TTA’s May Holiday Triple Feature: VA’s $840M ‘need for speed’ in the EHRM budget, Commure’s $70M raise, Innovaccer buys CaduceusHealth, Doximity vs. OpenEvidence, and two Perspectives on AI

May 23, 2026 | by

Friday 23 May 2026

Leading up to two holidays–Memorial Day in the US and the UK late May bank holiday–healthcare news remains light. Our roundup includes Congressional hearings on VA’s need for speed–needing 25% more in the EHR budget, an update on the recent VA fraud indictment, two fundings/M&A, and a long Must Read on the ongoing Doximity-OpenEvidence feud worthy of the Corleones and the Barzinis. Rounding it out are two Perspectives: the first on managing the risk of hallucinating AI chatbots and the second on moving AI tools from pilots to full operations.

Please feel free to comment on the articles and pass along this Alert. Let me know if this is worth it to you!

Holiday weekend roundup: VA asks for ‘cyberspeed’ 25% EHR budget bump, update on EHRM fraud indictment; Commure raises $70M; Innovaccer buys Caduceus, lays off staff; Doximity, OpenEvidence slugfest gets hot

Perspectives: AI Hallucinations in Behavioral Health–Why Access Needs Better Infrastructure, Not Better Chatbots

Perspectives: The Next Phase of Healthcare AI Will Depend on Operational Execution

Last Week’s Headlines

A Must-Read potpourri: the ‘math’ of AI data center builds, healthcare AI failures, telehealth in schools, Hippocratic AI’s problems, the loss of empathy.

US Senate Committee on Aging hearings on senior safety 20 May–available online

Plus…

Character.AI sued by Pennsylvania on its chatbots posing as licensed physicians and psychiatrists

Oracle steps back from the AI debt brink with $16.3B financing for MI data center, the Project Jupiter ‘clean energy’ experiment in NM, and a major Federal DOW contract

Is the health tech business neglecting validated deep learning medical AI models versus less proven LLMs and generative AI?

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Perspectives: AI Hallucinations in Behavioral Health–Why Access Needs Better Infrastructure, Not Better Chatbots

May 21, 2026 | by

TTA has an open invitation to industry leaders to contribute to our Perspectives non-promotional opinion and thought leadership area. Today’s topic is about the use of AI in the mental health area–how it is uniquely exposed to risk from LLMs and generative AI–and where best to use them. The author, Hari Prasad, is co-founder and CEO of  Yosi Health, a full-service technology ecosystem that connects patients with their providers through the entire care journey before, during and after the visit, modernizing the entire healthcare patient experience. 

One in three U.S. adults has now used an AI chatbot for health information in the past year, according to the most recent KFF tracking poll. Among adolescents and young adults, one in six has turned to a large language model specifically for mental health advice. That second number should stop every behavioral health leader in their tracks, because when those tools get it wrong, the consequences are not a bad product recommendation. They are clinical.

We are not witnessing patients casually Googling symptoms anymore. They are having extended, emotionally charged conversations with generative AI tools about medications, crisis management, and care decisions, often weeks or months before they ever speak to a licensed provider. And the technology they are confiding in has a well-documented failure mode that the industry still has not adequately addressed: the hallucination.

The Problem Is Not That AI Sounds Wrong. It Is That It Sounds Right.

An AI hallucination occurs when a model generates a response that is fluent, confident, and completely fabricated. In most consumer contexts, that is an inconvenience. In behavioral health, it is a patient safety event waiting to happen.

Consider the real scenarios already playing out: a generative AI tool confidently suggesting an incorrect dosage for a mood stabilizer. A chatbot offering therapeutic advice that directly contradicts evidence-based protocols for trauma. A model providing reassurance to a patient in acute crisis when the clinically appropriate response is immediate escalation. These are not hypothetical edge cases. They are the predictable output of systems designed to be helpful and conversational above all else.

What makes this uniquely dangerous is the packaging. These models are engineered to sound empathetic and authoritative simultaneously. A patient in a vulnerable mental state has no reliable way to distinguish a clinical fact from a statistically plausible guess. And once that trust is broken, the damage extends beyond the misinformation itself. A patient who receives hallucinated guidance during a moment of crisis may lose willingness to engage with the legitimate care system at all.

Why Behavioral Health Is Uniquely Exposed

Two structural realities make behavioral health the highest-stakes frontier for AI hallucination risk: subjectivity and scarcity.

First, subjectivity. Unlike a fracture that shows on an X-ray, mental health concerns are communicated through nuance, context, and subtext. Generative AI is exceptional at mimicking tone. It is incapable of the clinical judgment required to understand the weight behind a patient’s words. There is no lab value to cross-reference, no imaging to confirm. The entire diagnostic framework depends on the kind of human interpretive skill that cannot be approximated by next-token prediction.

Second, scarcity. As of late 2025, 137 million Americans live in a Mental Health Professional Shortage Area, and only about 27% of need is being met in those regions. Appointment wait times range from three weeks to six months depending on location and specialty. For the roughly 29.5 million adults with a mental health condition who received no treatment last year, the barrier was not awareness. It was access. When the pathway to a licensed professional is blocked by a three-month wait, an AI chatbot becomes the path of least resistance, not by preference, but by default.

This is the uncomfortable truth the industry must confront: patients are not turning to AI because they trust it more than their providers. They are turning to AI because the system has not given them a better option.

The Fix Is Infrastructure, Not Disclaimers

The standard industry response to AI hallucination risk has been to add disclaimers and guardrails to the models themselves. That approach treats the symptom while ignoring the disease. The reason patients are having clinical conversations with chatbots is that the operational infrastructure of most practices still makes it easier to talk to a machine at 2 AM than to get a timely appointment with a human being.

The real solution is to shift AI’s role from clinical logic to operational logistics. Practices need to deploy technology that removes the administrative barriers driving patients toward unregulated alternatives in the first place. That means rethinking three operational layers.

Deterministic intake over conversational intake. When an LLM “chats” its way through a patient intake, it introduces the same hallucination risk we are trying to eliminate. The alternative is structured, deterministic intake systems that gather discrete clinical data without improvising questions or advice. Symptoms, history, social determinants of health, all captured through validated frameworks and delivered into the EHR as clean, fact-based data. The clinician gets a head start. The patient gets accuracy.

Precision navigation over generic triage. AI’s greatest operational strength is processing complex variables at scale. That capability should be pointed at routing, not counseling. If a patient’s digital intake surfaces indicators of acute risk, the system should not offer a supportive quote. It should trigger immediate escalation to a crisis line or emergency clinician. The technology’s job is to get the right patient to the right level of care at the right time, not to play therapist in the interim.

Intelligent follow-up over passive waiting. The period between appointments is where behavioral health patients are most isolated and most vulnerable. This is where AI can add genuine value, not by providing care, but by acting as a monitoring layer. Structured check-ins, flagging of concerning patterns in patient-reported outcomes, and automated alerts to clinical teams when intervention thresholds are crossed. The AI serves as a tripwire, not a therapist.

From Advice to Access: The Shift That Matters

The practices getting behavioral health engagement right are not the ones deploying the most sophisticated AI interfaces. They are the ones using technology to collapse the administrative distance between a patient’s first expression of need and their first clinical encounter. When scheduling, intake, and insurance verification happen before the patient walks in, the clinical encounter starts on solid ground. That is what patient engagement actually looks like in 2026.

The hallucination problem is not an argument against AI in healthcare. It is an argument for precision about where AI belongs. Every time we deploy AI in the clinical layer without adequate safeguards, we introduce risk. Every time we deploy it in the operational layer to accelerate access, we reduce risk. The distinction is not subtle, and the stakes are too high to keep blurring it.

Behavioral health already has a trust deficit driven by stigma, scarcity, and systemic friction. The last thing this field needs is a technology layer that erodes trust further by giving patients confident answers that turn out to be wrong. The opportunity in front of us is to use AI to rebuild that trust by making the system itself faster, smarter, and more responsive. Not by replacing the clinician, but by making sure the patient actually gets to one.

Related article from TTA

Character.AI sued by Pennsylvania on its chatbots posing as licensed physicians and psychiatrists

 

Perspectives: Telehealth as Infrastructure–Building a Financially and Clinically Sustainable Virtual Channel

March 19, 2026 | by

TTA has an open invitation to industry leaders to contribute to our Perspectives non-promotional opinion and thought leadership area. Today’s topic is how clinicians can take advantage of the telehealth flexibilities extension to 2027 by integrating telehealth and virtual care fully within their operational workflow and within patient care. The author, Matthew Order, is Vice President of Business Development at Yosi Health. He has more than 20 years of healthcare technology and SaaS experience including previous roles at MEDITECH, athenahealth and Buoy Health. At Yosi, he leads enterprise adoption across health systems, translating product integrations into measurable operational improvements for practices and patients.

The Centers for Medicare & Medicaid Services (CMS) recently extended many Medicare telehealth flexibilities through December 31, 2027. That policy decision signals what providers already know: telehealth is no longer a short-term option to expand access to care, but a permanent channel of care delivery.

That policy certainty is welcome, but it also exposes a hard truth: simply offering video visits won’t deliver value unless telehealth is embedded into the day-to-day operational workflows of the practice. Clinics that want telehealth to reduce cost, improve access, and protect revenue must redesign the patient journey so virtual care is predictable, reimbursable and measurable.

Here are the practical, operational steps clinics should take now.

Move pre-visit upstream

The biggest operational losses happen around patient visits, e.g. when intake is incomplete, insurance is unknown, or staff must chase missing information. One way to change this is by moving pre-visit work upstream: require or encourage patients to complete digital intake forms before an appointment and surface those discrete data fields directly into the chart. This isn’t just a patient convenience, it fundamentally changes how front-office work gets done. Studies show that centralizing reminders and automating pre-visit tasks improves appointment utilization and reduces no-shows, two levers that matter for telehealth ROI.

Treat eligibility as clinical infrastructure, not an afterthought

Nothing kills collections faster than an unpaid copay or an ineligible telehealth claim. Embed real-time eligibility and benefits checks into the pre-visit flow so patients see their financial responsibilities before the encounter and staff can resolve red flags ahead of time. Organizations that operationalize eligibility verification as a revenue-cycle control point report fewer denials and faster time-to-cash. This is the difference between telehealth being a marginal convenience and a reliable revenue stream.

Design rule-based automation for phone and scheduling channels

Studies show over 60% appointments are still made by phone. If your telehealth offering can’t integrate with phone volume and scheduling rules, it is difficult to scale. Deploy rule-based automation that reads live availability, applies clinic booking policies, verifies benefits, and either completes the booking or hands off with full context to a human. For transactional tasks, rule-based systems often outperform free-form AI systems because they reduce follow-ups, corrections, and operational risk.

Measure clinical and financial outcomes, not vanity metrics

Define a tight set of KPIs tied to margin and access: completed telehealth visit rate, no-show reduction, denial rate for telehealth claims, point-of-service collections, and staff minutes reclaimed per patient. A simple 60–90 day pilot with baseline and target thresholds will tell you whether integration and automation are working. And instrument technical reliability as well; API success rates, data mapping accuracy, and escalation quality matter just as much as outcomes.

Protect equity and patient experience as you scale

Telehealth should expand access, not create new disparities. Make digital intake mobile-first and low-bandwidth, provide multilingual options, and maintain assisted touchpoints (phone registration, in-clinic support) for patients who need them. Evaluate patient satisfaction specifically by channel; a good telehealth system should reduce friction, not shift it elsewhere.

Make governance non-negotiable

Without clear operational ownership, telehealth programs drift and performance deteriorates. Who owns booking rules? Who maintains payer mappings? What are clear escalation policies for clinical red flags? Assign cross-functional ownership (e.g. operations, revenue cycle, clinical leaders, and IT) and lock in a change-control cadence that prevents “rule drift” as policies and payer contracts change. Evidence that EHR workload-per-visit can rise even when visit volume falls illustrates why governance and workflow redesign must accompany modality shifts.

Pilot pragmatically—and scale what earns results

Don’t rip-and-replace overnight. Start with two tightly scoped pilots: for example, telehealth follow-ups for chronic care and virtual urgent visits for same-day access. Keep pilots time-boxed, assign a process owner, and require week-over-week reporting on the KPIs that impact margin and access. If real-time eligibility and pre-visit intake reduce denials and nursing callbacks in the pilot, scale; if not, iterate.

Why this matters now

Policy windows like CMS’s telehealth extension present a rare opportunity – but only practices that pair clinical intent with operational discipline will secure lasting gains. With continued Medicaid churn and administrative pressure on primary care, clinics can’t afford telehealth programs that add friction or unpredictability. Integrating intake, eligibility verification, automation, and governance turns telehealth into a reliable channel for expanding access and stabilizing revenue. Recent analyses from KFF highlight how enrollment volatility is already increasing administrative burden across care settings.

By operationalizing telehealth – moving work upstream, protecting revenue at intake, automating predictable tasks, and measuring what counts – clinics can shift from friction to flow. That’s the difference between telehealth that breaks even and telehealth that delivers sustainable access, better outcomes, and measurable financial returns.

Job Posting: Yosi Health seeks Demand Generation Manager and Manager, Data Analytics & Reporting

May 28, 2025 | by

One of our Perspectives contributors, Yosi Health, is seeking candidates for two positions:

Demand Generation Manager

This role combines lead generation with marketing analysis, coordination with sales and marketing, and digital marketing to the B2B buyer customers. It is a full time position, remote/hybrid/in-office NYC.

About Yosi Health:
Yosi Health is a leader in pre-arrival patient intake and engagement solutions, transforming how healthcare practices interact with patients. Our platform streamlines administrative workflows, enhances patient experiences, and drives revenue growth for providers. As we continue expanding, we’re looking for a Demand Generation Manager to accelerate net new lead acquisition, optimize marketing performance, and position Yosi Health to be the segment leader.

About the Role – Is this You?
Yosi Health is looking to add a talented Demand Generation Manager to accelerate our company’s growth. We are looking for someone who wants to be part of a team that is making healthcare smoother and more pleasant for patients of medical practices. We want to bring on an energetic teammate who knows how to turn interested readers/viewers into potential buyers. If you are a demand generation expert who knows how to engage B2B healthcare buyers, that dives into solve problems rather than arranging meetings to study the situation and explores out-of-box ideas, then you may be the person to lead Yosi Health’s demand generation efforts.

For more details and to apply, see their Careers section and the job listing here.

Manager, Data Analytics & Reporting

This position oversees data collection, analysis, reporting and infrastructure maintenance to help gain valuable insights to make business decisions. It is a full-time position, hybrid and based in their NYC office.

About the Role – Is this You?
As Manager of Data Analytics & Reporting, you will play a central role in shaping Yosi Health’s data strategy — helping us unlock the full value of our data to drive growth, improve patient outcomes, and deliver outstanding client value. You’ll lead efforts across analytics, reporting, and insights while working cross-functionally with Sales, Marketing, Product, and Customer Success. You’ll dive into millions of patient records to uncover key trends, create powerful client KPIs, and guide strategic decisions across the company.

For more details and to apply, see their Careers section and the job listing here.

Perspectives: Bridging the Gap in Rural Healthcare Through Telehealth

April 7, 2025 | by

TTA has an open invitation to industry leaders to contribute to our Perspectives non-promotional opinion and thought leadership area. Today’s topic is the closure of rural hospitals and whether telehealth can bridge this access gap. The author, Hari Prasad, is co-founder and CEO of  Yosi Health, a full-service technology ecosystem that connects patients with their providers through the entire care journey before, during and after the visit, creating delightful patient experiences and modernizing the entire healthcare patient experience. 

Rural hospitals across the United States are at risk of closing especially if Medicare and Medicaid cuts are enacted. According to a March 2025 report by the Center for Healthcare Quality and Payment Reform, over the past two decades, nearly 200 rural hospitals have already closed. It’s an economic reality that could leave hundreds of thousands of Americans without local medical care.

Rural communities, which already face challenges related to limited healthcare resources, transportation, as well as staffing and economic constraints, are likely to experience even greater disparities in access to essential services. As these hospitals and clinics face potential shutdowns, telehealth is emerging as a critical tool to maintain healthcare connectivity and improve patient outcomes in these underserved areas.

The threat of rural hospital closures has far-reaching implications. For many residents, these facilities provide not only emergency care but also routine health services, chronic disease management, and preventive screenings. With the loss of a nearby hospital, patients are often forced to travel long distances for care—a situation that can delay treatment and exacerbate health conditions. Additionally, the closure of rural hospitals often leads to increased pressure on remaining facilities, further straining resources and limiting access.

Telehealth, which allows patients to connect with healthcare providers through digital platforms, offers a promising solution to these challenges. By enabling virtual consultations, remote monitoring, and digital care coordination, telehealth can mitigate some of the negative effects of hospital closures. It provides patients with timely access to medical advice and treatment without the need for long, costly journeys to distant facilities.

In my experience at Yosi Health, we are witnessing a notable trend: rural healthcare providers are increasingly turning to telehealth as a means of bridging the access gap. Digital tools and virtual care platforms have evolved to support not only routine consultations but also more complex care management needs. For example, remote patient monitoring is now being used to track chronic conditions such as diabetes and hypertension, ensuring that patients receive ongoing care – without the constant need for in-person visits.

Furthermore, telehealth solutions are proving effective in reducing hospital strain. By diverting non-emergency cases from overcrowded emergency departments, these platforms help ensure that hospital resources are preserved for patients in critical need. Virtual visits can also lead to more efficient use of healthcare resources, allowing providers to manage larger patient loads with improved workflow efficiencies.

There are, however, challenges that must be addressed for telehealth to reach its full potential in rural areas. One of the key issues is the digital divide. While broadband expansion initiatives and improved rural telecommunications infrastructure are making strides, many rural communities still lack reliable internet access—a crucial component for successful telehealth implementation. Policymakers at the state and federal levels, including considerations in the Federal 2026 budget, are beginning to recognize the importance of investing in these areas. Such investments are essential to ensure that telehealth can serve as a viable alternative to in-person care in rural settings.

Another challenge is ensuring that telehealth services are fully integrated with existing healthcare systems. Interoperability between telehealth platforms and electronic medical records (EMRs) is vital to maintain a seamless flow of patient information, which in turn supports continuity of care. As more healthcare providers adopt digital solutions, the need for standardization and robust data exchange protocols becomes increasingly important.

Ultimately, while telehealth is not a complete substitute for all in-person care, it is a powerful tool that can help maintain continuity in the face of rural hospital closures. By improving access to care, reducing travel burdens, and alleviating pressure on overstretched facilities, telehealth can play a central role in preserving the health of rural populations.

The ongoing evolution of telehealth technology offers a hopeful outlook for rural healthcare. As innovations continue to improve service delivery and integration, it is imperative for stakeholders—providers, policymakers, and technology developers alike—to collaborate in expanding these solutions. In doing so, we can help ensure that rural communities are not left behind, but instead have access to the high-quality, timely care they deserve.

For Perspectives editorial and additional opportunities such as supporting TTA through advertising, contact Editor Donna.