From 1960 to 2020, the proportion of Americans with obesity (BMI ≥30 kg/m²) more than tripled from 13% to 41.9%, with severe obesity (BMI ≥40 kg/m²) increasing tenfold from 0.8% to 9.2% [1-6]. Obesity is associated with significant morbidity, including an increased risk for type 2 diabetes, hypertension, heart disease, cancer, and osteoarthritis [7]. These obesity-associated conditions are a leading cause of preventable, premature death in the United States, accounting for approximately 112,000 deaths per year [8]. Health care expenditures attributable to obesity prevalence and associated conditions in the United States have increased from US $70 billion in 1995 to US $173 billion in 2019 [9-11]. These trends highlight the urgent need to promote healthy weight across the US population, as surgical and behavioral interventions alone have not reversed the obesity epidemic.

In 2021, the US Food and Drug Administration (FDA) approved the glucagon-like peptide 1 (GLP-1) receptor agonist semaglutide and, in 2023, the GLP-1 and glucose-dependent insulinotropic polypeptide (GIP) receptor agonist tirzepatide. These and more recent antiobesity medication (AOM) approvals are ushering in a radical shift in the treatment of obesity [12,13] because they produce more weight loss than prior AOMs and are poised to become essential tools in population weight management. In clinical trials, patients receiving a once-weekly semaglutide injection of 2.4 mg experienced an average weight loss of 15% within 1 year, with 77% of patients achieving clinically significant weight loss of greater than or equal to 5% [12,14]. Notably, trial patients receiving daily tirzepatide 15 mg achieved a weight loss of 21% within 1 year, with 91% of patients achieving greater than or equal to 5% weight loss [15]. Table 1 presents a concise overview of the effectiveness, safety, and costs associated with FDA-approved AOMs based on recent systematic reviews [13,16-18].

Despite their clinical benefits, we still lack a clear understanding of how to implement AOMs equitably and effectively in everyday practice, particularly given high costs and access constraints. The current environment creates critical disparities in access to AOMs for lower-income and underinsured patients, underscoring the need for timely real-world evidence on patterns and disparities in AOM use. Further, the high unit cost of these injectable medications, recent (December 2025) FDA approval of oral semaglutide, the large pipeline of new AOMs, and new federal pricing policy create uncertainty in a dynamic environment that patients, providers, and payers are finding challenging to navigate. Our study aims to address evidence gaps by identifying best practices, barriers, and strategies that address barriers to AOM use at multiple levels, including patient, provider, health system, and payer.

This work will provide a comprehensive characterization of the patterns and predictors of AOM use in 3 large, diverse health systems in the southeastern United States, generating real-world evidence on patient exposure to different AOMs as measured by provider prescribing practices (aim 1). Despite the upward trends in AOM initiation, prior evidence suggests only 1% to 3% of all eligible patients used AOMs as of June 2023, due to a variety of factors, including variable or limited insurance coverage, high out-of-pocket costs, patient and provider concerns about AOM safety, lack of confidence in the long-term efficacy of AOMs, insufficient knowledge of newer AOMs, and time constraints during health care visits [20-33]. These barriers to AOM uptake are described in the literature and outlined in Table 2. Long-standing stigma toward patients with obesity likely plays a role as well [34-39]. Interventions to address these critical barriers to AOM uptake are urgently needed to integrate AOMs more fully into clinical practice, as a complement to behavioral interventions and metabolic/bariatric surgery.

While these barriers appear well characterized, most of this evidence is based on older studies that predate the introduction of the incretin mimetics (GLP-1 and GIP), which appear to have significantly impacted public, provider, and payer perceptions of the utility of AOMs, as expressed in news reports [19,55]. In the DOMINO (Developing Robust Antiobesity Medication Implementation Approaches to Maximize Population Health) study, we will evaluate system-specific patterns of AOM access that span the incretin mimetic era to understand current facilitators and barriers to AOM provision (aim 1). We will then conduct interviews with patients, providers, health system leaders, and payers to understand their perspectives on access and affordability (aim 2). Aim 2 interviews and aim 1 results about patient access will inform the design of an AOM implementation road map (aim 3) for selecting and guiding implementation strategies that can be tailored to local barriers and facilitators to enable health systems to optimize patient access to AOMs. A detailed study timeline is provided in Multimedia Appendix 1.

The conceptual framework (Figure 1 [56,57]) informing this study is implementation mapping (IM) [58], a rigorous process that integrates theory, empirical evidence, and stakeholder input to develop strategies to enhance intervention adoption, implementation, and sustainability. The targeted evidence-based intervention is increasing appropriate AOM use. We will complete steps 1 through 4 in IM to develop implementation strategies, with plans to deploy and test them in a future project. In steps 1 and 2, we will use the tailored implementation for chronic diseases (TICD) framework [56], which is the most current consolidated determinants (barriers and facilitators to implementation success) framework in the implementation research literature.

The first step in IM is to identify stakeholders and participants in implementation by role and undertake a needs assessment. Aim 1 in our study will include identifying key stakeholders—patients eligible for AOM use, providers who prescribe AOMs, and relevant health system leaders. The second IM step (aim 2) involves identifying desired outcomes, such as more equitable AOM access, and their determinants intended to increase implementation success. In the third IM step (aims 2 and 3), we will identify implementation strategies expected to achieve desired outcomes, tailoring the strategies to health care organizational contexts based on input from key stakeholders to produce the AOM implementation road map (IM step 4). We show this in Figure 1.

The project is a continuation of long-term collaborations by a multidisciplinary team representing Duke University (MLM and VAS), Kaiser Permanente Washington (DEA, CH, and RM), the University of Missouri (AES), Vanderbilt University Medical Center (SBD), and Wake Forest (KHL).

A Stakeholder Advisory Board will be convened at study start and then annually to provide feedback on the detailed development of the work plan for each aim. This feedback is critical because the portfolio of AOMs and strategies of payers to address these changes are likely to be highly dynamic. For our work to be most responsive to these changes, we will seek guidance from the SAB to ensure that our planned work generates results that are most useful to these diverse stakeholders interviewed in aim 2. The SAB will be composed of volunteers from the patient, provider, health plan leadership, and payer groups.

We will conduct a longitudinal retrospective cohort study in aim 1 involving adult patients (aged ≥18 years) from these 3 health systems from 2014 to 2024 who were eligible for AOMs, corresponding to BMI greater than or equal to 30 kg/m² or BMI greater than or equal to 27 kg/m² with obesity-related comorbidity (eg, diabetes). We will identify all AOM prescriptions. The study period starts in 2014 to identify how characteristics of patients prescribed AOMs may be changing over time. We will include older adults (≥65 years) and younger adults (18-25 years), as these populations have not often been included in prior studies of AOMs. Inclusion criteria include having at least 1 height measurement and 2 or more weight measurements that correspond to the BMI criteria above. There will be no exclusion criteria because the goal of this study is to characterize real-world use of AOMs with the greatest possible generalizability. In the 3 sites, we expect to identify over 250,000 patients with obesity from 2014 to 2024 who are eligible for AOMs, of whom more than 35,000 will have been prescribed 1 or more AOMs in this period. The index date for all patients will be the date of the first eligible BMI recorded in each health system’s electronic health record (EHR) data in the study period (2014-2024). For those prescribed AOMs, a subsequent key date will be the order date for the AOM first observed.

Beginning in January 2014, the Patient-Centered Outcomes Research Institute funded 11 Clinical Data Research Networks (CDRNs, including the Science, Technology and Research Partnership (STAR) network) to develop the Patient-Centered Outcomes Research Network (PCORnet), an infrastructure to “create a large, highly representative, national network for conducting clinical outcomes research” [59]. During phase 1 of this work, these CDRNs created a common data model (CDM) that is consistent across each CDRN (Table 3). Unlike centralized data repositories, the CDM consists of parallel databases set up identically at each CDRN site that can be easily queried and merged across sites. Each CDRN has a different number of participating organizations or “nodes.” We will engage 3 STAR network nodes (Duke University Health System, Wake Forest Baptist Health, and Vanderbilt University Medical Center) to participate in aims 1 and 2.

We selected these sites based on population diversity, wide variability in AOM use, and completeness of CDM data tables. A data mart within each node in the STAR network has been constructed by extracting data from local EHR databases from participating organizations and reconfiguring those data into CDM data tables using standard variable names and coded values [60]. Using the CDM avoids most redundant preparatory work that would otherwise be needed to assemble cohorts or count potential events and other end points. Table 3 above describes the specific data elements in the CDM that will be used to accomplish each study aim. Table 4 also provides further detail on data analysis.

In preparation for grant funding and prior to submitting the grant application, we conducted a query through PCORnet to obtain aggregate data on the utilization of AOMs in these health systems (Table 5). There was significant variability in the number of AOM users, the number with a GLP-1 prescription, and race/ethnicity demographics.

Ethics approvals have been obtained from Duke University (single institutional review board [IRB] providing oversight for activities at Duke University, Vanderbilt University Medical Center, and Kaiser Permanente Washington; protocol number Pro00118475), the University of Missouri (deemed exempt from IRB oversight; protocol number 2118707), and Wake Forest University (deemed exempt; protocol number IRB00117747). We received a waiver of HIPAA (Health Insurance Portability and Accountability Act) authorization from the Duke IRB covering patient data in aim 1. All interviews are conducted as exempt under Exemption Category 2 of the Revised Common Rule, with information sheets distributed and verbal consent obtained prior to conducting the interview. Providers are compensated US $40 for an interview, patients are compensated US $75, and payers US $40. Provider and payer interviews are expected to last 30 minutes at most, while patient interviews may last up to 60 minutes. Interview transcripts are fully anonymized before analysis.

For aim 1, we will use data from 3 health systems participating in the multisite STAR network of the national PCORnet to understand the demographic, clinical, and provider characteristics of eligible patients that are associated with AOM receipt and greater duration of AOM use. Data from the PCORnet STAR network will facilitate investigation of real-world clinical practice patterns related to AOM use in 3 academic health systems with diverse populations located in the southeastern United States. These 3 academic health systems are Duke University Health System, Vanderbilt University Medical Center, and Wake Forest Baptist Health. All patient data are fully deidentified.

Funding began in 2024. Data extraction from the 3 health systems is complete as of April 2026, and data cleaning and preparation of analytic data sets are underway. As of August 2026, aim 2 has completed 40 patient interviews, 5 payer interviews, and 14 provider interviews. Aim 3, including the advisory board panels, is anticipated to begin in 2026.

We anticipated that start-up activities, including obtaining IRB approvals, data use agreements, and development of query code for aim 1 data pulls, would take 3 months to complete (original project timeline in Multimedia Appendix 1; note that the fifth year was cut from the project as part of the funding decision). Quarters 2-4 will be spent extracting and cleaning data from STAR network sites. Quarters 5-7 will be spent analyzing aim 1 results and writing aim 1 results and manuscripts. Quarters 8-10 will be spent conducting aim 2A, 2B, and 2C interviews with patients, providers, health system leaders, payers, and employers, respectively. Quarters 10-13 will be spent transcribing, analyzing, and writing up interview results, and writing aim 2 manuscripts and materials to be disseminated to stakeholders.

Quarters 14-18 will be spent presenting the materials to stakeholders and developing AOM use road map materials, reviewing with stakeholders, and iterating materials to generate their final versions. Quarters 18-20 will be spent writing aim 3 manuscripts, abstracts, and the final report. The SAB will be convened in quarter 1 to orient them to the proposed study and obtain suggested changes to the study plan. In addition, they will be convened in the third quarter of each year to review study progress and more detailed plans for subsequent work. The Duke team will convene coinvestigators and consultants for conference calls bimonthly. An experienced research project manager will schedule all project meetings and manage regulatory (IRB) issues.

The DOMINO study is designed to address an important implementation gap in the use of AOMs. We anticipate that this study will identify multilevel determinants of AOM use spanning patient, clinician, organizational, and health system contexts and will translate these findings into a practical implementation road map. Importantly, the study is not intended to establish the clinical efficacy of individual AOMs but to identify factors that influence whether evidence-based AOM treatment can be appropriately accessed, prescribed, initiated, and sustained in routine clinical care.

By integrating health system data with qualitative perspectives from patients, clinicians, and other relevant stakeholders, DOMINO is expected to provide a more comprehensive understanding of the barriers and facilitators influencing AOM use than could be obtained from either approach alone. The combination of real-world prescribing patterns and stakeholder perspectives may help distinguish gaps in AOM use that reflect patient preferences or circumstances from those arising from clinician practices, organizational workflows, medication access, or broader structural barriers. These findings will inform development of a timely implementation road map that is intended to be responsive to the realities of contemporary obesity care.

A particularly important anticipated contribution is the identification of opportunities to improve equity in AOM use. Access to obesity treatment may be influenced by medication cost, insurance coverage, availability of clinicians with obesity management expertise, geographic access to care, and differences in patient and clinician perceptions of obesity and its treatment. Examining these factors across diverse populations and health care settings may identify implementation strategies that can address modifiable barriers while recognizing that AOM use is shaped by factors extending beyond individual patient or clinician behavior.

Prior research has established the effectiveness of AOMs for weight reduction and, for several agents, improvements in cardiometabolic and other health outcomes [12-18]. However, evidence of clinical efficacy does not necessarily translate into equitable uptake in routine practice. Implementation of AOMs occurs within complex health care systems in which prescribing decisions may be influenced by medication availability, insurance coverage, cost, clinician knowledge and confidence, competing clinical priorities, organizational workflows, and patient preferences. Thus, understanding the gap between what is clinically possible and what occurs in practice requires an implementation-focused approach.

Existing studies have examined individual barriers to obesity treatment and AOM use, including stigma, treatment perceptions, affordability, insurance restrictions, and limited access to obesity-focused care [4,32-39,42-49]. However, these factors do not operate independently. A patient may be clinically eligible for an AOM but unable to obtain it because of insurance restrictions; alternatively, medication may be accessible but not offered because of clinician uncertainty, competing demands, or limitations in clinical workflows. The TICD framework provides a structure for examining these interacting determinants across multiple domains, while IM provides a systematic approach for translating identified determinants into implementation strategies [56,58]. By combining these approaches, DOMINO seeks to move beyond describing barriers toward identifying actionable opportunities for implementation.

The study also responds to the rapidly evolving nature of obesity pharmacotherapy. The AOM landscape continues to change as new formulations, indications, and therapeutic options become available. Consequently, implementation barriers identified for one medication or treatment era may not fully represent the barriers encountered as treatment options expand. Incorporating newly approved AOMs into stakeholder discussions as appropriate will allow the qualitative components of the study to remain responsive to changes in clinical practice and may help identify implementation needs that extend beyond any single medication.

First, the rapidly changing landscape of AOMs will likely result in the introduction of oral semaglutide and new AOMs during the course of the study. We will introduce any new AOMs into our conversations with stakeholders whenever they become approved by the FDA to help ensure that our findings are timely and relevant to future care delivery. Second, the 3 health systems from which we will extract data for aim 1 and use as sampling frames for aims 2 and 3 are all academic health systems affiliated with highly research-intensive universities. As a result, there are limits to the generalizability and applicability of our findings and implementation road map. However, there are numerous mitigating factors: all 3 health systems are in the southeastern United States with high prevalence of obesity, serving diverse populations, including high- and low-income, racial/ethnic, and culturally diverse populations. Our plans to recruit patients for aim 2A and stakeholders for aim 2C from sites outside these 3 systems will support greater diversity in the voices represented in this study.

The study team has developed a robust dissemination strategy based on prior experience with implementation and reporting of health services research. For our research to have an impact, stakeholders (clinicians, patients, and the research community) must be aware of the project from initiation through dissemination, which sets the stage for faster, widespread implementation of research results. To disseminate results to the clinician, health system, and payer communities that we engage in aim 2, we will develop and disseminate an evidence brief summarizing results. In addition, we will develop and disseminate a protocolized form of the AOM implementation road map in aim 3. To the extent possible given resources, we will develop 1 or more plain language summaries to share with interview participants and the public at large via advocacy groups. These may include audiovisual materials such as videos and podcasts narrated by patients and providers to support additional uptake.

Next steps include testing the effectiveness of at least 2 versions of the AOM implementation road map across multiple health systems using a cluster-randomized controlled trial design. These hybrid trial designs [63] were developed specifically to support more rapid implementation of promising clinical and system interventions, allowing generation of evidence for the intervention simultaneously with increasing levels of knowledge about implementation factors. Understanding the effectiveness and side effect profiles for the most common weight-loss medications for different patient populations will likely still be necessary as we conduct this further research, but given the rapid evolution of evidence for these medications, it may be feasible to conduct a hybrid type 3 study with primary outcomes focused on implementation.