Turning data into care: MSBA students partner with Lackey Clinic to support patient engagement

For students in William & Mary’s Master of Science in Business Analytics program, learning to build sophisticated machine learning models is only part of the equation. Understanding how data operates in the real world, especially in settings where human outcomes are significant, is central to the Raymond A. Mason School of Business experience.

That distinction became tangible this past semester when students in Professor Monica Chiarini Tremblay’s Machine Learning course partnered with the Lackey Clinic, a nonprofit healthcare provider serving uninsured patients across Virginia. The collaboration challenged students to analyze patient engagement, no-shows, and retention, while grounding their technical work in the realities of healthcare delivery.

“I think that working with a real client and with real healthcare data made it feel as though we were accomplishing something,” said Tai Chirasittikrn, MSBA ’26. “It was refreshing compared to assignments that exist only to test our understanding of a topic.”

As part of the project, students visited the Lackey Clinic to see the environment firsthand. The clinic team walked them through daily operations and explained their approach to care, including 45-minute appointments designed to give patients the time and attention they need.

“Seeing the clinic in person changed everything,” said Tremblay, Hays T. Watkins Professor of Business at the Raymond A. Mason School of Business. “The students were able to connect the analytics to the lived experience of patients and staff. That connection is where meaningful insights begin.”

For some students, that exposure stressed the human weight behind the data.

“When working with the healthcare data, I would scroll through diagnoses and realize that every record represented a real person,” said Turner Mathieux, MSBA ’26. “That awareness stayed with me throughout the project.”

Using analytics to address a critical challenge

At the heart of the project was a challenge familiar to many healthcare providers. When patients quietly disengage from care, often due to missed appointments or lapses in follow-through, their long-term health suffers. For clinics like Lackey, disengagement also limits the ability to provide consistent, ongoing treatment.

Student teams analyzed patterns in patient data to better understand the drivers of churn and no-shows. Their findings revealed clear signals. Patients with one or more missed appointments were significantly more likely to disengage from care, while those without active orders showed higher churn rates, highlighting the importance of consistent engagement.

From these insights, students developed practical recommendations, including earlier identification of at-risk patients, proactive outreach following missed appointments, clearer expectations during initial visits, and stronger follow-up for patients managing chronic conditions.

“What stood out to me was how quickly the students connected the analytics to the mission,” Tremblay said. “They understood that this was not just a modeling exercise. This was about improving access to care.”

Several students noted that the project reshaped their understanding of machine learning in practice.

“I initially thought machine learning was about building a black box that produces a highly accurate answer,” said Lousia Ferrell, MSBA ’26. “This project showed me that interpretability, transparency, and business context are just as important. In healthcare, models must be explainable to administrators, clinicians, and patients.”

Learning that extends beyond the classroom

For many students, the project was their first opportunity to apply advanced analytics in a healthcare nonprofit setting and to communicate findings to real stakeholders.

“To finish the semester, we presented our machine learning project to the Lackey Clinic and William & Mary’s executive partners,” said Conner Small, MSBA ’26. “It was a chance to provide the clinic with actionable recommendations and a predictive model they can continue to use.”

Others emphasized how the experience reinforced the importance of iteration and judgment.

“I realized it’s rarely just about running a model,” said Andrew Percy, MSBA ’26. “Data is messy and requires cleaning, interpretation, and the ability to explain results to non-technical stakeholders. That skill is just as important as the technical work.”

While several teams reported strong model performance, including one churn model that reached 96 percent accuracy, students were quick to note that metrics alone were not the ultimate measure of success.

“This project changed how I think about machine learning,” said Steven Alvarado, MSBA ’26. “Models are tools, not crystal balls. They require context, expertise, and humility to use effectively. Working on a problem that mirrored my own family’s healthcare experiences made the project especially meaningful.”

Data science for good

Across student reflections, a common theme emerged: analytics with purpose.

“It was incredibly rewarding to see how the skills I’ve been developing can have a meaningful impact in the nonprofit sector,” said Thirtha Poruthikode Unnivelan, MSBA ’26. “This is a space I care deeply about supporting.”

For Tremblay, that alignment between rigor and responsibility is intentional.

“I am incredibly proud of these students,” she said. “They approached this work with care, curiosity, and respect for the people behind the data. That is what data science for good looks like.”

The Mason School extends its gratitude to the Lackey Clinic team for opening their doors, sharing their data, and generously partnering in the learning process. The collaboration epitomizes how experiential learning can create value for students while supporting organizations that serve the broader community.