The dissertation defense is open and will be held virtually.
Participants are requested to RSVP to receive the link to the presentation.
The PhD in Translational Health Sciences Program is proud to announce the Dissertation Defense of:
PhD Candidate: Ana Morales, MS, CGC
Development of a Guideline-Based Innovation to Support the Genetic Evaluation of Cardiomyopathy and Arrhythmia: An Exploratory Mixed Methods Study
Dissertation Committee Chair: Lisa Schwartz, EdD, MS, CGC
November 9th, 2026 at 9am ET
Background. Genetic cardiomyopathy and arrhythmia (CM/ARRH) affect 1 in 250 individuals and can present with heart failure or a lethal arrhythmia. CM/ARRH genetic testing Guidelines recommend medical and family history intake, genetic counseling, and genetic testing, yet only 1% of patients are tested. Reported barriers include a need for clear guidelines and efficient workflows supported by genetics professionals. Addressing these barriers requires identifying: 1) the minimum necessary medical and family history questions; 2) clinical actions for CM/ARRH patients with a positive genetic test result.
Methods. A three-phase exploratory mixed methods study was designed, consisting of qualitative interviews, a three-round Delphi survey, and a design thinking workshop integrating qualitative and quantitative data to co-design an innovation and implementation plan.
Results. Twenty-seven CM/ARRH experts described barriers and facilitators and provided input for the Delphi survey. A total of 33 clinicians reached consensus on 30% of medical history items, 50% of family history items, and 73% of genetics-informed management items. A multidisciplinary team of 10 participants co-designed an 11-element workflow to simplify the genetic testing process. Participants also suggested 12 implementation strategies and a 6-part supportive toolkit.
Conclusion. Integrating qualitative and quantitative data facilitated the co-creation of a prototype to address the underutilization of CM/ARRH genetic testing. The study gained deeper understanding of implementation needs through enhanced efficiency, standardized data collection, and genetics-informed management.
