Data-driven scheduling cuts diabetes risks by 19% for vulnerable patients

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A new study from the University of Illinois Urbana-Champaign proposes a data-driven strategy to improve chronic disease care by rethinking how and when patients are scheduled to see clinicians — especially those most at risk of falling through the cracks.

Researchers developed a predictive framework that reduced population-level diabetes risk by up to 19.4%, particularly among underserved groups. The system uses machine learning to analyze electronic health records (EHRs) alongside U.S. Census data on income, education and other social determinants of health to guide appointment scheduling decisions.

“Managing chronic medical conditions such as diabetes is a major challenge for health care organizations because it requires both committing resources over a long timeline and high levels of patient engagement in the care process,” said Ujjal Kumar Mukherjee, PhD, MBA, one of the study’s authors and a professor of business administration at Illinois.

“But if you apply a strategic approach and essentially customize the care to the patient’s demographics, you can drive improvements in health outcomes,” he said.

The study, published in the Journal of Operations Management, analyzed clinical and demographic data from more than 10,000 patients across a multi-facility clinic network in the Midwest. Mukherjee and co-authors Dilip Chhajed, PhD, of Purdue University and Han Ye, PhD, of Lehigh University found that patients from low-income, less-educated, and predominantly minority communities were significantly less likely to have regular health care encounters — despite having higher average glucose levels.

“Many high-risk patients receive fewer health care encounters than needed, which means it’s important to tailor chronic care treatment to improve outcomes,” Mukherjee said.

The model identifies patients at elevated risk for diabetes-related complications and recommends prioritizing their care accordingly. The goal is to optimize limited resources — like clinician time and appointment slots — while avoiding preventable emergencies and costly care escalation.

“It’s well known that there is pervasive health inequity in the U.S. that’s coupled with a limited capacity to deliver chronic care — chronic being a progressive kind of a disease such as diabetes, chronic obstructive pulmonary disease (COPD), cancer or heart disease,” Mukherjee said. “These are all progressive diseases that, if left untreated, advance to costlier stages of care — which, of course, makes it more expensive and time-consuming for patients and practitioners.”

“But if you treat it early enough and often enough, then you can manage it and bend the cost curve down,” he said. “Chronic diseases don’t get cured. Rather, it’s a question of managing both the disease progression and the overall risk of it.”

The researchers also developed a “heuristic solution” to help health systems implement the model, making it possible for clinics to integrate this type of predictive scheduling into routine care planning.

“Many of the chronic care diabetes patients were from underserved, under-resourced communities, and they don’t have regular contact with medical professionals,” Mukherjee said. “They don’t go through preventative or primary care processes. Which means they’ll often end up in the emergency department with some kind of adverse health event, whether it’s a heart attack, kidney failure, retinal problems or a liver dysfunction, all from untreated diabetes.”

“And when that happens, both the patient and the health care system end up spending a lot more time and money than they would if the disease had been managed from the outset,” he said. “If you have regular contact with clinicians, you can avoid unnecessary emergency hospitalizations.”

Mukherjee said the model offers a scalable tool to help primary care practices address disparities in access and outcomes, even when clinical capacity is limited.

“The approach supports fairer access to chronic care and has the potential to reduce health disparities on a population level,” he said.