An Operations Approach For Reducing Glycemic Variability: Evidence from a Large Primary Care Setting

Abstract

Diabetes is a highly prevalent and expensive chronic disease that affects millions of Americans and is associated with multiple comorbidities. Clinical research has found long-term variation in a patient's glycated hemoglobin (A1c) levels to be linked with adverse health outcomes such as increased hospitalizations. Consequently, there is a need for innovative approaches to reduce long-term glycemic variability, and efficient ways to implement them. We draw on the management and healthcare literatures to hypothesize and then show that a key operational lever – continuity of care (CoC) – can be used to reduce glycemic variability, which in turn improves patient health. Additionally, we explore the moderating role of a key demographic characteristic – the patient's marital status – and the mediating role of medication compliance in the relationship between continuity and variability. We use a detailed and comprehensive dataset from the Veterans Health Administration, the largest integrated healthcare delivery system in the United States, which permits us to control for potential sources of heterogeneity. We analyze more than 300,000 patients – over an eleven-year period – with diabetes, a chronic disease whose successful management requires managing glycemic variability. We find that CoC is related to reductions in glycemic variability, more so for patients who are not married. However, this reduction is not linear in continuity; we find evidence of curvilinearity but with a sufficiently high stationary point so that benefits almost always accrue, albeit at a diminishing rate. Additionally, we find that an important mechanism through which CoC may reduce variability is through patients' adherence to medications. We also find evidence of partial mediation for glycemic variability in the CoC-outcomes process chain. Our counterfactual analysis reveals the extent of improvement and that enhanced continuity can bring, depending on where it is targeted. Our findings are validated by extensive robustness checks and sensitivity analyses. Academically, our study adds to the understanding of the importance of managing variability (via continuity in service) in settings where customers repeatedly interact with service providers. Identifying the process measures through which continuity of care reduces variability is also of interest to practitioners and policymakers as it can help design appropriate policies and pathways, both in terms of processes and staffing/work allocation.

Keywords: Glycemic Variability; Continuity of Care; Healthcare Operations; Learning; Empirical Operations