Novel strategies to FIND people living with genetic dyslipidemias: The family heart foundation flag, identify, network, and deliver (FIND) familial hypercholesterolemia collaborative learning network.

Background: Familial Hypercholesterolemia (FH) is among the most common genetic disorders. However, most people with FH are undiagnosed and many experience preventable premature cardiovascular disease. To improve identification of FH, the Family Heart Foundation established the Flag Identify Network Deliver™ Collaborative Learning Network (FIND FH™ CLN). This multi-year quality improvement initiative involves five healthcare systems, individuals with FH, and quality improvement/implementation scientists. This manuscript describes the methods and results of the FIND FH CLN. Methods: The FIND FH CLN leveraged a machine learning model (MLM) run on de-identified data from each healthcare system, coupled with implementation/quality improvement methods to enhance FH diagnosis. Healthcare systems were supported in identifying care gaps, engaging patients in diagnostic assessment, locating improvement opportunities, and implementing feasible interventions. Tracked outcomes included outreach volume, completed appointments, and new diagnoses of FH. Improvement approaches, care process changes, and challenges/lessons learned were recorded. Results: Across sites, 4476 individuals were flagged by the MLM; 847 patients were contacted following output review, 209 appointments were completed, and 175 diagnoses of definite, probable, or possible FH resulted. Two sites completed outreach to all patients deemed appropriate; three sites are still engaged in outreach. FH identification was facilitated by educational activities delivered to clinical teams, development of electronic health system-based features, and availability of web-based information targeting clinicians and patients. Conclusion: This multifaceted initiative provides insights and methods that can inform efforts to accelerate identification and improve care of individuals with FH at other institutions as well as other under-diagnosed conditions.