SART is the primary organization of professionals dedicated to the practice of IVF, or assisted reproductive technology (ART). The organization represents the majority of the ART clinics in the country. The mission of SART is to establish and maintain standards for ART so that you receive the highest possible level of care.
SART reports birth outcome data from its member clinics. This provides reliable information for patients to make informed decisions and understand the likelihood of success with different treatment options.
A note from SART: “It is understandable that patients would like to use SART clinic outcome reports both as a “report card” to judge quality of care and as a predictor of chance of success for each individual patient. Currently, the SART clinic summary reports cannot be used without context for either purpose.
Individual patients within any age range may have more “severe” or less “severe” infertility issues. These diagnosed factors will significantly impact the likelihood of success with ART treatments. Some well-intentioned physicians might strongly discourage or deny care to patients with a predicted low chance of pregnancy. Other physicians may feel ethically obligated to provide ART services to these same well informed “poor prognosis” patients. For this reason, the clinic summary reports are best used as a foundation to discuss the chance of success with your physician. Your SART member physician is in the best position to assess the diagnosed infertility factors and estimate your success in the context of your particular factors and the prior experience of the clinic.
Despite the limitations of the current clinic summary reports, it is our goal to collect and analyze information that might better help predict an individual patient’s chance of success within a clinic. We have developed a “patient predictor” that uses the data from over 2 million cycles collected nationally. This predictor does not take into account possible differences in program quality. Our goal is to collect additional data and develop mathematical models to calculate a “severity index” that can better reflect the differing patient population treated by different clinics.”