title: Implementing family health history risk stratification in primary care: impact of guideline criteria on populations and resource demand.
abstract: The Genomic Medicine Model aims to facilitate patient engagement, patient/provider education of genomics/personalized medicine, and uptake of risk-stratified evidence-based prevention guidelines using MeTree, a patient-facing family health history (FHH) collection and clinical decision support (CDS) program. Here we report the number of increased risk (above population-level risk) patients identified for breast/ovarian cancer, colon cancer, hereditary syndrome risk, and thrombosis; the prevalence of FHH elements triggering increased-risk status; and the resources needed to manage their risk.
  
  
  METHODS
  hybrid implementation-effectiveness study of adults with upcoming well-visits in 2 primary care practices in Greensboro, NC.
  
  
  METHODS
  1,184, mean age = 58.8, female = 58% (N = 694), non-white = 20% (N = 215). Increased Risk: 44% (N = 523).
  
  
  CONCLUSIONS
  genetic counseling = 26% (N = 308), breast MRI = 0.8% (N = 10), breast chemoprophylaxis = 5% (N = 58), early/frequent colonoscopies = 19% (N = 221), ovarian cancer screening referral = 1% (N = 14), thrombosis testing/counseling = 2.4% (N = 71). FHH elements: 8 FHH elements lead to 37.3% of the increased risk categorizations (by frequency): first-degree-relative (FDR) with polyps age ≥60 (7.1%, N = 85), three relatives with Lynch-related cancers (5.4%, N = 65), FDR with polyps age <60 (5.1%, N = 61), three relatives on same side of family with same cancer (4.9%, N = 59), Gail score ≥1.66% (4.9%, N = 58), two relatives with breast cancer (one ≤age 50) (4.1%, N = 49), one relative with breast cancer ≤age 40 (4.1%, N = 48), FDR with colon cancer age ≥60 (1.7%, N = 20). MeTree identifies a high percentage of individuals in the general primary care population needing non-routine risk management/prevention for the selected conditions. Implementing risk-stratification in primary care will likely increase demand for related-resources, particularly colon screening and GC. Understanding the prevalence of FHH elements helps predict resource needs and may aid in guideline development.
authors.forename: Lori A
authors.initials: LA
authors.lastname: Orlando
authors.forename: R Ryanne
authors.initials: RR
authors.lastname: Wu
authors.forename: Chris
authors.initials: C
authors.lastname: Beadles
authors.forename: Tiffany
authors.initials: T
authors.lastname: Himmel
authors.forename: Adam H
authors.initials: AH
authors.lastname: Buchanan
authors.forename: Karen P
authors.initials: KP
authors.lastname: Powell
authors.forename: Elizabeth R
authors.initials: ER
authors.lastname: Hauser
authors.forename: Vincent C
authors.initials: VC
authors.lastname: Henrich
authors.forename: Geoffrey S
authors.initials: GS
authors.lastname: Ginsburg
mesh_terms: D000328:Adult; D003661:Decision Support Techniques; D005817:Genetic Counseling / Q000379:methods; D005826:Genetics, Medical / Q000379:methods* / Q000639:trends; D006801:Humans; D008487:Medical History Taking / Q000379:methods*; D009369:Neoplasms / Q000235:genetics; D009657:North Carolina; D057285:Precision Medicine / Q000379:methods* / Q000639:trends; D011320:Primary Health Care / Q000379:methods* / Q000639:trends; D018570:Risk Assessment / Q000379:methods* / Q000706:statistics & numerical data; D013927:Thrombosis / Q000235:genetics
keywords: family history; health services; primary prevention; risk stratification