Obesity has been steadily increasing in prevalence and now affects more than 4 in 10 U.S. adults, leading to many adverse health outcomes including myocardial infarction, stroke, type 2 diabetes (T2DM), hypertension, sleep apnea, arthritis, and others. Effective surgical, pharmaceutical, and behavioral treatments for obesity are available, and the evidence to support the broad use of these treatments for obesity is very well established. However, active management of obesity defined as prescribing or referring adults with obesity for lifestyle, pharmaceutical, or surgical treatment of obesity, is greatly underused. Major underlying reasons for underutilization of effective obesity treatments include: (a) both patients and primary care clinicians (PCCs) frequently underestimate the effectiveness and potential benefits of obesity treatments; and (b) both patients and clinicians typically lack access to evidence-based, patient-specific estimates of the potential benefits and risks of appropriate patient-specific obesity treatment options.~To address this problem, the study team will integrate externally validated prediction equations that estimate benefits and risk of various obesity treatment options in adults with T2DM into a widely-used and successful clinical decision support system in order to deliver appropriate patient-specific obesity treatment suggestions at the point of care. The team will implement a scalable, web-based point-of-care decision-support intervention in a randomized trial in 40 primary care clinics with 15,810 eligible patients, and assess intervention impact on the following primary outcomes: (a) appropriate referral of eligible patients for evaluation for metabolic bariatric surgery (MBS); (b) appropriate initiation of FDA-approved medications for weight loss; (c) weight trajectories; and (d) patient-reported conversations with their PCC about weight loss and intentions to engage in weight loss. In addition, the study team collect and analyze clinician-reported and patient-reported data to identify factors that may impede or facilitate broad dissemination of this intervention strategy to other care delivery settings.~This innovative project will (a) provide state-of-the-art scientific evidence on obesity treatment to large numbers of obese American adults with T2DM and their PCCs at the point of care; (b) help PCCs identify appropriate patient-specific obesity treatment options; (c) implement in primary care a web-based electronic health record (EHR)-linked obesity treatment clinical decision support model that uses state-of-the-art Health Information Technology (HIT) standards, is broadly scalable, easy to update as evidence changes, and optimized for clear communication of information to patients and PCCs; and (d) improve the clinical return on ongoing massive private and public investments in outpatient health information systems.
Despite steady increases in obesity prevalence, the more than 12 million obese U.S. adults with type 2 diabetes (T2DM) and severe obesity encounter a number of barriers to adopting effective surgical and pharmaceutical treatments, including: (a) both patients and primary care clinicians frequently underestimate the effectiveness and potential benefits of obesity treatments; and (b) both patients and clinicians typically lack access to evidence-based estimates of the patient-specific potential benefits and risks of appropriate obesity treatment options. This project addresses these important obstacles to evidence-based obesity care by providing accurate, patient-specific estimates of benefits and risks of various obesity treatment options to inform shared decision making about obesity treatment.~In this project the study team will implement a scalable, web-based point-of-care decision-support intervention in primary care that provides patient-specific estimates of obesity treatment benefits and risks in a randomized trial in 40 primary care clinics with 15,810 eligible patients, and assess intervention impact on (i) appropriate active management of obesity in eligible patients, (ii) weight trajectories, and (iii) patient and clinician satisfaction with the decision support intervention.