Background Diabetes is associated with significant long-term costs for both patients and health systems. Regular primary care visits aligned with American Diabetes Association guidelines could help mitigate those costs while generating near-term revenue for health systems. Digital interventions prompting primary care visits among unengaged patients could provide significant economic value back to the health system as well as individual patients, but only few economic models have been put forth to understand this value. Objective Our objective is to establish a data-based method to estimate the economic impact to a health system of interventions promoting primary care visits for people with diabetes who have been historically unengaged with their care. The model was built with a focus on a specific digital health intervention, Precision Nudging, but can be used to quantify the value of other interventions driving primary care usage among patients with diabetes. Methods We developed an economic model to estimate the financial value of a primary care visit of a patient with diabetes to the health system. This model requires segmenting patients with diabetes according to their level of blood sugar control as measured by their most recent hemoglobin A1c value to understand how frequently they should be visiting a primary care provider. The model also accounts for the payer mix among the population with diabetes, documenting the percentage of insurance coverage through a commercial plan, Medicare, or Medicaid, as these influence the reimbursement rates for the services. Then, the model takes into consideration the population base rates of comorbid conditions for patients with diabetes and the associated current procedural terminology codes to understand what a provider can bill as well as the expected inpatient revenue from a subset of patients likely to require hospitalization based on the national hospitalization rates for people with diabetes. Physician reimbursement is subtracted from the total. Finally, the model also accounts for the level of patient engagement with the intervention to ensure a realistic estimate of the impact. Results We present a model to prospectively estimate the economic impact of a digital health intervention to encourage patients with documented diabetes diagnoses to attend primary care visits. The model leverages both publicly available and health system data to calculate the per appointment value (revenue) to the health system. The model offers a method to understand and test the financial impact of Precision Nudging or other primary care–focused diabetes interventions inclusive of costs driven by comorbid conditions. Conclusions The proposed economic model can help health systems understand and evaluate the estimated economic benefits of interventions focused on primary care and prevention for patients with diabetes as well as help intervention developers determine pricing for their product.

Public health promotes and protects the health of people and the communities in which they live. As such, community health assessment and engagement are critical elements that Master of Public Health (MPH) students must learn. Even so, those with graduate public health degrees often lack the ability to engage in this foundational aspect of public health practice. The purpose of this paper is to describe a curricular model that utilizes didactic preparation along with the collaboration of community-academic partners (CAPs) in a service-learning format to prepare MPH students for successful community assessment and engagement. In addition to enhancing the learning experience for students, this curricular approach assures meaningful engagement which benefits communities, especially those which are underserved. The relationships established through the implementation of this model were critical in the COVID-19 community response.

BACKGROUND Diabetes is associated with significant costs for both patients and health systems. Regular primary care visits aligned with American Diabetes Association guidelines could help mitigate those costs. Digital interventions prompting those visits among unengaged patients could provide significant economic value back to the health system as well as individual patients, but few economic models have been put forth to understand this value. OBJECTIVE This paper presents a data-driven model for estimating the economic impact for a health system of an email and text message intervention that drives appropriate primary care usage among patients with diabetes who have been historically unengaged with their care. METHODS We developed a model that segments patients with diabetes according to their level of blood sugar control as measured by their most recent HbA1c value. The model takes into consideration the population base rates of comorbid conditions for patients with diabetes, as well as physician reimbursement rates, inpatient admissions avoided, and historical impact of digital health solutions. RESULTS We present a conceptual model to measure the economic impact of an email and text message-based intervention used with patients with documented diabetes diagnoses and calculate a return on investment for a health care system implementing it for their population. The model offers a method to understand and test the financial impact of a primary care-focused diabetes population inclusive of costs driven by comorbid conditions. CONCLUSIONS The proposed economic model can offer a holistic way for health systems to understand the economic benefits delivered by interventions focused on primary care and prevention for patients with diabetes.