The Rise of AI-powered Health Care
Albert Katz (WG23) embraces a fundamental truth: nothing matters unless you’re healthy.
That simple philosophy, along with undergrad and master’s degrees in finance and computer science, and a Wharton School MBA in health care management, motivated him to launch Flagler Health, which combines patient data and the power of AI to improve health care.
Flagler’s AI algorithms analyze patient data, including medical records, to provide doctors with insights and recommendations for treatment and to optimize patient care. “I wanted to save money for the health care system,” notes Katz, whose startup was a finalist in Penn’s 2023 Venture Lab Startup Challenge and continues to innovate in areas like remote therapeutic monitoring and behavioral health. “Let’s try to gather that data so eventually we can help specialists adopt value-based care – providing optimal care to patients.”
It’s been called the health care AI revolution – an abundance of algorithms analyzing health care industry data to make medicine more efficient and effective. What does that look like? Flagler Health’s machine-learning for health care is just one of many approaches. (Btw, if you want to go deep on how that works, check out this podcast featuring Will Hu (GED19), Flagler’s co-founder and chief technology officer).
AI in the ER?
Flagler Health illustrates that this era of AI is inspiring entrepreneurial problem-solving in the health care industry. And it is also sparking innovation in research and pioneering algorithmic tools to improve health care on a global scale.
The application of AI in health care is widespread – and a fundamental focus of the Wharton Healthcare Analytics Lab, a research center launched at the Wharton School in October 2023.
Since then, co-directors Hamsa Bastani, a Wharton professor of operations, information and decisions who develops machine learning algorithms for learning and optimization in health care; and Marissa King, a Wharton professor of health care management, have led research and discussions about where AI is being used successfully in health care and where the challenges lie.
In conversation with Eric Bradlow, vice dean of Analytics at Wharton, Dr. King helped define the health care-AI landscape. “Machine learning and artificial intelligence have touched almost all aspects of health care at this point. If you think of everything from how you get reminders to pick up prescriptions, from who’s reading your radiology reports, to even how you’re being triaged in the emergency department, machine learning plays a key role in all of those facets,” she noted. “If you think about radiology reports, that’s arguably the place where AI has had the greatest penetration. Many, many of our radiology reports are read now by machines.”
The Wharton Healthcare Analytics Lab is collaborating with different stakeholders – patients, providers and policymakers — to design better algorithms across health care. Here’s a snapshot of where Wharton-led analytics are helping to inform the new health care AI revolution:
🩺 Resource allocation refers to equitably allocating health resources in environments that struggle with access to treatment or medicines. Angel Chung, a PhD student in Wharton’s Operations, Information and Decisions Department, is working with the Sierra Leone government in West Africa to use machine learning and optimization for essential medicine distribution across thousands of health facilities in the country. In a profile of her work published by the Wharton AI and Analytics Initiative, Angel said that 40% of patients were being turned away without receiving the medicines they needed. “We used a synthetic difference-in-differences model to evaluate the impact of our approach,” she said. “Our result shows around 20% improvement in people’s access to essential medicines and medical supplies by the second quarter of 2023. While introducing an innovative change into an existing government system is tremendously difficult, we have successfully incorporated AI technology on a national scale and showcased improvement in this resource-constrained setting.”
🩺 Workforce wellbeing addresses issues affecting the health care workforce, such as burnout. Large language models (which process vast amounts of text data) provide a way to mine data from sources that haven’t been tapped before, like the clinical notes kept on patients. “One thing we’re trying to do is to utilize data from electronic health records to understand where there’s likely to be a high risk of burnout or emotional overload in clinicians, especially nurses,” said Dr. King in an article posted on Penn’s Leonard Davis Institute of Health Economics. “There’s immensely rich data within clinical notes.”
🩺 Innovative trials are an area where algorithms can drive innovations in medical practice by improving the design of trials, or research studies that test the safety and effectiveness of new medical treatments. “Clinical trials tend to be statically designed,” Dr. Bastani told Penn’s Leonard Davis Institute of Health Economics (where she is also a senior fellow). “They’re not actually personalized or dynamically customized in any way. We’ve been thinking about leveraging data from historical clinical trials or pilots to warm start these predictive models.” The Wharton Healthcare Analytics Lab is collaborating with Penn’s Health Incentives and Behavioral Economics for Better Health on this project.
🩺 Treatment and care form the heart of health-care delivery, and large-scale health systems data can help researchers identify promising treatment strategies. The Wharton Healthcare Analytics Lab says that “Big data and the use of new algorithmic methods have an enormous potential to inform everything from treating patients with substance-use disorders…to establishing best practices for human decision-making in medical settings.” Recent data-driven research has looked at everything from trends in Buprenorphine treatment in the U.S., opioid prescribing and overdose deaths, to building algorithms to help fight sex trafficking.
🩺 Health equity is a key challenge as algorithms and AI are more prevalent in the design and administration of medical care. Datasets trained by AI and machine learning systems are often biased and can compromise systems and lead to imbalances in care. For example, an algorithm might improve the outcomes for one population of people, but not others. Dr. Bastani’s research on Rethinking Fairness for Human-AI Collaboration in part addresses this issue. She regularly discusses health equity as a priority for the analytics lab.
“Any health care dataset encodes biases because patients that face structural barriers are underserved by the health system and so we don’t have high-quality representative data on them,” noted Dr. Bastani, who has talked about biased datasets often in her research. “We’ve been thinking about addressing that by leveraging proxy data – for example, cheaper but more available data like Google Search terms and so on. A big challenge of machine learning is that the training datasets are very large and so it’s not always feasible to debias the training data. But what we’re really interested in are the downstream decisions…by leveraging auxiliary sets we can never perfectly debias the model, but at least the decisions that are coming out will be less biased.”
Conversation Starters
How are the entrepreneurs at Flagler Health using AI to make an impact in health care?
What is resource allocation in health care and how is Wharton PhD student Angel Chung using AI to impact this health care challenge?
Why are datasets often biased in health care and what is being done to make them less so?
Which area of health care-AI focus do you find most compelling? Share your thoughts in the comment section of this article.
The hero image was provided by canva.com.
I resonated deeply with this article. As a self-proclaimed technophile and someone who ventures beyond the textbook to neuroscience with my own NeuroEd platform, I know how technology can enhance the quality of life experiences. But to read about Flagler Health’s efforts to take its patients’ data and apply AI to determine the most effective route for care and cost reduction taught me that this intersection of technology comes from something beyond skilled programming. It comes from caring people.
I was even more touched by the Wharton researchers, including Angel Chung, who apply machine learning to distribute essential medications across Sierra Leone, improving availability by 20%. That’s not genius programming; that’s lifesaving technology on the international scale.
From remediative suggestions on refill notifications to predictive uses before patients even step into the ER to improved radiology accuracy , AI is changing the healthcare experience for everyone involved . I’m also glad to read that the article acknowledges setbacks—data bias, burnout, patient privacy—allowed for within typically underserved socioeconomic communities . An approach to compassionate, ethical oversight is required to temper technological expansion with humanity.
This is important to me because I use AI within my NeuroEd platform to not only foster an academically driven environment but also champion mental wellness and equity of access for my colleagues and myself. Using the health-oriented wellness components from this article as inspiration, I will forever strive to apply such deliberate purpose to EdTech or HealthTech one day through compassion and ethics.
I completely agree, Suriya—empathy fuels technology’s effect, not just code. Your comment speaks to the human element of AI creativity.
Expanding on that: what would happen if AI projects included a community advisory committee—a handful of real users who review model output in real-time and flag issues? In my work at a nonprofit, stakeholders reviewing campaign drafts allowed us to catch messaging misfires early. Would a feedback loop add ethical review to AI pre-deployment?
This article’s exploration of AI in healthcare sparked a cascade of reflections, bridging the future of technology with generations of lived experience. As I read about AI’s expanding role in healthcare—from the precision of burnout detection to Angel Chung’s equity-driven work in Sierra Leone—I kept coming back to one central question: Who gets to be represented in the data? Dr. Bastani’s insight that “health care datasets encode biases” resonated deeply. It reminded me of my own experience with the underserved, particularly seniors.
I live with my grandmother, a Chinese immigrant whose relationship to healthcare is rooted in trust, intuition, and community—not in wearable tech or digital tracking. She doesn’t log symptoms online or search diagnoses on Google. Yet her health, like anyone’s, deserves visibility and care. But if AI models are trained on data from only the most digitally fluent patients, where does that leave people like her?
That’s where BridGEN, the intergenerational civic-tech initiative I co-founded, comes in. We teach digital literacy to seniors—helping them navigate telemedicine, manage online prescriptions, and stay connected to virtual health resources. In a society where older adults are increasingly marginalized by innovation, our work bridges the digital divide in a way that honors their wisdom while safeguarding their access to critical services. For me, this work underscored a powerful truth: healthcare transformation isn’t just about new technologies—it’s about who gets included in the future they create.
Wharton’s article affirms that AI in healthcare can’t be driven by efficiency alone. It must be rooted in representation and empathy. While patient-centered tools like Flagler Health’s algorithms are promising, they must also confront the inherent tension of biased data. What happens to those whose medical stories aren’t recorded, whose lives fall outside conventional datasets? If healthcare is shaped only by those who leave digital footprints, we risk automating exclusion at scale.
This is more than a technical issue—it’s a societal one. To build AI that truly advances care, we must interrogate what’s missing from the data and design models that reflect the full spectrum of human experience. That’s why Angel Chung’s work matters: her algorithms address structural inequity in medicine distribution, not just abstract performance metrics. And it’s why clinician burnout research matters too—because emotional labor in healthcare, especially among nurses, is too often invisible in the data and the discourse.
AI holds the potential to reshape healthcare—but only if it is built with intentionality. At BridGEN, we remind seniors that digital equity is about more than access: it’s about dignity, agency, and connection. If the future of medicine is to be just, it must be designed for those who’ve historically been left out of its narrative. Because no model can truly care if it forgets whom it’s meant to serve.
This article highlights a deep truth: AI in healthcare isn’t algorithms—it’s redistributing human care and attention to where it matters.
The Flagler Health example is interesting, but I was especially touched by the case of Angel Chung—using machine learning to increase medication availability in Sierra Leone by 20%. That directly speaks to the ways that technology can be a bridge in low-resource environments.
My Python and AWS training showed me data pipelines are potent—but only if they are designed with equity in mind. When I worked on a project with SMS data from underbanked populations, I put fairness first by filtering language data meticulously to prevent misclassification—seeing firsthand how algorithmic bias can exacerbate inequalities.
The section on predicting clinician burnout spoke to me: Mining EHR notes for emotional cues is brilliant but also requires advanced model interpretation and continuous monitoring. My nonprofit internship grappled with data transparency vs. human review in donor reporting—a smaller-scale analogue of checks and balances for clinical AI.
This raises a fundamental question: How do labs like Wharton’s Healthcare Analytics Lab turn bias audits and open monitoring into a routine aspect of all AI projects—without slowing innovation cycles? That is: how do we build ethical governance into the DNA of AI development?
Thank you for these observations. It is clear that the future of healthcare is not just technological but ethical. If we design AI in a considered manner, it can be the greatest tool for equity in a generation.