of the US population is obese.
estimated annual cost of treating obesity and obesity-related related illnesses in the US.
Rimidi's Approach to Obesity
Like Rimidi’s other condition-specific views, Rimidi’s view for obesity aggregates pertinent information from the EHR that impacts clinical decision-making in management of obese patients. In addition, the obesity view alerts clinicians to opportunities to address risk factors associated with obesity, ensures appropriate diagnostic coding and risk-scoring, and identifies gaps in prescribing standards.
Aggregate Pertinent EHR Data
Rimidi aggregates data from the EHR that reflects assessment of obesity as a chronic disease, including biometrics like BMI, current medications, and screening for secondary causes of obesity. This ensures the patient gets guideline-based care, and enables clinicians to easily identify potential gaps in care.
Identify High Risk Patients
Based on the aggregated data, Rimidi presents the patient’s atherosclerotic-cardiovascular-disease (ASCVD) risk score so clinicians can easily see their 10-year risk for heart disease or stroke, and alerts clinicians to comorbidities associated with obesity (e.g. type 2 diabetes, hypertension, sleep apnea, PCOS, non-alcoholic fatty liver disease, and depression.)
Remotely Monitor High Risk Patients
If a patient is higher risk, clinicians may also choose to remotely monitor their progress through the Rimidi platform. Whether it’s daily weights, blood pressure, or glucose levels--Rimidi pulls patient-generated data from connected devices into the aggregated EHR data view so the care team can see how the patient is doing the 360 days a year they are not at the doctor’s office.
Intervene with Guideline-Based Management
Rimidi embeds clinical decision support cards within the clinician’s workflow. For example, based on a patient's data, the cards may--consistent with guidelines--recommend the next best step in care, such as weight management and lifestyle education or anti-obesity medication.