Health Information & AI with Dr. Ronda Chakolis-Hassan & Dr. Robin Austin
Stan, Clarence, Barry, and the Health Chatter team welcome Dr. Ronda Chakolis-Hassan and Dr. Robin Austin for a conversation on Health Information and Artificial Intelligence and the ongoing interdisciplinary work at the University of Minnesota merging health informatics, population health, and AI.
Dr. Ronda Chakolis-Hassan, President of the Minnesota Board of Pharmacy, has experience spanning Pharmacy Benefit Management, Medication Therapy Management, and public health, her work focuses on improving medication access, advancing healthcare policy, and combining pharmacy practice with public health to improve health outcomes.
Dr. Robin Austin, Associate Professor at the University of Minnesota School of Nursing, Director of the Center for Nursing Informatics, and Specialty Coordinator for the Nursing Informatics Doctor of Nursing Practice Program, is a leader in health informatics research. Her work explores how technology can support whole-person health, empower patients, and advance person-centered care.
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Brought to you in support of Hue-MAN, who is Creating Healthy Communities through Innovative Partnerships.
More about their work can be found at https://www.huemanpartnershipalliance.org/
Research
Artificial Intelligence (AI)- is a branch of computer science dedicated to building machines capable of performing tasks that typically require human intelligence.
Common examples of AI in everyday life are virtual assistants like Alexa and Siri.
Overall, four in ten (39%) adults say they actively use AI tools at least several times a week, while eight in ten say they come across AI-generated content at least several times a week, even if they are not actively looking for it.
How can AI improve health care?
Preventative Care
Cancer screenings that use radiology, like a mammogram or lung cancer screening, can leverage AI to help produce results faster.
AI Risk Assessment - In a Mayo Clinic cardiology study, AI successfully identified people at risk of left ventricular dysfunction, which is the medical name for a weak heart pump, even though the individuals had no noticeable symptoms.
Faster care for emergencies
Triaging people based on urgency
Alerting the medical team automatically
Coordinating care behind the scenes to keep everyone in sync
Tracking changes in your health
Until recently, radiologists manually measured nodules and compared the measurements to see if they’d changed between scans. But there could be small variations in measurements from one radiologist to the next.
With AI tools automatically noting and measuring nodules, nothing gets missed or lost in translation.
Streamlining administrative tasks
AI also powers virtual assistants and chatbots that handle simple tasks, like:
Pulling up your medical history faster
Helping you schedule a follow-up appointment
Sending medication reminders
Supporting virtual visits
AI and Health Information
Tracking Poll on Health Information and Trust finds about a third (32%) of adults are turning to AI for health information and advice
This includes about three in ten (29%) who say they’ve used AI tools in the past year for information or advice about their physical health, and one in six (16%) who’ve used them for mental health information or advice
A majority (77%) of the public says they are concerned about the privacy of personal medical information provided to AI tools, including similar majorities across age groups and those who use AI for health information.
Looking Up Information About Symptoms or a Health Condition Is the Most Common Use of AI For Physical Health Questions
Many Adults Who Used AI for Health Information Did Not Later Follow Up With a Doctor
Larger Shares of Younger Adults, Black and Hispanic Adults, and Those Who Are Uninsured Are Turning to AI for Mental Health Advice
Drawbacks to AI
The AMA has called on federal lawmakers to create stronger safeguards to ensure AI tools like chatbots complement, but not replace, the clinical guidance from a physician
Risks in sharing personal or identifying details, as chatbot privacy protections may differ from a physician’s practice.
AI programs may be difficult to understand and overly ambitious - Physicians may find it challenging to understand AI programs, particularly in complex domains like cancer diagnosis and treatment.
Implementation issues - BES embedded in electronic health care systems are commonly used but may lack the accuracy of algorithmic systems based on Machine Learning
Biases - Machine Learning systems in health care can be prone to algorithmic bias, leading to predictions based on noncausal factors like gender or ethnicity [51]. Prejudice and inequality are among the risks associated with health care AI
Mistakes in disease diagnosis or AI cannot be held accountable -
Data availability and accessibility - Large amounts of data from various sources are required to train AI algorithms in health care. However, accessing health data can be challenging due to fragmentation across different platforms and systems . Data available
Regulatory concerns - The autolearn feature of AI software poses regulatory challenges as algorithms evolve continuously with use. This creates the need for additional policies and procedures to ensure patient safety.
Social challenges - Misconceptions about AI replacing health care jobs lead to skepticism and aversion to AI-based interventions
1. Public health decisions are only as good as the data behind them. Communities generate enormous amounts of information about health needs, disparities, behaviors, and outcomes, but much of this information remains fragmented, underused, or disconnected from decision-making systems. Programs like TRIUMPH help train professionals who can transform raw data into actionable public health insight.
2. We need professionals who can bridge community experience and data systemsPublic health informatics is not just about technology—it is about understanding people, communities, and context. The workforce trained through initiatives like TRIUMPH can connect what is happening on the ground in neighborhoods, clinics, schools, and community organizations with the data infrastructure used by health systems, policymakers, and funders.
3. Data drives funding, policy, and resource allocation. Whether determining where to invest resources, identifying health disparities, responding to outbreaks, or evaluating interventions, organizations increasingly rely on data to make decisions. Without a skilled workforce able to collect, interpret, analyze, and communicate data accurately, communities risk becoming invisible in policy and funding conversations.
4. Public health informatics supports health equity and population health improvement. Communities experiencing the greatest health disparities are often the least represented in structured datasets. Training professionals in public health informatics helps ensure that diverse populations, social determinants of health, and community voices are represented in the data used to guide public health strategy and healthcare transformation.
5. Healthcare and public health are increasingly interconnected. Health systems can no longer operate separately from public health. Chronic disease management, mental health, aging, environmental exposures, maternal health, and infectious disease response all require integration of clinical data, community data, and population-level analytics. TRIUMPH helps prepare professionals to work across these sectors collaboratively.
6. The future workforce must be fluent in both data and human-centered care. Artificial intelligence, predictive analytics, and digital health tools are rapidly changing healthcare and public health practice. However, technology alone is not enough. We need professionals who understand ethics, community engagement, communication, and the real-world meaning behind the numbers to ensure data is used responsibly and effectively.
References
https://www.energy.gov/science/doe-explainsartificial-intelligence
https://health.clevelandclinic.org/ai-in-healthcare
