From 2025 Insights to 2026 Impact: AI and the Human Center of a Digital Future

By Brian Chee, Director of Content, Net Health

Perspectives on Interoperability, Specialty, and Reimbursement

A little over 12 months ago, Net Health began tracking sentiment toward artificial intelligence (AI) in the specialty healthcare space. Through surveys with partners and other collaborators, we asked rehab therapy and wound care professionals about how the idea, application, and reality of AI was changing their perspectives on providing care. With national surveys of health system leaders via Becker’s, and our own specialty care surveys, we aimed to better understand the perceived and real impact of AI — from the C-Suite to the bedside. Taken together, the findings show how the healthcare relationship with AI has evolved from a hidden benefit found in dashboards to a powerful solution for efficiency and decision-making.

Across our surveys and the span of time, one insight stands out: AI’s success will depend on how well it serves people. Not just patients, but also clinicians and nurses, administrative staff and more. It is unmistakably changing workflows and redefining our approach to care delivery and outcomes; it’s humanizing healthcare, connecting data to restore confidence, and strengthening reimbursement through evidence-based, integrated systems. The future will include a new vision for AI: with providers and patients at the center of care.

2025: Shifting Sentiment and Signs of Change

In late 2024 and the first quarter of 2025, we found rehab therapy and wound care professionals overwhelmed by documentation burden and unsure of how useful AI would be, especially in light of this high volume. Burnout was emotional; people were exhausted and defensive. Leaders and clinicians were shifting their opinion about AI as a solution in specialty care.

Rehab Therapy Professionals

• 38% were somewhat positive about AI use in their organization
• 37% were neutral to very negative about AI use in their organization
• 25% said AI/predictive analytics for decision support was the most effective technology at reducing burnout
• 60% of clinicians were neutral to very negative about AI use in their organization

Wound Care Professionals

• 25% were very positive about AI use in their organization
• 26% were neutral to very negative about AI use in their organization
• 45% said AI was the most effective technology at reducing burnout
• 16% of nurses were neutral to very negative about AI use in their organization

When asked specifically about ambient documentation, sentiment was much more positive, as was the willingness to adopt. The findings reveal openness to investigating and assessing AI, but perhaps uncertainty about capability and scale. Much like other industries, it seems healthcare professionals think there’s some promise with AI, but they’re not quite yet certain how extensive that will be.

Rehab Therapy Professionals

• 80% of non-clinicians were positive to very positive about ambient listening
• 64% of clinicians were positive to very positive about ambient listening

Wound Care Professionals

• 81% non-nurses were positive to very positive about ambient listening
• 87% of nurses were positive to very positive about ambient listening

The second and third quarters of 2025 saw increased enthusiasm, and a sharpening of opinions about the use and practical application of AI. As providers get more used to using AI in their practices, they’re more willing to apply it broadly to their work.

Rehab Therapy Professionals

• 52% were somewhat positive about AI use in their organization (+14%)
• 11% were neutral to very negative about AI use in their organization (+26%)
• 35% said AI/predictive analytics for decision support was the most effective technology at reducing burnout (+10%)
• 14% of clinicians were neutral (none negative) about AI use in their organization (-46%)

Wound Care Professionals

• 39% were very positive about AI use in their organization (+14%)
• 15% were neutral to very negative about AI use in their organization (+11%)
• 44% said AI was the most effective technology at reducing burnout (-1%)
• 14% of nurses were neutral to very negative about AI use in their organization (+2%)

Sentiment around ambient documentation grew more positive over time, to almost universal acceptance. This could be one of the keys to helping reduce the strain on practitioners and allow more time with fewer interrupts to spend with patients.

Rehab Therapy Professionals

• 93% of non-clinicians were positive to very positive about ambient listening (+13%)
• 86% of clinicians were positive to very positive about ambient listening (+22%)

Wound Care Professionals

• 89% non-nurses were positive to very positive about ambient listening (+8%)
• 80% of nurses were positive to very positive about ambient listening (-7%)

Leadership Perspectives on AI Are Continuing to Evolve

Our partnership with Becker’s showed that, in terms of reimbursement, technology decision-makers viewed AI with optimism, but many lacked clarity. Nearly half (49%) of health system leaders saw AI and advanced analytics as solutions for cost efficiency and ROI related to reimbursement, and a quarter (26%) saw significant potential. Essentially, a majority of technology and finance respondents believe AI has the potential to improve ROI and efficiency, but almost as many are unsure of how exactly that might come to be. Changes related to AI were also a concern: 23% cited AI compliance and the ethical use of predictive analytics as a top challenge.

By fall, the conversation had matured again. Health leaders began thinking about ‘agentic’ AI: systems capable of making contextual recommendations, executing actions, and connecting data across applications. The transition from using AI for simple tasks to using AI for more integrated processes, like those involving agentic AI, is defining the next era of AI in healthcare, but it’s nonetheless currently a moving target..

The same year that brought record optimism also exposed widening knowledge gaps that may be impacting adoption and transition AI from pilot to practice. Indeed, some technology partners struggle to map AI capabilities to the complexity of specialty care and reimbursement; it takes deep knowledge of codes, compliance, rules, and required fields to meet the needs of specialty care.

Across all the settings we studied, leaders seemed to say that AI must automate to improve efficiency, stabilize operations, and build revenue resilience, but that it must be done to meet their custom needs. AI must do more than take notes; it should also offer clinical support around decision-making. We’re still early in the process of figuring out how AI can be that solution for healthcare, but by linking clinical quality to financial stability, AI can be instrumental in putting people — both clinicians and patients — back at the center of care.

That’s the story of 2025: a year when AI optimism met operational reality to paint a more accurate picture of its applications going forward. Our studies show that as 2026 nears, healthcare professionals are preparing to shift from just accepting AI to actively using AI as an invisible support that propels a greater connection with patients and a reminder of their calling to care. The mechanics of operations should never again take priority over the mission to deliver better outcomes.

From Efficiency to Evidence

According to the fall 2025 Becker’s/Net Health survey on AI Adoption, over 90% of leaders said their organizations plan to prioritize AI for clinical decision-making within the next 12–24 months. Separately, leaders also prioritized documentation support, with 70% indicating that it is a high priority in the same timeframe. This signals that health systems see AI as a potentially critical lever to enhance care quality, reduce administrative burden, and improve clinician experience.

The use cases they’re pursuing, such as documentation accuracy, clinical decision support, and regulatory compliance, show that health systems and specialty providers are investing in AI to save time and to generate defensible proof of care and reimbursement integrity. In specialty care, where documentation errors can cost negative outcomes and lost revenue, the real promise lies in its ability to transform data into evidence in a constantly shifting regulatory world.

Interoperability Is About IT and Trust

When 63% of health system leaders ranked interoperability as one of the top three desired AI capabilities, they were likely expressing frustration with a deeper, more human problem: fractured data breaks trust between systems, clinicians, and payers… and makes it harder to provide high-quality care. AI could be the forcing function for addressing this, as predictive models, decision-support tools, and revenue analytics all depend on complete, clean, and connected data. This reality moves interoperability from  technical aspiration to a strategic mandate.

Burnout Is an Operational Risk that AI Can Solve

Staff burnout and retention issues have long been treated as a morale problem, something to fix with wellness programs and staffing adjustments. Today, burnout is evidence of core operational issues. It’s no surprise that across specialty care, we found that administrative burden was the top cause of burnout, which in turn led to emotional exhaustion. That connects human fatigue directly to financial and quality outcomes. Excess documentation can exhaust teams, slow throughput, increase denials, and reduce care consistency.

Close to 80% of wound care participants reported that burnout had a “very significant” or “significant” impact, evidenced by high turnover rates and difficulty filling positions. Reduced treatment quality and financial losses were among the top five ways that burnout has affected organizations. Nurses offered more clarity: over 4 in 10 said that administrative work, such as excess documentation, was to blame for burnout.

It’s no surprise that the same respondents pointed to specialty EHRs, AI, and predictive analytics as the top two solutions for fatigue. What’s more, over two-thirds of respondents aged 18-34 felt that AI and predictive analytics for decision support were the most effective technologies for reducing staff burnout. Across rehab therapy, wound care, and health systems in general, the most effective interventions identified in our surveys were ambient documentation tools, predictive analytics, and integrated specialty EHRs; all were rated positively as strong solutions for reducing practitioner stress and reclaiming time.

AI’s Knowledge Gap Threatens Momentum

Despite the enthusiasm for the benefits that AI delivers, 57% of health system leaders in the Becker’s/Net Health survey were “not very familiar” with AI-enabled EHR enhancements, and almost a third (27%) said they were “not at all familiar.” Healthcare leaders know that AI can help with staff shortages, audits, and reimbursement volatility, but many are figuring out how to use it effectively and within their own systems. The idea of AI is one thing, but the daily practice of it is a more complicated affair.

In addition, part of the confusion comes from semantics. When finance or technology executives talk about “AI,” they often mean predictive analytics or automation tools; clinical leaders might think of ambient documentation or agentic systems. Ultimately, leaders are confident in the strategic potential of AI but might be less fluent in the specific capabilities embedded in EHRs.

Staff Adoption Has Become the Leading Overall Concern about AI

When asked about their top concern in deploying AI, health system leaders most often cited staff adoption and training, ahead of even cost, ROI, and integration with existing systems — though these were also leading concerns. Teams aren’t fully on board with adoption yet.

When asked to select their top three most pressing issues, leaders cited “integration with existing systems” (21%) as first.. However, almost 90% ranked staff adoption and training as an overall top three concern. In addition to the 20% who ranked it as the number one concern, nearly 30% ranked it second, and 40% ranked it third. These findings signal that technology performance alone is not enough. AI must be workflow-aware, easy to use, and conscious of ethical and privacy concerns. It can never replace the clinician, but can serve as a trusted, invisible assistant, providing support and facilitating patient engagement.

From Insight to Impact: Five Takeaways for 2026

AI Is Humanizing Healthcare

In 2025, nearly half of health system leaders said they viewed AI primarily as a solution for ROI and cost efficiency, but 29% remained neutral, citing uncertainty about its “true potential”. Yet closer to the point of care, professionals across wound care and rehab therapy pointed to very human values, from clinical decision-making support to a compelling antidote for burnout.

It’s a sign that perhaps the value of AI lies in amplifying human effectiveness. Even by focusing on the human element, it will provide persuasive cost and ROI benefits. In 2026, the organizations that lead will be those that measure outcomes in minutes returned to patients, time reclaimed from documentation, and clinician satisfaction. This mindset is emerging in rehab therapy, where younger clinicians view AI as a path to balance and progress.

Interoperability Is Job One on Day One

In the Becker’s AI Adoption survey, interoperability ranked as the single most appealing AI functionality for 63% of respondents. That statistic tells us that the path to transformation is through connection. In 2026, “integration readiness” should be a factor for AI investment… and that means vetting technical partners not just for functionality, but also for data literacy. Can they align with FHIR APIs, payer access rules, and the interoperability mandates now embedded in CMS 2026 requirements?

Build a “Reimbursement-Resilient” Infrastructure

In our 2025 reimbursement research with Becker’s, over 40% of finance and technology leaders said reimbursement delays had a very significant impact on financial health. Another 38% said they were pessimistic about 2026 policy changes. That’s a signal that sustaining predictable revenue amid regulatory volatility is a defining priority. The systems that will thrive in 2026 are those that invest in infrastructure that blends AI with compliance intelligence: think EHRs that validate codes in real time, analytics that predict denials before they occur, and documentation engines that automatically build audit trails.

Close the Knowledge Gap Before Scaling AI

With a healthy majority of health system leaders admitting they’re only “somewhat familiar” or “not familiar at all” with AI-enabled EHR enhancements, an industry already piloting clinical AI in high-risk environments could see, at best, sub-optimal outcomes.

Education and governance must catch up. In 2026, expect leading systems to formalize Responsible AI Councils: multidisciplinary oversight bodies that combine compliance, data science, and clinical voices. The most effective models will follow the approach we saw in early adopters: real-world validation, continuous performance monitoring, and bias-mitigation frameworks. This next phase of AI in healthcare should be about discipline.

Partner for Expertise, Not Just Innovation

AI vendors are multiplying faster than health systems can evaluate them, and many lack experience in the specialty environments where the stakes are highest and specialized workflows are required—not to mention, the high stakes as it relates to health data and privacy. These sectors and settings require technology partners who understand reimbursement metrics and policy, interoperability constraints, and the daily realities of patient documentation in specialty settings.

As we discussed earlier, in our 2025 surveys, clinicians said their top concern was adoption anxiety. It’s critical that vendors can deliver technology that fits the healthcare world and operate as experienced partners. That’s why in 2026, “specialty fluency” will become a priority alongside security and scalability. In fact, when it comes to wound care and rehab therapy, no matter where the care is provided, the ability to map to the setting and specialty is a critical part of compliance, privacy, and scalability. The application of AI with the human experience of specialists is the real edge when it comes to turning healthtech into humantech—and creating better outcomes for all.