Healthcare IT Today: Healthcare AI Potpourri – 2026 Health IT Predictions

As we wrap up another year and get ready for 2026 to begin, it is once again time for everyone's favorite annual tradition of Health IT Predictions! We reached out to our incredible Healthcare IT Today Community to get their insights on what will happen in the coming year, and boy, did they deliver. We, in fact, got so many responses to our prompt this year that we have had to narrow them down to just the best and most interesting. Check out the community's predictions down below and be sure to follow along as we share more 2026 Health IT Predictions!

Check out our community's Healthcare AI predictions:

Ajai Sehgal, Chief AI Officer at IKS Health
To unlock the full value of AI and digital transformation in 2026 and beyond, leaders should prioritize several key shifts and trends that move beyond foundational adoption to sophisticated, value-driven implementation. While early AI efforts focused on automating discrete, repetitive tasks, the future lies in creating autonomous systems that can manage complex, end-to-end processes. The reliance on off-the-shelf, generic AI models will diminish.

The next wave of value will come from developing or fine-tuning AI that is highly customized and context-aware. This means AI that understands the specific nuances of an organization's operations, culture, and competitive landscape.

Dr. Heidi Jannenga, Co-Founder and Chief Clinical Officer at WebPT
2025 was the year of overpromising and underdelivering on AI, but in 2026, we'll see a shift from AI being a buzzword to it being a backbone for operational efficiency in healthcare. As clinicians, we don't need technology to replace us; we need it to restore the time and focus we've lost with direct patient care.

Providers that use AI as a true co-pilot will feel this most: tools that help diminish documentation and administrative burden, identify co-morbidities and red flags for certain treatment pathways, create patient exercise protocols based on individualized and evidence-based training, and maximize provider payments based on ongoing updated rules engines.

By the end of 2026, I expect AI-supported clinical decision support tools to become much more common in outpatient rehab as EMRs were a decade ago: practical, reliable, and woven into everyday care.

Rafael Sidi, Senior Vice President & General Manager of Health Research at Wolters Kluwer Health
The future of health research isn't just about more data; it's about turning knowledge into impact faster than ever before. In 2026, AI will help us move beyond searching and reading to truly understanding and applying insights in real time. Imagine a world where clinicians don't have to wait months or years for guidelines to catch up, because AI is continuously synthesizing global evidence and surfacing what matters most. That's not just efficiency, it's better care, everywhere.

AI is rapidly becoming an essential partner for researchers, helping them find, summarize, and synthesize the latest evidence and literature. It's transforming how journals are published, peer-reviewed, and consumed, making scholarly content more dynamic, accessible, and personalized.

AI will also play a pivotal role in upholding research integrity by helping make the peer review process more open, efficient, and trustworthy, automating corrections and retractions, and ensuring that the most current, high-quality evidence is always available. Rather than replacing human judgment, AI will strengthen it, creating a future where evidence-based medicine is continuously informed by the latest science delivered faster, smarter, and with greater impact.

The opportunity ahead is extraordinary. By leading with purpose, responsibility, and a commitment to innovation, we can shape a future where technology and human expertise work together to advance health research and improve lives.

Karen Thomas, Vice President, Clinical Solutions at CorVel
Workers' compensation will undergo a fundamental transformation in 2026, shifting from reactive claims handling to proactive risk identification powered by AI-driven early intervention. This evolution represents the industry's most significant opportunity since managed care emerged, with success measured not merely by claims processed but by injuries prevented and lives improved. The critical window for intervention lies within the first 30 days following an injury. Early identification and targeted clinical support yield lower costs and faster resolution.

This transformation isn't about replacing healthcare professionals with algorithms. Instead, it's about empowering nurses and clinicians with AI-enhanced capabilities. While artificial intelligence handles complex data analysis, healthcare providers can focus on what they do best: delivering compassionate patient care.

To succeed in this new paradigm, organizations must invest in integrated platforms that seamlessly connect claims data with medical records, train staff to collaborate effectively with AI tools, build strategic partnerships with providers who understand early intervention protocols, and develop metrics that prioritize prevention over simple claims closure. Companies that master this balance between advanced technology and human-centered care will achieve dramatic improvements in both clinical outcomes and financial performance.

Shelli Pavone, Co-Founder and President at Inlightened
In 2026, generative AI and predictive analytics will start to shift from abstract promise to practical applications. Rather than replacing expertise, AI will amplify it. We'll see this specifically in the amount of time clinicians and researchers spend synthesizing data. AI will enable them to focus on interpretation and decision-making instead of administrative chores. Clinicians will become validators of AI-generated insights, verifying that they are clinically acceptable, ethical, and rooted in real-world situations.

On the front lines, they will play a greater role in shaping and testing new tools so that the future of care delivery is less about clinicians versus AI, and more about clinicians supported by AI. This frontline involvement will also help to strengthen trust in how AI is used in healthcare.

Sam Dorison, Co-Founder and CEO at ReflexAI
AI will become the new standard for healthcare training. AI-driven simulations will become a go-to training tool for healthcare teams in 2026. With 83% of executives seeing AI's potential to improve clinical decision-making, but nearly half citing proper use as a barrier, simulation-based learning offers a safe, scalable, and cost-effective solution.

AI-powered simulations let clinicians and support staff practice critical conversations, triage decisions, and patient interactions in realistic, risk-free environments. At scale, healthcare organizations can design, test, and QA these simulations, building smarter, faster, and more resilient teams while reducing errors and improving patient outcomes.

Ajoy Ranga, Chief Digital Officer - Health Care at UST
I believe 2026 will be defined by how effectively enterprises can convert AI-first intent into operational reality. The opportunity is extraordinary - unprecedented productivity gains, automated workflows, predictive insights, and real-time personalization. AI will fundamentally reshape how work gets done. But with that opportunity comes a very real challenge: a meaningful portion of today's work will be automated. AI-driven productivity is going to be huge, and it will inevitably displace certain roles.

The question is not whether this shift will happen but how enterprises will respond. My view is that organizations need to be bold about reinvesting the value created by AI. Businesses must channel these gains into new business models, new services, and new revenue streams, so that there is enough meaningful work for the workforce. This is essential not just for enterprise competitiveness, but for economic stability and long-term prosperity.

What surprised me most this year was how quickly the healthcare industry moved from 'AI as an experiment' to 'AI as a baseline expectation.' Across payors, providers, and partners, the conversation shifted almost overnight toward operationalizing AI safely, responsibly, and at scale. The speed of that change underscored something important: enterprise architecture, data quality, and interoperability matter just as much as the models themselves. This realization has shaped the way I think about digital transformation, so that it's not just about adopting AI but designing systems that can continually absorb and leverage AI in real time.

Charlie Lougheed, Co-Founder and CEO at Axuall
AI, running on top of large data models that complete the picture of providers' credentials and practice patterns, while predicting alignment, attrition, and optimization, will continue to emerge as the most economical and practically viable way to manage the growing supply-and-demand gap in the United States. Meanwhile, with administrative documentation and paperwork accounting for up to 30% of healthcare workers' time, the rollout of AI in areas such as ambient charting and pre-authorization will begin to relieve some supply shortages by virtue of increasing capacity.

Nicole Miller, Chief Operating Officer at Nordis Technologies
2026 will reward healthcare organizations that build resilience for AI-scale change. Think Y2K urgency, minus the panic. They'll set 3-5-year goals so AI serves strategy; assemble leaders who normalize change; move fast but deliberately; and partner where they lack specialists. Budget for experimentation, expect some wrong turns, and keep people in the loop as roles evolve. The point isn't to chase hype but to translate uncertainty into innovation through disciplined iteration, investment, and workforce planning because AI's 'Y2K moment' has arrived.

Bryson Tombridge, Co-Founder and CEO at Tono Health
2026 is the year AI stops trying to replace clinicians and starts replacing delay. The biggest breakthroughs won't be diagnostic AI; they'll be around workflows and how AI compresses all of the friction around specialty access from weeks to days, and even hours. Health systems will realize that the fastest ROI in AI comes from eliminating friction, not practicing medicine.

Frank Vega, CEO at The Efficiency Group
Like most enterprises, Healthcare is struggling with incorporating AI. Where does it fit? Is it just a different search engine, or will it take my job? Questions and concerns about implementation and how it can make enterprises more efficient and productive will be clarified. Like most new technologies, it takes time for people and organizations to see where it works best for them. Each application will use AI technology in different ways.

Healthcare is wary about the application of AI in diagnostics. But there are many other areas where AI can be applied that greatly improve patient care and operations within the enterprise. As healthcare grapples with rising costs and reduced staff, AI will be used to drive efficiency and increase productivity. Specifically, AI will accelerate and facilitate process improvement across healthcare, an initiative that exists today.

Time and resource allocation for process improvement has always been a challenge and labor remains tight. AI-enabled process creation, mapping, analysis, improvement, and documentation will be a paradigm shift for 2026. Healthcare teams tasked with improving the enterprise will use AI to quickly visualize a process and identify repetitive, low-value tasks.

Staff will be given back time for patient care, collaboration, and innovation. These improvements will help healthcare organizations reduce burnout, improve throughput, and build a foundation for continuous improvement.

Philipp von Gilsa, CEO at Kontakt.io
More than half of the Top 20 Health Systems will move off legacy RTLS and onto AI-native care operations platforms. The driver will be application consolidation, cost savings, and use-case expansion with a focus on AI and throughput. Providers will adopt platforms that combine real-time signals with EMR context to improve patient flow, eliminate bottlenecks, and give clinicians back meaningful time. In 2026, RTLS stops being a 'find-it' tool and enables a new era of agents focusing on physical operations.

Jon Wang, Co-Founder and Co-CEO at Assort Health
AI is allowing engineers to code at 10x the amount as in the past. Historically, healthcare has been burdened by highly customized implementations producing clunky solutions that clinicians, providers, and patients have to custom-fit to code rather than the code being built specifically for them.

Heading into 2026, we are in this unique place where you can actually build agents and frameworks to rapidly deploy codes that are extremely bespoke to a given provider's workflow, delivering outcomes that require less clinician time to implement.

Thank you so much to everyone who took the time out of their day to submit a prediction to us, and thank you to all of you for taking the time to read this article! We could not do this without all of your support. What do you think will happen for Healthcare AI in 2026? Let us know on social media. We'd love to hear from all of you!

Be sure to check out all of Healthcare IT Today's Healthcare AI content and our other 2026 Health IT Predictions.