Addressing the critical need for cancer genetic evaluation by implementation of an embedded coordinator-based genetics alternative delivery model: The Fast-Track Program.

Journal: Journal of genetic counseling

This publication describes the design and early outcomes of a “Fast-Track” coordinator-based model for delivering hereditary cancer germline testing within a comprehensive cancer genetics program.

Key points:

  • Rationale: Demand for germline testing has risen sharply, while the availability of genetic counselors is limited. The program aims to preserve quality while increasing throughput and reducing wait times.
  • Model structure:
  • • Referrals to the cancer genetics program are reviewed and triaged by genetic counselors.
  • • Patients who meet NCCN criteria for germline testing are assigned to the Fast-Track pathway.
  • • Patients receive standardized pre-test education via a program-specific video.
  • • Trained genetics clinical coordinators (non-GC staff) handle pre-test education, consent, and test coordination.
  • • Genetic counselors review all cases after the initial visit (whether or not testing is completed) and provide management recommendations.
  • • Patients with pathogenic/likely pathogenic variants or complex results have results disclosed directly by a genetic counselor; all others receive results from the coordinators under GC oversight.
  • Implementation and early outcomes (6/12/2023–3/29/2024):
  • • 415 patients were seen in the Fast-Track pathway over 9 months.
  • • 12.3% of those tested had a pathogenic or likely pathogenic germline variant.
  • • Time from referral to appointment was significantly reduced compared with the standard genetic counselor pathway (mean ~22 days vs ~90 days; p < 0.001).
  • Conclusions:
  • • A coordinator-based model, with genetic counselor oversight, can substantially shorten access time to germline testing while maintaining triage and oversight by genetics professionals.
  • • The program is expanding institutionally.
  • • Further work is needed to assess additional quality metrics, including patient satisfaction, to fully evaluate the model’s impact.

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