Ferris State University is actively participating in Tech Week Grand Rapids, spotlighting the expanding influence of artificial intelligence (AI) across various sectors, particularly in healthcare.

Taking place from September 14 to 19, Tech Week Grand Rapids is a community-focused “unconference” comprising around 160 events throughout the Grand Rapids area, featuring panels, workshops, showcases from students, and networking sessions, all offered free to the public. Full event details can be found online.

Sessions during this week will delve into how AI, data, and emerging technologies are revolutionizing clinical practices, drug development, and patient care, with contributions from leading health tech organizations in the region.

Ferris State faculty and staff will host several noteworthy discussions, including:

**AI, Digital Equity, and Rural Education: Closing the AI Divide in Rural and Tribal Communities**  
Scheduled for Wednesday, September 16, from 11 to 11:45 a.m. at the Kendall College of Art and Design, this presentation will address the lack of focus on rural and Tribal areas in the ongoing AI revolution. As technological advancements predominantly benefit urban and suburban communities, the session will analyze the educational disparities faced by rural and Tribal populations in Michigan and propose solutions to bridge these divides.

**Leading AI Adoption: People Drive Success**  
On the same day from 1 to 2 p.m., this interactive session will explore the human dynamics of AI integration within organizations. It will highlight how leadership, effective communication, and change management are crucial for successful AI adoption—beyond just the technology itself. Attendees will engage in discussions and gain actionable insights applicable across industries.

**AI at the Bench: PKS Engineering Case Studies**  
Taking place on Thursday, September 17, from 11 a.m. to noon, this session will focus on the role of AI in engineering polyketide synthases (PKS), which are vital for producing chemicals used in biotechnology. Two student presentations will illustrate how AI can enhance the design-build-test-learn cycle in scientific inquiry, positioning AI as a collaborative tool rather than a standalone entity.

**CHARM: Practical AI for Healthcare Data**  
Also on September 17, from 1:15 to 2 p.m., attendees will learn about CHARM (Collaboration to Harmonize Antimicrobial Registry Measures), which utilizes healthcare data for improved antibiotic stewardship via interactive dashboards. This session will feature two AI-driven projects that streamline prescription data processing and leverage machine learning to detect outlier prescribing patterns, enhancing both accuracy and patient privacy.

**AI Adoption for Healthcare and Public Health: Practical Steps for Responsible Healthcare AI Adoption**  
That same afternoon, from 2:15 to 3 p.m., a presentation will discuss the transition from AI experimentation to effective implementation within healthcare systems. It will cover essential applications and address potential risks linked with privacy and bias, supported by a real-world case study illustrating how advanced data pipeline techniques can enhance antimicrobial stewardship.

**Applied LLMs in Pharmacy Practice: AI Projects Improving Pharmacy Workflows and Decisions**  
Concluding the day on September 17, from 3:15 to 4 p.m., this session will unveil innovative AI applications designed to support pharmacy practice. One project enhances access to data through a user-friendly conversational interface on a centralized dashboard, fostering faster and more efficient information retrieval while ensuring compliance with privacy standards. The second project automates various routine tasks in pharmacy service planning, significantly reducing manual workload and allowing professionals to concentrate more on patient care and strategic decisions.

Through these engaging sessions, Ferris State University aims to illuminate the pivotal role of AI in evolving healthcare and education, ultimately fostering a better understanding of its implications and potential in both urban and rural contexts.

Ferris State's Tech Week sessions in Grand Rapids emphasize the increasing importance of artificial intelligence.

A bright, sunlit conference hall at Ferris State University’s Grand Rapids campus, a large “Tech Week” banner draped across a warm wood-paneled stage, professional attendees in business casual clustered around demo stations. Sleek laptops display AI-generated data visualizations in natural tones, and a polished robotic arm gently hands a small chip to a researcher. Floor-to-ceiling windows reveal a Grand Rapids skyline under clear blue skies.

Ferris State’s Tech Week Grand Rapids sessions focus on the growing role of artificial intelligence - Ferris State University

- Ferris State University hosts Tech Week Grand Rapids, focusing on AI's societal impact.  
- Sessions address AI's role in healthcare, education, and addressing digital divides in communities.  
- Presentations showcase practical AI applications, improving workflows and decision-making in healthcare fields.  

Ferris Stateâs Tech Week Grand Rapids sessions focus on the growing role of artificial intelligence

GRAND RAPIDS, Mich. â

Ferris State University is playing a key role in Tech Week Grand Rapids presentations looking at how artificial intelligence is playing a growing role in health care and other aspects of society.

Tech Week Grand Rapids is a six-day, community driven âunconferenceâ taking place from Sept. 14 to 19 across the greater Grand Rapids area.

The event has about 160 events including panels, workshops, student showcases and networking mixers that are free and open to the public. Details about the entire event are located here.

These events will explore how AI, data, and emerging technology are transforming clinical care, drug discovery, and patient outcomes through a week of talks, demos, and hands-on sessions from the region's leading health tech organizations.

Sessions hosted by Ferris State faculty and staff are:

AI, Digital Equity, and Rural Education: Closing the AI Divide in Rural and Tribal Communities

Wednesday, Sept. 16, 11 to 11:45 a.m. at Ferris State Universityâs Kendall College of Art and Design, 17 Pearl Street NW in Grand Rapids.

Registration information is here.

While society is in the middle of a burgeoning AI revolution, such unprecedented growth has largely been focused on urban and suburban spheres throughout the nation.

However, scant attention has been given to the unequal playing field in rural and Tribal communities, especially here, in Michigan.

This presentation will attempt to cover educational inequities and provide possible solutions to redress such trends.

The presentation will examine the growing trends in AI, but from an often overlooked and lesser-focused perspective, a rural and Tribal community-lens.

This session will not only document the current technological paradigm and disconnect between rural / Tribal and urban / suburban communities, but also note how this gap has been shrinking, as of late.

The session will also explore the paradox of AI in the rural and Tribal sectors, and show how it can be leveraged as a useful tool to bridge the proverbial 'digital divide,' but also how it risks further alienating such traditionally disenfranchised communities.

Leading AI Adoption: People Drive Success: Lead AI Change with Trust, Champions, and Confidence

Wednesday, Sept. 16, 1 to 2 p.m. at Ferris Stateâs Kendall College of Art and Design, 17 Pearl Street NW in Grand Rapids.

Registration information is here.

AI is transforming every industry, but successful AI implementation is rarely limited by the technology itself. More often, organizations struggle because of uncertainty, resistance to change, unclear leadership, and a lack of organizational readiness.

This interactive presentation explores the human side of AI adoption and provides practical strategies for leading successful organizational change. Rather than focusing on the latest AI tools, we'll examine the leadership, communication, and change management practices that determine whether AI initiatives succeed or fail.

The session includes audience discussion, real-world examples, and actionable takeaways that leaders can immediately apply regardless of their industry or organization size. It is designed for business leaders, managers, HR professionals, educators, entrepreneurs, and anyone responsible for helping people navigate technological change.





AI at the Bench: PKS Engineering Case Studies

Thursday, Sept. 17, 11 a.m. to noon at Ferris Stateâs Kendall College of Art and Design, 17 Pearl Street NW in Grand Rapids.

Registration information is here.

Polyketide synthases are large, assembly-line enzymes that manufacture a variety of chemicals essential for American biotechnological manufacturing, including fuels, medicines, and bulk chemicals. The engineering of PKS systems is complex, however, owing to the large size and complexity of the enzymes.

Engineering of these proteins requires repeated cycles of designing experiments, building genetic constructs, testing performance, and interpreting results. Artificial intelligence can support several stages of this designâbuildâtestâlearn, or DBTL, cycle when its outputs are converted into testable scientific hypotheses.

This session features two student-led presentations describing practical AI applications in PKS engineering. The speakers will demonstrate how AI can assist with hypothesis generation, experimental interpretation, identification of promising engineering strategies, and selection of subsequent experiments.

Together, the talks introduce the concept of âAI-in-the-DBTL-cycle,â placing AI within the scientific process rather than above it. AI serves as a scientific sounding board, helping undergraduates anticipate experimental outcomes and interpret their results more quickly.

CHARM: Practical AI for Healthcare Data: AI and Dashboards for Smarter Antibiotic Stewardship

Thursday, Sept. 17, 1:15 to 2 p.m. at Ferris State Universityâs Kendall College of Art and Design, 17 Pearl Street NW in Grand Rapids.

Registration information is here.

How can AI move beyond experimentation and solve practical problems using real-world healthcare data?



This session will introduce Collaboration to Harmonize Antimicrobial Registry Measures â known as CHARM -- an antimicrobial stewardship initiative that transforms healthcare data into actionable insights through interactive dashboards and analytics.

Attendees will see how CHARM uses data from participating healthcare organizations to examine antibiotic prescribing patterns, identify opportunities for improvement, and support antimicrobial stewardship efforts.



The session will then showcase two AI projects being developed around the CHARM data ecosystem. The first uses locally hosted, open-source large language models to standardize prescription data while keeping sensitive healthcare data within a controlled environment.

The project combines structured data processing, retrieval-augmented generation, fine-tuning, systematic evaluation, and human review to improve accuracy while reducing hallucinations and manual data-cleaning effort.



The second project, CHARM Outlier, uses machine learning to identify antibiotic prescriptions that differ from expected prescribing patterns. Flagged records are presented through a web application where researchers and healthcare professionals can review them and provide feedback.

That feedback is then used to improve the modelâs future predictions, creating a human-in-the-loop approach to identifying potentially inappropriate prescribing.



Together, these projects demonstrate how dashboards, locally deployed LLMs, machine learning, and human expertise can work together to turn complex healthcare data into useful information while addressing practical concerns around privacy, accuracy, and responsible AI implementation.

AI Adoption for Healthcare and Public Health: Practical Steps for Responsible Healthcare AI Adoption

Thursday, Sept. 17, 2:15 to 3 p.m. at Ferris State Universityâs Kendall College of Art and Design, 17 Pearl Street NW in Grand Rapids.

Registration is here.

Artificial intelligence is advancing rapidly, but many healthcare and public health organizations still struggle to move from experimentation to meaningful and sustainable adoption.



In this presentation and interactive Q&A, attendees will explore the difference between simply using an AI tool and successfully integrating AI into real organizational workflows.



Attendees will learn about practical AI applications such as data cleaning and standardization, automated reporting, prescribing outlier detection, dashboards, decision support, and natural-language analytics. The session will also examine important risks, including privacy, bias, hallucinations, incorrect outputs, explainability, and the need for human review.



A real-world case study from the CHARM will demonstrate how secure data pipelines, analytics, small language models, machine learning, and natural-language reporting can support antimicrobial stewardship.



The session is designed for healthcare professionals, public health leaders, technology professionals, students, researchers, business leaders, and organizations exploring practical AI implementation.

Applied LLMs in Pharmacy Practice: AI Projects Improving Pharmacy Workflows and Decisions

Thursday, Sept. 17, 3:15 to 4 p.m. at Ferris State Universityâs Kendall College of Art and Design, 17 Pearl Street NW in Grand Rapids.

Registration information is here.

We are presenting novel AI capabilities in support of the CHARM, whose centralized dashboard is hosted and supported by the Ferris State University College of Pharmacy.

The first of two completed projects extends that dashboard â a web-based Power BI environment â with a conversational interface. Instead of drilling through filters and report screens, users simply ask a question in plain language.

Behind the scenes, a text-to-SQL layer paired with a large language model interprets the question, translates it into a valid query, and returns the matching data.

Every request is validated before it runs, and privacy safeguards are built-in to keep patient data protected and HIPAA-compliant, so faster access never comes at the cost of trust.

âThe second project applies the same technology to the business side of pharmacy practice. It automates the groundwork behind pharmacy service planning: prospect identification, market analysis, and the assembly of a comprehensive report that a pharmacy owner or health-system leader can review and act on.

Work that once consumed many hours of manual research now takes a fraction of the time, freeing pharmacists and administrators to focus where their expertise actually matters â judgment, strategy, and patient care.

Ferris Stateâs Tech Week Grand Rapids sessions focus on the growing role of artificial intelligence

Regenstrief Institute and Suki, a pioneer in AI-driven ambient clinical documentation, are joining forces to investigate the implications of ambient AI technologies on healthcare practitioners, patients, and healthcare systems overall. Their collaboration aims to generate independent research and evidence that will enable healthcare organizations to grasp the effects of these technologies.

As ambient AI solutions become more woven into the fabric of healthcare workflows, health systems are confronted with significant inquiries regarding their influence on clinician documentation loads, communication between patients and providers, care quality, operational efficiency, and overall trust. Both Regenstrief and Suki are committed to promoting evidence-based strategies that empower healthcare organizations to make educated choices about the incorporation and utilization of these technologies.

Suki specializes in developing AI-driven ambient clinical intelligence tools that streamline documentation and administrative processes for clinicians. In contrast, Regenstrief Institute is globally recognized for its proficiency in health informatics, electronic health records, implementation science, and generating real-world evidence. Together, they aim to enrich the existing knowledge surrounding ambient AI and its applications within healthcare.

Rachel Patzer, PhD, MPH, who serves as the president and CEO of Regenstrief Institute, remarked on the necessity for healthcare organizations to rely on rigorous, independent evidence to comprehend the effectiveness of AI technologies and their real-world implications on clinicians, patients, and healthcare delivery. She stated, “As ambient AI continues to advance, research and evaluation will be essential in ensuring the responsible and effective implementation of these technologies.”

The buzz around ambient AI technologies stems from their promise to alleviate administrative strain, enhance clinical documentation, and enable clinicians to dedicate more time to patient interactions. Despite this potential, healthcare leaders, policymakers, and providers are still in search of concrete evidence regarding implementation success, outcomes, user satisfaction, and the long-term effects of these technologies. Ambient AI platforms aim to capture conversations between patients and clinicians while aiding in documentation, which may lead to reduced time spent on administrative duties, thereby fostering more attentive patient care.

As healthcare institutions assess the latest AI advancements, both Regenstrief and Suki stress the significance of transparency, accountability, and making decisions based on solid evidence. With its extensive background in healthcare data analytics, clinical informatics, and comprehensive evaluations of new technologies, Regenstrief is particularly well-equipped to address crucial questions about the real-world performance of AI tools in clinical settings.

Regenstrief collaborates with Suki to assess ambient AI in the healthcare sector.

A bright, modern hospital consultation room flooded with warm morning light. A physician sits at a sleek touchscreen console reviewing patient vitals while slim ambient AI sensors are discreetly mounted in the ceiling. On the screen side by side are the Regenstrief logo and the Suki logo, linked by a softly glowing waveform that weaves around medical charts and a stethoscope. Natural daylight streams through large windows, emphasizing a hopeful, innovative partnership in healthcare.

Regenstrief and Suki partner to evaluate ambient AI in healthcare - Regenstrief Institute

- Regenstrief Institute and Suki collaborate to study ambient AI's impact on healthcare delivery.  
- They aim to address clinician workload, communication quality, and operational efficiency concerns.  
- Research is essential for informed adoption of AI technologies in real-world healthcare settings.  

Organizations share a commitment to understanding how ambient AI technologies impact clinicians, patients and healthcare delivery

Regenstrief Institute and Suki, a leader in AI-powered ambient clinical documentation technology, are collaborating to explore how independent research and evidence generation can help healthcare organizations better understand the impact of ambient artificial intelligence technologies on clinicians, patients and healthcare systems.

As ambient AI tools become increasingly integrated into healthcare workflows, health systems face important questions about their effects on clinician documentation burden, patient-provider communication, care quality, operational efficiency and trust. Regenstrief and Suki share an interest in advancing evidence-based approaches that help healthcare organizations make informed decisions about the adoption and implementation of these technologies.

Suki develops AI-powered ambient clinical intelligence solutions that assist clinicians with documentation and administrative tasks. Regenstrief Institute is internationally recognized for its expertise in health informatics, electronic health records, implementation science and real-world evidence generation. Together, the organizations are exploring opportunities to contribute to a growing body of knowledge around ambient AI and its role in healthcare.

“Healthcare organizations need rigorous, independent evidence to understand not only whether AI technologies work, but how they affect clinicians, patients and care delivery in real-world settings,” said Rachel Patzer, PhD, MPH, president and CEO of Regenstrief Institute. “As ambient AI continues to evolve, research and evaluation will play a critical role in ensuring these technologies are implemented responsibly and effectively.”

Ambient AI technologies have gained attention for their potential to reduce administrative burden, support clinical documentation and allow clinicians to focus more fully on patient interactions. However, healthcare leaders, policymakers and providers continue to seek evidence regarding implementation, outcomes, user experience and long-term impact. Ambient AI platforms are designed to capture patient-clinician conversations and assist with documentation workflows, potentially reducing time spent on administrative tasks while supporting more focused patient care.

As healthcare organizations continue evaluating emerging AI technologies, both organizations emphasize the importance of transparency, accountability and evidence-based decision-making. Regenstrief’s longstanding expertise in healthcare data, clinical informatics and rigorous evaluations of innovative technologies positions it to help answer critical questions about how AI technologies perform in real-world clinical environments.

Regenstrief and Suki partner to evaluate ambient AI in healthcare

As autumn arrives, discussions surrounding cancer gain prominence in national dialogues. The interval between summer's end and winter's onset is marked by various awareness initiatives, inspiring survivor narratives, and crucial reminders to prioritize health check-ups. This year, these conversations are intermingling with a noteworthy trend: a growing number of Americans are seeking health information through artificial intelligence (AI).

A recent survey revealed that 76% of Gen Z adults and 63% of millennials are now relying on AI as their primary resource for healthcare inquiries, often before consulting a physician.

With just a few clicks, individuals can quickly search for symptoms, decipher medical jargon, and gain insights into potential health risks. When used judiciously, AI can serve as a valuable resource; however, simply obtaining information does not equate to knowing the appropriate next steps, particularly when it comes to cancer. Regular check-ups with healthcare professionals are essential for accurately detecting serious health issues, especially when symptoms may not seem relevant at first glance. This underscores the idea that while AI can assist, it should not lead our healthcare decisions.

In my experience as the president of a Fortune 500 company, I have learned that transforming ideas into actionable decisions requires careful guidance, as aimless actions can quickly lead to stagnation. Therefore, having a well-defined strategy is crucial to creating an effective healthcare plan.

Unfortunately, in the realm of personal health management, we continue to miss the mark.

Despite many years of awareness initiatives, nearly two-thirds of Americans still postpone essential health screenings. This reluctance is not due to ignorance about the importance of screenings; rather, life often gets in the way. Additionally, many individuals harbor fears about what they might discover or convince themselves that feeling well is enough to forgo check-ups.

Here, technology can play a pivotal role in mitigating these challenges. AI can help clarify various screening options or prompt individuals to prepare important questions for their medical appointments. However, the capabilities of AI have limitations.

AI cannot physically accompany you to an appointment if you're hesitant about going. It’s incapable of performing necessary procedures like mammograms and colonoscopies, nor can it analyze your entire health history to identify new potential conditions. Importantly, it cannot replace the reassuring voice of a loved one asking, “Have you gotten that checked?”

Supporting this idea, research on cancer screening behaviors indicates that many people view encouraging family members or friends to undergo screenings as an expression of love, which in turn influences their own health behaviors. This illustrates that the most effective health interventions often come from those who care about us, rather than through mere technological interfaces.

Like many families, my own has been affected by cancer. I have witnessed friends and loved ones face diagnoses, treatments, and the accompanying uncertainties. Such experiences fuel my commitment to encourage others to transition from uncertainty to action.

This principle also aligns with Aflac's mission: we support families who face challenges when screenings are overlooked or diagnoses are delayed, stepping in when health insurance falls short. Our perspective on the AI dialogue is rooted in real-world implications reflected in our claims data.

You can embody this principle by taking charge of your health. For that reason, AI should serve as a support tool rather than the primary guide in our healthcare journeys.

Virgil Miller holds the position of president at Aflac Incorporated and Aflac U.S., recognized as a leading provider of cancer insurance in both the United States and Japan.

The viewpoints expressed in this commentary are those of the author and do not necessarily represent the opinions of Fortune.

Aflac President: reasons AI shouldn't lead your healthcare decisions

A dimly lit hospital corridor under flickering red alarm lights, a sleek robotic arm holding a medical syringe looms over an empty gurney, while a determined doctor in scrubs steps forward, hand raised to block it. Above the double doors behind them, the Aflac logo shines boldly in stark white. Shadows stretch across cracked tile floors, conveying a sense of caution and unease about AI taking the wheel in healthcare.

Aflac President: why AI shouldn’t drive your healthcare - Fortune

- A significant number of Gen Z and millennials rely on AI for primary healthcare inquiries.  
- Many Americans delay health screenings despite understanding their importance, often due to life distractions.  
- AI should enhance, not replace, personal healthcare practices and the importance of human encouragement.  

Every fall, cancer starts sounding louder in the national conversation. The months between summer and winter bring multiple awareness campaigns, survivor stories, and reminders to get checked. That attention really matters. This year, however, that conversation is happening alongside another major shift: More Americans are turning to artificial intelligence (AI) for answers about their health.

A recent study found that 76% of Gen Z adults and 63% of millennials now turn to AI as their first line of primary healthcare — before ever seeing a doctor.

In seconds, we can search for symptoms, understand medical terminology and learn more about potential risks. Used responsibly, AI can be an extraordinarily useful tool, but knowing more is not the same as knowing what to do next. This is especially true when it comes to cancer. Consistent physician care can more accurately identify signs of more serious conditions associated with symptoms that might not always seem relevant. That is why AI should copilot but not drive our healthcare.

Being president of a Fortune 500 company means turning concepts into decisions, and decisions into action. After more than two decades in leadership roles at Aflac, I have learned that action without guidance can quickly turn into inaction. The best cure for that is a solid strategy that results in a viable care plan.

And this is where, when it comes to our own healthcare, we continue to fall short.

Despite decades of awareness campaigns, nearly two-thirds of Americans still delay recommended health screenings. It is not because people don’t understand that screenings matter; rather, life just gets busy. We also know that people worry about what they might learn, or convince themselves that because they feel fine, there is no reason to get checked.

Technology can help address some of those barriers. AI can be a component of the plan. It can help someone understand the difference between screening options or help prepare questions before a doctor’s appointment. But there is a limit to what AI technology can do.

AI cannot take you to an appointment when you are hemming and hawing about going. It cannot perform a mammogram or a colonoscopy. And it cannot personally view your health history and connect the dots on a possible condition that didn’t exist before, and certainly cannot replace the voice of someone you trust asking, “Did you get that checked?”

Separate research on cancer-screening behavior backs this up: most Americans describe encouraging a loved one to get screened as an act of love, and say that encouragement changes their own behavior. It’s a reminder that the most powerful health intervention tool is not information coming out of a screen. It often can be someone who knows and cares about you.

Like so many families, mine has been touched by cancer, and I have watched loved ones and friends navigate diagnoses, treatment and the uncertainty that comes with both. Those experiences are why I will continue to raise my voice to help move people from uncertainty to action.

That’s also, frankly, Aflac’s business model: we exist because a screening gets missed, a diagnosis comes late, and a family needs help covering what health insurance alone doesn’t. The AI conversation isn’t abstract for us — it shows up in our claims data.

You can apply this principle by being the leader of your health. That is why AI should be the copilot but not the driver of our healthcare.

Virgil Miller is president of Aflac Incorporated and Aflac U.S., a leading provider of cancer insurance in the United States and Japan.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

Aflac President: why AI shouldn’t drive your healthcare

Healthcare leaders must not mix up the potential of models with actual operational effectiveness.

The challenges faced within healthcare administration stem from a lack of cohesive information, fragmented workflows, and unclear accountability—not from an absence of data. For decades, the industry has invested heavily in systems designed to track various activities, such as electronic health records, billing software, payer portals, scheduling tools, call center systems, and analytical applications. While each of these systems captures vital information, very few are equipped to analyze the entire array of decisions that dictate whether patients receive timely care, if clinicians obtain the necessary documentation, and how providers are reimbursed correctly.

This is the challenge that artificial intelligence (AI) is now tasked with addressing.

The revenue cycle has emerged as a key area for AI testing in healthcare.

The revenue cycle encompasses the entire process that healthcare providers follow to secure payment for services rendered—initial scheduling and registration through coding, billing, payer interactions, and collection of payments.

This sector presents a unique opportunity for robust AI implementation due to its combination of high transaction volumes, intricate decision-making, and both structured and unstructured data, all of which necessitate measurable outcomes and exhibit widely varying operational dynamics. Moreover, the revenue cycle is pivotal, linking financial performance with patient access and administrative workload.

The complexity of a single insurance claim illustrates this intricacy; it can be impacted by factors such as the patient's insurance details, clinical documentation, coding regulations, payer policies, prior authorization prerequisites, medical necessity standards, and numerous other data inputs and operational facets. A failure in any of these areas can trigger significant repercussions weeks or even months later.

This complexity explains why one-size-fits-all automation has frequently been inadequate.

Traditional robotic process automation tends to excel in environments where workflows are stable and rules are clear-cut, but the reality of healthcare administration is quite the opposite. Payer demands shift. Documentation standards are in constant flux. Exceptions are prevalent and can be significantly impactful.

The next challenge for healthcare AI is integration.

A sunlit modern hospital operating room where a female surgeon in scrubs studies a sleek tablet displaying live patient vitals, diagnostic scans, and AI-suggested treatment paths. Warm natural light spills in through large windows, and soft, glowing circuit-pattern accents trace the edges of the tablet screen—symbolizing seamless integration of AI into healthcare.

Healthcare AI’s next test is integration - MIT Technology Review

- Healthcare's administrative challenges stem from fragmented information and workflows, not lack of data.  
- The revenue cycle process is highly suitable for rigorous AI deployment in healthcare.  
- Traditional automation fails due to unstable workflows and unpredictable payer requirements in healthcare.

But healthcare leaders should not confuse model capability with operational capability.

Healthcare’s administrative challenges are caused by fragmented information, fragmented workflows, and fragmented accountability, not a lack of information. The industry has spent decades investing in systems that capture activity: electronic health records, billing platforms, payer portals, scheduling systems, call center platforms, and analytics applications. Each system records something important. But few were designed to reason across the full chain of decisions that determines whether patients get timely access, clinicians have the right documentation and providers are reimbursed appropriately.

This is the problem that AI must now confront.

Revenue cycle is becoming one of healthcare AI’s proving grounds

The revenue cycle is the process healthcare providers use to get paid for care — from scheduling and registration through coding, billing, payer follow-up, and payment collection.

It is unusually suited to rigorous AI deployment because it combines high transaction volume, complex reasoning, structured and unstructured data, measurable outcomes, and significant operational variation. It also sits at the intersection of financial performance, patient access, and administrative workload.

A single claim can be influenced by patient insurance information, clinical documentation, coding rules, payer-specific policies, prior authorization requirements, medical necessity criteria, and many other data sources and operational processes. A breakdown in any one of those areas can create downstream consequences weeks or months later.

This is why generic automation has often fallen short.

Traditional robotic process automation works well when workflows are stable and rules are predictable, but healthcare administration is neither. Payer requirements change. Documentation expectations evolve. Exceptions are common and often material.

Healthcare AI’s next test is integration

As Wisconsin legislators explore the regulation of artificial intelligence in healthcare, the pace of technological advancements for both patients and healthcare professionals is accelerating. These AI tools are becoming integral to modern medical practices and are taking on more significant responsibilities.

During a recent committee meeting, Frank Meyers from the Federation of State Medical Boards highlighted the rapid proliferation of AI tools in healthcare. "About a year and a half ago, it was challenging to find high-quality tools from reliable providers. Now, they're everywhere," he noted.

This meeting, part of the 2026 Legislative Council Study Committee on AI in Healthcare, featured insights from industry players, regulators, educators, and healthcare providers. They delved into the swift adoption of AI technologies and the implications for oversight.

Meyers pointed out that Verona's Epic Systems has integrated multiple AI applications within its widely used electronic health records systems. He also mentioned Microsoft's Dragon Copilot AI platform, which utilizes ambient scribe technology to document clinical encounters in real-time.

"In the past, this technology primarily documented encounters. However, it has significantly evolved and can now input data directly into medical records, facilitate automatic prescription renewals, and check medical contraindications," Myers explained. "It even surfaces relevant research from medical journals."

Patients are also benefiting from enhanced AI applications, such as Amazon's Health AI program, which interprets medical records and lab results, addresses symptom-related inquiries, and directs requests to healthcare providers. Additionally, tools like ChatGPT offer functionalities for analyzing health data.

"It's essential to note that these tools are not intended to provide diagnostic advice. However, with the nature of generative AI, users can often elicit various responses by framing their queries differently," Meyers cautioned.

Dr. Richard Bruce from UW-Madison's Department of Radiology also spoke at the meeting, emphasizing AI's transformative impact on imaging quality and safety. He observed that the technology leads to improved image resolution, reduced radiation exposure, and fewer mistakes, marking significant advances in radiology.

AI is also streamlining processes like real-time quality checks during ultrasounds and enhancing technicians' capabilities to produce better images. "The result is more efficient and higher-quality healthcare," Dr. Bruce said.

However, speakers acknowledged that AI systems are not infallible and their increased usage has resulted in some notable errors. For instance, a recent incident involved an AI scribe mistakenly documenting that a patient had consumed psychedelic mushrooms, a detail that was absent during their healthcare visit.

Meyers noted that this error was incorporated into the patient's health record and later proposed as a potential cause of her symptoms to her primary care physician. “This isn’t an isolated issue,” he said, citing other instances where AI inaccurately documented medical histories.

As the integration of AI accelerates, regulatory frameworks are struggling to keep pace. In response, several states are beginning to enact measures aimed at safeguarding personal data in light of concerns over AI systems' access and control.

Jordan Francis, a senior policy counsel for the Future of Privacy Forum, observed that states are increasingly empowering consumers by granting them the right to access information regarding third-party data sales and implementing new privacy protections related to data-driven profiling.

"Many states are also introducing sector-specific privacy legislation focusing on consumer health data managed by data brokers," Francis added.

Some states are experimenting with “sandboxes” that permit companies to deploy AI healthcare tools under a defined regulatory environment. Utah, for example, is temporarily suspending certain regulatory requirements from its medical board to facilitate unlicensed AI initiatives under these pilot programs.

Meyers mentioned that evaluations of Utah’s ongoing pilot project have yielded encouraging initial results, but it remains in the early stages. "The primary goal of these sandboxes is to foster experimentation with AI tools, allowing both developers and regulators to understand optimal regulatory approaches," he explained.

Currently, six states are operating active AI pilot programs—Arizona, Connecticut, Delaware, Kansas, Texas, and Utah—while eight others have proposals in the pipeline, though variations exist among them.

As for what lies ahead for Utah's AI regulatory initiatives after the pilot's one-year mark, Meyers indicated uncertainty remains. "No one knows what will come next," he concluded.

AI's involvement in healthcare is growing with technological advancements.

Sunlit modern hospital ward: a female physician in warm-toned scrubs reviews a large touchscreen showing a detailed human anatomy model with glowing predictive analytics overlays; beside her, a sleek white medical robot arm holds a surgical instrument; an adult patient reclines calmly; soft natural light floods through floor-to-ceiling windows, symbolizing AI’s expanding role in healthcare.

AI’s role in healthcare expanding as tech develops - WisBusiness

- Wisconsin lawmakers are addressing the rapid evolution of AI tools in healthcare regulation.  
- AI applications enhance clinical efficiency but have led to significant recording errors.  
- Several states are piloting “sandbox” initiatives for AI in healthcare experimentation and regulation.  

As Wisconsin lawmakers grapple with how to regulate the use of AI in healthcare, tools for both consumers and clinicians are quickly evolving to play a larger, more autonomous role in the medical field.

“There’s many tools out there now,” Frank Meyers of the Federation of State Medical Boards said yesterday during a committee meeting. “Just about a year and a half ago, it was very difficult finding high-level tools from legitimate providers. Now they’re everywhere.”

The meeting of the 2026 Legislative Council Study Committee on the Use of Artificial Intelligence in Health Care featured presentations from industry representatives, regulators, educators and care providers. They discussed how AI is being rapidly adopted in healthcare and what that means for those tasked with keeping it in check.

Meyers, the federation’s director of regulatory innovation and member services, noted Verona’s Epic Systems has built various AI applications into its ubiquitous electronic health records systems.

At the same time, Microsoft’s Dragon Copilot AI platform is built on an ambient scribe tool that was one of the first on the market, he said. The technology listens in on the clinical encounter while documenting and summarizing what happens.

“In the past, that’s really all it did … but those have significantly advanced, to the point where now they are entering information into the medical record itself, they’re being built into automatic prescription renewals so they can pre-load renewals for the clinician’s sign-off,” Meyers said. “Or they’re checking medical contraindications, they’re doing some research surfacing relevant medical journal entries.”

Along with these applications for clinical use, patients too now have more AI options than ever. That includes Amazon’s Health AI program that can read patient records and explain lab results, answer questions about symptoms and route requests to providers. ChatGPT also has a consumer-side health tool for analyzing medical records and related data.

“It’s not meant to provide diagnostic or medical advice, but like with everything with generative AI … if you push it certain ways, you can always kind of get it to respond in certain different ways,” Meyers said.

Meanwhile, Dr. Richard Bruce with UW-Madison’s Department of Radiology yesterday highlighted how AI is supercharging the scientific side of healthcare. He noted the technology is producing “much better” imaging quality based on available data, lower radiation doses and fewer errors — “all things that are slam-dunk home runs from an imaging perspective.”

AI is also being used for real-time quality checks during ultrasounds, automating labelling and even “coaching” technicians on how to get better quality images.

“We get more efficient, higher-quality healthcare as a result of that,” Bruce told the committee.

Still, speakers yesterday cautioned AI tools still aren’t perfect, and their expanded role has led to high-profile mistakes. In one example from just last month, an AI scribe erroneously recorded that the patient had taken psychedelic mushrooms, despite that not being mentioned during the provider visit.

Meyers said this potential “misunderstanding” by the AI system was entered into the patient’s health record, and later surfaced in a letter to her primary care doctor as a possible explanation for her symptoms.

“Watchdog reviewers found this was not an isolated case,” he said. “They have found another scribe had named the wrong breast cancer diagnosis, epilepsy in a patient that had no such epilepsy … a Google bot making up and combining two different body parts to create a whole new body part that didn’t exist.”

As progress and implementation is outstripping regulatory bodies in some cases, certain U.S. states are taking action to protect people’s personal data amid concerns about how much access and power is granted to AI systems.

Jordan Francis, senior policy counsel for the Future of Privacy Forum, yesterday said states are increasingly giving consumers the right to access lists of third parties their information is being sold to, along with new protections around data-based profiling.

“States are also enacting sectoral privacy laws, addressing specific issues like consumer health for data brokers,” Francis said.

Other states are conducting pilots called “sandboxes” allowing companies to roll out AI tools for healthcare within a specified framework. Utah, for example, is waiving certain regulatory requirements from the state’s medical board for this purpose, allowing unlicensed AI-related activity within the scope of the pilot.

Meyers said Utah’s pilot is still ongoing, and initial findings seem promising. But the effort is still in its early days.

“The main takeaway for sandboxes is they’re primarily designed to allow experimentation of these tools, both by the developers but by the regulators to understand what would be the potential best mechanism for regulating these things,” he said.

Six states currently have active sandbox AI pilots — Arizona, Connecticut, Delaware, Kansas, Texas and Utah — while eight others have been proposed, though Meyers noted they differ substantially state-to-state.

“What comes next after the one year? And, you know, the answer is they don’t know,” Meyers said of Utah’s AI policy office. “Nobody really knows.”

Watch the video.

AI’s role in healthcare expanding as tech develops

In a significant gathering at the Wisconsin State Capitol, lawmakers, healthcare professionals, and experts in artificial intelligence convened to explore the possibility and necessity of regulating AI within the medical field. This meeting highlighted the urgent need for data privacy legislation in the U.S., as pointed out by Jordan Francis, senior policy counsel for the Future of Privacy Forum, who noted that the U.S. currently lacks a federal law to safeguard data privacy, unlike 144 other countries.

As the healthcare industry rapidly evolves, both Wisconsin legislators and federal lawmakers are preparing to address these changes. A proposed bill in Washington, D.C., introduced for 2025, would permit AI systems to prescribe medications—a concept still met with skepticism. Dr. Ramsey Larson, a family medicine practitioner, expressed his concerns during the discussions, emphasizing the daunting questions surrounding responsibility for patient care when machines take on such roles.

Sara Froenke, a clinical informatics nurse specialist at UnityPoint Health-Meriter, urged legislators to consider the long-term implications of their decisions. “Think seven generations ahead and aim to prevent reactive legislation in the future,” she advised. Froenke encouraged the lawmakers to leverage their unique position to foster meaningful change.

The legislative study committee plans to convene several times over the coming fall, with the new legislative session set to commence in January.

Wisconsin legislators consider regulating AI in the healthcare sector.

A bright legislative hearing room bathed in warm natural light streaming through tall windows. A group of Wisconsin lawmakers in suits and a doctor in a white coat sit around a polished wooden table, studying digital tablets that project a glowing AI chip entwined with a red medical caduceus. The Wisconsin state flag stands solemnly behind them, and a stethoscope and policy papers lie in the foreground.

Wisconsin lawmakers weigh AI regulation in healthcare - WMTV 15 NEWS

- Lawmakers and experts convened to discuss regulating AI in healthcare settings.  
- The U.S. lacks a national data privacy law, unlike 144 other countries.  
- Future legislation may allow AI to prescribe medications, raising responsibility concerns.  

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MADISON, Wis. (Gray) - Lawmakers, doctors and AI experts met at the Wisconsin State Capitol to discuss whether and how artificial intelligence should be regulated in healthcare settings.

The United States does not have a national law to protect data privacy, compared with 144 other countries, according to Jordan Francis, senior policy counsel for U.S. legislation at the Future of Privacy Forum, a non-profit focused on data protection, who spoke at the hearing.

Lawmakers in Wisconsin and in Congress are looking to respond to the fast-evolving industry. In Washington, D.C., a bill introduced in 2025 would allow AI to prescribe medications.

“It sounds kind of like science fiction, like machines replacing family physicians,” Dr. Ramsey Larson, a family medicine physician, said during the hearing. “Who is responsible in the care of that patient? These are very big questions that haven’t been answered.”

Sara Froenke, a clinical informatics nurse specialist at UnityPoint Health-Meriter, told lawmakers to consider long-term effects of legislation.

“Think seven generations ahead and think upstream to prevent knee-jerk legislation later,” Froenke said. “You are all in an incredibly good position to be forward thinking and you have the power to make real change.”

The legislative study committee is expected to meet multiple more times this fall. The next legislative session begins in January.

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Wisconsin lawmakers weigh AI regulation in healthcare

With artificial intelligence increasingly integrated across various sectors, healthcare systems are actively investigating ways to leverage this technology to enhance the efficiency of patient care.

Implementing AI in the medical field

A bright, sunlit hospital room with large windows. A doctor in scrubs consults a patient’s chart on a sleek digital tablet displaying AI-analyzed scans. Beside them, a small, friendly medical robot on wheels holds a tray of instruments. Warm, hopeful atmosphere highlighting human-AI collaboration in healthcare.

Using AI in healthcare - WSAW

- Health systems are adopting AI to enhance efficiency and streamline patient care processes.  
- AI technology aims to improve outcomes and reduce operational burdens in healthcare.  
- The integration of AI is becoming widespread across various sectors, including healthcare.

As artificial intelligence finds its way into nearly every industry, health systems are exploring how the technology can streamline patient care.

Using AI in healthcare

A recent study conducted by Cotiviti in collaboration with MedCity News indicates that the primary challenge surrounding artificial intelligence in healthcare has shifted from adoption to governance. As AI becomes integrated into various operational facets—clinical, administrative, and financial—healthcare executives are now prioritizing security, governance, and accountability to ensure responsible usage. The 2026 Healthcare AI Readiness Index collected insights from 70 healthcare leaders during the summer of 2026.

The findings reveal that over 70% of participants have started implementing AI tools, with nearly 40% of health insurers identifying AI as a fundamental component of their operations. However, healthcare providers appear to be at an earlier adoption stage, with more than 70% still navigating the initial phases of AI integration.

Despite the advancement in AI application, the necessary governance frameworks are still in the works. Only 42% of payers and 32% of providers express feeling “very prepared” to tackle AI-related cyber threats. Additionally, a significant portion of the workforce is engaging with unauthorized or non-integrated "shadow AI" tools, with 60% of payers and 64% of providers reporting such incidents. Alarmingly, less than 40% have established comprehensive policies to oversee employee use of AI technologies.

Ric Sinclair, CEO of Cotiviti, emphasized the urgent need for enhanced security and governance as AI becomes a staple in healthcare infrastructure. He stated, “While AI is increasingly embedded in critical workflows, the measures for safety and oversight must evolve equally fast. It’s crucial for health plans and organizations to ensure that the technology they leverage and their strategic partners uphold the highest standards for security, governance, and responsible usage. Building trust will be essential for fully unlocking AI’s potential in the healthcare sector.”

The Index not only assesses the current adoption levels but also investigates how healthcare leaders are constructing the operational frameworks essential for the ethical use of AI.

Survey Shows Rapid AI Adoption in Healthcare, Yet Readiness Lags

Modern hospital ward bathed in warm natural light from tall windows. In the foreground, two faceless medical professionals in scrubs study a wall‐mounted digital dashboard: a green bar graph labeled “AI Adoption” soars skyward, while beside it a round readiness gauge is pegged deep red. On a stainless steel cart, a sleek AI diagnostic robot arm, its surface etched with circuit patterns, holds a stethoscope as if offering it to the staff. The scene feels hopeful yet tense, underscoring rapid AI growth amid lagging preparedness.

Survey Reveals AI Adoption is Accelerating in Healthcare, But Readiness is Not - MedCity News

- Over 70% of healthcare leaders have started using AI tools for operations.  
- Only 42% of payers and 32% of providers are prepared for AI-related cyberattacks.  
- Many employees utilize unauthorized "shadow AI" tools, indicating weak governance policies.  

A new report from Cotiviti and MedCity News suggests healthcare’s biggest AI challenge is no longer adoption. As AI becomes embedded in clinical, administrative and financial workflows, healthcare leaders are increasingly focused on the governance, security and accountability needed to use it responsibly. The 2026 Healthcare AI Readiness Index surveyed 70 healthcare leaders over the summer of 2026.

More than 70% of respondents said they have begun using AI tools, with nearly 40% of health insurers describing AI as a core aspect of their business. Providers, however, are earlier in the process, with more than 70% still in the early stages of adoption.

Yet the systems needed to govern AI use are still developing. Only 42% of payers and 32% of providers report being “very prepared” to respond to AI-assisted cyberattacks. More than half of payers (60%) and 64% of providers report employees are using unauthorized or non-integrated “shadow AI” tools, while fewer than 40% have detailed policies governing employee use of AI.

“AI is quickly becoming part of the infrastructure of healthcare, but the security and governance around it have to advance just as quickly,” said Ric Sinclair, CEO of Cotiviti. “As health plans and healthcare organizations rely on AI across more critical workflows, they need confidence that the technology and partners throughout their ecosystems meet the same high standards for security, governance and responsible use. Trust will be foundational to realizing AI’s full potential in healthcare.”

Beyond measuring adoption rates, the Index explores how healthcare leaders are building the operational foundations required for responsible AI use.

To access the 2026 Healthcare AI Readiness Index, please fill in the form below:

Survey Reveals AI Adoption is Accelerating in Healthcare, But Readiness is Not

As the integration of artificial intelligence in healthcare accelerates, discussions are evolving from merely assessing AI’s technical capabilities to evaluating the preparedness of organizations as a whole. While many healthcare institutions acknowledge AI's transformative potential, several are still struggling with essential data management practices that enable effective implementation, scaling, and operational functionality.

Transitioning from isolated AI applications to comprehensive growth necessitates a solid base of reliable, integrated, and actionable data. The core issue often lies not with the AI technologies themselves but with the ability to utilize data confidently across diverse platforms and areas—ranging from clinical applications to financial assessments and performance metrics.

Widening the focus beyond just data quality

Historically, the discourse around AI in healthcare has centered on data quality, an undeniably vital aspect of the technology. Flawed, incomplete, or inconsistent data can lead to inaccurate AI outcomes and negatively affect patient care. While healthcare organizations have dedicated substantial resources over the years to enhance data quality, this focus alone is no longer sufficient—it is now a fundamental requirement.

To effectively scale AI initiatives, organizations must consider several key factors:

1. **Robust Governance**: Establishing a well-defined framework that outlines the necessary processes, roles, standards, responsibilities, and metrics associated with the AI tools and models, along with the input data that drives them.

2. **Interoperability**: Given the fragmented and siloed nature of healthcare data, organizations must implement advanced tools that integrate various data sources into a singular, reliable resource for AI models to utilize before making critical clinical decisions.

3. **Data Lineage**: Trust in AI-generated results hinges on the ability to trace the origin of data, understand its transformation during integration, and confirm it as a reliable source for operational and clinical scenarios.

4. **Transparency**: The algorithms and data that inform AI outputs often operate in a “black box” environment, which can jeopardize trust as AI applications become more widespread. Ensuring visibility into the data and processes behind AI insights diminishes error risk, fosters confidence, and helps protect patient and organizational interests.

5. **Detectability**: Organizations must be equipped with tools that can identify and resolve data discrepancies early on, preventing any negative impact on financial or clinical decisions.

Failing to establish these essential pillars may cause AI to exacerbate existing data issues rather than provide substantial benefits. By implementing these structures, organizations can confidently adopt and scale AI across various departments, ultimately enhancing patient care.

The implications for healthcare AI are substantial

With the rise of AI across numerous sectors, discussions surrounding safety and effectiveness are gaining momentum. This is particularly critical in healthcare, where the deployment of AI tools holds far greater stakes than in other fields, as decisions made can have life-or-death consequences for patients. The implications of AI also extend to regulatory compliance and can either reinforce or challenge the fundamental trust between patients, providers, and healthcare institutions striving to deliver high-quality care.

As the utilization of AI advances in healthcare, those entities lacking a robust data framework risk encountering significant and damaging repercussions.

Impact on clinical efficacy

Utilizing AI models that rely on incomplete or fragmented data can lead to subpar clinical decision-making and compromise patient safety. It is imperative that the data used is both comprehensive and precise. Furthermore, AI solutions developed using generic open-source large language models (LLMs) typically fall short in clinical environments since they must be specifically tailored to interpret complex medical language and facilitate nuanced, individualized care provisions. Even in administrative contexts, AI tools must be trained on targeted medical terminologies and billing systems to minimize errors and enhance performance.

Importance of context

Understanding the specific challenges that users aim to address with AI is crucial, and these needs can vary widely among stakeholders. The requirements of a clinically integrated network of providers diverge significantly from those of a self-funded employer seeking different insights. Within a single organization, various users will demand distinct outputs and insights from AI to tackle clinical, financial, and operational issues. For an AI tool to deliver relevant insights, it must be capable of accounting for the unique circumstances surrounding each user's inquiry.

Data security challenges

Healthcare is one of the most regulated industries, where patient privacy is paramount, and violations of the Health Insurance Portability and Accountability Act (HIPAA) can lead to severe penalties. Consequently, AI tools must be designed with robust security measures, particularly those using open-source LLMs, to prevent unauthorized access to sensitive health information (PHI). A strong data foundation is essential to ensure both HIPAA compliance and inherent security within AI systems.

In addition to compliance, organizations face challenges in ensuring their AI models are supported by a substantial and secure data source for learning and refinement. Utilizing open-source LLMs for training can heighten the potential for data breaches, making it critical for healthcare providers to strike a balance between innovation and data security through a purpose-built architecture. Such infrastructure secures data and ensures user interactions are contained within a protected framework, allowing AI to refine its insights over time while safeguarding sensitive information.

Emphasizing outcomes over mere outputs

When healthcare payers, providers, and self-funded employers prioritize a mature, AI-optimized data environment, they enhance their capacity to fulfill vital components of value-based care—such as population health, care management, quality improvement, cost control, and collaborative efforts. This foundation also positions organizations to leverage AI for informed decision-making at scale, where the integration of detailed individual insights alongside wider population trends becomes a critical advantage, reliant on dependable, transparent, and interconnected data throughout the organization.

Ultimately, the future of AI in healthcare hinges not just on an organization's ability to manage data, but also on its commitment to building a foundational data framework that fosters trust, visibility, and operational proficiency. By establishing AI on a secure, compliant, and clinically-informed basis, organizations can transform their data into actionable insights and scalable AI capabilities, propelling advancements in healthcare delivery.

Transforming disjointed healthcare information into smart solutions in the era of AI

A sunlit modern hospital office with natural light pouring through large windows. On a sleek desk lie torn patient charts and floating translucent data panels. A doctor in scrubs watches as an ethereal network of glowing lines emerges from a slim tablet, weaving the fragments into a unified interface. In the background, a wall-mounted monitor displays stable vital signs, symbolizing AI-driven intelligent action in healthcare.

From fragmented healthcare data to intelligent action in the age of AI - Healthcare Dive

- Healthcare organizations recognize AI potential but often lack necessary foundational data management capabilities.  
- Effective AI implementation requires strong governance, interoperability, data lineage, transparency, and detectability.  
- A mature, AI-ready data foundation supports value-based care and enables scalable decision-making.  

As AI adoption accelerates across healthcare, the conversation is increasingly shifting from AI model capabilities to broader organizational readiness. Most healthcare organizations recognize the potential of AI, but many still lack the foundational data management capabilities to effectively implement, scale and operationalize that potential.

Moving from early AI use cases and segmented solutions to strategic growth requires a foundation of trusted, connected, actionable data. This challenge is often less about the AI tools themselves and more about whether you can confidently leverage your data across multiple platforms and domains—from clinical implementation and support to financial and performance measurement.

Priorities must expand beyond data quality

The healthcare AI conversation has long focused on data quality because it is a critical component of this evolving technology. Inaccurate, incomplete, or inconsistent data inevitably leads to flawed AI outputs and poor patient outcomes. But healthcare organizations have spent years investing in data quality, so that alone is no longer a differentiator—it’s table stakes.

Organizations looking to scale AI still need high-quality data, but taking the next step now requires:

Strong governance: A structured, clearly defined framework that includes all the processes, roles, standards, responsibilities and metrics for the AI tools and models in use, as well as the data that feeds into those systems.

Interoperability: Healthcare data is notoriously fragmented and siloed, so organizations must have advanced tools that bring all the disparate sources together into a single, trusted, connected source for AI models to evaluate before making treatment decisions or clinical recommendations.

Data lineage: Before trusting AI outputs, healthcare organizations must be able to track where data originated, how it was transformed in the process of bringing it together and whether it remains a trustworthy source for operational and clinical use cases.

Transparency: The data and calculations underlying AI outputs are often a “black box,” purposefully opaque to maintain a competitive edge, or protect proprietary or sensitive information. But that opacity won’t work as AI use expands in healthcare. The ability to see all the underlying data and validate insights with explainable calculations reduces the risk of errors, improves trust and can prevent patient or organizational harm.

Detectability: Organizations must have tools that detect and resolve data issues early, before they impact any financial or clinical decisions.

Without these critical five pillars, AI may amplify existing data challenges rather than create meaningful value. With them, organizations have the confidence necessary to implement AI at any level and scale it across entire teams, divisions and organizations to improve care.

The stakes are higher for healthcare AI

As AI proliferates in many industries, critical conversations around efficacy and safety are emerging. Nowhere is that more important than in healthcare. Deploying an AI tool in a healthcare setting is not the same as optimizing an e-commerce funnel or streamlining shipping logistics—the stakes are much higher when AI informs decisions that could literally be life and death for a patient. AI in healthcare also affects regulatory compliance and can reinforce or destroy the core trust between patients, providers and the healthcare systems that are supposed to provide exceptional care.

As AI-enabled healthcare grows, organizations that lack the data infrastructure to support it could encounter serious and detrimental consequences.

Clinical consequences

When an AI model only has access to fragmented and incomplete data, the underlying information it’s using to make decisions is flawed. That can directly impact clinical decisions and patient safety, so precise and complete data must be non-negotiable.

Additionally, AI tools built and trained on generic open-source large language models (LLMs) will almost always fail in a clinical setting. Healthcare AI tools must have training to analyze and decipher complex medical terminology and make very nuanced and personalized care delivery decisions. Even when AI is utilized for healthcare administration, the tools must be trained on industry-specific terminology and billing codes to avoid errors and optimize performance.

Context matters

Context is critical to understand the specific problem that a user is trying to solve with AI—and that varies by stakeholder. The insights and interventions a clinically integrated network of providers needs from their AI tools are vastly different from the information a self-funded employer is seeking. Even within a single organization, different users will need different AI outputs and insights to solve clinical, financial and operational challenges. For a healthcare AI tool to provide actionable and appropriate insights, it must be able to understand the nuances specific to each user’s situation and align its outputs accordingly.

Data security

Few industries in the world are as highly regulated as healthcare. Patient data is personal and protected and HIPAA violations lead to strict penalties and financial liabilities. AI tools that are not properly secured, particularly those using or connected to open-source LLMs, can introduce another opportunity for bad actors to access protected health information (PHI). Thus, the data foundation underlying AI tools must have HIPAA compliance and security architected in from the start.

HIPAA compliance is also not the only AI-related challenge healthcare organizations face. AI models need a robust data source from which to learn and train and a secure feedback loop to help improve insights, workflows and ultimately outcomes. But using open-source LLMs to train and improve AI models increases the risk of data breaches.

Balancing data security with innovation requires a purpose-built architecture that ensures data and interactions are not shared outside an organization’s secure data infrastructure and user prompts or insights are never co-mingled with other organizations. Proper architecture allows AI to learn—generating better insights over time and increasing productivity—without introducing unnecessary risk to the underlying data. It also enables users to develop proprietary AI that is unique to their organization.

Focus on outcomes over outputs

When healthcare payers, providers and self-funded employers invest in a mature, AI-ready data foundation, they can support all the critical aspects of value-based care: population health, care management, quality improvements, cost reduction and collaborative care. They are also better positioned to support AI-enabled decision-making at scale. The ability to combine granular member-level insights with broader population trends will be a significant differentiator, but it’s only possible when the underlying data is reliable, trustworthy, governed and connected across your entire enterprise.

The defining principle for healthcare’s AI future is not whether organizations can manage data. It is whether they are committed to building the necessary data foundation to enhance trust, visibility and operational readiness. AI built on a secure, compliant and clinically-aware foundation will enable users to transform data into actionable intelligence and scalable AI capabilities.

From fragmented healthcare data to intelligent action in the age of AI

The collaboration with AI is not a recent development, yet the rise of foundation models marks a new chapter in the evolution of large language models (LLMs) capable of reasoning. Evidence is mounting that these advanced models can carry out clinical tasks with competence akin to that of human professionals.

India is progressing towards an AI-ready healthcare system, bolstered by governmental support, a vibrant start-up landscape, increased adoption of electronic medical records (EMRs), private investment, and a growing acceptance of AI technologies among clinicians.

Presently, the value generated from AI applications is largely focused on a limited number of use cases, such as workflow automation and ambient scribing. These initiatives aim to enhance efficiency and patient experience, although demonstrating a clear return on investment (ROI) for businesses in the short term proves challenging.

Despite noticeable demand, various promising AI applications still show significant potential due to the wide gaps existing in the market. Opportunities abound in areas like remote patient monitoring, optimization of operating rooms and intensive care units (ICUs), management of chronic diseases following hospital discharge, and claims processing—making these attractive prospects for AI-focused startups.

Nevertheless, the readiness to implement these technologies is inconsistent. Key obstacles such as data quality, effective workflow integration, clinician trust, and managing change hinder large-scale adoption. Successful start-ups typically possess a strong understanding of the healthcare sector combined with robust AI capabilities.

For those interested in further insights, a comprehensive report is available for download (PDF). 

HealthQuad, one of the foremost investment platforms in India focusing on innovative healthcare models, is actively investing from its HealthQuad Fund III. The organization continues the successful strategy and track record established in its previous funds. Founded in 2016, HealthQuad manages assets exceeding US$500 million and operates as the early-growth investment branch of Quadria Group, a prominent healthcare investment firm in Asia. HealthQuad supports outstanding founders who are creating category-defining companies in sectors such as HealthTech, MedTech, Bio/PharmaTech, and innovative care delivery, leveraging deep industry knowledge and operational capabilities to foster growth both in India and on a global scale.

Artificial Intelligence in Healthcare Services in India

A sunlit Indian clinic room where a doctor in a crisp white coat leans over a sleek tablet glowing with colorful neural-network overlays of a patient’s vitals. The adult patient sits calmly on the examination table as soft natural light streams through a nearby window, illuminating charts on the wall and a small Indian flag discreetly displayed. Subtle digital data pulses seem to flow from the tablet into the room, symbolizing AI’s life-saving reach in rural and urban healthcare alike.

AI in Indian Healthcare Delivery - Bain & Company

- The emergence of reasoning LLMs demonstrates human-level competence in clinical tasks.  
- India's AI-ready healthcare infrastructure is enhancing through government and start-up initiatives.  
- Barriers to AI implementation include data quality, clinician trust, and integration challenges. 

Written in collaboration with Written in collaboration with AI is not new, but foundation models are: The agentic era of reasoning large language models (LLMs) is emerging, with mounting evidence that clinical tasks can be performed at a human level of competence.





India’s healthcare infrastructure is becoming AI-ready, supported by government initiatives and a thriving start-up ecosystem, as well as rising electronic medical record (EMR) penetration, private capital, and clinician acceptance.





In AI deployments, value is currently concentrated in a narrow set of use cases (e.g., workflow automation and ambient scribing, among others) directed toward efficiency and patient experience gains while business ROI remains difficult to demonstrate in near-term.





Several high-potential AI use cases present large whitespace despite clear demand—including remote patient monitoring, operation theatre and intensive care unit (ICU) optimization, post-discharge chronic disease management, and claims management—creating compelling opportunities for AI start-ups.





Implementation readiness remains uneven—data quality, workflow integration, clinician trust, and change management remain the primary barriers to scale, favoring start-ups with deep healthcare domain expertise alongside AI capabilities. Download the report (PDF) About HealthQuad HealthQuad is one of India’s leading investment platforms focused on new-age healthcare models, and is currently investing from HealthQuad Fund III, continuing the strategy and track record established across Funds I and II. Founded in 2016, HealthQuad currently has an AUM of over US$500 million, and acts the early-growth investment arm of Quadria Group, one of Asia’s leading dedicated healthcare investment platforms. HealthQuad backs exceptional founders building category-defining companies across HealthTech, MedTech, Bio/PharmaTech and novel care delivery, leveraging deep sector expertise, operating capabilities and Quadria’s extensive healthcare ecosystem to scale in India and globally.

AI in healthcare News