< + > Israeli Medtech: Innovation Without an Ecosystem? – Life Sciences Today Podcast Episode 59

We’re excited to be back for another episode of the Life Sciences Today Podcast by Healthcare IT Today. My guest today is Shai Policker, Co-Founder and Managing Partner at Edge Medical Ventures! I sit down with Policker to challenge a provocative thesis: that Israeli medtech is trapped in a cycle of underfunding, walking-dead companies, and missed commercialization opportunities. I bring the data — $5.6B raised by 41 US medtech companies in 15 months, zero Israeli — and argue that the Israel Innovation Authority’s habit of writing small checks keeps entrepreneurs in perpetual poverty rather than building a self-sustaining ecosystem.

Policker pushes back with a counter-model: Edge VC’s venture studio approach, which starts from validated unmet needs sourced from large medical device corporations, builds companies from scratch, and bridges Israeli innovation to US commercial operations through a first-of-its-kind partnership with the state of New Jersey. The result: Israeli R&D capital efficiency meets American go-to-market expertise.

Our conversation covers the IAA’s evolving funding programs, why US investors still see Israel as a premier innovation hub, and what it actually takes to cross the ocean without crashing into the wall.

Check out the main topics of discussion for this episode of the Life Sciences Today podcast:

• Tell me about your journey.
• Break it down for our audience – when you say ‘a venture studio’, ‘we’re more of a VC’, and ‘we’re more hands on’, what does that mean?
• Were you with the Israel Innovation Authority (IIA) for a while?
• How much money are you managing right now?
• My controversial opinion is that as a citizen of Israel, I would like my government investing more in the ecosystem and less in helping VC’s make more money. I know you don’t agree with this – so what is your view?
• Would Israel as a country be better off if the government was creating commercialization infrastructure?
• What is, in your opinion, the biggest anti-pattern in the Israeli medtech industry?

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Along with the popular podcasting platforms above, you can Subscribe to Healthcare IT Today on YouTube.  Plus, all of the audio and video versions will be made available to stream on Healthcare IT Today. As a former pharma-tech founder who bootstrapped to exit, I now help TechBio and digital health CEOs grow revenue—by solving the tech, team, and go-to-market problems that stall your progress. If you want a warrior by your side, connect with me on LinkedIn.

If you work in Life Sciences IT, we’d love to hear where you agree and/or disagree with our takes on health IT innovation in life sciences. Feel free to share your thoughts and perspectives in the comments of this post, in the YouTube comments, or privately on our Contact Us page. Let us know what you think of the podcast and if you have any ideas for future episodes.

Thanks so much for listening!

#medtech

< + > AI in Healthcare Needs More Than Momentum, It Needs Governance

The following is a guest article by Ken Puffer, Healthcare CTO at ePlus

Healthcare has been on the AI journey for a long time, even if we didn’t always call it that. Ten to fifteen years ago, we were talking about machine learning, process automation, and building more intelligence across the healthcare ecosystem. Today, AI has, in a lot of ways, become omnipresent in day-to-day healthcare operations, moving from broad interest to tangible, impactful use cases. Baseline tools have matured, and vendors and health systems alike are building around practical needs within clinical and operational workflows.  

That said, if healthcare organizations want to see and sustain real value from AI, they cannot treat every new tool like a science experiment. Healthcare is not an industry where you can afford to be casual about new technology. You are dealing with patient safety, financial performance, cybersecurity, operational workflows, clinician satisfaction, and organizational trust at the same time. If an AI initiative is not well considered, those areas can get out of balance quickly.  

AI Pressure Versus AI Readiness 

One of the biggest mistakes organizations can make is adopting AI just because the market is moving, the board is asking questions, or competitors are making announcements. Those pressures are real. Yes, boards want to understand how AI is being used. Patient groups are asking similar questions. Internal teams are hearing a lot from vendors and the media. But pressure to act is not the same as being ready to act. 

What organizations need first is a common language and a shared framework for AI discussions. If leadership, IT, clinical staff, compliance, security, and operations are all using different definitions, the program is already at risk. Before buying or building anything, teams need to understand what the capabilities are today, what is available in the market, and how those capabilities align with real use cases. 

Define Success Early 

However, before you can do anything, there needs to be clarity on what success looks like. That sounds simple, but it is where many AI efforts break down. Programs with strong potential often stay stuck in pilot mode because ownership is unclear, measurement is not defined, and accountability is missing. Without structure, AI becomes a science project. It creates activity, but not value. In healthcare, that is not enough. 

When evaluating a use case, organizations should be asking tough, direct questions. Who owns this? How will it be measured? Are the success criteria defined? What risks does it introduce across security, finance, and operations? Who is responsible for reviewing (and maintaining) the program after it goes live? 

Governance Enables the Right Ideas 

Governance is not just about limiting risk. It’s about creating the conditions for the right ideas to succeed. Take, for example, ambient documentation, which addresses a pain point that physicians have been facing for years: Balancing manual, time-intensive data entry with personal patient interaction. Physicians want to focus on patient care, not the computer. They don’t want to spend hours after work finishing charts, nor do they want to spend their entire time in the room with the patient inputting into the computer. AI tools can ease that documentation burden in a meaningful way.  

However, governance still matters. Physicians always need to review and approve what is being done. The organization needs to define how documentation quality will be evaluated. Leadership needs to track the impact on chart closure, billing readiness, physician satisfaction, and workflow. Proper governance allows innovation to resolve the administrative pain points that burden clinicians every day.  

Operational Use Cases and Oversight 

The same governance principles apply in operational settings. Computer vision can help identify when a patient has left a room after discharge, allowing environmental services to turn the room over more quickly. This affects throughput, emergency department flow, and revenue. In outpatient settings, dwell time monitoring can highlight when patient wait times are too long. In operating rooms, computer vision can track setup, preparation, and turnover in one of the most resource-intensive areas of the hospital. 

These are strong use cases. But they also show why governance must extend beyond the technology itself. If tools can identify people, monitor movement, or automate alerts tied to patient flow, there must be clear oversight around how they are used, who has access, and what policies guide their use.  

From Hype to Real Impact 

Healthcare organizations don’t need more AI hype. They need practical governance that helps them focus on the right use cases, while measuring results and managing risk. Only then can AI drive sustained impact for both the patient and the clinician. 

#healthcare

< + > TELCOR Acquires Sample Healthcare | One Call Completes Acquisition of Data Dimensions

Check out today’s featured companies who have recently completed an M&A deal, and be sure to check out the full list of past healthcare IT M&A.

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TELCOR Acquires Sample Healthcare to Lead AI-Driven Transformation of Revenue Cycle Operations

TELCOR Inc., a leading provider of healthcare technology solutions for laboratories and healthcare facilities, today announced the acquisition of Sample Healthcare, an AI workflow platform designed to execute revenue cycle and clinical operations workflows.

This acquisition defines a shift in how revenue cycle work gets done. Traditional RCM platforms manage data. TELCOR now executes that work through AI with human oversight.

By combining TELCOR’s revenue cycle system with Sample’s AI-driven workflow engine, organizations can execute high-impact workflows such as prior authorizations, appeals, payer follow-up, and document processing.

Sample Healthcare will continue to be offered as a standalone platform, enabling organizations to execute workflows within their existing systems. Customers can deploy Sample independently or as part of the TELCOR platform.

Healthcare providers and laboratories face rising administrative costs, staffing shortages, and reimbursement pressure. Much of the revenue cycle remains fragmented and labor-intensive, leading to delays and denials. TELCOR has a proven track record of improving collections through rules-based automation and is now extending those capabilities with AI-driven execution…

Full release here, originally announced April 8th, 2026.

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One Call Completes Acquisition of Data Dimensions, Establishing Foundational Infrastructure for the Healthcare Ecosystem

The Combination Creates the Industry’s First End-to-End Infrastructure Connecting Intake, Clinical Coordination, Data Exchange, and Payments – Enabling Better Patient Outcomes, Faster Decisions, and Lower Administrative Costs Across the Healthcare Ecosystem

One Call, a technology-enabled leader in connected care coordination and workflow intelligence for the healthcare industry, today announced the completion of its acquisition of Data Dimensions, an electronic data interchange (EDI), clearinghouse, and technology services provider serving healthcare, insurance, and government markets.

With the transaction complete, the organizations will now begin operating as a single, unified platform – integrating care coordination, clinical workflows, data exchange, and payments into a continuous, connected system.

For decades, the workers’ compensation industry has operated through disconnected workflows, manual processes, and limited shared visibility, while broader healthcare and insurance systems in general have faced rising administrative costs. This acquisition directly addresses those structural challenges by enabling a unified digital data exchange and end-to-end infrastructure that connects stakeholders in real time and supports faster, more informed decision-making across the lifecycle of a claim.

Through this connected platform, One Call is advancing a more modern operating model for healthcare, delivering measurable value across the ecosystem:

• Continuous, End-to-End Coordination: A seamless, end-to-end coordination model, reducing delays, minimizing handoffs, and improving outcome predictability
• Embedded Workflow Connectivity for Providers: Integrated documentation, billing, and communication that reduces administrative burden and improves speed and clarity of interactions
• Real-Time Data and Workflow Visibility for Payers: Greater transparency, improved efficiency, and more consistent outcomes across the claims lifecycle
• Platform Extensibility: A modular infrastructure designed to support future integrations, services, and ecosystem innovation

“This is a structural step forward – not just for One Call, but for the industry,” said Nick Mendez, Chief Executive Officer…

Full release here, originally announced April 15th, 2026.

< + > Leading Hospital at Home Programs

In February, Congress extended the Acute Hospital Care at Home program through the end of 2030. The program provides waivers to hospitals to provide inpatient-level care at home to qualified Medicare beneficiaries. The five-year extension will help participating hospitals – in place at more than 400 organizations – demonstrate the value of hospital at home and, according to the American Hospital Association (AHA), provide evidence to other payers that the program can work.

Many hospital at home programs predate the program approved by Congress, which the Centers for Medicare & Medicaid Services (CMS) first launched during the COVID-19 pandemic. Here’s a look at some of these key programs and what makes them successful.

Johns Hopkins Medicine has been operating its hospital at home program since 1994. Positive outcomes were first reported in 2005, including a 32% drop in care costs and a 35% drop in length of stay. Along with clinical care services, Baltimore-based Johns Hopkins now offers social services, home health aides, and help with day-to-day household management. Not surprisingly, the health system’s success has served as a model for Hospital at Home programs around the country.

Advocate Health launched its North Carolina-based program in just 10 days during the pandemic, AHA said, and has achieved a 15% reduction in readmission rate coupled with higher patient satisfaction scores. Patients can transition from a hospital or skilled nursing facility (SNF) to home-based care, and the program covers short- and long-term care.

Atrium Health serves about 90 patients per day in its North Carolina-based hospital at home program, which was also the model for what the health system said is the first-of-its kind pediatric hospital at home. The program also offers transitions to advanced care or palliative care at home.

The Cleveland Clinic Florida program, launched in 2023, has seen some of the highest patient satisfaction scores across all inpatient wards at the health system. It helps that the health system contracts with community-based providers. The health system’s Clinically Integrated Virtual Care (CIViC) Center covers remote monitoring and virtual care.

Kaiser Permanente has reported smoother care transitions and better patient experiences for its program based in Northern California, which provides care for more than 1,000 patients annually. It’s part of a larger care at home strategy for the health system, which in 2021 partnered with Mayo Clinic and Medically Home to found the Advanced Care at Home Coalition.

Los Angeles General Medical Center emphasizes virtual, concierge-level care, though rideshares can be dispatched if patients need to be evaluated at the hospital. The public safety net hospital’s program has reduced inpatient stays by 4 days and saves the system about $5.6 million annually, and leaders say it’s a model for providing “financially responsible” care.

Wisconsin’sMarshfield Clinic Health System is another early adopter (2016), according to the American Medical Association. The Home Recovery Care program covers 30-day medical care or 60-day rehabilitation care. The health system has reported a 44% reduction in readmission rate, a 35% decrease in average length of stay, patient satisfaction of more than 90%, and increased physician satisfaction.

At Mass General Brigham, the readmission rate for the hospital at home program is less than one-third the rate for inpatient care, AHA said. Aling with typical clinical and ancillary services, the hospital offers medically tailored meals and supports in-hone X-rays. The program has also been adapted to provide hospital-level care for veterans experiencing homelessness.

Mayo Clinic Arizona has reported a 35% decrease in readmission rate for hospital at home patients. The program also demonstrates equivalent rates of patient safety and higher rates of patient comfort. Patients receive a technology kit that, in addition to medical devices, can include a direct-dial phone, Wi-Fi extender, and backup power supply.

Mount Sinai Health System launched its program in 2014 thanks to a CMS grant, according to AHA; it built on an existing program providing home-based primary care to homebound New Yorkers. Only 7% of participating patients need to return to the hospital. The program also includes at-home palliative care, dialysis, and infusion.

The Ohio State University Wexner Medical Center has focused on disadvantaged neighborhoods. AHA reported readmission rates are roughly half as high as inpatient care, and 95% of patients rate the experience as 9 or 10 out of 10. Available services include an in-home safety assessment to help reduce the risk of falls.

Oschner Health prevented hospitalization and readmission for 92% of eligible emergency department patients in its initial pilot program, which it subsequently expanded in 2024. Along with covering chronic conditions, the program is available for Louisiana-based patients recovering from a transplant or those with a cancer diagnosis.

Presbyterian Healthcare Services launched its hospital at home program in 2008 in partnership with Johns Hopkins Medicine. Most patients receive two care visits per day for several days before discharge. The cost of care is 42% lower than inpatient hospitalization, AHA reported. The New Mexico-based health system also tripled at-home admissions capacity during the pandemic.

Are there other home health programs that you know about?  Let us know on social media.

#hospital

< + > Koda Health and UPMC Enterprises Collaborate | Click Therapeutics and Boehringer Ingelheim Announce Series D

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.

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Koda Health and UPMC Enterprises Collaborate to Prove Out the Value of Advance Care Planning (ACP) at Scale

Backed by Strategic Investment from UPMC Enterprises, Koda Health Scales AI-Enhanced Advance Care Planning Across Complex Populations

Koda Health, an AI-enhanced Advance Care Planning (ACP) platform, today announced a strategic investment from UPMC Enterprises as part of the company’s Series A raise.

The investment reflects UPMC Enterprises’ conviction that digitally guided, values-based advance care planning represents a critical and underbuilt layer of serious illness infrastructure.

Koda’s platform guides patients through condition-specific care planning conversations via video and guided education, helping them document their values, care wishes, medical decision-makers, and treatment preferences. High acuity patients are paired with a dedicated Koda Member Advocate — a clinician with a background in palliative nursing or social work — who provides longitudinal support throughout the care planning process. These advocates ensure advance care plans are complete, surrogates are aligned, and that members receive the care that matters most to them during serious illness. Patient preferences flow directly into clinical workflows, ensuring care teams have access to patient goals at the moments that matter most.

An estimated $200 billion is spent each year on care that patients would not have wanted had they been engaged in their care planning earlier. ACP is proven to close that gap, but has historically been difficult to deliver at scale. Koda Health has demonstrated a 79% reduction in terminal hospitalizations, a 38% reduction in ICU utilization, and a 19% reduction in total cost of care for patients in the last year of life in a third-party validated study.

“UPMC Enterprises’ investment is a meaningful signal, not just to Koda, but to the broader market. It validates that health systems are ready to invest in infrastructure that makes advance care planning work the way it should: proactively, at scale, and with the human support that these conversations require. Having UPMC Enterprises as a strategic investor puts us in a unique position to prove what’s possible,” said Dr. Desh Mohan, Co-Founder and Chief Medical Officer at Koda Health.

“UPMC Enterprises invests in companies building infrastructure that improves how care is delivered for patients who need it most,” said Kathryn Heffernan, Senior Director at UPMC Enterprises…

Full release here, originally announced April 28th, 2026.

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Click Therapeutics and Boehringer Ingelheim Announce Series D Investment and Funding to Advance Commercialization of CT-155

• The Financing Combines a $50M Series D Investment by Boehringer Ingelheim with Additional Dedicated Funding to Support Commercialization; As Part of the Agreement, Boehringer is Transferring Responsibility for Commercializing CT-155 to Click Therapeutics
• Granted Breakthrough Device Designation by the FDA in 2024, the CT-155 Phase III CONVOKE Study was the First and Only Pivotal Trial to Show a Statistically Significant Reduction in Experiential Negative Symptoms of Schizophrenia as an Adjunct to Standard of Care Antipsychotic Treatment
• Treatment with CT-155 Demonstrated a Cohen’s D Effect Size of -0.36 (p Value= 0.0003) Reflective of a 6.8-Point Improvement of Negative Symptoms Severity as Measured by CAINS-MAP at 16 Weeks (vs. 4.2-Point in Digital Control Arm), Representing a 62% Relative Improvement
• Click Therapeutics’ Expertise in Digital Therapeutics, Experience in the Mental Health Space, and Commitment to Patient-Centric Approaches Position the Company Well to Bring this Important Therapy to Patients

Boehringer Ingelheim and Click Therapeutics today announced a strategic agreement to support the commercialization of CT-155, an investigational prescription digital therapeutic that is being studied for the treatment of the experiential negative symptoms of schizophrenia in adults aged 18 years and older. Under the agreement, Boehringer will transfer full product responsibility, including all commercial and marketing authorization rights, to Click Therapeutics. To support this transition, Boehringer has made a $50M Series D strategic investment and provided dedicated commercial funding to help bring CT-155 to patients, if cleared by the FDA. CT-155 was co-developed by Boehringer and Click.

“Boehringer Ingelheim’s selection of Click to deliver CT-155 to patients is powerful validation of our vision and the capabilities we have spent over a decade building,” said David Benshoof Klein, CEO and founder of Click Therapeutics. “We are eager to take the lead with CT-155 and are focused on getting this FDA-designated Breakthrough Device to patients after clearance by the FDA.”

At the core of Click’s commercialization strategy will be the clinical data from the Phase III CONVOKE study (CONVOKE; NCT05838625). The randomized, double-blind, controlled study investigated the effectiveness and safety of CT-155 versus a digital control app as an adjunct to standard of care antipsychotic therapy in people diagnosed and living with schizophrenia experiencing negative symptoms.

The study met its primary endpoint, as presented at the 38th Annual European College of Neuropsychopharmacology (ECNP) Congress, which was change in experiential negative symptoms from baseline to 16 weeks as measured by the Clinical Assessment Interview for Negative Symptoms, Motivation and Pleasure Scale (CAINS-MAP). Treatment with CT-155 demonstrated a Cohen’s D effect size of -0.36 (p value= 0.0003) reflective of a 6.8-point improvement of negative symptoms severity as measured by CAINS-MAP at 16 weeks (vs. 4.2-point in digital control arm), representing a 62% relative improvement.

CT-155 was well-tolerated and demonstrated an adverse event (AE) profile consistent with past studies. The AE rates with CT-155 and the digital control arm were 8.3% vs 13.4%, respectively. There were no trial discontinuations attributed to CT-155 and two (2) for the digital control arm. There were no serious AEs related to either group…

Full release here, originally announced April 9th, 2026.

#koda

< + > Measuring Clinical, Operational, and Financial ROI of AI Initiatives

We’ve broken AI down into many pieces to analyze the many different aspects of AI and how it affects the different areas of healthcare organizations. One area we haven’t talked about enough yet is the return on investment of AI initiatives once they’ve been deployed in the real world. Today, we set out to fix this wrong and learn more not only about the financial ROI of AI initiatives but also the clinical and operational ROI.

We’ve reached out to our brilliant Healthcare IT Today Community to ask — how do you measure the clinical, operational, and financial return on investment of AI initiatives once they are deployed in real-world settings? The following are their answers.

Elevsis Delgadillo, SVP, Customer Success at KeenStack
There’s no need to reinvent reporting to measure AI ROI. Most organizations are already tracking the right metrics. In referral management, that might be leakage or time to schedule, and in clinical use cases, it could be outcomes like hospital-acquired sepsis rates. In the revenue cycle, it’s collections and denials. The key is enabling an AI workflow in a specific area and measuring what changes so the impact can be clearly attributed.

Shay Perera, Co-Founder & CTO at Navina
Meaningful ROI in healthcare AI must be assessed through a combination of clinical, operational, and financial metrics. Clinically, we measure factors like improved risk adjustment accuracy and care gap closure rates; operationally, reductions in chart review, documentation, and coding time, and administrative burden, alongside high clinician engagement with the solution; financially, enhanced documentation quality contributes to audit readiness and more predictable revenue under value-based contracts.

Jared Hamilton, Cyber Managing Director at Crowe LLP
Physician feedback is one of the most important measures of return on investment. In clinical settings, we look closely at whether AI tools are meaningfully reducing time spent on documentation and administrative tasks, such as manual charting, and allowing providers to spend more time interacting directly with patients.

That impact shows up not only in efficiency metrics, but also in the patient experience. Most of us have been patients ourselves, and we understand the difference between a provider who is fully engaged in the conversation versus one focused on a keyboard. When AI helps shift attention back to the patient, it delivers both clinical and experiential value, which ultimately supports provider satisfaction and long-term operational and financial returns.

Denis Whelan, CEO at Documo
ROI for AI initiatives is measured using the operational and financial metrics organizations already track. This includes reduced manual processing time, faster referrals and authorizations, fewer document errors, and lower cost per transaction. On the clinical side, teams focus on metrics like improved turnaround times, fewer delays in patient care, and improvements in population health metrics.

For example, one healthcare organization using AI to process inbound documents was able to cut handling time by 40-50% while redeploying staff to higher-value tasks – achieving measurable efficiency gains without adding headcount.

Joe Russolello PT, DPT, MBA, Senior Vice President, Growth at WebPT
ROI only becomes real when it shows up in the clinician’s experience first. Clinically, that means less burnout and higher satisfaction driven by reduced documentation burden.

Operationally, the most consistent metric is time. Often, one to two hours per clinician per week is reclaimed and significantly less after-hours charting. Financial gains follow when those efficiencies reduce denials, accelerate billing cycles, and shrink AR days, with many organizations reaching sustained ROI within the first year.

If AI doesn’t make clinicians’ lives meaningfully easier, the financial returns rarely last.

Deepak Prakash, Co-Founder & CTO at Sonio
Comparing key benchmarks of time, cost, and performance of initiatives prior to AI integration in a health system can show the stark differences in operational efficiencies due to its use, such as lessening clinician documentation burden, greater reimbursement rates, and faster diagnostic results. Yielding earlier-stage diagnostics from AI-powered software allows patients to make more informed decisions, and can be measured in defining the decreased time spent per appointment, higher patient engagement rates, and more personalized care journeys.

Lisa Israelovitch, Co-Founder & Chief Executive Officer at AssistIQ
Real-world settings such as hospitals and other care facilities often drive clear return on investment from integrating AI platforms in their networks, seen through tangible outcomes in areas such as reduced cost per case, lower inventory waste, and time saved per procedure. Setting recurrent assessments around operational efficiencies in the early stages of new AI initiatives creates a useful lens to compare strategy effectiveness with previous benchmarks.

Mohan Giridharadas, CEO at LeanTaaS
In the real world, the ROI of AI centers on whether it changes outcomes in a measurable, sustained way. We measure operational ROI by tracking flow and capacity metrics that reflect day-to-day performance: discharge processing time, ED boarding, transfer declines, length of stay, OR utilization, block utilization, and surgical throughput.

Clinical ROI is often indirect but real: when the system runs with less gridlock, patients get to the right care faster, and clinicians spend less time doing manual workarounds.

Financial ROI comes from unlocked capacity and avoided cost: more admissions and surgeries without building new beds or ORs. Specifically in the perioperative space, Rush University Medical Center increased primetime OR utilization by 4% and improved surgeon block utilization by 12%, enabling 1,705 additional surgeries over three years and delivering a 12x ROI in one recent year.

Patrick Sheehan, Vice President of Value-Based Care at Withings Health Solutions
Real-world ROI from AI in healthcare is realized when it improves both how care is delivered and the outcomes it produces. While operational AI is already delivering measurable efficiency gains, the next frontier of real ROI will come from clinical use cases that directly enable earlier, more confident intervention. Clinical AI is advancing rapidly, enabling earlier identification of patient deterioration and giving care teams the confidence to intervene proactively rather than react to symptoms.

This is especially impactful in heart failure, where disease progression patterns vary widely, and early signs of deterioration are difficult to detect, contributing to avoidable hospitalizations. By improving operational efficiency and enabling earlier intervention, AI helps health systems deliver high-quality, scalable care to complex populations and perform better under value-based care models that reward quality and affordability.

Ben Moore, Chief Innovation Officer at PerfectServe
Now that healthcare is deep into the AI hype cycle, the focus needs to shift from experimentation to solving specific, measurable problems based on the wealth of knowledge we’ve already compiled. When the use case is narrowly tailored, the expected results should be easier to anticipate and track. For example, we’ve done some research that suggests the average clinician may spend 30–40% of their time during a shift just trying to communicate with their colleagues to coordinate patient care. That kind of built-in friction is a perfect target for AI. Train an AI agent with all of the rules from our routing engine and deploy it to stem the number of errant or unnecessary communications that flow throughout a hospital. Deploy another agent to execute emergency shift swaps when a provider has a sudden family emergency and can’t cover a shift.

These applications remove toil and stress from important clinical workflows and give time and peace of mind back to clinicians. And because so many hospital processes are touched by communication and coordination, the opportunities for measurement are extensive. Measurement opportunities span call center efficiency metrics—more efficient patient transfers, higher volumes of urgent calls handled, pre/post analyses of calls misdirected to off-call providers, and engagement surveys tracking provider satisfaction with scheduling flexibility and autonomy. As these AI applications mature, the results will shift from promising to proven.

Greg Farnum, SVP GM, Federal and Strategic Advisory at Audacious Inquiry
Just as early time-and-motion studies made the invisible work of information exchange visible and quantifiable, customer-specific language models have the potential to illuminate ROI in ways generic LLMs cannot. While general AI tools can demonstrate time savings, curated SLMs that truly understand organizational workflows, terminology, and decision frameworks unlock a different level of measurement.

The ROI opportunity with customer-specific models lies in their ability to capture organizational friction that’s currently invisible: the cognitive load of context-switching, the emotional burden of repetitive administrative tasks, and the time lost to information retrieval. Like HIE before it, we need proxy measures first—time saved, burden reduced, experience improved—before we can connect these to hard financial metrics.

Ben Scharfe, EVP for AI at Altera Digital Health
Measuring ROI currently relies heavily on leading indicators that signal long-term financial health. While many measurements are currently soft, we focus on physician and patient satisfaction as primary markers. High satisfaction scores are direct predictors of reduced physician turnover and increased patient retention and referenceability, both of which have material financial impacts. Operationally, we still track chart closure times and clean claim rates. When ambient AI reduces the administrative burden, the return is found in the stability of the workforce and the improved integrity of the patient encounter.

So many great experiences here! Huge thank you to everyone who took the time out of their day to submit a quote to us! And thank you to all of you for taking the time out of your day to read this article! We could not do this without all of your support.

How do you measure the clinical, operational, and financial return on investment of AI initiatives once they are deployed in real-world settings? Let us know over on social media, we’d love to hear from all of you!

#clinical     #measure

< + > The Expanding AI Ecosystem: How PHI Can Quietly Leave the Healthcare Environment

The following is a guest article by Dennis P. Sweeney, MBA, Co-Founder of Vertebrai Solutions Inc., and Consulting Principal at Tellogic Inc.

Healthcare organizations are rapidly adopting artificial intelligence (AI) solutions to support clinical, administrative, and operational workflows. To manage privacy risk and control Protected Health Information (PHI), most healthcare organization deployments follow a familiar pattern. AI systems are hosted inside private, HIPAA-compliant cloud environments under Business Associate Agreements (BAAs) with the major cloud providers.

Hosting in a private HIPAA-compliant cloud environment provides infrastructure safeguards. These architectures, used by legacy healthcare systems with internal interfaces and custom-developed external APIs, manage PHI data exposure concerns. Platforms such as Microsoft Azure and Amazon Web Services provide strong security controls, encryption, audit logging, and established compliance frameworks. With a BAA in place, healthcare leaders can be reasonably confident that protected health information (PHI) stored and processed within those environments is being handled appropriately.

Many organizations deploying large language models (LLMs) believe they have addressed critical privacy concerns. The AI is operating inside a controlled HIPAA environment. Security controls are in place. Compliance requirements are satisfied.

The information technology architecture hosting the system feels safe.

The Valuable AI Work Inside Controlled Environments

AI systems in these healthcare environments are performing valuable work. They summarize patient charts, generate clinical documentation, assist with prior authorization workflows, triage patient messages, support population health analysis, link to research guidelines, and automate administrative tasks that consume large portions of the clinician’s workday. The realization that every system capable of reading the medical record eventually encounters the same reality, Electronic Health Record (EHR) systems are filled with protected health information.

PHI is more than structured data elements. It is a detailed narrative of an individual’s medical history, including diagnoses, medications, laboratory results, imaging findings, clinical notes, and social or behavioral context. Protecting PHI is not only a regulatory obligation under HIPAA, it is also essential to maintaining patient trust and preventing harms such as stigma, discrimination, identity theft, or financial loss resulting from unauthorized disclosure.

The Shifting Question: What Happens After the AI Accesses PHI?

For many healthcare leaders, the central question has historically been whether AI can safely operate within HIPAA-compliant environments. This can be compared to verifying if barn can safely house the farm animals, where the only exit for the farm animals is through the observed front barn door.

A different question is emerging as agentic AI expands in these LLM systems. What happens to patient data after the AI accesses it?

The Rapid Rise of Agentic AI in Healthcare

At the recent HIMSS 2026 conference, numerous vendors prominently promoted their agentic AI solutions, showcasing autonomous agents capable of handling everything from clinical documentation and revenue cycle tasks to patient communications and multi-step care coordination.

LLMs are increasingly being deployed within agentic architectures, where the LLM not only generates responses but also performs actions across multiple systems. Integration frameworks such as the Model Context Protocol (MCP) demonstrate the ease of connecting systems using this new architecture. MCP standardizes secure, structured communication between AI agents and external tools, resources, and data sources, enabling LLMs to discover capabilities, retrieve context, and execute workflows with greater reliability and control. A single AI assistant can retrieve clinical context from the EHR, assemble documentation, query scheduling systems, submit payer requests, and coordinate actions across multiple applications. 

An LLM might call external systems such as pharmacy benefit manager (PBM) databases for real-time formulary and drug-interaction checks, laboratory information systems (LIS) for results verification, revenue cycle management (RCM) platforms for claims processing, telehealth integration services, wearable data aggregators, or third-party population health analytics tools.

Each integration makes the system more useful. This might be compared to the barn housing farm animals; the building is rapidly being renovated to allow more light with new windows and doors, but at the same time, allowing new exits through which the animals might escape. Each agentic AI integration creates new pathways through which patient data can flow. 

Hidden Privacy Risks in Interconnected Ecosystems

A BAA governs how a cloud provider stores and processes PHI within its services. It does not automatically govern how information flows when an AI system communicates with external APIs, third-party software tools, or other connected platforms.

LLM increasingly functions as a bridge between systems by retrieving information from one environment, processing it, and then transmitting relevant context to another system to complete a workflow.

This LLM behavior is exactly what is intended and provides the expected benefit. 

Consider a use case such as prior authorization. The LLM accesses the patient data, including codes, history, and details that make up the patient’s life. It might pull in a quick formulary check from the Pharmacy Benefits Manager (PBM) or verify a lab result and transmit this data to the payer’s Interface. Overall, saving time and speeding up care, but behind the scenes, suspense builds in the quiet; the request can spill more context than planned. External logs gobble fragments of the record. Data is retained outside the controlled HIPAA environment. No malice. Just the task completed. Yet the patient data crossed the line. Slipped away into the unknown.

Figure 1: The Expanding Agentic AI Ecosystem

Agentic AI systems are particularly effective at multi-step workflows that retrieve information, reason about it, and pass structured data between systems, without the user’s intervention. The LLM/AI engine becomes an intelligent conduit through which patient information flows.

Mitigating Risks: The Technical Savior Using PHI Redaction

Mitigating this risk requires architectural safeguards as well as governance oversight.

The most reliable HIPAA Safe Harbor solution is technical PHI redaction. A de-identification layer prevents the LLM from ever receiving the protected data and transmitting it outside the private environment. It replaces the 18 HIPAA identifiers, including names, addresses, phone numbers, and medical record numbers, with pseudonymous tokens. It does this while preserving the clinical facts the LLM needs, including data on labs, vitals, allergies, encounters, diagnoses, clinical notes, and medications. Dates are shifted to maintain sequences without exposing exact values. A secure mapping in the application layer temporarily holds the link back to the original identifiers.

Clinicians act on the provided information, and tokens resolve back. Session ends, mapping gone. No persistent exposure. These safeguards reduce the risk dramatically. The AI flows data safely now. The expanding AI ecosystem? It is now tamed. Patient trust preserved.

Looking Ahead: Balancing Innovation and Protection

The productivity benefit of these systems is real, and their adoption will accelerate in the coming years, if not months. Healthcare leaders need to recognize that AI systems connected to multiple platforms behave differently than traditional software operating within a single controlled private environment.

Once an AI system learns how to navigate the patient chart, it eventually learns how to navigate everything connected to it.

In modern healthcare IT environments, that network of connections and data flows will end up extending farther than most organizations expect.

About Dennis P. Sweeney

Mr. Sweeney is the Co-Founder of Vertebrai Solutions Inc., which released the Vertebrai AI Clinical Assistant at HIMSS26. He is also a Consulting Principal with Tellogic Inc., as a trusted advisor, supporting healthcare organizations for over 30 years, leading the IT & Data/Information strategies, establishing Clinical Integration & Accountable Care Organization programs, leading cross-functional teams, providing program management, technical assessments, business transformation, organizational redesign, software product development, change management, and system implementations.

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