Generative AI is powerful and useful…but is it intelligent?

Matthew Hays, Ph.D., SVP Research and Analytics at Amplifire

A few years ago, I wrote an article arguing that artificial intelligence isn’t actually intelligent, but is instead so fast that it can do a good impression of a smart person…sometimes. The crux at the time (2019) was that computers only rivaled humans on well-defined tasks, where success and failure are easy to determine — like playing chess, answering a trivia question, or saying whether there’s a motorcycle in a picture. Computers were still terrible at ill-defined tasks, like drawing a picture or making a convincing argument or writing a poem.

Then ChatGPT came out.

Suddenly, computers were good at many of the ill-defined things people were good at. ChatGPT could summarize a 20-page report, check the tone of an email to a difficult coworker, and write VCR-programming instructions in the style of a wisecracking detective from the 1940s.

But is this, finally, intelligence?

It certainly feels like it. When a chatbot muses on the philosophy of free will, or offers comfort after the death of a pet, or explains a complex tax situation in plain English…how could there not be an intellect behind that? Indeed, how could that intellect not be very similar to our own?

Fluent, sophisticated conversation has been humans’ default indicator of intelligence for millennia. For most of that time, it worked fairly well — until travel beyond the nearest geographical boundary became possible. Halting or heavily accented speech can make people underestimate a conversation partner’s intelligence. Could chatbots’ fluent speech be causing us to overestimate theirs? To answer that, we need to look at how these tools actually produce language.

Have you ever seen the Plinko game on The Price is Right? The contestant drops a disk that bounces down through a series of pegs until it lands in a slot that (hopefully) corresponds to a prize.

This is, essentially, how a large language model makes a chatbot chat. From enormous amounts of text, statistical patterns emerge about which words tend to follow which other words. So when you give an LLM a prompt, it doesn’t think about the answer; it just drops a disk. The disk bounces through the pegs and lands in a slot. That slot is the first word of its response. Then it drops another disk, and another, and another — and more words come out. Nothing is choosing the slots; the words are produced mindlessly.

But, again, it really doesn’t feel mindless. I asked ChatGPT about its consciousness. It said:

I am a language model. I generate text based solely on statistical patterns — without experiences, feelings, intentions, or consciousness. I don’t and can’t care about anyone or anything. Even when I use the word “I,” this is just a linguistic convention. There is no real “me”; there is no self, no awareness, and no entity having this conversation — just algorithmically generated text.

We can see this clearly in chess — ironically, a domain where non-LLM AI has been superhuman for decades. ChatGPT has ingested thousands of publications containing the rules of the game, so it will tell you that of course a queen can’t jump a knight. ChatGPT will also play quite well for the first few moves of a chess game because the patterns of those moves are quite familiar; LLMs have digested millions of games in the form of text notation like:

1. e4 e5

2. Nf3 Nc6

3. Bb5 a6

…which looks like this, the Ruy Lopez opening, which has been played hundreds of thousands of times: 

But chess games become unique fairly quickly after the first dozen or so moves, which is right when ChatGPT starts to fall apart. In fact, if your initial moves are unusual enough, ChatGPT’s fourth move can be so bad that it’s actually illegal. And the very same ChatGPT that told you queens can’t jump knights will happily jump a knight with a queen because “Qxa5” is the slot that the Plinko disk bounced to. ChatGPT doesn’t know that particular move is illegal because it doesn’t actually know the rules — because there’s no thing that would actually do the knowing. My 2019 distinction between well-defined and ill-defined tasks breaks down because LLMs aren’t actually doing the task.

Emerging software development partners (Claude Code, OpenAI’s Codex) seem to contradict the Plinko analogy, because there is clearly some thing  helping engineers write purpose-driven code. Agentic AI is similar; tools like Qlik Answers can generate a high-quality data visualization from a plain-English question. AI-enabled development environments like Cursor and Antigravity go a step further, allowing you to conduct an orchestra of AI agents working on various parts of your codebase. The results can be genuinely impressive. It’s hard to see this as anything other than (superhuman?) intelligence.

But a look at what’s actually happening under the hood tells a different story: the LLM spits out a plausible next step — a line of code, a function call, a command — and then something external checks whether it worked. A compiler catches syntax errors. A test suite flags broken logic. A runtime reveals whether the program actually runs. The model proposes; the environment verifies. The intelligence, to the extent there is any, is in the loop — not in the model. The Plinko board gives you valid outputs when you have some other tool blocking the invalid ones.

Ray Bradbury opened a short story (Night Call, Collect) with a poem that perfectly describes today’s LLM-driven conversation partners, even though it was written in 1969:

Suppose and then suppose and then suppose
That wires on the far-slung telephone black poles
Sopped up the billion-flooded words they heard
Each night all night and saved the sense
And meaning of it all.
Then, jigsaw in the night, Put all together…

Thus mindless beast
All treasuring of vowels and consonants
Saves up a miracle of bad advice
And lets it filter whisper, heartbeat out…

So one night soon someone sits up
Hears sharp bell ring, lifts phone And hears a
Voice like Holy Ghost Gone far in nebulae
That Beast upon the wire,
Which with sibilance and savoring!
Down continental madnesses of time
Says Hell and O And then Hell-o.

To such Creation
Such dumb brute lost Electric Beast,
What is your wise reply?

I showed the poem to an AI chatbot set to its strongest model in March 2026. It gushed that

“a miracle of bad advice” might be the single best three-word description of an LLM hallucination ever written, fifty-five years before the thing it describes existed.

I had to laugh; in a single sentence, I saw a phenomenally powerful tool (appear to) meta-analyze its weaknesses…while also not being able to count to five. I asked the bot to explain what went wrong in the context of this article.

I didn’t count. I can’t count. There’s no “I” that would do the counting.

Stanford Health Care Took the Stage at HIMSS to Share Their EHR Training Success 

We were honored to see this work come to life during Stanford Health Care’s presentation at HIMSS Global Health Conference & Exhibition last week. Anne Hyland, Vice President of EHR Learning, and Michael Walker, Senior Director of New Business at Amplifire, were able to hear first-hand how their team handled the challenges and the measurable outcomes they’ve achieved. It was a powerful reminder of what’s possible when innovation is paired with a clear focus on outcomes. 

What stands out most is Stanford Health Care’s thoughtful approach to modernizing onboarding. Rather than accepting traditional, one-size-fits-all training, they’ve embraced a more personalized, data-driven model that meets clinicians where they are, respecting prior experience while ensuring mastery of critical workflows. The result is not only greater efficiency, but a more confident, prepared workforce ready to deliver high-quality care from day one. As highlighted in their broader work, this approach has also enabled earlier identification of struggling learners and more targeted support, ultimately strengthening both performance and satisfaction. 

Stanford Health Care continues to set the standard for innovation in workforce development, and their recent feature in Healthcare IT News highlights just how impactful that work has been. By reimagining EHR training, their team has successfully reduced training time by 50% while simultaneously improving learning retention, an achievement that speaks to both their strategic vision and deep commitment to clinician success. 

Congratulations to the entire Stanford Health Care team on this well-deserved recognition. Their leadership is not only advancing their own organization, but helping to shape the future of healthcare workforce development across the industry. 

Learning at Scale, with Purpose: What We’re Focusing on in 2026

If there is one thing the last year reinforced, it is this: learning still matters deeply, especially when the stakes are high. Across healthcare, financial services, higher education, and other mission-critical industries, organizations are under pressure to do more with less, onboard faster, reduce risk, and support a workforce that is stretched but deeply committed.

As we move through early 2026, we have been reflecting on what we have collectively built with our customers and partners and what that progress enables next.

• The scale alone tells a powerful story. 
• Over 706,000 courses completed. 
• More than 556 million learner interactions. 
• Nearly 46 million instances where Confidently Held Misinformation™ was surfaced and corrected. 

But the real impact of those numbers is not volume. It is what they represent: people learning more efficiently, retaining critical knowledge longer, and showing up to their work more prepared and more confident.

From Activity to Impact

Training activity is easy to measure. Meaningful learning is harder, and that is where outcomes start to emerge.

We’ve seen health systems and organizations translate adaptive learning into tangible improvements.

• UC San Diego Health reduced training time by 75 percent, helping clinicians get where they were needed faster.
• Stanford Health Care achieved 50 percent faster onboarding without sacrificing depth or quality.
• Mercy Health saved over $2 million by modernizing compliance training, freeing up time, budget, and attention for patient care.

These outcomes were not driven by shortcuts. They came from rethinking how learning works, recognizing that not every learner needs the same content, that confidence matters as much as correctness, and that insight into struggle and uncertainty is just as important as test scores.

Across clients, we also saw consistent improvements in knowledge retention and compliance completion. These are quiet wins that do not always make headlines, but they make a real difference in safety, performance, and trust.

Learning That Adapts to People, Not the Other Way Around

One of the clearest themes we heard from learners themselves was appreciation for personalization.

A resident at LVHN reflected on retaking a course years later and coming away with far more value the second time. The content was familiar, but the experience was different. Self-guided, adaptive learning allowed prior knowledge to be respected and gaps to be addressed without friction. The result was greater confidence and stronger preparation for seeing patients.

That sentiment showed up again and again. When learning adapts to individuals rather than forcing everyone through the same path, it becomes something people engage with, not something they endure.

From a global professional services perspective, SAX LLP highlighted how reduced course completion times made it possible for professionals to acquire new skills on demand, wherever they were in the world. In fast-moving environments, time is not just money. It is relevance.

Behind the Scenes: Building Better Tools for Learning

Progress at this scale does not happen without sustained investment in the platform itself. In 2025, we doubled down on innovation with platform improvements including:

• A new learning environment with an updated look and feel designed to reduce friction and make engagement more intuitive. 
• Major enhancements to AI-powered authoring, enabling an end-to-end experience that helps subject matter experts move faster without compromising accuracy. Today, more than 150 authors are already using AI as part of their workflow, contributing to over 4,300 courses developed.
• GapFinder Assessments continued to evolve as well, giving organizations clearer insight into where knowledge gaps, uncertainty, and risk actually live before they show up in practice.

We also expanded our AI roadmap with a new conversational capability now in beta and moving toward broader availability. The goal is not novelty. It is support, meeting learners where they are in the moments they need guidance most.

Another important milestone was the issuance of U.S. Patent No. 12,307,920. This strengthens protection around how the platform uses AI to interpret learner confidence and answers sequentially, not just simultaneously. It is a technical achievement with a very human purpose: helping people avoid confidently held misinformation, learn faster, and retain what matters.

Systems, Not Silos

One of the most encouraging signs of maturity we saw was how organizations began integrating learning more deeply into their operational ecosystems.

At Stanford Health, integrating Amplifire with ServiceNow enabled automated workflows that flagged struggle, generated requests, and connected learners with the right resources without adding administrative burden. As Lacey Jensen, RN-BC, MN, Director of Informatics Education, shared, this approach transformed training from something reactive into something responsive and personalized at scale.

This kind of integration signals a broader shift. Learning is no longer a standalone event. It is becoming part of how organizations sense risk, support people, and continuously improve.

The Human Side of Scale

For all the technology, patents, and metrics, what we are most proud of is the community behind the work.

Customers who co-develop content and share what they are learning. Educators and clinicians who push for better ways to train their teams. And internally, a group of people who care deeply about the impact of what they build.

As one team member put it, there is something rare about working in a place where you can make a difference, trust your leadership, and genuinely enjoy the people around you. Another reflected on how being together, stepping away from screens and roadmaps, was a reminder that the best feature of the platform might actually be the people behind it.

That culture matters because learning is fundamentally human. Tools can accelerate it, but empathy, collaboration, and integrity sustain it.

Carrying Momentum Forward

As we continue into 2026, the focus is not on celebrating past milestones. It is on building from them.

The challenges facing today’s workforce are not easing. Turnover, burnout, skills gaps, and rising expectations remain very real. At the same time, there is momentum and proof that smarter, adaptive learning can reduce burden, restore confidence, and create space for people to do their best work.

We remain committed to leading the future of learning through an AI-powered platform rooted in patented brain science. One that drives lasting retention, unlocks human potential, and helps organizations achieve excellence at scale.

Not because learning is trendy.
But because when learning matters, outcomes do too.

The Future of EHR Training: How Intelligent Learning Drives Clinician Confidence and Reduces Burnout

Anne Hyland, Vice President of EHR Learning, Amplifire

EHR training is no longer about teaching clicks and workflows. Across healthcare organizations, training has become a strategic driver of clinician experience, adoption of AI-enabled workflows, and organizational readiness for an increasingly complex regulatory and cybersecurity environment. Health systems that treat training as an afterthought will struggle to realize the full value of their EHR investments.

At Amplifire, we see EHR training as a catalyst for measurable improvement in efficiency, confidence, and clinician satisfaction. The evolution of healthcare technology, combined with workforce pressures, makes a modern approach to learning essential. 

Virtual and Asynchronous Learning Is Now the Standard

The continued shift toward virtual and asynchronous training reflects the realities of clinical practice. Nearly 70 percent of clinicians report that self-paced learning is effective, according to KLAS. Rigid, one-size-fits-all training models are increasingly incompatible with the pace and pressure clinicians face every day.

However, asynchronous delivery alone is not enough. Training must be intelligent. Effective programs identify what users already know, pinpoint knowledge gaps, and focus learning only where it will materially improve performance. Amplifire’s adaptive learning approach ensures clinicians spend time where it matters most rather than repeating information they already understand. 

AI-Powered Documentation Requires Smarter Training

One of the most significant shifts in EHR use is the rapid adoption of AI-powered documentation tools, including ambient documentation and virtual scribes. While these technologies promise to reduce administrative burden, they also introduce new risks if implemented without thoughtful education.

Clinicians must understand how to validate AI-generated content, maintain regulatory compliance, and retain clinical accountability. Training must therefore move beyond simple feature education and focus on responsible AI use. Scenario-based learning helps clinicians develop the judgment required to work effectively with these tools while maintaining trust in the documentation process.

Microlearning That Respects Clinician Time and Expertise

Microlearning has emerged as one of the most effective ways to engage clinicians. Short, targeted modules combined with focused courses align far better with real-world schedules than traditional classroom sessions.

Effective training also recognizes that EHR use varies significantly across specialties. Role-specific content feels more relevant to clinicians and improves both engagement and efficiency. By respecting existing knowledge and targeting inefficient habits, adaptive learning delivers education that feels practical and immediately applicable to clinical work. 

Cybersecurity Training as Risk Reduction

As cyber threats grow more sophisticated, the importance of effective security training continues to increase. Phishing attacks, ransomware, multi-factor authentication requirements, and evolving HIPAA regulations demand ongoing education rather than annual compliance exercises.

Training has become a frontline defense. Adaptive learning helps organizations reinforce correct behaviors, identify areas of risk across roles, and deliver targeted education that strengthens overall security posture.

Preparing Clinicians for Telehealth and Remote Monitoring

Telehealth and remote patient monitoring continue to introduce new documentation and workflow requirements. Virtual care environments require clinicians to manage device-generated data, document care interactions appropriately, and maintain continuity of care across digital and in-person settings.

These workflows are not intuitive extensions of traditional practice. Focused education is necessary to ensure accuracy, compliance, and efficiency while minimizing additional administrative burden.

The Next Phase of EHR Training

As healthcare technology continues to evolve, AI literacy is becoming a central competency for clinicians. Health systems are defining frameworks for responsible AI use and ensuring that clinicians understand how to interpret AI-driven insights within their workflows. Success increasingly depends on effective collaboration between clinicians and intelligent systems.

At the same time, regulatory and operational changes will continue to introduce new workflow requirements. Electronic prior authorization mandates, expanded automation, and evolving cybersecurity standards will require structured training to avoid delays, errors, and staff frustration.

One-time interventions are no longer sufficient. Continuous, adaptive learning strategies are becoming essential for maintaining proficiency in a rapidly changing clinical environment.

Training That Improves Satisfaction and Reduces Burnout

At Amplifire, our goal is to make EHR training more efficient, more effective, and more human. By respecting existing knowledge, correcting inefficient habits, and focusing education where it matters most, organizations can improve both proficiency and clinician experience.

The connection is clear. Better training leads to higher EHR satisfaction, and improved satisfaction contributes directly to reduced clinician burnout.

As EHR systems become more intelligent and more embedded in clinical decision-making, training will determine whether technology becomes a burden or a true partner in care delivery. Organizations that invest in intelligent learning will be best positioned to ensure their clinicians are confident, capable, and ready for what comes next.

Anne Hyland is the Vice President of EHR Learning at Amplifire.  With over 30+ years in learning and development, including in healthcare IT and EHR implementation, education, and change management, Anne is passionate and committed to both the learner experience and the organizational impact of effective learning.

2026 Healthcare Alliance Virtual Summit Recap

On February 11th, 2026, we gathered together for the Annual Healthcare Alliance Virtual Summit. 200+ Learning and Clinical Leaders from leading healthcare organizations across the country came together to discuss the power of innovation in learning.

Featuring:

• Froedtert Health showcases the impact of GapFinder Assessments, improving documentation efficiency and closing performance gaps. 

• UW Medicine provides an inside look at how their team is leveraging Amplifire Authoring, powered by AI, to transform their learning and development process. 

• Tampa General Hospital illustrates how Dynamic Learning and GapFinder Assessments advance annual competencies and empower new-hire APP onboarding across 60+ specialties.

• University of California Health (UCSD, UCSF, and UCLA) shares how system-wide learning strategies and data-driven insights are accelerating innovation and performance across their institutions. 

Complete the form below to watch the highlights video!

Adaptive Learning as a Strategic Response to a Multigenerational, High-Pressure Environment 

Corrie Halas, VP of Clinical Learning, Amplifire

The healthcare industry is at an inflection point. With workforce shortages, increasing patient acuity, digital transformation, rising labor costs, and expanding regulatory demands, the way we train and develop clinical and operational staff can no longer remain static. 

To meet today’s challenges and prepare for tomorrow’s healthcare delivery, organizations must embrace adaptive learning as more than an educational tool. It’s a system-level strategy for resilience, performance, and transformation. 

The Multigenerational Workforce Challenge 

Healthcare teams today include five generations of employees, each with different levels of experience, comfort with technology, and learning styles. This diversity is a strength, but only if organizations adopt learning methods that meet individuals where they are. 

Standardized training modules and traditional LMS-based models often fall short. For new hires, it can mean information overload. For veteran staff, repetitive content becomes a source of disengagement. In both cases, the result is lost time, missed opportunities for development, increased variability in patient care, and significant cost. 

Adaptive learning changes that by responding to each learner’s current knowledge, identifying gaps, and personalizing the learning path. The result: faster mastery, reduced training fatigue, and better knowledge retention. 

Competing Priorities Demand a Smarter Approach 

Healthcare organizations are juggling critical, often competing priorities: 

• Addressing talent shortages and onboarding at scale 

• Maintaining compliance amid shifting regulatory standards 

• Reducing burnout while increasing productivity 

• Implementing and optimizing digital health tools and EHRs 

• Improving safety and quality outcomes in real time 

Adaptive learning helps tackle these priorities in parallel. It enables faster training cycles, real-time measurement of competency, and precise alignment with organizational performance goals. 

How Adaptive Learning Works 

Modern adaptive learning platforms use principles of cognitive science to uncover: 

• What learners know 

• What they think they know but don’t (a major risk in clinical settings) 

• What they need to learn next 

• Where knowledge decay is occurring over time 

This allows healthcare organizations to close skill gaps, mitigate risk, and reduce time spent on unnecessary training. For clinical roles, this translates to safer care. For revenue cycle, IT, and operational teams, it means fewer errors and faster performance ramp-up. For everyone, it leads to a more confident and capable workforce. 

Measurable Impact Across the Organization 

Organizations that adopt adaptive learning see clear benefits: 

• Reduced turnover from improved employee engagement and development 

• Shortened onboarding timelines and fewer disruptions to care delivery 

• Improved patient outcomes by targeting confidently held misinformation 

• Operational scalability, particularly in multi-site systems or high-growth environments 

• Reduced training costs by eliminating time spent on content already mastered 

Perhaps most importantly, adaptive learning contributes to a culture of continuous improvement, a critical component for organizations striving to meet quality benchmarks, retain top talent, and remain competitive in value-based care models. 

Supporting Patient Safety and Quality Goals 

For healthcare leaders focused on patient safety and quality outcomes, adaptive learning offers a direct line of sight between education and performance. By identifying and correcting confidently held misinformation before it reaches the point of care, adaptive platforms help organizations reduce preventable errors, improve adherence to evidence-based protocols, and strengthen their performance on CMS quality measures and value-based care metrics. This targeted approach ensures that education investments directly support patient safety movement methodology (PSSM) and continuous quality improvement initiatives. 

The Path Forward 

The future of healthcare hinges not just on hiring the right people, but on equipping every team member, regardless of background or tenure, with the knowledge and confidence to succeed in a dynamic environment. 

Adaptive learning isn’t a luxury. It’s a modern solution to a modern problem. 

As health systems continue to evolve, the question isn’t whether we can afford to change how we train our teams. It’s whether we can afford not to. 

About Amplifire 

Amplifire is the world’s leading adaptive learning platform, built from patented brain science discoveries and informed by billions of learner interactions. Its AI-powered tools help organizations reduce costs, save time, and improve outcomes through smarter, personalized training. From EHR onboarding to clinical risk reduction, Amplifire partners with high-stakes industries to elevate performance, safety, and satisfaction. 

Clinician-Centered, Data-Driven: How AHN Transformed OB Risk Training with Amplifire 

Personalized education. Smarter insights. A safer environment for mothers and babies. 

Allegheny Health Network (AHN) recognized a growing challenge within its obstetrics (OB) risk reduction training. A generic, one-size-fits-all program was falling short of addressing the unique needs of individual clinical roles. Clinicians with extensive experience were required to sit through content they had already mastered, while new staff lacked the targeted support they needed to build competence and confidence. On top of that, AHN had limited visibility into who was struggling, and the platform could not be tailored to reflect their specific protocols. 

Rather than settle for the status quo, AHN took action. By partnering with Amplifire, they shifted to an adaptive, clinician-centered training model that respects prior knowledge, surfaces knowledge gaps, and delivers role-specific content that reflects the way their teams actually work. 

The Turning Point: From Generic to Purposeful Learning 

Before 2024, AHN’s OB training was built on a platform that treated all learners the same. Everyone received identical content regardless of experience level or specialty. The system lacked flexibility, which meant that even as protocols evolved, AHN could not easily update or customize training materials. 

Clinicians were frustrated by training that didn’t feel relevant. Leadership lacked the tools to track progress and identify areas of concern at the individual level. Most critically, there was no way to detect the presence of misinformation—a dangerous blind spot in a high-stakes field like obstetrics. 

AHN knew it needed a smarter approach. 

Partnering with Amplifire: Tailored, Adaptive, and Data-Rich 

OB subject-matter experts at AHN worked closely with Amplifire’s instructional design team to create customized content that aligned with facility-specific workflows and protocols. This collaboration resulted in a modern learning experience that felt relevant to every clinician, regardless of role or tenure. 

Amplifire’s Confidence-Based Learning™ platform enabled clinicians to move quickly through content they already understood while focusing learning time on areas of uncertainty or misinformation. The platform’s adaptive engine delivered personalized learning paths and immediate feedback, creating a more efficient and engaging training experience. 

Equally important, AHN gained access to detailed analytics through Amplifire’s dashboard. For the first time, Nursing Professional Development specialists could see exactly where clinicians were struggling, identify Confidently Held Misinformation™, and intervene before gaps in knowledge reached the bedside. 

As Emily Hempel, MSN, RN, RNC-MNN, C-EFM, NPD-BC, Nursing Professional Development Specialist for Labor & Delivery and Antepartum shared: 

“We can now identify areas where additional support is needed for our team and provide that support so that everyone has the best training experience possible.” 

Early Results: Positive Feedback and Time Savings 

While AHN did not have baseline metrics to quantify every outcome, early indicators showed clear improvements in learner engagement and training effectiveness. Following implementation, 99 percent of clinicians rated the training experience as effective, signaling strong adoption across roles and experience levels. 

Clinicians responded positively to the adaptive learning experience and the ability to focus only on areas where reinforcement was needed. Learners shared feedback such as, “Love this learning style,” and “I like the fact that I only have to repeat what I did not get correct the first time.” This approach allowed experienced clinicians to move quickly through familiar content while providing newer team members with targeted support. 

Learners also noted practical improvements in their day-to-day work. One clinician shared, “I will definitely be able to write better progress notes for the postpartum patients concerning course of delivery,” while another commented, “I will carry this knowledge to the workplace.” 

Behind the scenes, Nursing Professional Development specialists gained clearer visibility into learner performance across individuals, topics, and locations. This insight enabled earlier intervention and more precise coaching.

Why It Matters: Building a Safer Environment 

AHN’s ultimate goal was not just to improve training efficiency, but to reduce patient safety risks. In obstetrics, the cost of misinformation or delayed knowledge can be significant. By surfacing Confidently Held Misinformation and ensuring every clinician mastered essential protocols, AHN took a proactive step toward safer care. 

This shift also reflects a broader commitment to workforce development. By respecting clinicians’ time and expertise, AHN strengthened satisfaction and engagement—key drivers of retention and performance. 

A Model for What’s Next 

AHN’s success with OB risk reduction training is already serving as a model for other departments. With Amplifire, they now have a scalable approach to delivering personalized, high-impact learning that adapts to their needs and evolves with their protocols. 

In a time of workforce shortages and increasing complexity, AHN’s clinician-centered, data-driven approach sets a powerful example of how smarter training can drive better outcomes. 

Amplifire Recognized by Brandon Hall Group for Breakthrough AI Authoring Technology

BOULDER, Colo., Feb. 3, 2026 /PRNewswire/ — Amplifire, the leading adaptive learning platform built on patented brain science, has been honored by Brandon Hall Group with two bronze awards in the 2025 Excellence in Technology Awards for Best Advance in Content Authoring Technology and Best Advance in AI for Business Impact. The recognition highlights Amplifire’s continued innovation in solving critical training and workforce development challenges with AI-powered tools that deliver measurable impact.

At the core of this recognition is Amplifire’s latest AI-driven authoring platform. Released as part of Version 8.0, the platform delivers an intuitive, end-to-end course development experience that empowers instructional designers and subject matter experts to accelerate training while improving accuracy and trust. New capabilities include AI-generated course planning, integrated storyboarding, smarter refinement tools, and advanced content verification. These enhancements are helping teams reduce development time by weeks while maintaining the rigorous standards required in high-stakes industries like healthcare, accounting and aviation.

“We built AI authoring capabilities that deliver step-change improvements in speed without sacrificing the accuracy required in mission-critical environments,” said Nitin K. Walia, President of Amplifire. “The result is training learners can trust, improved knowledge retention, and better workplace performance. We’re grateful to Brandon Hall Group for recognizing these innovations and the results our customers are achieving.”

Amplifire’s clients are already seeing results. Devan Berkley, Senior Training Specialist at UW Medicine, said, “It used to take two and a half, almost three months to create a new training course. Now we’re doing it in just two weeks. That’s not just a technology win, it’s a workforce win.”

Built on more than 5 billion learning interactions and trusted by leading health systems, Amplifire uses patented confidence-based learning to surface Confidently Held Misinformation™ and deliver actionable analytics. Its flexible, AI-supported authoring platform makes it easier than ever to scale personalized learning for onboarding, compliance, career laddering, and more.

About Amplifire

Amplifire is the world’s leading adaptive learning platform, built from patented brain science discoveries and informed by billions of learner interactions. Its AI-powered tools help organizations reduce costs, save time, and improve outcomes through smarter, personalized training. From EHR onboarding to clinical risk reduction, Amplifire partners with high-stakes industries to elevate performance, safety, and satisfaction.

Learn more at www.amplifire.com

About Brandon Hall Group™ 
Brandon Hall Group™ is the home of the HCM Excellence Awards® – the most prestigious and sought-after awards in Human Capital Management. For over 30 years, these awards have set the gold standard in recognizing organizations for innovative and effective HCM practices across Learning and Development, Talent Management, Leadership Development, Diversity, Equity & Inclusion, Human Resources, Sales Performance, and Technology.

Source: PR Newswire

Precision Training Saves 2,920 Hours Annually for Froedtert Health

To reduce documentation burden and free nurses to focus more on patient care, Froedtert Health partnered with Amplifire to take a smarter, more efficient approach to training. Using Amplifire’s scenario-based GapFinder Assessment, the health system assessed all 1,887 inpatient nurses and identified exactly who needed support—only 24 percent. This meant 1,440 nurses avoided unnecessary training, saving over 4,000 hours immediately. Those who did require support received targeted in-person coaching designed to close performance gaps in areas like documentation latency and system efficiency.

The results were transformative. Within three months, nurses who had been struggling improved their documentation performance by up to 85 percent, achieving near parity with their top-performing peers. Across the full cohort, Froedtert saw a 4.6 percent reduction in active system time per shift hour, which equated to 2,920 hours saved annually. This success underscores the power of precision learning to drive measurable efficiency, ensure competency across the board, and alleviate one of healthcare’s most persistent pain points, all while honoring clinicians’ time and expertise.

Read the full Case Study

Amplifire Transforms Epic Training: New KLAS Case Study Shows 75% Reduction in Training Time

BOULDER, Colo., Jan. 14, 2026 /PRNewswire/ — Amplifire, a leader in adaptive learning and workforce development, is sharing best practices and improved learning outcomes in collaboration with UC San Diego Health and the Arch Collaborative at KLAS Research. A new case study published by the Arch Collaborative at KLAS Research documents how UC San Diego Health improved their Epic EHR training program. The collaboration resulted in a 75% or more reduction in provider training time while simultaneously improving content relevance and learning outcomes.

UC San Diego Health onboards approximately 1,000 clinicians annually, each requiring mandatory Epic training within two weeks of their start date. The goal was to improve efficiencies and consistencies with the learning process and outcomes.

The training used Amplifire’s science-based approach and patented Confidence-Based Learning® algorithms to personalize training. Beyond the 75%+ reduction in training time reported, providers were twice as likely to report content relevance to their role compared to prior training, and 577 instances of Confidently Held Misinformation™, incorrect knowledge that learners don’t realize they have, which poses the greatest risk in clinical settings.

“Our Confidence-Based Learning platform adapts to each learner to provide an individualized experience, focusing time only where it’s needed most but ensuring mastery for all,” said Anne Hyland, VP of EHR Learning at Amplifire. “The results of the collaboration demonstrate what’s possible when adaptive technology for education provides not only time-savings, but also a personalized learning experience that respects existing knowledge, closes the gaps, and corrects bad habits and misinformation.”

The platform’s analytics capabilities enabled the training team to identify specific knowledge gaps across their provider population and develop targeted microlearning modules to address them. This data-driven approach ensures training resources are allocated where they’ll have the greatest impact.

Following the success of the Epic training program, there will be an expansion to additional user groups, including inpatient nurses, and developing ongoing education strategies based on the platform’s detailed analytics.

The complete KLAS case study is available here.

About Amplifire

Amplifire’s adaptive learning platform, with over four billion learner interactions, drives measurable outcomes in high-stakes industries including healthcare, accounting and professional services, government, and education. Its AI-powered, brain science-based solutions reduce training time, improve employee satisfaction and retention, and enhance performance. Amplifire partners with a Healthcare Alliance of 35+ leading health systems, working collaboratively to improve the learning experience, engage clinicians, reduce costs, and improve clinical performance. Learn more at www.amplifire.com.

About the Arch Collaborative at KLAS Research

The Arch Collaborative at KLAS Research is a group of healthcare organizations committed to improving the EHR experience through standardized surveys and benchmarking. KLAS amplifies providers’ voices to enhance healthcare delivery. Follow KLAS on X and LinkedIn at klasresearch.com.

Source: PR Newswire