Wednesday, May 6, 2026

< + > The Quintuple Aim of Value Based Care Enabled by The Garage

Hard to resist the opportunity to go to a user conference that includes attending the F1 Miami race in Miami which also became the theme for the conference.  The Garage’s recent user conference did just that as they leveraged some of the learnings from F1 racing to illustrate how they’re racing to enable value based care in organizations.

Throughout the event I captured a number of the insights that were shared on stage at the conference.  I was only able to go to part of the event, but below you’ll find some of the key perspectives along with a little additional commentary.

One of the things that the FUSE 2026 organizers from The Garage deserve credit for is the opportunity they provided for attendees to be able to connect with each other.  We hear all the time that one of the most valuable things at conferences is connecting with other attendees.  The Garage did a great job helping those in attendance to meet and connect with each other.  The F1 Race helped and this breakfast by the water helped as well.  Nothing like connecting with other attendees and I’m sure The Garage benefitted from that time too.  Plus, those connections reframed the rest of the conference experience because we were already familiar with each other.

As mentioned, the racecar theme drove a number of the topics of discussion at the event.  That included Sabré Cook, one of the most successful female drivers on the circuit, sharing some insights from her experience as a female driver.  I loved when she said that “Doing whatever it takes isn’t always comfortable”, but then acknowledged that pushing yourself into the uncomfortable is the way to grow and do the very best for patients.

Hearing about Sabré Cook’s struggle to survive in a sport that’s really expensive was inspiring.  Particularly when she shared how it’s important to bet on yourself even when the outcome is not guaranteed.  Lots to chew on there, but it’s powerful to trust in yourself even when something seems hard or even impossible.

In a world where most startup companies are trying to become unicorns overnight (rarely happens), The Garage actually illustrates the path that most of the best health IT companies take.  14 years in to their journey and they’re just getting started.  I’m sure there are lots of reasons why Pranam Ben said they’re just getting started, but it definitely illustrates how far both value based care and technology have come.  It does feel to me like we’re at a really interesting confluence of policy, data, and technoogy that is going to push value based care in new and novel ways.

I love when companies take a good thing like the triple aim and are even more ambitious with their goals.  Far too many in health IT code their product to the standard.  That’s not a bad thing, but it usually doesn’t end up well to just code to the bottom of what’s required.  It’s much more powerful to push far beyond what’s required.  That’s what I feel about The Garage’s Quintuple Aim.

What do you think of Pranam Ben, CEO of The Garage’s assertion that healthcare will be human centric?  There are a lot of people out there shouting about how the robots are going to take over parts of healthcare.  Will human centricity in heatlhcare change?  I personally agree with Pranam that it will always be out the human.

One thing that’s unique about F1 is that the cars are optimized for performance and much of the competition is about those optimizations.  Sabré Cook shared that she’s learned over time that when optimizing the car, it’s better to optimize it for the driver’s style and preferences rather than making it a tenth faster regardless of driver.  Feels like there’s a lesson there as we customize value based care efforts to clinicians and healthcare organizations.

Another great analogy from car racing is making sure that the right systems are in place for the driver to respond with the right decision quickly.  That is exactly what we need for clinicians.  They need the right data at the right place and the right time to be able to treat the patient in the most effective way possible.  We don’t always do that in healthcare, but it was great to hear how The Garage is helping more clinicians live this reality.

Everyone loves seeing the stats for companies.  This was really true for the above stats that The Garage shared at the event.  Most of these numbers are hard to comprehend, but the clinical data that they have available is particularly powerful.  I’ve often said that the future of healthcare is built on the back of data.  Having this much data in a usable format enabling the AI solutions The Garage is rolling out is a powerful value proposition.

The stats on what it takes to make an impact in healthcare are useful.  However, it’s great to see that all that data, LLMs, and AI are actually moving the needle on measure that matter like ACO savings and reduced hospitalizations.

I’m pretty sure this approach to AI is what is happening at many health IT companies.  However, Pranam Ben was the first I’ve seen articulate it.  Plus, having talked to The Garage CTO on the bus back from F1, I can confirm that they’re not just layering a few AI features on to their product.  They’re being thoughtful in how they roll out AI, but they’re integrating AI deeply into their product.

It was great to see even critical access hospitals being successful with value based care efforts.  This was even more profound in the context of Jodi Nelson’s comments about the importance of cirtical access hospitals to their communities.  Is value based care a key to saving rural health?

Dr. Meera Kanhouwa was impressive in her description of the various stages of AI.  The big challenge right now is that most of what’s being done with AI in healthcare is still in the summative AI category.  This is exciting because we’re already seeing impact from the AI that’s being implented, but it also illustrates we’re just getting started in unleashing the value of AI.

There were a few callouts to the Kobe Bryant Mamba Mentality, so Isaiah Nathaniel created a Kobe Bryant analogy based on the offense he played in called the Triangle Offense.  He compared healthcare to a triangle between primary care, the specialty system, and hosptials.  Much like the Triangle Offense requires players to work effectively together.  We need that same coordination in the team sport that is healthcare.

I was intrigued how at an event that was largely focused on value based care, there were still a lot of people talking about the value that ambient clinical documentation is having on improving the lives of clinicians.  As Nathaniel said, reducing the burden on providers allows them to better focus on the patients.  One of those focuses could be spending time addressing the value based care initiatives for that patient.

Kanhouwa was pretty direct with the reality that cost avoidance isn’t as powerful as revenue.  That’s a common phrase in business, but can be challenging for provider organizations.

This satement from Pranam Ben from The Garage may be one of the most profound of the event.  We’ve all heard the statement, Garbage In leads to Garbage Out.  However, Pranam added that the real challenge is that with AI, many are starting to believe the garbage out that many AI solutions are producing.  That’s a challenge very healthcare AI vendor will face.

As I said in the social share above, I don’t think we’re going to need regional models.  However, we are going to need data from all of the regions or we’ll be doing a disservice to a lot of populations.  What do you think?

I really loved reading these predictions.  Peter DiFondi’s prediction that we’re going to have utilization constraints is profound.  There are a lot of ways to take that comment and it will have good and bad impacts.  I think these constraints are likely to sneak up on us faster than we think.

At the core of value based care is trust.  Without trust, patients won’t do the actions they need to do to stay healthy.  AI has the opportunity to grow that trust or hurt that trust.  It depends on how we implement it.

Pawan Shah nailed it when he gave these 2 challenges to value based care.  I think the later can actually be more challenging.  Especially because in many ways its an unclear or moving target.  What other challenges would you add to the list?

I’ll admit that I’m not an expert on all the models, so I’ll take Shah’s word that ACOs have been the most stable

Scale really does impact so many aspects of value based care.  Much of what’s needed to be successful requires scale.  Look back at the numbers that The Garage shared.  Now try and imagine every healthcare organization trying to replicate that kind of scale.  It’s not going to happen, so it’s great that The Garage can bring that scale to providers organizations in a wide variety of sizes.

I wish I’d been able to stick around for more of The Garage  FUSE 2026 conference, but I was really impressed by the practical dicsussions that happened at the event.  Value based care really has come a long way and in many ways is coming into its own.  Plus, all of those value based care efforts are being infused with technology and AI to improve the entire process.



< + > Ensuring a Successful Epic Go-Live with Real-Time Training Dashboards & Personalization

We recently had the chance to sit down with Rajeeb Khatua, MD, Chief Operating Officer at ReMedi Health Solutions, and Sara Helvey, MD, Chief Clinical Information Officer at Care New England to talk about the Epic Go-Live experience at Care New England.  In our discussion, we dive into some of the specialized training and support Remedi Health Solutions provided leading up to, during, and after their Epic Go-Live.

One of the challenges that Care New England faced was ensuring that phyisicians across dozens of specialties were ready for their Epic Go-Live.  To address this challenge, ReMedi worked with Care New England to provide training on basic workflows, but then specialized training on specialty specific workflows that ensured the clinicians were well prepared for the Epic Go-Live.

Another focus of our discussion was on personalization, which is critical to making Epic convenient and efficient for users. The quality of the personalization sessions was measured by asking each doctor to fill out a form indicating what was accomplished. The results were then checked against the planned accomplishments.

ReMedi also sends out a user survey right after the session. In the case of Care New England, they found that 97% of the doctors felt more confident after the session. Users were also asked to measure their confidence level with Epic from a scale of 0 to 10 before and after their training.  Doctors advanced on average from 7.1 before the session to 8.8 after the session, indicating a good degree of readiness.  Khatua shared that a doctor with a readiness level of 8.8 leads to a much more succeessful Epic implementation and happy user than one at a 7.1.

One unique aspect of Care New England’s Epic implementation was that they were already using Epic in addition to another EHR, but they wanted to consolidate to just Epic.  This meant that many of their physicians had Epic experience either at their hospital already on Epic or from other hospitals or practices in the region. 40% could test out of their basic training and could start with the personalization training sessions. Even doctors who have used Epic before benefit from personalizations in the new system, partly because Epic evolves and partly because the workflows change from one hospital to another. The introduction of AI, especially ambient note-taking, also required updates to templates.

Part of the training aimed to show doctors how personalization made things easier for them while also meeting Care New England’s workflow needs. A motivation to prepare was also provided by “clinical champions” who were willing to train their fellow doctors. The physical presence of trainers on site, along with the close relationship CMOs had with staff in a relatively small hospital, helped to ward off problems before they began.

Finally, metrics on the transition itself were collected and used to improve results. The hospital worked together with ReMedi to hit the metrics, which were ambitious: 90% of the doctors received personalization before the go-live. The metrics were also shared in real-time dashboard that were used to keep the hospital board up to date on progress along with other leaders.

Check out our interview with ReMedi Health Solutions and Care New England to learn more about their Epic Go-Live experience and how they ensured a successful implementation with specialized training across their physician specialties.

Learn more about Care New England: https://www.carenewengland.org/

Learn more about ReMedi Health Solutions: https://www.remedihs.com/

Listen and subscribe to the Healthcare IT Today Interviews Podcast to hear all the latest insights from experts in healthcare IT.

And for an exclusive look at our top stories, subscribe to our newsletter and YouTube.

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ReMedi Health Solutions is a proud sponsor of Healthcare Scene.



< + > We Need More than the Model Context Protocol in Healthcare

The following is a guest article by Adam Farren, CEO at Canvas Medical

The Model Context Protocol (MCP) has already started to reshape how AI systems access data across every software market. For any organization looking for better outputs from LLMs and agents, MCP holds real promise. It gives agents a standardized way to reach external context and invoke tools in a structured interface. Originally proposed by Anthropic and now maintained as an open standard with contributions from OpenAI, Google, and Microsoft, MCP has significant industry momentum behind it as a foundational layer for AI applications.

But in healthcare, MCP by itself is not enough. Not because the protocol is flawed; it is well-designed for what it does. The problem is that healthcare’s hardest challenges exist at a layer MCP was never designed to address.

MCP Solves Communication, Not Coordination

MCP extends a large language model’s capabilities by giving it access to new context and new tools through a structured client-server interface. That is a meaningful unlock.

However, in healthcare, the challenges are not about communication between agents and data sources. They are about coordination, governance, and state management across systems where the patient record can span multiple context sources and systems.

Consider a multi-agent system handling prior authorizations. One agent gathers clinical documentation, another checks formulary requirements, and a third submits to the payer. What central system coordinates their work? Who ensures they are not operating on stale data? Who resolves conflicts when two agents attempt to update the same record? Who guarantees observability of the agent’s autonomous action?

MCP does not answer these questions. It is a protocol for connections, not a framework for coordination, and healthcare organizations need both.

The Infrastructure Problems MCP Leaves Open

Identity and access are out of scope for MCP. MCP intentionally keeps organizational identity governance out of scope. Healthcare organizations still need role-based access controls, audit logging, and the ability to prove to regulators exactly which agent accessed which patient data and when. This is a table-stakes capability that any production-ready healthcare technology must offer.

Shared state is genuinely hard, and MCP doesn’t solve it. When one agent updates a patient’s chart, every other agent working on that patient’s care needs to know immediately. Without a centralized coordination layer, you end up with multiple agents operating on outdated or conflicting information. One may recommend a medication another has already flagged, or document a visit a third agent is still processing. In healthcare, that kind of lag is a major patient safety risk.

Lifecycle management isn’t provided by MCP. Sampling agent outputs for quality, shutting down an agent that’s making errors, managing schema versioning, and resolving conflicts when agents work at cross purposes are all examples of the need for centralized oversight that MCP does not provide.

Healthcare AI Needs a Hub-and-Spoke Model

When every agent communicates directly with every other without a central authority, complexity scales exponentially. In healthcare, this is not an engineering problem; it is a patient safety issue. The answer is not to abandon MCP, but to recognize that MCP needs orchestration around a centralized coordination authority. Healthcare requires a hub-and-spoke architecture where:

  • The EMR is the hub; it is the source of truth for patient data and the center of clinical workflows, and it is already established for deterministic workflows and in the right system, extensible for agentic ones
  • Agents are spokes; they operate in a coordinated runtime, hosted centrally within the EMR runtime, which means with shared access to structured, real-time context centered on the patient record
  • MCP enables connections at the periphery; external data sources and tools connect through MCP, and that access is managed through the governed orchestrator, not freelancing across a mesh

This is where a well-designed software development kit becomes essential infrastructure. The right SDK gives developers the ability to build and deploy custom workflows natively within the EMR environment, across scheduling, charting, and billing, without standing up separate systems. The SDK provides a consistent, low-complexity toolset for building multi-agent systems without incurring the combinatorial cost of stitching together context, tools, and state externally.

That foundation delivers:

  • Native EMR deployment across scheduling, charting, billing, and beyond — no separate systems to stand up or maintain
  • Real-time event streams emitting clinical, operational, and financial data, so agents always operate on the current patient context
  • Built-in governance with conflict resolution, safety rails, and end-to-end auditability from a single point of control
  • Pre-built integrations with AI providers, cloud services, and communication tools that reduce the surface area developers have to manage

The entire model stays auditable from a single point of control.

Implications for Tech Leaders

For leaders building or buying healthcare AI infrastructure, the priorities are clear.

Demand an orchestration layer, not just MCP connectivity. The ability to govern, monitor, and coordinate agents in real time is what separates a compelling pilot from a production-ready system. If a vendor shows you MCP connections but cannot explain the coordination authority, keep looking.

Treat the EMR as the coordination hub. Any architecture that routes agents around the system of record will eventually break under the weight of state synchronization failures and compliance gaps. Build from there.

Build auditability from day one. In regulated environments, the ability to trace every agent action back to a responsible system is not optional. It is the baseline for earning clinical trust.

Where MCP Goes From Here

MCP will succeed in healthcare, but only as one layer of a multi-layer stack, not as the entire solution.

The multi-agent future in healthcare is coordinated intelligence, centered on the system of record. MCP handles external connections, while a robust platform handles coordination, governance, task orientation, and safety. This is not a limitation of MCP,  but rather an acknowledgment that healthcare’s problems require solutions at multiple layers, and that agent coordination with shared context is foundational to patient safety.

About Adam Farren

Adam leads Canvas Medical with over 15 years of experience in startups and healthcare. He combines his deep technical expertise with a passion for transforming care delivery. Before joining Canvas, he served as Chief Growth Officer at Elation Health and Osmind, where he played a pivotal role in expanding both companies into national markets, driving rapid customer acquisition, and creating new revenue streams alongside their EMR solutions. Adam holds a BA from Princeton University and an MBA from the Haas School of Business at UC Berkeley.



< + > This Week’s Health IT Jobs – May 6, 2026

It can be very overwhelming scrolling through job board after job board in search of a position that fits your wants and needs. Let us take that stress away by finding a mix of great health IT jobs for you! We hope you enjoy this look at some of the health IT jobs we saw healthcare organizations trying to fill this week.

Here’s a quick look at some of the health IT jobs we found:

If none of these jobs fit your needs, be sure to check out our previous health IT job listings.

Do you have an open health IT position that you are looking to fill? Contact us here with a link to the open position and we’ll be happy to feature it in next week’s article at no charge!

*Note: These jobs are listed by Healthcare IT Today as a free service to the community. Healthcare IT Today does not endorse or vouch for the company or the job posting. We encourage anyone applying to these jobs to do their own due diligence.



Tuesday, May 5, 2026

< + > Proofpoint’s Intelligent Monitoring Catches Both Accidental and Malicious Email Breaches

Proofpoint understands that data breaches can take place through outgoing as well as incoming communications, and through a wide variety of channels: email, chat, collaboration tools. At the recent HIMSS conference, Brittany Quemby from Healthcare IT Today saw a demo by Proofpoint’s Director of Product Marketing, Andrew Goodman, that shows the sophistication of analysis performed automatically by Proofpoint’s solution.

Some of the simpler checks include finding a URL that has been altered in the hope of making the victim send data to a malicious site. But Proofpoint can also notice that a sender might be accidentally sending data to the wrong recipient or attaching the wrong file. The system has been trained to detect common traits of malicious email, such as an artificial urgency. The system forms a baseline of typical behavior for each user, and recognizes suspicious changes such as large amounts of attachments.

Goodman says promulgating good policies is not enough, because users are often tempted to send sensitive data to their personal accounts. One of the large gaps in many healthcare organizations is addressing their users sending sensitive and private data from their work email account to external emails or their own personal email.

The reality today is that business email compromise is one of the most common types of breaches and most bad actors are leveraging AI to accelerate the number of malicious emails they’re sending.  Every healthcare organization needs to have systems in place to address malicious and inappropriate inbound and outbound emails.

Watch the video below to learn more about how Proofpoint is helping healthcare organizations to address potential security and privacy issues with inbound and outbound emails.

Learn more about Proofpoint: https://www.proofpoint.com/us

Listen and subscribe to the Healthcare IT Today Interviews Podcast to hear all the latest insights from experts in healthcare IT.

And for an exclusive look at our top stories, subscribe to our newsletter and YouTube.

Tell us what you think. Contact us here or on Twitter at @hcitoday. And if you’re interested in advertising with us, check out our various advertising packages and request our Media Kit.

Proofpoint is a proud sponsor of Healthcare Scene.



< + > Why MDM Implementations Fail—and How to Get Them Right

The following is a guest article by Cheryl Griffin, VP of Professional Services at Verato

Master data management (MDM) is often positioned as a technical initiative, but at its core, it solves a fundamental human problem: determining whether different records represent the same individual. In healthcare, the question carries real operational and clinical consequences. It determines whether a patient’s history is complete, whether care teams are aligned, and whether organizations can confidently act on their data.  

When MDM works, identity becomes an invisible foundation – something every system and team can rely on without hesitation. When it doesn’t, the consequences ripple across the organization. Duplicate records, fragmented profiles, and mismatched data force registration, Health Information Management (HIM), and operations teams into manual reconciliation and record validation workflows. Time that should be spent delivering care or driving insights is instead spent questioning whether the data is even correct.

Despite MDM’s critical role, many implementations fall short. Not because the technology fails, but because governance, accountability, and decision-making are not consistently aligned to the intended outcome.

Where MDM Implementations Go Wrong 

One of the most common pitfalls in MDM initiatives is the lack of a clearly defined, shared outcome. Business stakeholders may have a vision—improving patient matching, enabling better analytics, or supporting a system migration—but that vision often becomes diluted as work moves into execution.

Technical teams focus on building pipelines, configuring match rules, and integrating systems. Progress is measured through technical milestones rather than business impact. Over time, the “why” behind the work fades into the background.

Without that shared understanding, teams lose the ability to make effective trade-offs. Scope expands. Priorities shift. What began as a focused initiative becomes a sprawling effort that tries to solve everything at once—and ultimately delivers less.

This tendency to “boil the ocean” is one of the most consistent drivers of MDM failure.

What Successful MDM Implementations Do Differently

Successful MDM programs aren’t necessarily simpler—but they are more intentional.

They begin with a clearly defined outcome and keep it visible throughout the project’s lifecycle. This clarity aligns not just leadership, but also the architects, developers, and analysts responsible for execution.

When everyone understands what success looks like and why it matters, decision-making improves at every level. Teams can evaluate trade-offs in real time, prioritize effectively, and ensure that each step forward meaningfully contributes to the end goal.

In these environments, MDM stops being a technical project and becomes a business capability.

Start Small—but Start with Purpose

In large healthcare environments, duplicate record rates can reach greater than 8% to 12%, creating meaningful downstream cleanup, rework, and safety issues in registration, billing, and clinical workflows. Even targeted improvements within a single workflow can reduce manual reconciliation efforts and reduce patient safety risks significantly.

Organizations are often tempted to pursue enterprise-wide transformation from day one, especially given how broadly identity impacts systems and workflows. But the most effective implementations take the opposite approach.

They start small, with purpose and precision.

That means:

  • Defining a Specific, Measurable Objective: For example, reducing duplicate patient records in a single workflow or preparing clean identity data for a system migration
  • Anchoring the Work to a Real Business Driver: A CRM transition, regulatory requirement, or new digital initiative creates urgency and alignment
  • Making Deliberate Choices About Scope: Not everything needs to be solved immediately; deferring non-critical capabilities allows teams to build momentum and demonstrate value early

Consider an organization preparing for a CRM migration. Their immediate need is not a fully mature, enterprise-wide MDM ecosystem, but rather to establish confidence in who their customers or patients are before data is moved into the new system.

By focusing first on identity resolution in that specific context, they can establish a trusted foundation. Additional capabilities—real-time integrations, expanded domains, advanced analytics—can be layered in over time without derailing progress.

Trust is the True Measure of Success

Technology can create identity records, but only trust makes those records usable.

Governance and stewardship are essential, including reviewing potential duplicates, resolving exceptions, and validating match outcomes against real-world scenarios. This is not simply a technical function—it is the mechanism through which organizations establish confidence in their data.

When trust is present, identity becomes actionable.

  • Care teams can access complete, reliable patient views
  • Operations teams can coordinate without duplication or delay
  • Analytics teams can generate insights without second-guessing their inputs

Without trust, even the most sophisticated MDM platform will fail to deliver value. Teams will continue to validate, reconcile, and question—undermining the very purpose of the system.

The Path Forward

The lesson for organizations is straightforward, even if execution is not:

  • Start with a clear, outcome-driven purpose
  • Build trust in identity data through governance and stewardship
  • Expand capabilities deliberately, not all at once

MDM success is not about solving everything immediately. It’s about solving the right problem first—and doing it well.

When identity is done right, it becomes a force multiplier. Systems align. Workflows streamline. Decisions improve. And organizations gain a true, reliable understanding of “who is who” across every interaction.

That clarity is what makes everything else possible.

About Cheryl Griffin

Cheryl Griffin is an experienced executive in the healthcare technology industry, with a proven track record of delivering high-quality professional services to healthcare clients using SaaS solutions. As the VP of Professional Services at Verato, Cheryl leads and manages a team that provides implementation, customization, training, and consulting services to customers, ensuring all engagements are successful and exceed expectations. She ensures that Verato’s solutions are designed and delivered with customer needs in mind. She also plays a key role in developing and implementing the company’s overall customer experience strategy.



< + > Ethermed Raises $8.5 Million Series A | Joyful Health Raises $22M

Check out today’s featured companies who have recently raised a round of funding, and be sure to check out the full list of past healthcare IT fundings.


Ethermed Raises $8.5 Million Series A

Ethermed, a Philadelphia, PA–based healthcare technology company building AI-driven automation for prior authorization and medical-necessity workflows, has raised $8.5 million in Series A funding.

Investors 

The round was led by Enfield Capital Partners and Blue Marlin Partners, with participation from Jumpstart Ventures, Healthliant Ventures, Woodard Family Office, and Gaingels, bringing Ethermed’s total funding to over $15 million.

Enfield Capital Partners is a private equity and investment firm founded in 2021. Leveraging a “founders for founders” philosophy, Enfield manages a diversified portfolio spanning growth equity, real estate, and private credit, with recent notable activity in the InsurTech and Fintech sectors.

Blue Marlin Partners is a Bethesda-based private equity firm that operates through a unique, deal-by-deal investment model, eschewing traditional fund structures to offer its network of high-net-worth operators greater transparency and choice. The firm focusing on sectors such as logistics, energy, and consumer services, where its partners can provide direct operational value…

Full release here, originally announced April 20th, 2026.


Joyful Health Raises $22M to Build Denial Intelligence & Recovery Infrastructure

We Raised $22M to Build the Financial Infrastructure Healthcare Revenue Needs; What this Funding is For, What We’ve Learned, and What We’re Building Next

The Market Mechanism

U.S. providers lose $125 billion in earned revenue every year. The reason is structural.

Healthcare providers are losing revenue at the systems level. The care is delivered. The billing is submitted. But between the claim and the bank account, financial data moves through a network of disconnected systems that were never designed to share information with each other.

A single claim moves through an electronic health record, a billing platform, a clearinghouse, a payer portal, and eventually a bank account. Each system touches a portion of the story. None of them tell the whole story. The result is that providers can see revenue is missing, but can’t reliably explain where it went or why.

The underlying issue is structural. Financial data is scattered across systems that were never designed to connect, and revenue cycle teams have had to build their operations around that reality without having the infrastructure to change it.

“We spent our first year working alongside clinics as fractional CFOs for dozens of practices. What we consistently found wasn’t a staffing problem or a workflow problem; it was a data infrastructure problem. Financial information was scattered across multiple systems, and no one could see the full lifecycle of a claim,” said Eliana Berger, Co-Founder & CEO.

The Denial Architecture

Diagnosing denials at scale.

When a claim is denied, the event generates a signal. That signal encodes information about what broke down: a documentation gap, an authorization failure, a payer policy shift, a workflow misconfiguration. But because denial data moves through multiple disconnected systems: ERA, EHR, PM, clearinghouse, and BI. The signal rarely arrives intact…

Full release here, originally announced April 16th, 2026.



< + > The Quintuple Aim of Value Based Care Enabled by The Garage

Hard to resist the opportunity to go to a user conference that includes attending the F1 Miami race in Miami which also became the theme for...