarity and Confidence

Uncertainty about job loss are common when new technologies are introduced, with 65% of physicians reporting concerns about AI making diagnosis and treatment decisions. But in healthcare, most AI tools are designed to streamline administrative tasks, not replace clinical judgment. Charting, patient follow-up, and care coordination are just a few examples of workflows AI can help simplify, giving staff more time to focus on patients.

Leaders play a key role in shaping how these tools are received. Clear communication about what AI can and can’t do helps reduce fear and misinformation. Staff should know how AI will be monitored, how it fits into care delivery, and how to raise concerns. With the right education, teams are more likely to adopt these tools with confidence and use them effectively.

Look for a Technology Partner with a Clear Responsibility Framework

Responsible AI begins with clear values. As developers adapt to a shifting ethical landscape, they should be able to show how their platforms are built with safety and accountability in mind. When evaluating vendors, ask whether their principles are designed specifically for healthcare and whether they’re embedded in the product, not just marketing.

At Commure, our framework focuses on:

• Safety and reliability: Built to perform as intended, with safeguards in place from the start.
  
  
• Human oversight: Designed to keep clinicians in control, with transparent review and feedback loops.
  
  
• Equity and fairness: Developed to reduce bias and expand access, especially for underserved groups.
  
  
• Privacy and security: Structured to meet HIPAA standards and protect patient data at every level.

Trust Drives Impact

AI has the potential to reshape how care is delivered. But its value depends on trust. For platforms to succeed, patients and care teams need to know the technology is safe, reliable, and thoughtfully designed. That trust is built when developers listen to users, address real concerns, and ensure every solution reflects the needs of the people it serves.

Ready to see why Commure Engage is trusted by both patients and providers?

Explore the Engage Platform

AI is taking the healthcare industry by storm, with nearly two-thirds of physicians reporting utilizing AI in 2024, up from only 38% in 2023. Being able to easily integrate AI tools into existing workflows and quickly find new use cases is key to success. This is where AI healthcare assistants are becoming essential tools. Unlike past digital assistants (such as chatbots or phone-based interactive voice response (IVR) systems), today’s AI healthcare assistants can adapt, learn, and operate across clinical, operational, and financial workflows.

Innovation leaders across the country are reviewing AI healthcare assistants and agents to streamline workflows from patient access to back-office operations. But with so many options, the questions remain: Which use cases are real? How are AI healthcare assistants actually delivering value today?

AI Healthcare Assistants vs Traditional Healthcare Automation

Traditional automation tools in healthcare (like IVR systems or chatbots) follow fixed scripts and often lack the flexibility to respond to real-world complexity. AI healthcare assistants (also known as AI Agents or Agentic AI) are built differently. By utilizing generative AI/natural language processing and integrating in real-time with EHRs and hospital systems, they support a wide range of users and tasks, from guiding patients through appointment scheduling to helping staff manage intake, billing, follow-up, and more.

These assistants work across clinical, operational, and financial workflows, processing information in real time. This ability makes them far more effective than earlier systems that required manual oversight or constant updates.

Commure Agents take typically point-solution-based AI healthcare assistants a step further by delivering these capabilities through a single, modular platform. Built in close collaboration with clinical teams, the tools are designed for ease of use and rapid deployment. They integrate with existing infrastructure, allowing health systems to address targeted needs first and scale over time. The result is a smarter approach to automation that improves outcomes without adding complexity.

Clinical, Operational, and Financial Use Cases for AI Healthcare Assistants

Clinical Use Cases

Leading health systems are embedding AI healthcare assistants into clinical workflows to increase efficiency and support care quality. One of the most prominent uses is ambient documentation, where the assistant captures and structures notes during a patient encounter. This reduces the time providers spend charting and improves the completeness of clinical records. Providers using Commure Ambient AI see an average 81% reduction in overall clinical documentation time.

AI healthcare assistants can also trigger timely post-visit follow-ups, checking on symptoms, recovery progress, or medication adherence. By automating these touchpoints, health systems can stay connected with patients between visits without adding to clinician workload.

Operational Use Cases

Front office staff also benefit significantly from AI healthcare assistants by automating patient intake, verifying demographic data, and flagging missing information before the visit even starts. AI healthcare assistants can also handle appointment scheduling and rescheduling, reducing call volumes and administrative backlogs.

Pre-visit instructions, such as fasting requirements or parking details, can be sent automatically and adjusted based on appointment type. These improvements enhance operational efficiency and patient preparedness, which translates into fewer delays and a smoother front-desk experience.

Financial Use Cases

For billing teams, AI healthcare assistants ensure smoother transactions and better communication. They can verify insurance eligibility, confirm benefits coverage, and flag issues that might delay payment. By prompting patients to update insurance information or submit missing documentation, these assistants help reduce claim denials.

They also automate payment reminders and offer flexible payment plan options based on patient eligibility. This improves revenue cycle performance while making the process less confusing and more transparent for patients.

Closing the Gap Between Hype and Results

Virtual assistants used to mean clunky phone trees or websites that couldn’t answer basic questions. That’s no longer the case. The most effective tools today use advanced AI and are embedded in clinical and operational workflows, built for outcomes from the start. They integrate with existing systems, adapt to user needs, and begin delivering value without long implementation cycles.

Commure brings this into a modular, interoperable platform that is co-developed with the clinical teams using it. This foundation keeps the focus on what matters most, providing excellent care, capturing revenue, and making the most out of clinician time.

See What’s Possible With AI Healthcare Assistants

“I was getting to the point where I was considering changing my career because I just had no life… I’m now incredibly happy. I left for a two-week vacation with absolutely nothing on the table and could walk away without any stress.” - Andrea Hazelton, FNP-C @ Pioneers Medical Center

This is just one of countless examples of clinicians across the country who are drowning in documentation, and how utilizing AI-generated doctor’s notes can help.

In fact, clinicians now spend an average of 16 minutes per encounter in the EHR, 11% of it after hours. Nurses report spending 19–35% of their entire shift documenting, a sharp increase from 9% in the paper era.

With this much time spent on administrative work, it's no surprise that 43% of clinicians report at least one symptom of burnout.

To add to that, health systems are under more pressure than ever to improve efficiency and maintain rigorous documentation standards. AI-powered doctor's notes offer a promising path forward. Here's what you need to know.

What Health Systems Should Expect From AI Doctor’s Notes

AI-enabled documentation is rapidly becoming a baseline capability. But as the market grows more crowded, the gap between commoditized features and enterprise-grade performance is widening.

For health systems, the real question is not whether to adopt AI-generated notes, it’s how to choose a solution that delivers measurable, system-wide value. That starts with identifying the operational outcomes that matter most:

• Reducing denial rates and billing friction
  
  
• Accelerating reimbursement through more streamlined claims
  
  
• Lowering clinician after-hours charting time
  
  
• Improving consistency in documentation across teams
  
  
• Scaling seamlessly across departments without heavy lift

Vendors may check the same boxes on a feature sheet, but implementation success and sustained value hinges on how well those tools work in reality.

Why Going Beyond Dictation is a Necessity

Legacy AI doctor notes tools still require clinicians to record their thoughts after the visit and often involve manual entry or editing. That workflow hasn’t kept up with modern clinical demands.

More advanced AI-powered documentation tools change the dynamic:

• Documentation starts and ends in the exam room: No need to circle back post-visit or piece together notes from memory.
  
  
• Every note reflects the full clinical picture: Tools pull in prior labs, diagnoses, and medications automatically.
  
  
• Nuance is preserved, noise is removed: Small talk is filtered out while clinically relevant cues are retained.
  
  
• Less screen time, more patient time: Providers can engage directly without toggling between note fields.
  
  
• Notes are directly integrated with EHRs and beyond: Structured output drives billing, coding, reporting, and workflow handoffs.

These advanced tools are already in use at leading health systems. At Dignity Health, providers using AI-generated documentation cut their charting time by up to 41%, freeing them to focus more on patient care.

What Health Systems Should Look For When Choosing an AI Doctor’s Note Solution

Not all AI-generated notes are created equal. Health systems evaluating tools should ask:

• Does the solution support multiple languages across diverse care settings?
  
  
• Does it support real-time documentation during multi-speaker conversations?
  
  
• Are the outputs structured enough to feed into coding, billing, and compliance systems?
  
  
• How fast can the platform be deployed and configured for different specialties?
  
  
• What clinician satisfaction or adoption metrics has the vendor demonstrated post-implementation?
  
  
• How customizable is the system for different note styles, specialties, and documentation templates?
  
  
• What level of human-in-the-loop quality control or review is included in the workflow?
  
  
• How transparent is the AI’s decision-making when it comes to clinical inferences or omissions?

Ultimately, to drive real impact, documentation tools must enhance, not complicate, how providers work day to day.

Commure Ambient AI is Built to Deliver at Scale

Commure Ambient AI moves beyond basic transcription by generating complete, structured, EHR-integrated notes without added steps.

At Commure, health systems using Ambient AI report up to 81% less time spent on charting and a 25% drop in claim denials. That impact is driven by the system’s ability to understand clinical context, reference relevant history, and produce notes that are ready for coding, compliance, and reimbursement.

For health systems looking to modernize documentation, the goal should be better outcomes through streamlined workflows. Ambient AI offers a path to scale efficiency and quality without adding complexity.

Explore Commure Ambient AI

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

I love to travel, my lifelong goal is to visit every World Wonder from the Civilization franchise. I’m about 35% of the way there so far!

Outside of that, I dabble in a bit of everything: I bike, ski, game with friends, read manga, watch movies, and avidly follow the NBA. One of the best things about living in the Bay Area is that there's always a place to do whatever you’re into, and the weather makes it easy to stay active year-round.

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

A Jedi Knight. Unfortunately, my lack of Force sensitivity meant I had to explore more practical options. Thankfully, software development turned out to be a pretty good alternative.

In all seriousness, I’ve always known I’d end up doing something computer-related. I considered careers in research, computational linguistics, robotics, and even aerospace. But when I got to college, I realized that software development was what I genuinely enjoyed most. There’s something deeply satisfying about watching your code run. The moment you realize your logic rendered a reflective object, created an unbeatable Connect 4 player, or simulated particle movement in an accelerator, it’s impossible to beat that feeling.

Describe a day in the life of your role.

As the tech lead, my role revolves around two core responsibilities. First, I lead development for major features on our product roadmap, whether that’s building infrastructure to integrate with EHRs or developing an AI clinical copilot that helps physicians edit their notes. Second, I focus on team enablement, making sure engineers are aligned, growing, and enjoying their work.

Day to day, it varies. Some days I’m in the trenches with the team, pair programming, reviewing code, or validating designs. Other days, I block off time to focus and tackle complex technical problems on my own. The common thread is that every day brings something new, and that keeps things fresh and exciting.

What made you decide to join Commure?

What drew me to Commure was the speed. Things move fast here. You're only limited by your own initiative, and that’s something I really value as a developer.

The second big draw was the mission. If you talk to any clinician, their biggest complaint is always documentation, the typing, the clicking, the endless paperwork that pulls them away from patient care. A few years ago, the idea of a “touchless” clinical workflow felt like fantasy. But now, with transformer-based LLMs and generative AI, it's a reality. These models can ingest huge volumes of natural language and produce fluent, human-like summaries. It’s a complete paradigm shift, and I knew I didn’t want to sit on the sidelines. Commure gave me the opportunity to be on the front lines of that transformation.

How would you describe the Commure company culture?

Fast-paced and results-oriented. At Commure, you can stand up the infrastructure for an entire product line in two days, no questions asked. 

It’s also highly impact-driven. You’re not working on abstract projects that disappear into the void. Everything you build has clear, visible value, and you can observe the difference you're making.

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

Don’t be afraid to ask questions. The worst thing you can do is pretend to know something and end up going down the wrong path. People are always happy to help, and it’s much faster to learn the right way than to spend time undoing mistakes.

A piece of wisdom from my Youth Symphony conductor that has stuck with me: “If you’re going to make a mistake in rehearsal, make it loud.” In other words, communicate openly; it’s always better than struggling in silence.

What has been your greatest accomplishment so far at Commure?

The growth of Commure Ambient AI has been nothing short of incredible. We’ve launched seamless EHR integrations, built an AI copilot, and even created tools that can learn a provider’s documentation style from just a few examples. The progress has been astonishing, and it’s just the beginning.
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Interested in a career building the next generation of healthcare technology powered by AI? We are always looking for talented people across our departments.

Explore Career Opportunities

You likely had several options — why did you choose Commure?
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Nikki Solanki, Chief of Staff to CEO (Biology and Economics ‘24): I chose Commure because I was drawn to the fast-paced, high-impact environment and the opportunity to work directly alongside the CEO on a mission that matters. Healthcare is often seen as slow-moving, but Commure proves you don’t have to follow industry norms to make real change happen. In my role as Chief of Staff, no two days look the same. I get visibility into every part of the business, from sales and product to engineering and operations. That breadth not only keeps the work exciting but also pushes me to grow quickly as a strategic operator. I was excited to join a team of people who genuinely care about the impact of their work. What stood out was how deeply committed everyone was to solving real-world problems that matter, and doing it with urgency. Our core principle of “speed above all else” isn’t just a tagline; it creates a culture where smart, driven people rally together to move fast and make a difference.

Hugh Sagawa, Software Engineer (Computer Science ‘23): I originally heard of Commure, when my friend referred me and told me the work here is very intellectually stimulating. Personally, I have had family members struggle to get vital appointments that they need in the healthcare space. If I can help save time for providers so that they can see more patients, that is a win.

Athan Liu, Software Engineer (Statistics and Economics ‘21): I always wanted to be a software engineer, and after COVID I knew I wanted to work in the healthcare space, which is why I joined Epic out of college. After a few years, I wanted to work in a faster-paced environment, like Commure, where you’re going full throttle all the time. I’ve done a lot of immersion trips to healthcare systems, and what really stood out to me was how much time providers spend doing documentation. The moment all the new LLMs and transcription models came out, it was pretty obvious that this would be a game changer to a future “hands-free” documentation process in health care. So when I saw what Scribe Growth was doing, and how much impact I could have as an engineer in shaping and pushing that product forward, it was a no-brainer to join.

Milton Lee, Operations Manager (Economics ‘22): I was previously working at an older, more established EHR company, and while I loved the mission of helping providers help patients, I was looking for a company that offered a higher degree of agency and ownership while maintaining a product suite with a similar mission. I really liked the opportunity to work directly with providers to use cutting-edge technology to decrease the documentation burden in a significant way. I was drawn to the idea that working hard here would not only benefit me, but would also help patients get better care from doctors who can afford to give them more time and attention.

How have you grown?

Nikki: In a fast-paced startup environment, there’s rarely a perfect roadmap. Over time, I’ve learned to trust my judgment, get comfortable making decisions with imperfect information, and rally the right people to execute quickly.

Hugh: I’ve definitely become a stronger programmer at Commure and have also become better at working collaboratively. 

Athan: At Commure, I’ve learned how to actually manage a team while still getting to do engineering tasks. At my previous company, becoming a team lead meant hanging up your IC hat, so I never really wanted to become one, but here it’s different. I learned a lot more about training new engineers, setting team culture, and having fun while doing so.

Milton: As part of a quickly growing and changing team, I’ve had the opportunity to personally design and implement a far wider range of approaches to software implementation than I could have otherwise.

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

Nikki: We work hard, but we genuinely enjoy spending time together outside of work - whether it’s Tuesday-night dinners, weekend trips to Tahoe or Yosemite, or spontaneous flights to Europe. When your coworkers are also your friends, it makes the day-to-day more fun and the tough moments easier. It’s what makes the team at Commure really special.

Hugh: Sometimes we have board game nights where we play Dominion. I already enjoyed this game and owned it before I even started working at Commure. It was awesome to learn that many members of my team also played it.

Athan: The Warriors have never lost when we have been in attendance. Therefore, in order to secure another championship before Steph retires, I think the team should get courtside season tickets for free. Call me Joe Lacob.

Milton: I find it inspiring that my team takes pride in their work and constantly pushes toward our shared goals.

What would you tell another University of Chicago student considering Commure today?

Nikki: I’d say: if you’re looking for a place where you’ll be challenged, learn fast, and actually see the impact of your work, Commure is it. It’s not the kind of place where you wait your turn or follow a set playbook. You’ll be thrown into real problems from day one, surrounded by smart, driven people who care deeply about what they’re building. If you like the intellectual intensity of UChicago and want to apply that to something fast-paced and meaningful, you’ll feel right at home here.

Hugh: Coming out of UChicago, you’ll definitely have to learn a lot on the job, as the computer science major at UChicago definitely is more focused on the academic side rather than engineering. However, you’ll definitely be able to learn quickly and become more than proficient applying what you learned at UChicago.

Athan: Coming straight out of school into the workforce, there’s going to be a bit of a learning curve. You might not even be on the radar for a lot of positions because UChicago isn’t renowned for being an ‘engineering’ school. But that doesn’t mean you shouldn’t apply. If you have the passion and the drive, you will excel!

Milton: I’d tell them that it’s a good place to find opportunities for creative problem solving and meeting interesting, driven colleagues.

Tens of thousands of patients undergo surgical procedures every day in the United States. Successful outcomes depend on both a well-executed surgery as well as the ability of the patient to follow a post-operative care plan at home. Even with thorough discharge instructions and after-visit summaries, key details often get lost in the transition from hospital to home. One survey found 66% of patients leave their appointments without asking important questions, leading to confusion, complications, or missed steps in recovery.

The reality of healing at home often brings unexpected challenges including new medications and unexpected symptoms that can overwhelm even those familiar with the process.

But that’s starting to change. AI is helping patients navigate recovery more confidently while giving care teams new ways to extend support beyond the hospital stay.

‍The Challenges of Post-Surgery Care Management

Surgery may be the turning point in a patient’s treatment, but what happens afterward often presents its own set of complications. Without steady support, the following challenges can slow progress and increase the risk of complications.

Managing Medications

Medication management after surgery is rarely straightforward. While prescriptions are a standard part of most recovery plans, following them precisely requires more effort than many realize. Instructions can be complex, especially when they involve multiple drugs with different timing, dosages, and side effect profiles.

Roughly half of patients are prescribed medications after surgery, but adherence isn't just a matter of remembering. Some struggle to get to the pharmacy. Others second-guess whether a symptom is normal or cause for concern. And when confusion sets in, it’s not always clear where to turn for answers.

Missed or mismanaged medications can delay healing, trigger avoidable complications, and undermine the hard work of the surgical team. For something as routine as a prescription, the margin for error is surprisingly thin.

Communication Gaps

Clear, timely communication is one of the most important factors in post-surgical recovery. Patients often leave the hospital with lingering questions, unsure of what symptoms to expect or when to ask for help.

The consequences of this disconnect are well-documented. A multi-study analysis found that patients who received thorough education before abdominal surgery had fewer complications, shorter hospital stays, and better psychological outcomes. When patients understand what’s ahead, they’re more prepared to manage it.

Transportation and Accessibility

Mobility challenges are common after surgery, which makes access to transportation a critical part of recovery. Follow-up visits, physical therapy, and prescription pickups all require patients to leave home, often before they feel ready.

Yet for many, getting there isn’t simple. An estimated 3.6 million Americans miss medical care each year because they don’t have reliable transportation. That barrier is even more pronounced for patients recovering from surgery, especially those living in rural areas or managing care on their own.

Unexpected Symptoms or Side Effects

No matter how well providers prepare patients for recovery, surprises are almost inevitable. New symptoms can appear without warning, and even common side effects can feel alarming when they happen at home.

Take nausea for example. One study found that 20 to 40 percent of patients experience nausea or vomiting after surgery. For someone recovering alone, that can feel like a serious setback, and without clear guidance, it may prompt a return to the hospital that could have been avoided.

How Conversational AI Can Support Postsurgical Patients

Conversational AI is emerging as a practical way to reduce friction in the recovery process. By helping care teams deliver timely, personalized support, these tools can improve how patients recover after surgery. From reinforcing medication plans to spotting potential risks, here are a few ways AI-driven platforms are making recovery more manageable for everyone involved.

Supports Medication Adherence

Patients who don’t clearly understand their instructions are less likely to follow them. Phone check-ins aren’t scalable, and many patients fall through the cracks.

Conversational AI helps close that gap. These tools remind patients when to take medications, explain how each one fits into their recovery, and respond to common questions about treatment. They also check whether doses were taken and ask patients to share reasons if they weren’t. That information is recorded in a clinician-facing dashboard, giving care teams a clearer picture of who may need follow-up and why.

‍Answers Common Recovery Questions

After surgery, it’s normal for patients to have questions about symptoms, side effects, and when something might signal a more serious issue. But with limited access to providers, many end up calling or showing up in person for reassurance.

Messaging tools have tried to help, but often just shift the burden. Inboxes fill up, response times slow down, and clinicians lose time they don’t have.

Conversational AI offers a more scalable option. By using natural language processing to understand patient concerns, these tools can deliver accurate answers based on clinical guidance.

Surfaces Barriers to Recovery

Recovery plans often assume patients can access what they need; a ride to the pharmacy, a stable place to rest, or the means to follow up. But that’s not always the case. Challenges tied to social determinants of health, like housing, food security, or transportation, can easily disrupt care.

The American College of Surgeons emphasizes the importance of identifying these risks early. Conversational AI makes that possible by prompting patients to share obstacles they’re facing. That information is logged for care teams and can trigger tailored support, like transportation help or financial resources, to keep recovery on track.

Routes Urgent Issues to the Right Provider

Most patient questions after surgery are routine, but some signal something more serious. In a hospital, a nurse or doctor is just steps away. At home, patients often don’t know who to contact, or whether they need to.

Conversational AI helps make sure nothing serious gets overlooked. When a patient flags a concern, the system can detect urgency and alert the right provider to step in. It also makes it easier to coordinate across specialties, so the patient gets the right support without unnecessary delays or missed signals.

Bringing Recovery Into Reach

The days and weeks after surgery are just as critical as the procedure itself. With the right digital tools, health systems can give patients the support they need to recover safely, ask better questions, and avoid preventable setbacks.

Commure Engage makes this kind of support possible. Built with clinical input and proven in the field, it helps care teams extend their reach without adding more to their workload.
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See AI Patient Engagement In Action

Today, nearly 129 million Americans live with at least one chronic condition. These long-term illnesses (such as diabetes, heart disease, and kidney disease) now account for a staggering 90% of the nation’s $4.5 trillion in annual healthcare spending. Unlike other care journeys, chronic conditions require continuous care, personalized monitoring, and long-term treatment strategies, placing a growing burden on already-stretched clinical teams.

With the number of patients diagnosed with chronic conditions expected to rise sharply over the next decade (driven in large part by an aging population), health systems must act now to equip their care teams with the tools and support they need.

In this article, we explore the key challenges of chronic care management and how AI-powered solutions like Commure Engage are helping providers meet those challenges head-on.

What Makes Chronic Care Management So Challenging?

Chronic care management (CCM) is designed to help patients with long-term conditions (such as diabetes, heart disease, cancer, and chronic kidney disease) stay on track with their health goals and improve their overall quality of life. But delivering consistent, effective chronic care at scale is no small feat, especially because these conditions are complex and require collaboration across multiple stakeholders. Providers face a range of structural, clinical, and social barriers, including:

Low patient engagement and adherence: Many patients struggle to follow treatment plans once they leave the clinic. Medication routines, lifestyle adjustments, and regular check-ins can feel overwhelming, especially for individuals who don’t fully understand their diagnosis or why these steps matter. At the same time, care teams spend two hours on administrative work for every hour of engagement with patients, making it nearly impossible for care teams to be available to respond in real-time whenever someone has a question or concern during their care journey.

Gaps in access and workforce capacity: Patients in rural or underserved areas often lack access to specialists, resources, or even basic healthcare services, in fact as of September 2024, 66.33% of Primary Care Health Professional Shortage Areas (HPSAs) were located in rural areas. Meanwhile, the healthcare workforce is increasingly stretched thin, leaving little time to proactively manage chronic conditions manually.

Fragmented care coordination: When providers across primary, specialty, and community care settings operate in silos, it can lead to communication breakdowns, duplicated work, and inconsistent care. Poorly managed care transitions in critical moments, such as hospital discharges, often result in confusion and missed follow-ups, putting patients at risk.

Outdated tools and disjointed data: Many care teams still rely on manual workflows, disconnected EHRs, and legacy systems that make it difficult to track patient progress, share information across teams, and manage long-term care plans effectively.

Social and behavioral health factors: Social determinants of health (SDOH) (including income level, housing stability, access to transportation, and health literacy) play a major role in a patient’s ability to manage their condition. Coexisting mental health challenges, such as depression or anxiety, can further complicate care and increase the need for support.

How AI Helps Relieve the Burden of Chronic Care Management

Chronic care management places a heavy burden on both patients and care teams. For patients, managing symptoms, treatment plans, and follow-ups outside the clinic can be confusing and isolating. For providers, fragmented systems and manual workflows make it hard to deliver consistent, coordinated care. AI can help on both fronts, giving patients timely, personalized support while reducing the administrative load on clinical staff.

Here’s how solutions like Commure Engage are helping:

1. Smarter Symptom and Side Effect Support

Managing chronic illness at home can be unpredictable. Patients often face flare-ups or medication side effects that cause concern and disrupt their care plans. Even when these issues are minor, they can lead to patient anxiety, a surge in messages to care teams who are already operating at capacity, and even unnecessary hospital visits.

Commure Engage helps alleviate this pressure. The platform uses AI and natural language processing (NLP) to respond to most symptom-related questions instantly, with answers tailored to each patient’s care journey. The guidance is pulled from a trusted, clinician-curated knowledge base, ensuring patients receive accurate, relevant information. This allows care teams to focus their time where it matters most, while patients stay informed, on course, and out of the hospital.

2. Better Data Collection and Clinical Visibility

Effective chronic care depends on data, but turning that data into meaningful insights has traditionally relied on time-consuming, manual work from clinicians.

Commure Engage helps streamline this process. The platform automatically collects patient data through SMS check-ins and connected monitoring devices, tracking changes in key indicators like blood pressure, blood glucose, and weight. All of this information appears in a clinician-facing dashboard integrated with the EHR, making trends easy to view and act on. Care teams get the insights they need, right when they need them, without the burden of manual data entry.

3. Stronger Support for Social Determinants of Health (SDOH)

Social factors like food insecurity, unreliable transportation, and limited health literacy can create major challenges for patients managing chronic conditions. These issues often occur outside the clinical setting, but the responsibility to address them frequently falls to care teams.

Commure Engage helps lighten that load. The platform checks in with patients through SMS-based conversations and automated assessments, using targeted questions to uncover common SDOH challenges like food insecurity, transportation issues, and limited health literacy. This gives care teams a clearer picture of the external factors affecting each patient’s ability to manage their condition. Based on those insights, the platform surfaces relevant resources such as nutrition support, ride services, or pharmacy access so patients can get help without adding more work for clinical staff.

4. Scalable Provider Communication and Care Coordination

Chronic conditions often require patients to interact with multiple specialists and providers. Managing these relationships can be overwhelming for patients and creates added complexity for care teams trying to coordinate treatment plans across roles.

Commure Engage helps simplify this process. The platform enables providers to collaborate in a shared communication thread, tagging one another to streamline coordination. It also uses AI to route patient concerns to the appropriate team members, helping ensure timely, personalized follow-up without adding friction to the care workflow.

The Future of Chronic Care Management Is AI-Enabled

Supporting patients with chronic conditions is a complex and resource-intensive task, but AI is making it more manageable. Platforms like Commure Engage are already transforming how health systems deliver chronic care by reducing manual workload, improving care coordination, and helping patients stay engaged between visits.

As AI continues to evolve, providers will be even better equipped to deliver proactive, personalized support at scale. The path to more effective chronic care is no longer a distant goal. It’s already taking shape.

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Clinical teams are overwhelmed by the bloated tech stacks meant to support them. A 2023 survey of health system clinicians and CIOs found that 55% of respondents use between 50 and 500+ software solutions to run their healthcare operations. Innovations in healthcare tech that were supposed to streamline care have instead turned into a fragmented maze of point solutions, each solving a narrow problem while creating broader inefficiencies across the health system.

At the May 2025 Commure Nexus event, Ian Shakil (Chief Strategy Officer at Commure, and former Founder of Augmedix) explored how health systems can break free from this cycle and adopt a platform-based strategy for AI healthcare innovation.

“There are far too many point solutions degrading the digital health revolution, and now is the time to consolidate rationally to platform partners.” - Ian Shakil

The Problem with Point Solutions

The current market is oversaturated with narrow tools that address individual sections of clinician needs, including documentation, coding, order entry, RCM, prescriptions, and more. But for healthcare organizations, layering multiple vendors means stacking logins, integrations, and risks. This creates a massive work tax by disrupting workflows, takes attention away from patient care, and can also have huge financial implications. Ian Shakil captured the absurdity of this approach when he asked: 

“Can you imagine stacking all those per-user-per-month fees and what that would come to? I don't even know how you calculate it.”

Beyond user experience and cost, the burden of oversight is unsustainable for health systems. “I'm very familiar with health systems and their security reviews, compliance reviews, and responsibility committees,” Shakil continued. “I'm not sure there are enough people in the world to do all these committees for all of these different solutions.”

Fragmented digital systems can also create frustrating, disjointed experiences for patients, including missed follow-ups, redundant questions, delayed diagnoses, and difficulty accessing care.

What it Takes to Implement an AI Platform

Co-Development with Health Systems

Implementation requires deep collaboration and flexibility.

Commure’s approach centers on a Forward-Deployed Engineering (FDE) model (embedding product and engineering teams directly alongside health system partners). This allows for faster iterations, tailored customizations, and deeper alignment with clinical and operational workflows. Whether deploying across multiple EHRs at a system like HCA or optimizing for multilingual care at NEMS, Commure’s teams operate as an extension of the health system, not an external vendor.

A Headless EHR Approach to Healthcare Transformation

Health systems today face a strategic inflection point: consolidate around a single EHR, gamble on emerging point solutions, or invest in a flexible platform. Ian Shakil warned that relying solely on a singular EHR is shortsighted:

“To be all in on [your EHR] and perceive that as your strategy and to be beholden to it and its pacing and its tech debt… can sometimes be dangerous."

Ian then pointed to the emerging concept of the “headless EHR”, an open, intelligent layer that interacts with existing systems without being limited by them. This concept is core to Commure’s approach: by operating above/around the EHR layer, Commure enables health systems to innovate rapidly, deploy AI capabilities without being bottlenecked by vendor roadmaps, and unify workflows across fragmented systems.

Platform-Powered Agentic AI

“We're moving from documentation assistants to workflow orchestration agents, enabling autonomous care actions.” These AI agents are designed from the ground up to jump in, take on routine tasks, and help clinicians move smoothly from one step to the next.

Combining agentic AI with Ambient AI is where it starts to feel like a real teammate. Shakil described it as “this super resident being able to go so much further beyond the note… teeing up ambient nudges to help you with CDI, CDS, patient safety.” Whether it’s pulling together a post-visit summary, catching a missed diagnosis code, or teeing up a care plan handoff, agents work quietly in the background without slowing anything down.

Commure is building for flexibility, with modular agents that plug into real-world care settings. It’s all about creating value fast and making sure AI works for the clinician, not the other way around.

Point solutions are dead. Platform thinking is the path forward.

Curious how health systems like HCA and NEMS are putting these strategies into practice today?

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