Studies examine the connection between primary care provider resources and health outcomes for type 2 diabetes patients

by University of Massachusetts Amherst

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Within the medical ecosystem, health guidelines aim for timely diagnosis and regular follow-up of chronic illnesses, such as type 2 diabetes, to ensure that no patient gets left behind. However, two studies by researchers at the University of Massachusetts Amherst show that a patient's actual care experience can vary, and not just along the typical demographic lines that research usually investigates.

"To predict whether you are going to experience a potential delay in detecting your type 2 diabetes or whether your follow-up care after you are diagnosed occurs in a timely manner is not so much about your age or your gender, but more about the clinical practices in your primary care provider," says Muge Capan, assistant professor of industrial engineering in the Riccio College of Engineering at UMass Amherst. "I think that's very powerful if you think about it: Where you go to primary care says something about your health outcomes."

This idea that clinic-level decisions impact patients' chronic disease diagnosis and follow-up was reflected in two papers by Capan's lab, focusing on patients with type 2 diabetes.

In the first paper, published in IISE Transactions on Healthcare Systems Engineering, the researchers found that prevailing policies on scheduling follow-up appointments in primary care can both over- and underschedule patients. The time between primary care appointments impacts how quickly increases in blood sugar (the measure of hemoglobin A1c) are detected before a patient has a health crisis. At the same time, cookie-cutter schedules could generate unnecessary visits, which have their own burdens (both financial and otherwise) for patients.

The researchers used electronic health records of 17,499 patients across 10 primary care practices in the mid-Atlantic region to develop a dynamic framework for determining follow-up intervals. The model did not uniformly prescribe frequent follow-ups. Instead, it recommended more intensive follow-up for high-risk patients as A1c levels increased, while maintaining longer follow-up intervals for a wider range of A1c measures in the low-risk group. High-risk patients were predominantly 60 years or older with existing higher rates of chronic kidney disease, heart disease and other type 2 diabetes-related complications. Low-risk patients were younger and generally healthier.

They also compared their optimized follow-up policy to other policies commonly instituted by doctors' offices, namely fixed one-, three- and six-month follow-up policies and an American Diabetes Association (ADA)-like benchmark. The ADA-like benchmark was quarterly primary care follow-up for type 2 diabetes patients whose therapy has changed or who have not achieved the A1c target, and at least twice yearly for those who have achieved glycemic control.

The goal was to find the follow-up interval that could best balance the trade-off between an interval that is too short to provide new information and one that is too long to support timely detection of worsening of the condition and clinical intervention. Compared to more standardized follow-up intervals, their optimized policy met this goal best across all patients and across the high- and low-risk groups (followed by always one-month, always three-month, the ADA schedule and always six-month follow-ups).

Capan explains that getting the follow-up regimen correct is important for long-term blood sugar control since A1c is an average of blood sugar in the last three months, "so we need some time to detect meaningful changes. High frequency is not always helpful nor informative," she says, and can be costly.

Another study by this research group, published in BMC Primary Care, suggests that decisions made by doctors' offices may also influence how quickly a person is diagnosed with type 2 diabetes after an elevated A1c blood test result.

"Type 2 diabetes is a huge burden, not just financially, but also health-wise," says Capan. "The sheer magnitude of people impacted by it justifies why we should look into the processes of how these people are diagnosed."

"At the beginning of the study, we thought: Once you have the abnormal lab signal, the diagnosis should be documented quickly," says Capan. "In the guidelines, it's very straightforward. After a confirmation test, it should follow quickly that you do have diabetes, and the sooner, the better, because then we can start a treatment. But that is not what is happening."

Same-day diagnosis accounted for 22% of patients. For the remaining patients, they compared timing across two clinics. The study was a pilot, so it did not have a large enough sample size to capture statistically significant differences in diagnosis time between the two clinics, but the researchers observed diverging trends. Between the two offices, the average time to diagnosis was 1.6 and two months after the blood test. After one year, 6.2% of patients remained undiagnosed at one clinic, and 14.4% remained undiagnosed at the other.

There were also noticeable differences in continuity of care and regularity of primary care visits that are worth further investigation. "We found that the first few visits after a diabetes-range A1C appeared to be especially important opportunities for diagnosis," adds Parisa Lotfibagha, a graduate student in Capan's lab and first author on the paper.

The next steps for this research are to look at more patient data and consider what factors differ between the offices. "What's happening here is more geographical differences between locations, because, although they are both part of the same system, they have different resources," says Capan, including the numbers of physicians, nurses, social workers, diabetes educators and nutritionists they have on staff. How this translates to things like appointment availability and time clinicians have to spend with their patients—and ultimately trickles down to influence diagnosis timing—is yet to be seen.

"If we look at this paper as exploration, what it really shows is that there is value in exploring not just demographics, because the demographics themselves don't explain the delay," says Capan.

More information

Parisa Lotfibagha et al., Context-aware optimization of follow-up intervals for type 2 diabetes care using Markov decision processes, IISE Transactions on Healthcare Systems Engineering (2026). DOI: 10.1080/24725579.2026.2740607

Parisa Lotfibagha et al., A time-to-event analysis to conceptualize and predict delay in type 2 diabetes diagnosis in primary care, BMC Primary Care (2026). DOI: 10.1186/s12875-026-03529-6

Key medical concepts

Diabetes Type 2Hemoglobin A1C Measurement

Clinical categories

Common illnesses & PreventionFamily medicineEndocrinology Provided by University of Massachusetts Amherst Who's behind this story?

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