< + > Data Analytics and Predictive Modeling’s Role in Identifying High Risk Patients and Optimizing Care Plans

Identifying high-risk patients and optimizing care plans are some of the main goals and purposes of implementing value-based care in your organization. Goals that require a lot of coordination and work to accomplish. Like most things in life, there are plenty of ways to go about this, but today we are going to be focusing on data analytics and predictive modeling.

We reached out to our amazing Healthcare IT Today Community and asked — what role do data analytics and predictive modeling play in identifying high-risk patients and optimizing care plans in a value-based care setting? Their answers are below.

Christopher Bayham, Chief Operating Officer at Xsolis
Predictive analytics has become the cornerstone of successful value-based care operations. We’ve seen this first-hand, having deployed more than a dozen machine learning models trained across billions of actual encounters to assess medical necessity, with accuracy rates over 85-90%. But the real value isn’t just in the predictions — it’s in the workflow integration it’s enabling, especially between payers and providers. Identifying high-risk patients is only valuable if you can intervene effectively. This means embedding prediction capabilities directly into clinical workflows, automating notifications and communication across the care team, and enabling interoperability between health systems and health plans.

Today’s sophisticated value-based care leaders are now moving beyond reactive risk stratification to proactive care optimization. They’re using natural language processing on clinical notes to identify care gaps, predictive models to assess discharge readiness and disposition, and generative solutions to increase the productivity and efficiency of over-burdened staff. The organizations succeeding in value-based care arrangements are those treating analytics not as a reporting function but as an operational necessity integrated into real-time care delivery.

Jason Prestinario, CEO at Particle Health
Data analytics and predictive modeling play a critical role in identifying high-risk patients and optimizing care in value-based settings. Whether you’re a health plan accepting risk for an employer group or a value-based care provider taking on downside risk, every risk-bearing organization needs to excel at two fundamentally different things. First, they need to understand the baseline chronic condition burden of their population and then create care plans that help manage the needs of this population. That part is table stakes. Then, they must be prepared to effectively manage care when patients have acute episodes that change their level of risk.

Unfortunately, most patients don’t gradually slide from high risk back to low risk over time. This is the most important part of predictive modeling and why better analytics matter. It’s easy to use previous claims history to identify chronic conditions, but what really sets you apart is the ability to predict and rapidly identify rising risk moments so you can optimize how you handle patient care. That’s the difference between analytics that describe what happened and intelligence that lets you actually do something about it.

Frank Vega, CEO at The Efficiency Group
Predictive analytics gives clinicians visibility for flagging high-risk patients before they fall through the cracks and aligning care plans with real-world data, not guesswork. The organizations doing well at providing value-based care are the ones treating analytics as a core clinical tool, not an afterthought.

Mary Sirois, Senior Vice President, Strategic Solutions at Nordic
Data analytics and predictive modeling can play an essential role in identifying high-risk patients and optimizing care plans. In this use case, analytics should be treated as a progression that advances from forecasting risk to averting events, then tightening accuracy, and finally tailoring interventions to each patient’s goals. AI can then be used as a spellcheck-like tool for surfacing patient details, risks, and recommendations into workflows exactly when clinicians need them.

This application of AI means that clinicians can spend more time interacting with patients instead of digging into a chart. By curating the information that matters in the moment (meds, allergies, care history, goals) at the point of care, computers can frame recommendations, leaving diagnoses and care planning for the provider, as well as the uniquely human traits of observing and listening and being present for a patient in need.

The key is seamlessness and the right build. Analytics are most valuable when they disappear into workflows, quietly elevating risks, gaps, and goals so care teams can act at the right time, in the right way, for each patient.

Erik Moore, Chief Technology Officer at Bamboo Health
In value-based care, analytics and predictive modeling are only as powerful as the actions they enable. Risk models can flag a high-need patient, but if that alert doesn’t trigger timely outreach or connect them to the right treatment, the opportunity is lost. That’s especially true in behavioral health, where missed interventions can escalate into emergency visits or hospitalizations. The future of value-based care lies in real-time, closed-loop systems that not only identify risk but mobilize care teams and networks to intervene when it matters most.

Ganesh Nathella, Executive Vice President and General Manager – HLS Business at Persistent Systems
In value-based care, analytics and predictive modeling shift risk management from retrospective review to proactive intervention. The most effective organizations use these tools not simply to label patients as “high risk,” but to understand why deterioration is likely and which factors are driving it. When clinical histories, utilization patterns, behavioral cues, and social determinants are combined into a longitudinal record, patient trajectories become far more predictable.

This level of insight enables earlier outreach, more precise care plans, and interventions calibrated to each patient’s actual barriers, ultimately leading to fewer avoidable hospitalizations and better chronic-condition stability. The key is embedding predictive intelligence into daily clinical decisions, not isolating it in reports.

For instance, one large payer organization now applies real-time clinical and claims data to intervene proactively, while another healthcare services firm uses predictive models to map Chronic Kidney Disease (CKD) progression and tailor care plans over time.

Bob Farrell, CEO at mPulse
Shifting care from reacting to conditions to preventing them is where value-based care starts. Anticipating risk before it becomes a cost can only happen if organizations are identifying inflection points in a patient journey through data analytics and predictive modeling. However, data analytics and insights mean nothing without action, and that’s where healthcare struggles most.

In today’s healthcare landscape, organizations that succeed in value-based care are those that collect and analyze diverse data sources, including clinical records, claims, social and environmental determinants, and engagement behavior to build a holistic picture of each member’s needs and risks. Predictive models capture inflection points and patterns that may otherwise be difficult to uncover, including patients likely to experience avoidable acute events, disengage from care, or those that may need additional support on treatments and next steps. These models can also catch care gaps and identify patients likely to progress to a chronic care condition.

However, data and insight alone are not enough; we need to connect the dots. The true value emerges when insights and points of inflection are paired with meaningful actions like personalized outreach that drives engagement through carefully tailored care plans, culturally relevant communication, and support models that empower individuals to engage in their health. That’s where a Health Experience and Insights approach becomes essential – creating a connective workflow with predictive analytics, omnichannel engagement, and health navigation portals under one streamlined ecosystem to predict risk, engage members, and drive care.

In a value-based environment, it’s not just about predicting who is high-risk; it’s about using those predictions and progressing from insight to action to close gaps, build trust, and guide members through their care journey in ways that improve outcomes and reduce avoidable costs. Sure, data analytics and predictive models are a foundation, but guidance on actionable steps that members need to take is true value-based care. It allows organizations to scale empathy, become proactive rather than reactive, and design care experiences that are member-centered and outcome-driven.

AJ Patel, CEO at TeleMed2U
In a value-based care setting, I believe that data analytics and predictive modeling are essential for proactively managing a patient’s health and optimizing outcomes while controlling costs. By analyzing large sets of clinical, behavioral, and demographic data, these tools help us identify high-risk patients before their conditions worsen, enabling earlier interventions and more personalized care plans.

In specialty telemedicine, specifically, we can leverage these analytics to detect gaps in care, for example, patients struggling to manage chronic conditions like diabetes or hypertension, and ensure they are promptly connected with the multidisciplinary care teams to support them. Predictive models also support capacity planning and resource allocation, helping providers better anticipate patient needs, prevent avoidable hospitalizations, and deliver the right care where and when it is needed most.

Ultimately, this data-driven approach enhances care coordination, improves patient outcomes, and supports the overall goals of value-based care: better health, better care experiences, and lower costs.

John Nash, Vice President, Strategic Initiatives at Redpoint Global
For success in VBC performance models, health systems and payers must transform unfit data into a complete, unified view of each patient. Analytics and predictive modeling to identify high-risk patients require complete and accurate patient data that is reflective of their health status at the moment of care. A complete picture of a patient’s data, including clinical, behavioral, digital, and social data, can help organizations anticipate risk and better allocate resources to deliver hyper-personalized engagement to guide patients to take action in their care journey.

Despite significant investments in data technology, most health organizations and plans still struggle with poor data quality, unresolved patient identities, and fragmented care journeys. These issues severely limit the effectiveness of patient engagement campaigns. Aligning IT environments with VBC goals requires updating “unfit” data from disparate sources- health records, claims, demographic, etc.- to create a unified view of each patient. When data is truly ready for use, it’s not just stored, it’s trusted, connected, and actionable. Organizations should invest in data solutions that transform data so it is right and fit for purpose, creating a foundation where analytics and AI can finally deliver the hyper-personalized, outcome-oriented care that value-based models demand.

Deb Jones, Senior Director, Insights Strategy at Tendo
Data analytics and predictive modeling are really the driving force behind proactive, personalized care in a value-based world. They shift the focus from reacting to illness to anticipating it—helping teams identify patients most at risk for complications, readmissions, or gaps in care before those issues surface. When done well, these tools do more than highlight risk; they provide context. By bringing together clinical data, behavioral insights, and social determinants, they paint a full picture of a patient’s needs.

That perspective helps care teams coordinate more effectively, target interventions, and focus resources where they’ll make the biggest difference. In the end, this results in better outcomes, smarter use of resources, and more meaningful patient impact.

Melissa Tyler, Vice President of Advisory Services at Lightbeam Health Solutions
Data analytics and predictive modeling work hand in hand in value-based care by analyzing historical and current performance trends to reveal risk patterns within a population. Predictive models then build on these insights to forecast which patients are most likely to deteriorate, enabling care teams to intervene early and get ahead of patient-management risks before they escalate. Together, these capabilities drive proactive, targeted care planning that improves outcomes and strengthens performance under value-based reimbursement models.

Kempton Presley, CEO at AdhereHealth
Data analytics and predictive modeling are indispensable in value-based care. They help identify who is most at risk—whether for poor medication adherence, an avoidable hospitalization, or an unmanaged social determinant of health. Advanced models can flag those members early, prioritize outreach, and surface the next best actions that care teams can take to prevent decline. But identifying risk isn’t the same as addressing it.

The real work happens when a person connects with the patient to understand why that risk exists—maybe it’s transportation, food insecurity, or medication affordability. Predictive insights are only as powerful as the human conversations that follow them. Medication adherence, in particular, is a linchpin for keeping members healthy and out of the hospital. Combining data-driven predictions with empathetic, person-centered outreach allows health plans to close gaps faster and deliver on the true intent of value-based care: better outcomes and a better experience.

Chandra Osborn, Chief Experience Officer at AdhereHealth
Analytics solutions today are incredibly sophisticated—they can track, report, and even predict outcomes with remarkable precision. But what they often lack is behavioral science. We can use advanced analytics and machine learning to risk-stratify, predict non-adherence, and prioritize outreach, yet those models are still incomplete if they don’t account for how people actually think, feel, and behave. The next step is to design predictive models that work with human behavior, not around it. That means embedding behavioral science into algorithms so they reflect real-world decision-making—why someone might delay filling a prescription, ignore a call, or disengage from care.

Many of the toughest challenges in value-based care, like medication adherence, are fundamentally human problems rooted in psychology and social determinants of health. AI can tell us who needs help and when, but behavioral science tells us how to reach them in ways that motivate change. The future of healthcare IT isn’t just more data—it’s smarter empathy, built into the models that drive action.

Sandhya Ravi, Principal Product Manager at AGS Health
In a value-based care setting, data analytics and predictive analytics are critical in identifying high-risk patients and optimizing their care plans. Predictive models can be built using clinical data, claims data, and even social determinants of health, which can help in identifying patients who are likely to experience complications, hospitalizations, or higher costs in the near future. This allows us to move from a reactive to a proactive approach.

Once those high-risk patients are identified, data analytics can help in stratifying the patients into risk tiers and personalize their care plans. For e.g., scheduling more frequent follow-ups, setting up remote monitoring, etc. Predictive analytics can also flag medication non-adherence or potential readmissions, so interventions can be planned before an issue occurs.

This not only helps improve patient outcomes but also helps in reducing avoidable hospital visits and the overall cost of care.

Shay Perera, Co-Founder & CTO at Navina
Analytics and predictive models are most powerful in value-based care when they stop being scoreboards and start being compasses. A risk score on its own doesn’t change outcomes. What matters is whether it’s grounded in the full longitudinal record—claims, labs, diagnoses, medications, prior admissions, even unstructured notes—and tied to a concrete next step in the care pathway.

The models that add the most value are those that answer specific questions: who might get worse in the coming months, whose chronic conditions aren’t staying on track, who may be headed for an avoidable ER visit, and what intervention is realistic in this setting. The real value comes from the move from generic ‘high risk’ lists to patient-specific recommendations that clinicians can actually act on at the point of care.

Such great answers! Huge thank you to all of you who took the time out of your 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.

What role do you think data analytics and predictive modeling play in identifying high-risk patients and optimizing care plans in a value-based care setting? Let us know over on social media, we’d love to hear from all of you!

< + > Revolutionizing Healthcare with Agentic AI: The Breakthroughs Hospitals and Health Plans Can’t Afford to Overlook

The following is a guest article by Heather A. Haugen, Ph.D. Healthcare Consulting Practice Leader, NTT DATA

I recently participated in a discussion centered around the impact of generative AI (GenAI) and agentic AI on healthcare. The healthcare industry is currently seeing positive results from using agentic AI in their daily processes. However, there is still work to be done to fully commit to this new technology.

This article dives into our discussion on how agentic AI is transforming healthcare practitioners’ workflows, along with the strategies behind its successful implementation for patients, practitioners, health plans and larger health organizations.

The burden on healthcare providers and the promise of agentic AI

It’s no secret healthcare workers face stressful workloads that can take a toll on not only them, but patients and families receiving care as well. During the discussion, a colleague said, “There was never a day, not a single day, where I left work and all the work was done. It was simply what could wait until the next day.” Healthcare providers are faced with overwhelming administrative and cognitive burdens, which contribute to burnout and inefficiency. Having a handle on their workloads or even getting ahead can seem like a pipe dream to most.

Implementing a GenAI solution like agentic AI can ease some of the administrative workload across healthcare roles. In our recent research report, GenAI: The care plan for powering positive health outcomes, 95% of those surveyed agreed that GenAI is a crucial differentiator in proactive patient/member experiences that drive value.

By streamlining administrative processes and improving operational efficiency, AI agents help reduce wait times for appointments, tests and procedures. Because of these impacts, many organizations are preparing their GenAI strategies to focus on such areas. Among respondents, 51% have already assessed the GenAI opportunity to streamline patient experiences and 94% will have done so within the next year. This frees up more of the cognitive load on practitioners and helps them to foster a collaborative, trusting relationship with patients.

Rethinking business models for adopting AI agents

While many are optimistic about the successful results GenAI is harboring, there is still work to be done to fully accept and incorporate it into the healthcare system. Healthcare organizations are considering how they are viewing their business models and care delivery strategies to fully leverage the potential of agentic AI. It’s important to integrate a GenAI strategy into your organizations strategic planning. Aligning the GenAI strategy with the business strategy, ensuring they grow and match with the evolving nature of care delivery is key.

Our research revealed 81% of organizations have a well-defined GenAI strategy in place. However, only 40% of healthcare leaders agree that the GenAI strategy strongly aligns with their business strategy.

It can be hard to align a GenAI strategy with business goals, especially if in some areas the goals compete or differ altogether. To help ease this alignment, focus on the key outcomes that are important to patients, families and caregivers. For example, if your strategy has the end goal of increasing revenue, you may want to shift the focus to increasing your client base. Since agentic AI helps ease the administrative burden of patient care, it allows health practitioners to see more patients.

Overcome AI strategy challenges by understanding governance

While bridging the gap between GenAI and business strategies, it’s important to include governance in this process. You can implement a strong GenAI strategy, but how will you ensure it remains unbiased, ethical and following the guidelines and parameters set in the software?

Incorporating a governance strategy within the GenAI strategy helps instill trust and confidence in GenAI solutions. It allows you to be sure a solution like agentic AI gets deployed, prioritized, adopted and monitored properly. Research states 83% say it is very important to have confidence in the security of the GenAI technology. Establishing a governance review board or center of excellence is highly recommended to ensure your GenAI strategy is on track, and the technology remains true.

The future of healthcare with agentic AI

While there may be challenges in strategy alignment, the results of agentic AI can speak for themselves. You may see it as starting small and helping automate and complete administrative tasks, but the future is bright. Agentic AI unburdens healthcare workers and patients/members alike. It can provide more personalized and proactive care, reducing the need for patients to travel and ensuring adherence to recommended interventions.

The current healthcare system is volume-driven, but agentic AI can help shift focus toward managing populations and reducing avoidable medical expenditures. Agentic AI can help with patient education, chart summarization and messaging, improving both patient and provider experiences. With tools like ambient listening, practitioners gain more insights and can feel better equipped to manage their patient care than ever before.

The integration of agentic AI in healthcare represents a significant shift toward more efficient, patient-centric care. By alleviating the administrative and cognitive burdens on healthcare providers, agentic AI enables practitioners to focus more on patient care, fostering a collaborative and trusting relationship. However, the successful adoption of agentic AI requires healthcare organizations to carefully align their goals across business models and care delivery strategies. Moreover, aligning AI strategies with overall business goals and implementing robust governance frameworks are crucial steps in ensuring the effective and ethical deployment of agentic AI.

As the healthcare industry continues to evolve, embracing agentic AI will be pivotal in driving positive health outcomes, improving experiences and reducing burnout for healthcare workers.

Healthcare, like many industries, is successfully using AI. To hear more use cases, meet up with NTT DATA at the ViVE and HIMSS Conferences.

About Dr. Heather A. Haugen

Dr. Heather A. Haugen leads the Healthcare Consulting practice for NTT DATA. Her previous roles include the Chief Science Officer at Atos in the Digital Health Solutions division and the Managing Director of The Breakaway Group, A Xerox Company. Haugen’s experience spans industry and academia including large EHR implementations, revenue cycle management, clinical trials, and healthcare operations.

Dr. Haugen holds a faculty position at the University of Colorado Denver- Anschutz Medical Center as the Director of Health Information Technology, where she actively mentors doctoral students and teaches courses. She serves as an Advisory Board Member for DU in the Daniels College of Business Management.

NTT Data is a proud sponsor of Healthcare Scene

< + > Sword Acquires Kaia Health | Premise Health and Crossover Health Sign an Agreement

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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Sword Acquires Kaia Health, Extending Its Lead in AI Health and Expanding Reach to 100 Million People Worldwide

$285M Deal Expands Sword’s AI Care Platform in the U.S. and Establishes a Major Foothold in Germany’s Digital Health Reimbursement Market, Covering 70M+ People

Sword Health, the world’s leading AI Health company, today announced the acquisition of Kaia Health, a digital health company focused on musculoskeletal (MSK) and pulmonary care, in a deal valued at $285 million. The acquisition reinforces Sword’s position as the fastest-growing AI Care platform in the world, expanding its ability to deliver high-quality, scalable care to new populations in the US while entering the German market.

“This acquisition will accelerate our already rapid growth in the United States while also opening Germany as a major new market, in our mission of democratizing, through AI, access to high-quality care all over the world. We’re excited to work with Kaia’s clients and partners, further expanding our presence in the U.S. market,” said Virgilio Bento, Founder & CEO at Sword.

Following the acquisition, Sword Health will replace Kaia’s MSK solution in the U.S. market, ensuring a seamless transition for existing clients and members. Kaia Health’s millions of American members will gain access to Sword Health’s expanded AI Care platform. This ensures continuity of care while delivering Sword’s market-leading outcomes, engagement metrics, and cost performance.

In Europe, Kaia Health’s solution is available through Germany’s digital health reimbursement pathway, which covers more than 70 million people in the country…

Full release here, originally announced January 27th, 2026.

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Premise Health and Crossover Health Sign an Agreement to Create One Unified Company

Premise Health and Crossover Health Will Offer Comprehensive Advanced Primary Care and Occupational Health Services for Employers, Unions, Tribes, and Health Plans

Premise Health and Crossover Health today announced that they have entered into a definitive agreement to merge. The unified organization will deliver onsite, nearsite, and virtual care to more than 400 organizations and millions of members, operating nearly 900 wellness centers across the United States.

Premise and Crossover deliver advanced primary care and occupational health services, including behavioral health, care management, care navigation, physical therapy, and chiropractic care. Premise also offers pharmacy, which includes pharmacist-led chronic condition management and patient education, virtual pharmacy services, and provider dispensing services. Both companies have made significant strides in developing alternative payment models, including a primary care-centered health plan, and progress on this important product will accelerate as a result of the companies coming together.

As a combined organization, Premise and Crossover will advance their work to improve the healthcare experience and clinical outcomes for organizations and members, which leads to better health and lower healthcare costs. In 2024, Premise published a study of more than 200,000 lives that showed patients who used Premise for advanced primary care saved an average of 30%, or $2,434 per year, on the total cost of their healthcare compared to those who accessed care in their communities. Cost savings were driven primarily by increases in primary care utilization, enhanced chronic condition management, and reductions in emergency room visits and inpatient hospital admissions.

Stu Clark, Chief Executive Officer at Premise Health, will lead the new organization. He noted that Premise and Crossover have long shared a vision to transform commercial healthcare by delivering innovative, comprehensive, scalable primary care services.

“Our two organizations share a firm conviction that primary care should be the foundation of any high-functioning healthcare system,” Clark said. “Our separate efforts have focused on easy access to care, more time with providers, and a team-based approach, and now we are excited to bring those efforts together to create a transformative new company. Crossover has earned an excellent reputation with employers and is known for passion, creativity, and innovation…

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

< + > Guidance from The Sequoia Project on Computable Consent and Privacy

Clinicians, health IT professionals, and policy makers all want to protect patient privacy. This is a hard goal, made harder by the increasing pressures to open up data and share it for treatment and research purposes, and harder still by the proliferation of state laws on data privacy. These laws are only getting stricter and more detailed, and are fragmenting wildly even as the federal government tries to bring everyone together around standards.

The Sequoia Project, a nonprofit consortium, is dedicated to implementing data interoperability in health care, securely and respecting patient needs. In our recent interivew, we hear from two co-chairs of the Privacy and Consent Workgroup at The Sequoia Project: Mel Soliz and Kevin Day, where we learn more about these complex regulations and how their workgroup is providing guidance to make it simpler to navigate.

Soliz laid out the challenges created by proliferating laws, many of which focus on sensitive data in the areas of reproductive health, behavioral health, and genetics. The laws are written from a policy perspective and therefore are hard to translate into the clinical and technical terms needed for implementation.

Most clinical organizations that operate in multiple states have a single EHR system that must be programmed consistently. Thus, they generally conform to different state laws by making the most restrictive statute in one state apply to every patient in every state because their IT systems don’t allow for state differences.

For instance, a patient in one state might not be able to send data about their substance abuse to another clinician in that state because of laws in a different state imposing strict controls on the transmission of substance abuse PII.

Soliz called for “a common technical foundation to define categories of data” to help governments define their policy laws in terms that technologists know how to implement

Soliz and Day talked about the multi-stakeholder process that produces their guidelines. Participants include lawyers, technical experts, providers, application developers, privacy advocates, and policy makers. Day cited the contributions of day-to-day operations staff as particularly important, because ultimately they are the people who have to make things work.

In April 2025, the project published the white paper Moving Toward Computable Consent: A Landscape Review. Soliz describes it as covering a range of topics from policy challenges to implementation challenges, along with solutions that are being tested.

This month, the project published a guide that provides more guidance and lays out exactly what information is needed to support a health care data disclosure, which individuals and data are involved, the operational process, and even a sample disclosure form.

They invite stakeholders to join their working groups where they can help shape and improve the guidance that The Sequoia Project workgroup produces.  Check out our interview with the co-chairs of The Sequoia Project’s Privacy and Consent workgroup to learn more.

Learn more about The Sequoia Project: https://sequoiaproject.org/

Learn more about the Privacy and Consent Workgroup: https://sequoiaproject.org/interoperability-matters/privacy-and-consent-workgroup/

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

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

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

The Sequoia Project is a proud sponsor of Healthcare Scene.

< + > Why Health IT Still Struggles to Move as One System According to Robert Fox

Healthcare IT leaders are surrounded by dashboards, standards, and roadmaps. Yet clinicians still juggle systems, duplicate work, and wait on data that should already be there Adding more technology won’t resolve this friction. It’s a structural issue, not a tooling gap.

Robert Fox, CEO of OntarioMD, joined Healthcare IT Today to talk through a pattern he sees accelerating across the industry and in particular the province of Ontario. His focus wasn’t products. It was how systems, teams, and technology either converge or continue to create drag.

What This Conversation Revealed

• Interoperability only accelerates when incentives, governance, and vendors align around a shared agenda
• Team-based care exposes integration gaps most health IT stacks were never designed to handle
• AI value emerges when it supports the entire clinical workflow, not just documentation

Convergence is the secret sauce in healthcare integration

Fox describes convergence as more than cooperation. It is what happens when governance, funding, technology standards, and vendor behavior pull in the same direction.

“We’re seeing a lot of people coming together, systems coming together, people coming together, care providers coming together. And I think that that’s the secret sauce to accelerating our healthcare system.”

At the system level, he points to Ontario Health, the Ministry of Health, the OMA, and primary care networks operating as an integrated whole rather than parallel actors.

“It’s not like they’re independent. They are completely integrated.”

That same expectation now extends to vendors.

“Vendors are now creating solutions that can integrate with any of the EMRs, can integrate with any of the hospital information systems like Epic, Oracle Health, and MEDITECH.”

When vendors collaborate, there are fewer seams between systems. Fewer seams mean less friction for clinicians.

Why interdisciplinary care stresses today’s digital foundations

As care delivery shifts beyond the solo physician model, integration complexity increases quickly. Fox is direct about both the opportunity and the lift.

“I’m super excited about team-based care. But as the board chair of the OMA says, health is a team sport.”

The challenge is that team members often rely on different systems, data models, and workflows.

“When a chiropractor or a physiotherapist or a dietician uses different systems than a primary care physician would, it’s about more integration and interoperability with systems that we haven’t considered in the past.”

Standards help, but they don’t eliminate the work.

“It is going to involve a lot more work to create those APIs or other abilities to connect those systems.”

Team-based care exposes the limits of point integrations and reinforces the need for vendors to collaborate earlier and more intentionally.

AI and the 360 Visit

AI scribes may be the most visible example of AI adoption today, but Fox is clear they’re only the starting point.

“AI Scribe has been a fantastic solution. We have incredible adoption in Ontario right now.”

The next phase is broader and more operational.

“In the clinical practice, it’s more about an iterative AI capability.”

Fox describes a future built around a full “360 visit,” where AI supports pre-visit preparation, in-visit decision-making, and post-visit follow-up.

“There’s the pre visit and the post visit too. We’re calling that the 360 visit. And that’s where AI is going to play a key role.”

More collaboration, less illusion

Across convergence, team-based care, and AI, the pattern is consistent. Healthcare IT advances fastest when systems reflect how care actually happens across people, workflows, and time.

Fox sums it up simply: “We can’t do it by ourselves. And the more people involved, the faster and better it’s going to be adopted.”

What Healthcare IT Leaders Are Asking

1. Why does interoperability still stall even when standards and integrations are in place?
   Because alignment matters as much as architecture. When incentives, governance, and vendor priorities are misaligned, data may technically move, but workflows remain fragmented and adoption slows.
2. How does team-based care change the way we should think about integration strategy?
   Team-based care introduces more systems, roles, and handoffs than most point integrations were designed to support. Without broader coordination across platforms, the burden simply shifts to clinicians and staff managing the gaps.
3. Where does AI reduce operational friction today beyond documentation?
   Real gains come when AI supports the full clinical workflow, before, during, and after the visit. That includes preparation, decision support, follow-up, and inbox management, not just note creation.

Learn more about OntarioMD at: https://www.ontariomd.ca/

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

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

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

< + > NOCD Announces Noto As New Parent Brand, Acquires Rebound Health for PTSD | Wisp Acquires TBD Health

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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NOCD Announces Noto As New Parent Brand, Acquires Rebound Health for PTSD

World’s Largest Virtual Specialty Therapy Company Announces its AI-Enabled Platform, Noto, as its New Parent Brand, Positioned to Power and Transform the Future of Specialty Therapy for Complex Psychiatric and Behavioral Conditions (CPBCs)

This week NOCD, the world’s largest specialty behavioral health treatment company and leader in serving OCD and Related Disorders, announced that Noto, the AI-enabled platform built from scaling NOCD over the past decade, will serve as its new parent brand. In Latin, Noto means “to be known.” Virtual specialties powered by Noto will help people with complex psychiatric and behavioral conditions know what they’re experiencing, access evidence-based treatment, and get better.

The Noto platform has enabled NOCD to deliver over 1 million evidence-based OCD therapy sessions per year, with industry-leading clinical outcomes. Now, Noto is poised to power the next generation of virtual specialties for other complex psychiatric and behavioral conditions, starting with the acquisition of Rebound Health for Trauma Disorders. Noto will serve as the parent brand for both NOCD and Rebound, enabling both to continue scaling nationwide.

Stephen Smith, Co-Founder and CEO at Noto and NOCD, shares the origins of the Noto platform, “We set out to develop a better treatment system that identifies hard-to-reach people with OCD, builds trust with them, and offers them effective, affordable, and convenient care for the root of their suffering, rather than surface-level symptoms. This led to world-class clinical outcomes for our therapy members and substantial savings for our payer partners, allowing us to re-invest in serving more people in need of care. Today, we’ve engaged millions in our online community and enrolled hundreds of thousands in specialized therapy. We couldn’t have scaled to this point if we hadn’t created Noto: the natively-built technology that enables our operations teams to work with payers, enroll hard-to-engage members, and manage treatment operations for our network of full-time, specialty-trained therapists. Now, we feel the responsibility to leverage our Noto platform to scale other virtual specialties for severe, overlooked, but treatable conditions—starting with Rebound for PTSD.”

The Noto platform powers payer administrative functions, member identification & enrollment operations, and clinical training & management—often the biggest and most costly challenges faced by virtual specialty therapy services that focus on complex psychiatric and behavioral conditions. For example, partnerships with health plans nationwide are streamlined with AI-enabled revenue cycle management and purpose-built processes for credentialing and enrolling new therapists. To identify and engage Members of the OCD community, Noto powers awareness campaigns, personalized community feeds, self-help tools, and live events led by experts. And for NOCD’s 1000+ primarily full-time therapists, Noto houses AI-enabled clinical interviewing, a specialized training experience, continuous support and oversight, AI-assisted note-taking, Member communications, outcomes tracking, AI practice sessions, and more.

“As we worked to give our members a personalized, VIP treatment experience, we were limited by the off-the-shelf technology solutions that existed, so we set out to build the technological infrastructure we needed ourselves. After a decade of work, I can confidently say that Noto allows us to deliver a VIP treatment experience and more,” says Anil Vaitla, Co-Founder and CTO at Noto and NOCD.

New specialties launched on Noto will be able to leverage support in these areas more quickly and easily. This is why NOCD’s acquisition of Rebound Health is a significant milestone. “Noto allows my cofounder, Dr. Erin Berenz, PhD, and me to scale a leading specialty therapy service for trauma survivors across the entire country,” says Raeva Kumar, Co-Founder and CEO at Rebound Health…

Full release here, originally announced January 27th, 2026.

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Wisp Acquires TBD Health, Launching Enterprise and Hybrid Care Offerings

Acquisition Adds National Care Infrastructure, Diagnostics, and Hospital Partnerships to Wisp’s Women’s Health Platform

Wisp, the largest women’s telehealth company in the U.S., today announced the acquisition of TBD Health, a national sexual health platform and one of the nation’s most scaled TelePrEP infrastructures with deep partnerships with hospital systems, enterprises, and public health organizations. This marks Wisp’s first acquisition and milestone expansion beyond direct-to-consumer care, into enterprise and hybrid care models.

Care is delivered outside of traditional clinical settings now more than ever, through hybrid models that combine consumer-first digital care with hospital systems, enterprises, and public health programs. However, gaps in access remain, particularly in sexual health and preventive care. Despite the availability of preventative treatment, U.S. PrEP adoption is lagging. Of the 2.4 million people eligible for treatment, only ~25% are currently enrolled, signaling a major public health shortfall. Further, while accounting for 19 percent of new HIV diagnoses, women remain significantly underserved by existing prevention models, largely because most solutions have historically been designed and marketed for men.

“This acquisition reflects where healthcare is going and where women have been left behind,” said Monica Cepak, CEO at Wisp. “TBD Health brings the infrastructure and partnerships that allow us to move into hybrid and enterprise care quickly, while staying true to Wisp’s patient-first approach. Together, we are making preventative care more accessible, especially to women, and integrating them into proven care models.”

TBD Health operates a nationally scaled sexual health and diagnostics platform across all 50 states, combining routine STI and HIV testing, virtual clinical support, and strategic partnerships that help remove cost barriers for patients. By bringing together Wisp’s trusted women’s health platform with TBD Health’s national care infrastructure and established health system relationships — including Mount Sinai Health System, San Francisco AIDS Foundation, and Planned Parenthood Direct — the companies aim to expand access to sexual health, diagnostics, and hybrid care models that better reflect how and where patients seek care.

“By joining forces with Wisp, we can provide partners with a turnkey solution for PrEP along with sexual health diagnostics and care that integrates seamlessly into their existing workflows…

Full release here, originally announced January 27th, 2026.

< + > Epic Hosting in the Public Cloud

The question of where to host Epic in a hospital and health system is a really important decision.  It’s hard to argue that any system is more important to the operations of a healthcare organization than their EHR.  For the longest time, the Epic hosting decision was easy.  Everyone hosted Epic in their own data centers.  As time has gone on, many people started moving Epic to various private cloud environments including an Epic hosted cloud option.  Now we have some organizations choosing to host Epic in the public cloud.

A little over a year ago, we hosted an episode on our Healthcare CIO Podcast discussing the Epic Cloud Migration along with Michigan Medicine’s decision to go all public cloud.  Our guest on that podcast episode was Dr. Tim Calahan.  He recently decided to leave his position as CTO at Michigan Medicine to work full time as the Founder and Managing Member at EHC Consulting which focuses on hosting healthcare solutions like Epic in the public cloud.

In the interview below, we learn more from Dr. Calahan about his decision to work at EHC Consulting full time.  Plus, we dive into hosting Epic on the public cloud along with his experience moving other applications to the public cloud.

Tell us a little about yourself and EHC Consulting.

Dr. Tim Calahan: I began my technology career in the U.S. Marine Corps within the Judge Advocate General’s Office, where I was famously told to “fix the computer.” That directive launched a career spanning more than three decades in healthcare technology. Over that time, I’ve been privileged to witness — and help lead — the evolution from on-premises computing to modern cloud-based healthcare ecosystems. That constant state of change is what continues to energize and motivate me.

We founded EHC Consulting to address a clear gap we’ve seen across the healthcare industry over the past decade. Many health systems recognize the strategic advantages of the public cloud — agility, resilience, scalability, and innovation — but lack a clear roadmap, governance model, and execution playbook to migrate complex clinical workloads safely and effectively.

At EHC, our core focus is helping organizations move Epic EHR to the public cloud, but our expertise extends well beyond that. We also support migration and modernization of Epic third-party applications, imaging platforms, analytics environments, and general enterprise workloads. Our goal is not just to move technology, but to help organizations transform how they deliver care through modern infrastructure.

Why did you decide to leave your position as CTO at Michigan Medicine and go full time at EHC Consulting?

Dr. Tim Calahan: I am immensely proud of what our team accomplished at Michigan Medicine. During my tenure, we defined a comprehensive cloud strategy, began executing it at scale, and — most importantly — successfully migrated Epic to the public cloud, which is a significant milestone for any academic medical center.

Equally important, we built a strong, capable technology leadership team that I trust deeply to continue this journey. The organization is in excellent hands.

Ultimately, my decision comes down to impact. While I was able to drive meaningful change at Michigan Medicine, EHC Consulting allows me to bring that same experience, expertise, and approach to multiple health systems nationwide. By focusing full time on EHC, I can help accelerate cloud transformation across the broader healthcare ecosystem — which I believe is where I can have the greatest positive effect.

What are some of the big lessons learned while CTO at a health system when it comes to IT infrastructure?

Dr. Tim Calahan: There are three key lessons that stand out from my time as CTO at Michigan Medicine:

First, traditional on-premises infrastructure is increasingly inadequate for modern healthcare. Legacy architectures struggle to support real-time analytics, interoperability, AI, and the scale of data that today’s clinical and research environments demand. These limitations don’t improve over time — they compound.

Second, cloud transformation is not a quick project; it is a multi-year journey that requires disciplined leadership, patience, and trust in the long-term value of cloud. Organizations that lack consistent executive sponsorship or strategic clarity often stall or backslide. At Michigan Medicine, we were fortunate to have strong, unwavering leadership — particularly from Dr. Marshall Runge — which was critical to our success.

Third, your partners matter. Selecting the right cloud provider and systems integrators is one of the most consequential decisions a health system can make. You need partners who deeply understand both healthcare and large-scale cloud transformation, not just generic IT migration.

As CTO you decided to go all in on public cloud — what were the pros and cons of that decision?

Dr. Tim Calahan: Going all in on the public cloud was a straightforward decision for me, and one I would make again without hesitation.

The benefits are extensive: improved reliability, faster innovation, better security posture, elastic scaling, and the ability to integrate modern data and AI capabilities that are simply impractical in traditional data centers.

The primary “con” isn’t technical — it’s cultural. A transformation of this magnitude inevitably creates resistance. Some stakeholders are understandably cautious, and some incumbent vendors are invested in preserving the status quo. Throughout our journey, we encountered skepticism, fear, uncertainty, and doubt from various corners of the organization and industry.

Strong leadership and a clear vision were essential. We had to consistently remind people why we were doing this: to modernize care delivery, improve resilience, and position Michigan Medicine for the future of digital health.

Why are you so bullish on Epic in the public cloud?

Dr. Tim Calahan: I’ve been working on moving Epic to the cloud for nearly a decade, and I’ve seen firsthand how transformative it can be.

At a surface level, Epic performs well in the cloud, can be more cost-effective to operate, and allows for more flexible capacity planning. But the deeper benefit is organizational.

When Epic is delivered via cloud and managed services, IT teams are freed from routine operational maintenance and can focus more on innovation, clinical collaboration, and strategic initiatives that directly impact patient care. I’ve seen this shift dramatically change how IT functions within health systems — from infrastructure caretakers to strategic enablers.

That track record of real, measurable transformation is why I remain so confident in Epic’s future in the public cloud.

How have you seen Epic evolve in its approach to the public cloud?

Dr. Tim Calahan: When we first explored moving Epic out of traditional data centers, it was considered nearly impossible — and Epic initially told us as much.

Over time, through collaboration, engineering investment, and persistence across the industry, that mindset changed. What began as experimental architecture evolved into a validated, scalable model.

Today, Epic has fully embraced the public cloud. They’ve developed strong engineering capabilities, standardized best-practice architectures, infrastructure-as-code frameworks, and operational playbooks tailored specifically for cloud environments.

A great example of this evolution is Epic’s Cogito platform on Microsoft Fabric, which represents a truly cloud-native analytics strategy. This shift reflects a broader recognition that the consistency, scalability, and innovation velocity of the public cloud align better with Epic’s long-term roadmap than fragmented on-premises environments.

Are you concerned about cloud vendors raising prices once organizations are locked in? What can be done to mitigate that risk?

Dr. Tim Calahan: One of the advantages of the public cloud market is that it is competitive. There are three major cloud providers, and none can dramatically raise prices without risking significant customer migration.

While moving Epic between clouds is complex, it is absolutely feasible — and can be done without clinical disruption. That reality keeps pricing in check. If one provider acted in bad faith, others would quickly step in with incentives to attract customers.

Health systems can also hedge risk by designing architectures that are not overly dependent on proprietary services, negotiating strong contracts, and maintaining strategic flexibility. The key is thoughtful cloud governance, not avoidance of cloud altogether.

How do you see “AI at the edge” fitting with a public cloud-first strategy?

Dr. Tim Calahan: There is a lot of discussion about AI today — much of it still theoretical. What we do know is that effective AI depends fundamentally on data. Organizations that have centralized their data in the cloud are far better positioned to take advantage of AI at scale.

As AI tools mature, they will require ongoing monitoring, governance, and validation to ensure outputs remain accurate, ethical, and clinically reliable. That kind of oversight is far easier to manage from a centralized, cloud-based platform than from fragmented on-premises environments.

I do believe we’ll see more AI user interfaces and applications deployed at the edge — for example, in clinical workstations or medical devices. But the underlying data, analytics, and computational infrastructure will remain best suited for the cloud. In that sense, “AI at the edge” complements — rather than contradicts — a cloud-first strategy.