Virtual credit card (VCC) payments from insurance companies are on the rise and are often costing practices more than they realize. We sat down with Eric Cohen, CEO of Merchant Advocate, to unpack what providers should know about VCCs, the fees they often don’t see, and how to fight back without switching processors.
Q: What exactly are virtual credit cards, and why are they becoming more common in healthcare reimbursement?
Virtual credit cards are randomly generated, single-use credit card numbers typically issued by an insurance payer or payment aggregator for provider reimbursement. They’re often pitched as a secure and efficient alternative to paper checks or electronic funds transfers (EFTs). For insurance companies, VCCs streamline the payment process, allowing them to earn cashback rewards on the transaction. This makes VCCs attractive for payers, but the convenience comes at a cost to providers.
Q: What kind of costs are we talking about?
Most healthcare providers are unaware that virtual credit card payments can carry processing fees anywhere from 2% to 5%. On average, we see providers paying between an additional 3% to 4% per transaction with VCCs. Over time, those fees add up significantly, especially for high-volume practices.
Q: Isn’t that just the cost of doing business in a digital age?
Not necessarily. That’s a common misconception. The truth is that providers often have a choice, but they don’t realize it. In many states, insurance companies are legally required to offer alternatives to VCCs, including ACH EFT payments, which carry much lower fees, typically just a few cents per transaction. Many insurers still default to VCCs unless the provider explicitly requests something else.
Q: So, if a provider wants to avoid these fees, what should they do first?
Step one is understanding your rights. Several states, including Colorado, have passed legislation that prohibits insurance companies from making VCCs the only reimbursement method. Check with the American Medical Association or your state medical board to find out what the law says in your jurisdiction.
Reviewing your contracts is also crucial. Many providers sign agreements with insurers without realizing that payment terms are included or negotiable. If you’re not sure what your agreement allows, that’s a red flag that warrants a closer review.
Q: What other steps can providers take to reduce or avoid these hidden fees?
Audit your current payments. Understand how you’re getting paid, by whom, and how often. Separate VCCs from EFTs and patient payments. Many practices don’t even realize how much of their reimbursement is tied to VCCs until they do a line-item audit.
Request alternative payment methods. If your state allows it, formally request ACH EFTs from payers instead of accepting VCCs by default. Keep written documentation of those requests and any responses from the payer. If they deny your request without cause, you can file a complaint against the health plan with the Centers for Medicare & Medicaid Services.
Partner with experts. Outside experts can help providers navigate the complexities of payment systems and identify areas where fees can be reduced. These professionals can assist with contract analysis, clarify fee structures, and suggest more cost-effective payment arrangements, all without requiring a change in processors.
Q: Can you share an example of what fee reduction might look like in practice?
Every medical practice is different, but we often find that providers are unaware of how much they’re losing to virtual card fees until they take a closer look. By analyzing how reimbursements come in and working through the details of payer contracts, many practices can shift toward lower-cost payment methods, such as ACH, and significantly reduce overall processing costs. These improvements often don’t require changing processors or overhauling operations, just more transparency and better oversight.
Q: Why isn’t this issue more widely known in the healthcare industry?
I think it’s a combination of opacity and inertia. The payment space is complex and constantly evolving. Most administrators are focused on delivering quality care and managing day-to-day operations, not the nuances of interchange fees. VCCs are also marketed as the default, making them easy to accept and hard to question unless you know what to look for.
Q: Any final advice for healthcare administrators who want to get ahead of this?
Stay informed and proactive. Treat your payment methods like any other vendor or operational expense; you wouldn’t accept a 3% surcharge on your rent or utilities without questioning it. The same scrutiny should apply here. A few small changes could make a big difference to your bottom line.
Health Level Seven International (HL7), a leading global healthcare standards development organization, is pleased to announce the official launch of its HL7 Business Process Modeling (BPM) Community of Practice. Now open for membership, the community is dedicated to advancing interoperability, process consistency, and process automation through the use of formal modeling techniques promoting better modeling, sharing, and execution of clinical and administrative workflows across the healthcare ecosystem.
BPM Community of Practice builds upon three open standards-based languages – referenced together as “BPM+”, and include:
BPMN (Business Process Model and Notation): For prescriptive workflows
CMMN (Case Management Model and Notation): For reactive activities
DMN (Decision Model and Notation): For complex decision-making rules
The use of these standards, in concert, allows inherent ambiguities in natural-language guidelines to be clarified, providing precise, automatable guidance to improve care quality and consistency. Organizations use BPM+ to model and streamline processes, ensuring accurate and scalable healthcare delivery, process consistency, comparability, and repeatability.
“HL7’s focus is on bringing together communities to advance all aspects of interoperability, and that includes workflow and care processes,” said Ken Rubin, Community Coordinator of the HL7 BPM Community of Practice. “This launch marks an important step in providing the healthcare industry with tools, models, and frameworks to manage care processes more effectively, consistently, and collaboratively.”
The BPM Community of Practice builds on years of foundational work in clinical business process modeling and is now positioned to contribute meaningfully to FHIR-based implementation efforts. Members will collaborate to support real-world needs like shared care planning, cross-organizational process execution, human and system interaction modeling, and automation through service orchestration and decision support.
“Welcoming the BPM Community of Practice into our organization was a natural fit. We are excited about the possibilities it brings to leverage and build upon FHIR to enable and support shared care, consistency, and reliability in healthcare practices,” said Dr. Charles Jaffe, CEO of HL7 International. “Moving forward, our industry needs solutions that can effectively manage interactions between humans, systems, and AI to support process portability and seamless care.”
The BPM Community of Practice offers an open environment where stakeholders from healthcare, health IT, academia, and government can collaborate to shape how workflows are represented and executed within the FHIR ecosystem. It provides a platform for those working in this space to collaborate, learn from one another, collect and document best practices, and engage with peer experts.
“The HL7 BPM Community of Practice is a game-changer for aligning complex healthcare workflows with the precision and scalability of FHIR,” said Dr. Thomas Chon, CEO of Tetra Fields LLC. “By uniting leading modeling standards, this community empowers collaborative, interoperable solutions that improve care coordination and execution across the healthcare system.”
By Scott E. Rupp, editor, Electronic Health Reporter.
In 2025, AI in healthcare is no longer a distant ambition—it’s an operational force. But as we stare down the next five years, what matters isn’t what AI could do. It’s what it will do, based on current trajectory, real-world deployment, and policy infrastructure.
Let’s cut past the marketing fluff. Below is a grounded look at how AI is reshaping healthcare now—and how it will evolve by 2030—through the lens of diagnostics, documentation, monitoring, drug development, operations, and governance. This isn’t speculation. It’s what the tech, the economics, and the outcomes are already showing us.
AI in Diagnostics: From Hype to Clinical Utility
Recent developments in diagnostic AI underscore a leap beyond narrow models. Microsoft’s Multimodal AI Diagnostic Orchestrator (MAI-DxO), for example, has shown 85.5% accuracy in diagnosing complex conditions—significantly outperforming unaided physicians in a controlled study. It isn’t replacing clinicians, but rather augmenting them by synthesizing imaging, lab values, and clinical notes into actionable differentials.
What’s next? Between now and 2030, expect diagnostic support tools to become embedded into EHR workflows. AI won’t just suggest differential diagnoses—it will flag overlooked symptoms, propose appropriate next steps, and track care adherence. Clinicians who adopt this technology will find themselves practicing “assisted medicine,” with reduced cognitive load and more consistent care across patient populations.
Clinical Documentation: The Administrative Front Line
Physician burnout continues to correlate with time spent in EHRs—often charting late into the night. AI scribes and ambient listening tools like Suki, Abridge, and Nuance DAX are making measurable inroads. One recent study found documentation time dropped by over 60% after implementing voice AI, with corresponding improvements in patient satisfaction and physician experience.
This is one of the lowest-risk, highest-yield applications of AI in healthcare, and adoption is accelerating. By 2027, we should expect clinical documentation to be mostly machine-generated and human-edited in ambulatory care and some inpatient settings. Expect significant expansion into coding, utilization review, and real-time note summarization. In revenue cycle management, this will radically improve claims accuracy and reduce denials.
AI in Remote Monitoring: Early Intervention, Not Just Passive Data
The convergence of wearables, ambient sensors, and AI analytics is quietly becoming one of the most effective tools for managing chronic conditions. What’s changing now is contextualization: AI doesn’t just measure—it interprets and flags risk. Systems are already showing promise in detecting atrial fibrillation, early-onset heart failure, and even cognitive decline through pattern recognition in voice and movement.
Expect AI to play a growing role in longitudinal care between visits. More than 35% of U.S. health systems are expected to integrate AI-driven monitoring solutions by 2026. Hospital-at-home models will increasingly rely on these tools to support early discharge, flag adverse trends, and prevent readmissions—helping address the financial strain from value-based care models.
AI in Drug Discovery and Trial Design: Time-to-Therapy Will Shrink
AI is accelerating drug discovery by optimizing target identification, simulating molecular interactions, and streamlining trial recruitment. Insilico Medicine, Recursion, and Exscientia are examples of companies slashing preclinical timelines by up to 50% using AI.
By 2030, expect AI to redesign how clinical trials are run—from adaptive designs that learn during execution, to digital twins that simulate patient responses to reduce trial size. Large language models will also aid protocol writing, patient matching, and compliance documentation. The result? Fewer failed trials, faster paths to market, and dramatically lower costs.
Back-Office Automation: The Real Cost Frontier
Administrative complexity remains one of the largest sources of waste in the U.S. healthcare system. AI is already reducing this burden through automations in prior authorizations, denial management, supply chain logistics, and call center operations.
By 2030, back-office automation powered by AI will be table stakes. Health systems will deploy intelligent agents for high-volume tasks like eligibility checks, appointment reminders, claims scrubbing, and patient financial counseling. This will reshape the workforce, reallocating humans to oversight and exception handling, rather than repetitive processing.
Estimates from McKinsey and others suggest that automation could drive over $150 billion in annual savings across the U.S. healthcare system, without touching a single clinical procedure.
Regulatory Momentum and Ethical Infrastructure
As of mid-2025, over 340 AI-enabled tools are FDA-cleared, mostly in radiology and cardiology. The regulatory environment is slowly catching up to the pace of innovation, with a push toward lifecycle oversight, real-world performance data, and post-market surveillance.
The next challenge is equity and transparency. Recent studies highlight significant performance discrepancies across demographic groups. To avoid algorithmic bias becoming clinical harm, AI developers and health systems must prioritize diverse training data, model interpretability, and explainable outputs.
We’re also likely to see a move toward mandatory algorithm audits and AI “nutrition labels”—initiatives that clarify how models were trained, tested, and validated for real-world use.
What Health IT Professionals Should Do Now
As stewards of digital infrastructure, health IT leaders are at the center of this transformation. But the task isn’t just implementation; it’s orchestration. Here’s where to focus:
Pilot with a purpose: Start small, measure well. Focus on low-risk, high-reward areas like documentation or revenue cycle automation.
Govern with clarity: Stand up AI review boards and build governance frameworks now—before use cases scale.
Invest in interoperability: AI is only as good as the data it receives. Ensuring clean, accessible, and standardized data remains the most strategic move any IT team can make.
Push for explainability: If a vendor can’t explain how their AI reaches conclusions, don’t implement it. Full stop.
Final Thought: Beyond the Buzzwords
AI in healthcare is real, impactful, and increasingly essential. But this isn’t about science fiction. It’s about systems — designed, tested, and governed by people — serving other people.
By 2030, the systems that win will be those that operationalize AI in ways that are trusted, useful, and invisible to the patient. We don’t need to marvel at AI. We need to make it mundane, baked into the background, improving care every day, without fanfare.
From the dawn of the Internet to the advent of electronic health records, the healthcare industry historically has been slow to embrace new technologies and the improvements they can bring. One reason is the perceived risks associated with these technologies. Another is the perceived costs of implementing them.
The rise of cloud computing and artificial intelligence presents healthcare providers — traditional ones like hospitals and health systems, along with medical device providers and other entities that meet the “provider” definition — presents the industry with a similar tech conundrum. As new players join more conventional providers in reshaping the patient care ecosystem, opportunities abound for them to leverage the cloud, AI and other tools to reinvent healthcare business processes, services and the patient experience.
But with those upside opportunities come potential new risks and costs, including compliance challenges with HIPAA, a law that doesn’t readily reconcile with technologies like AI or cloud computing, which weren’t around when it was promulgated, nor with the growing diversity of entities now defined as patient care providers.
For this growing class of providers, the applications for AI and other intelligent technologies are indeed promising, for things like predicting certain elevated risks for patients, diagnosing issues and recommending treatments. Generative AI (genAI) copilots driven by large language models could support decision-making about diagnoses and treatments. GenAI also shows great promise for improving clinician and clinical productivity. As versatile as it is, AI also can help companies manage their compliance responsibilities — and the data required to meet them — across multiple jurisdictions.
What’s more, AI shows potential for connecting patient health with marketing, where, for example, based on an analysis of patient data, AI-powered capabilities serve shopping list recommendations to patients for vitamins, supplements, over-the-counter medications, etc., when they’re in-store or shopping online. This intelligent health-based marketing looks like a highly promising frontier for companies that can get it right.
Risk and reward
AI’s huge potential clearly isn’t lost on healthcare companies. In a 2024 survey of 100 upper-level U.S. healthcare execs conducted by McKinsey, 72% of respondents said their organizations are either already using genAI tools or are testing them. Another 17% said they were planning to pursue genAI proof of concepts. And now their AI investments have begun to pay off. About 60% of those who have implemented gen AI solutions are either already seeing a positive ROI or expect to.
This growing embrace of AI and cloud computing introduces a whole new set of issues, risks and responsibilities that healthcare providers — and their regulators — must contemplate. Ensuring patient privacy and data security in compliance with HIPAA is perhaps the most pressing of those issues. Because HIPAA became law in 1996, well before Amazon, Google, the cloud and AI entered the tech mainstream, and well before medical device companies, insurers and the Walmarts of the world were providing some form of care directly to patients, its provisions aren’t equipped to discern how compliance responsibilities and liability should be shared among the various parties that now touch patient data, including covered entities and their business associates. As the definition of “provider” changes, companies in many more industries now may touch patient data in some way.
The increasing use of AI by patient care providers brings new categories of associated entities into the compliance mix. That includes the hyperscalers that host the cloud-based AI capabilities and large language models providers are using, the software/tech companies that build and sell these systems, and the system integrators that are helping providers implement them. Who’s liable for a data breach? Who owns the risk associated with protecting patient information in this broader care ecosystem? It is a true legal quagmire with few clear answers.
The perception of AI as an untested technology (at least in a healthcare context) is also part of the risk equation. How to address potential bias and hallucination risk in large language models, for example? The cost of implementing cloud-based AI and other tech infrastructure, and internal resistance to embracing these new technologies, also factor into that equation.
Maximizing tech’s potential
A 2023 article in the Harvard Business Review contends that implementing cloud-based AI capabilities in a way that’s compliant will require extensive cooperation among stakeholders across the healthcare landscape. “Payers, health systems, and providers need to come to a common understanding about when it is appropriate to use an AI application, how it should be used, and how potential side effects will be identified and mitigated.”
That’s a necessary and worthwhile undertaking, the article’s author concludes. “It would be sadly ironic if the U.S. health sector lagged in reaping the benefits of this transformative new technology.”
The challenge here is a huge one: establishing widely accepted practices, standards and guardrails around cloud computing and AI so regulation can catch up to and keep pace with technology and the ethical and security issues it raises, as well as with the shifting patient care ecosystem.
The most viable vehicle for doing so, at least here in the U.S., could be to establish some kind of broad stakeholder consortium, perhaps led by the U.S. government (the FDA and/or HHS, for example), and including medical colleges/boards, along with covered entities and their business associates under HIPAA. The goal: develop consensus about how the responsibilities and liabilities associated with HIPAA will be divided and executed in the AI era.
A broader embrace of the cloud and AI within the patient care ecosystem increases the universe of covered entities and business associates that likely will be touching or at least have some role, direct or indirect, in the handling of patient data. That in turn necessitates formation of business networks, within which data can flow unimpeded, transparently and securely between relevant entities in the patient care ecosystem.
So, for instance, in the case of cell and gene therapies, a business network would enable the various stakeholders handling a patient’s treatment, from drawing a blood sample to producing, delivering and administering the actual therapy, to securely connect to share and analyze information in a timely and compliant way to yield the best possible patient outcome. Each member of the value chain thus must have the security and data-management capabilities in place to viably participate in such a network. This same concept would also apply to clinical networks.
As daunting as some of this may sound, technology like AI will not stand still. So neither should members of the patient care value chain in laying the necessary groundwork — standards, networks, etc. — to take full advantage of intelligent technologies in a way that’s compliant, profitable and most importantly, beneficial for patients.
By Chris Cronin, partner, HALOCK Security Labs and chair of the DoCRA Council
We strongly recommend an annual penetration test if your company is on the internet. Also known as a pen test, this is where you simulate a cyber attack to discover and exploit weaknesses in your network, app, wifi, or system.
Note, however, you have external threats, but you have what are thought of as internal ones too. Internal penetration testing is just as much required.
This type of testing will simulate the type of attack you could get from an unscrupulous insider, like an unhappy employee or contractor who would misuse their privilege.
Why Conduct Pen Testing?
It is also recommended that you hire a third party with expertise in the latest penetration test techniques. Think of it as hiring an ethical hacker to break into your digital infrastructure before the bad guys do. Some of the benefits of conducting a pen test include:
Although a pen test by itself is invaluable, it shouldn’t be looked at as a one-time event. Regular pen testing is needed to keep pace with evolving threats, uncover new vulnerabilities introduced by system changes, validate the effectiveness of security controls, and ensure ongoing compliance with industry standards
A New Incentive for Pen Testing
If your organization is responsible for HIPAA compliance, you may have another incentive to begin regular pen testing. That is because on December 24, the Office for Civil Rights (OCR) at the U.S. Department of Health and Human Services (HHS) issued a Notice of Proposed Rulemaking (NPRM) to modify HIPAA. Some of the details include the following:
Tests must be performed by qualified professionals with appropriate cybersecurity expertise.
Pen tests must simulate real-world cyberattacks to identify exploitable weaknesses in systems that create, receive, maintain, or transmit electronic protected health information (ePHI).
The frequency of penetration testing may be increased if a risk analysis determines it is necessary. The proposed rule would also require technical controls such as regular patching and vulnerability management, with penetration testing serving as a key validation method.
New Requirements for Incident Response Plans
Every digital organization today must have a well-crafted incident response plan (IRP) to guide their response and recovery efforts for an attack today. The new proposal for HIPAA also includes guidance for responding to security incidents. Some of the proposed requirements include:
Establish written security incident response plans and procedures documenting how workforce members are to report suspected or known security incidents and how the regulated entity will respond to suspected or known security incidents.
Establish written procedures to restore the loss of certain relevant electronic information systems and data within 72 hours.
Implement written procedures for testing and revising written security incident response plans.
Current HIPAA Obligation
As of right now, current HIPAA requirements do not require pen testing. While HIPAA does require organizations to have incident response plans in place, the existing rules allow considerable flexibility that allows each organization to tailor its incident response approach based on its unique risks, size, and resources.
Under the proposal, organizations would be required to adopt a formalized, fully documented incident response plan that clearly defines roles and responsibilities, outlines escalation procedures, and mandates thorough post-incident reviews. This shift aims to standardize incident response practices and ensure a consistent, proactive approach.
When Will the New Requirements Take Effect?
The updated HIPAA Security Rule was introduced in January 2025 and the public comment period closed on March 7, 2025. The Department of Health & Human Services (HHS) is now processing and evaluating the submitted comments and will subsequently issue the Final Rule in the Federal Register.
The proposed changes include additional requirements as well such as bi-annual vulnerability scan and multi-factor authentication (MFA) requirements.
Nutanix, a leader in hybrid multicloud computing, announced the findings of its seventh annual global Healthcare Enterprise Cloud Index (ECI) survey and research report, which measures enterprise progress with cloud adoption in the industry. The research showed that 99% of healthcare organizations surveyed are currently leveraging GenAI applications or workloads today, more than any other industry.
This includes a mix of applications from AI-powered chatbots to code co-pilots and clinical development automation. However, the overwhelming majority (96%) share that their current data security and governance measures are insufficient to fully support GenAI at scale.
“In healthcare, every decision we make has a direct impact on patient outcomes – including how we evolve our technology stack,” said Jon Edwards, Director IS Infrastructure Engineering at Legacy Health. “We took a close look at how to integrate GenAI responsibly, and that meant investing in infrastructure that supports long-term innovation without compromising on data privacy or security. We’re committed to modernizing our systems to deliver better care, drive efficiency, and uphold the trust that patients place in us.”
This year’s report revealed that healthcare leaders are adopting GenAI at record rates while concerns remain. The number one issue flagged by healthcare leaders is the ability to integrate it with existing IT infrastructure (79%) followed closely by the fact that healthcare data silos still exist (65%), and development challenges with cloud native applications and containers (59%) are persistent.
“While healthcare has typically been slower to adopt new technologies, we’ve seen a significant uptick in the adoption of GenAI, much of this likely due to the ease of access to GenAI applications and tools,” said Scott Ragsdale, Senior Director, Sales – Healthcare & SLED at Nutanix. “Even with such large adoption rates by organizations, there continue to be concerns given the importance of protecting healthcare data. Although all organizations surveyed are using GenAI in some capacity, we’ll likely see more widespread adoption within those organizations as concerns around privacy and security are resolved.”
Healthcare survey respondents were asked about GenAI adoptions and trends, Kubernetes and containers, how they’re running business and mission critical applications today, and where they plan to run them in the future. Key findings from this year’s report include:
GenAI solution adoption and deployment across healthcare will necessitate a more comprehensive approach to data security. Healthcare respondents indicate a significant amount of work needs to be done to improve the foundational levels of data security/governance required to support GenAI solution implementation and success. The No. 1 challenge faced by healthcare organizations when it comes to leveraging or expanding utilization of GenAI is privacy and security concerns of using large language models (LLMs) with sensitive company data. Furthermore, 96% of healthcare respondents agree that their organization could be doing more to secure their GenAI models and applications. Improving data security and governance at the scale needed to support emerging GenAI workloads will be a long-term challenge and priority for many healthcare organizations.
Prioritize infrastructure modernization to support GenAI at scale across healthcare organizations. Running modern applications at enterprise scale requires infrastructure solutions that can support the necessary requirements for complex data security, data integrity and resilience. Unfortunately, 99% of healthcare respondents admit they face challenges when scaling GenAI workloads from development to production – with the No. 1 issue being integration with existing IT infrastructure. For this reason, we believe it is imperative that healthcare IT decision-makers prioritize infrastructure investments and modernization as a key enabling component of GenAI initiatives.
GenAI solution adoption in the healthcare sector continues at a rapid pace, but there are still challenges to overcome. When it comes to GenAI adoption, healthcare metrics are excellent, with 99% of industry respondents saying their organization is leveraging GenAI applications/workloads today. Most healthcare organizations believe GenAI solutions will help improve levels of productivity, automation, and efficiency.
Meanwhile, real-world GenAI use cases across healthcare segments gravitate towards GenAI-based customer support and experience solutions (e.g., chatbots), and code generation and code co-pilots. However, healthcare organizations also note a range of challenges and potential hindrances regarding GenAI solution development and deployment, including patient data security and privacy, scalability, and complexity.
Application containerization and Kubernetes® deployments are expanding across the healthcare industry. Container-based infrastructure and application development has the potential to allow organizations to deliver seamless, secure access to patient and business data across hybrid and multicloud environments. Application containerization is pervasive across industry sectors and is set to expand in adoption across healthcare as well, with 99% of industry respondents saying their organization is at least in the process of containerizing applications.This trend may be driven by the fact that 92% of healthcare respondents agree their organization benefits from adopting cloud native applications/containers. These findings suggest that the majority of IT decision-makers in healthcare will be considering how containerization fits into expansion strategies for new and existing workloads.
For the seventh consecutive year, Nutanix commissioned a global research study to learn about the state of global enterprise cloud deployments, application containerization trends, and GenAI application adoption. In the fall of 2024, U.K. researcher Vanson Bourne surveyed 1,500 IT and DevOps/Platform Engineering decision-makers around the world. The respondent base spanned multiple industries, business sizes, and geographies, including North and South America; Europe, the Middle East and Africa (EMEA); and Asia-Pacific-Japan (APJ) region.
MDaudit, a portfolio company of Bregal Sagemount & Primus Capital and an award-winning cloud-based continuous risk monitoring platform that enables the nation’s premier healthcare organizations to minimize billing risks and maximize revenues, and Streamline Health Solutions, Inc., a leading provider of solutions that enable healthcare providers to improve financial performance, announced today that they have entered into a definitive merger agreement pursuant to which MDaudit will acquire Streamline.
This combination brings together two organizations that share a common vision: enabling healthcare organizations to expand patient care and access by improving financial stability. By joining Streamline’s pre-bill integrity solutions with MDaudit’s robust billing compliance and revenue integrity platform, the parties believe that the combined organization will be uniquely positioned to unify disparate data silos, broaden executive insights, and drive coordinated actions across the revenue cycle continuum to accelerate revenue outcomes and mitigate risk.
Ritesh Ramesh
“At a time when health systems are facing mounting financial and operational pressures, we believe the future belongs to those who can connect the dots across the revenue cycle continuum with data- and AI-driven solutions,” said Ritesh Ramesh, CEO of MDaudit. “Streamline’s RevID and eValuator solutions complement MDaudit’s current strengths in billing compliance and revenue integrity capabilities by enabling pre-bill visibility in real-time to unlock revenue opportunities. These solutions reflect our shared belief that human-driven revenue cycles deserve proactive, systemwide intelligence with closed feedback loops that are actionable”.
“MDaudit and Streamline have always believed that the most sophisticated technology won’t drive successful outcomes without an unwavering focus on customer satisfaction,” said Ben Stilwill, CEO of Streamline Health. “Our teams have built trust by being true partners to our customers. Together, we’re building a broader platform that reflects the reality of today’s revenue cycle: distributed teams, disconnected data, and immense responsibility. Together, we’re delivering foresight and action; not just reports or alerts.”
Transaction Summary
At the effective time of the merger, a wholly-owned subsidiary of MDaudit will merge with and into Streamline, with Streamline surviving the merger as a wholly-owned subsidiary of MDaudit. The closing of the transaction is subject to certain customary closing conditions, including approval of the merger agreement by the Streamline stockholders. The transaction is not subject to a financing condition, and MDaudit intends to finance the transaction using a combination of cash on hand and available funds from existing credit facilities.
The merger is expected to close during the third quarter of 2025. Following the closing of the merger, Streamline’s common stock will no longer be listed on the Nasdaq Stock Market, and Streamline will become a private company.
Trackable Health AI, in partnership with Vantiq, has developed a groundbreaking real-time biometric monitoring solution that is redefining force readiness for the U.S. Air Force. By integrating wearables, AI-driven analysis, and edge computing, the initiative accelerates deployment decisions and enhances personnel well-being.
The Readiness Challenge
Traditional health assessments in military environments—typically manual, post?mission evaluations—are slow and reactive. Trackable Health AI CEO Greg Hayward recognized that while wearable devices like Garmin, Whoop, Oura, and Somatix generate valuable biometric data, fragmented ingestion and lack of real-time processing left a critical readiness gap.
A Unified, Real-Time Approach
Trackable Health AI selected Vantiq’s event-driven, low-code real-time intelligence platform to address this challenge comprehensively:
Data harmonization from multiple wearables: Vantiq ingests and normalizes metrics across devices into a unified readiness score
Instant alerts and insights: Threshold-triggered notifications inform commanders as soon as fatigue, stress, or readiness anomalies emerge
User-friendly mobile interface: A secure, scalable mobile application empowers personnel with transparency and consent-based data sharing
Results That Matter
Delivered in just 18 months—vs. an originally projected 3 years—the platform is operational, scalable, and in use across multiple Air Force units:
Deployment readiness 50% faster
Thousands of biometric events processed instantly
Frictionless adoption from day one
Commanders now have real-time dashboards for both individual and unit-level readiness. Data-driven gamification initiatives have even boosted engagement and helped reduce fatigue-related attrition
Beyond the Military
Buoyed by this success, Trackable Health AI is extending its solution into civilian healthcare applications—from hospitals and eldercare to corporate wellness programs. Future innovations aim to incorporate asset monitoring and digital health passports.
A Blueprint for Proactive Health
By harnessing wearable tech, real-time processing, and a secure, consent-driven interface, Trackable Health AI offers a powerful model for proactive health management. Its early results—faster mission readiness, healthier personnel, and policy compliance—are just the beginning. With the foundation in place, future enhancements promise growth in both scale and impact.
Trackable Health AI, in partnership with Vantiq, has developed a groundbreaking real-time biometric monitoring solution that is redefining force readiness for the U.S. Air Force. By integrating wearables, AI-driven analysis, and edge computing, the initiative accelerates deployment decisions and enhances personnel well-being.