Clinical trial NCT05277116: The Electronic Medical Records and GEnomics (eMERGE) Network Genomic Risk Assessment
Official title: The Electronic Medical Records and GEnomics (eMERGE) Network Genomic Risk Assessment
Study type: INTERVENTIONAL
Overall status: ACTIVE_NOT_RECRUITING
Phase: NA
Conditions: Genetic Disease
Keywords: Genomic Risk
Genomic Medicine
GIRA
Genome Informed Risk Assessment
Risk Report
Lead sponsor: Vanderbilt University Medical Center
Sponsor class: OTHER
First posted: 2022-03-14
Enrollment: 26877
Sex: ALL
Minimum age (years): 3
Maximum age (years): 75
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: All participants will receive a genome informed risk assessment (GIRA) report. Participants designated as high risk based on their genomic risks will be compared to those without such risks for each condition.
Primary purpose: OTHER

Brief summary: The eMERGE Network embraces the opportunity to use new methods in genomic medicine, information science, and research participant engagement to identify people at very high risk for specific diseases and recommend individualized approaches to prevention and care. The investigators will conduct a prospective study, with diverse and underserved participants, across ten eMERGE study sites to evaluate clinical implementation of a Genome Informed Risk Assessment (GIRA) tool that combines genetic, family history, and clinical risk information from participants.

Detailed description: The purpose of the study is to determine if providing a Genome Informed Risk Assessment (GIRA) will impact clinical actions taken by providers and patients to manage disease risk and the propensity of participants to develop a disease reported in the GIRA. New tools in Genomic Medicine - polygenic risk scores, monogenic genetic screening tests, platforms to capture family history, and advanced electronic phenotyping - offer the prospect of early identification of people at especially high risk of common diseases. The investigators developed methods to generate integrated genomic risk assessments for ten conditions; a plan to engage, recruit, and retain \~25,000 subjects to receive these assessments; and methods to study outcomes in those designated high risk and those designated non-high risk. By enhancing understanding of new methods to create and deliver integrated genomic risk assessments, this project will enable prevention and early treatment of people at high risk for common diseases.

Eligibility criteria: Inclusion Criteria:

* Adults 18-75
* Children 3 to \< 18
* Able to read or understand English or Spanish
* Able to provide a healthcare provider or clinician to receive results
* Willing to accept GIRA report

Exclusion Criteria:

* Inability to provide consent
* Transplant (solid organ or bone marrow) or transfusion within 8 weeks
* Research staff and investigators in eMERGE
* Not a patient at parent institution

Primary outcomes:
measure: The number of new health care actions after return of the genome-informed risk assessment
timeFrame: Baseline to 6 months post return of results to participant
Secondary outcomes:
measure: Number of newly diagnosed conditions after return of the genome-informed risk assessment
timeFrame: 6 months and 12 months post return of results to participant
measure: Number of risk-reducing interventions after return of the genome-informed risk assessment
timeFrame: 6 months and 12 months post return of results to participant
Other outcomes:
Locations:
city: Birmingham
country: United States
facility: University of Alabama Birmingham
state: Alabama
city: Tempe
country: United States
facility: Mountain Park Health Center
state: Arizona
city: Chicago
country: United States
facility: Northwestern
state: Illinois
city: Boston
country: United States
facility: Massachusetts General Hospital
state: Massachusetts
city: Boston
country: United States
facility: Brigham and Women's Hospital
state: Massachusetts
city: Rochester
country: United States
facility: Mayo Clinic
state: Minnesota
city: New York
country: United States
facility: Mount Sinai
state: New York
city: New York
country: United States
facility: Columbia University
state: New York
city: Cincinnati
country: United States
facility: Cincinnati Children's Hospital Medical Center
state: Ohio
city: Philadelphia
country: United States
facility: Children's Hospital of Philadelphia
state: Pennsylvania
city: Nashville
country: United States
facility: Vanderbilt University Medical Center
state: Tennessee
city: Seattle
country: United States
facility: University of Washington
state: Washington
Study officials:
affiliation: Vanderbilt University Medical Center
name: Josh Peterson, MD, MPH
role: PRINCIPAL_INVESTIGATOR
References:
citation: Roden DM, Pulley JM, Basford MA, Bernard GR, Clayton EW, Balser JR, Masys DR. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin Pharmacol Ther. 2008 Sep;84(3):362-9. doi: 10.1038/clpt.2008.89. Epub 2008 May 21.
citation: Pulley J, Clayton E, Bernard GR, Roden DM, Masys DR. Principles of human subjects protections applied in an opt-out, de-identified biobank. Clin Transl Sci. 2010 Feb;3(1):42-8. doi: 10.1111/j.1752-8062.2010.00175.x.
citation: Bowton E, Field JR, Wang S, Schildcrout JS, Van Driest SL, Delaney JT, Cowan J, Weeke P, Mosley JD, Wells QS, Karnes JH, Shaffer C, Peterson JF, Denny JC, Roden DM, Pulley JM. Biobanks and electronic medical records: enabling cost-effective research. Sci Transl Med. 2014 Apr 30;6(234):234cm3. doi: 10.1126/scitranslmed.3008604.
citation: Pulley JM, Brace MM, Bernard GR, Masys DR. Attitudes and perceptions of patients towards methods of establishing a DNA biobank. Cell Tissue Bank. 2008 Mar;9(1):55-65. doi: 10.1007/s10561-007-9051-2. Epub 2007 Oct 25.
citation: Rosenbloom ST, Madison JL, Brothers KB, Bowton EA, Clayton EW, Malin BA, Roden DM, Pulley J. Ethical and practical challenges to studying patients who opt out of large-scale biorepository research. J Am Med Inform Assoc. 2013 Dec;20(e2):e221-5. doi: 10.1136/amiajnl-2013-001937. Epub 2013 Jul 25.
citation: Bowton EA, Collier SP, Wang X, Sutcliffe CB, Van Driest SL, Couch LJ, Herrera M, Jerome RN, Slebos RJ, Alborn WE, Liebler DC, McNaughton CD, Mernaugh RL, Wells QS, Brown NJ, Roden DM, Pulley JM. Phenotype-Driven Plasma Biobanking Strategies and Methods. J Pers Med. 2015 May 14;5(2):140-52. doi: 10.3390/jpm5020140.
citation: Brothers KB, Westbrook MJ, Wright MF, Myers JA, Morrison DR, Madison JL, Pulley JM, Clayton EW. Patient awareness and approval for an opt-out genomic biorepository. Per Med. 2013 Jun;10(4):10.2217/pme.13.34. doi: 10.2217/pme.13.34.
citation: Pulley JM, Denny JC, Peterson JF, Bernard GR, Vnencak-Jones CL, Ramirez AH, Delaney JT, Bowton E, Brothers K, Johnson K, Crawford DC, Schildcrout J, Masys DR, Dilks HH, Wilke RA, Clayton EW, Shultz E, Laposata M, McPherson J, Jirjis JN, Roden DM. Operational implementation of prospective genotyping for personalized medicine: the design of the Vanderbilt PREDICT project. Clin Pharmacol Ther. 2012 Jul;92(1):87-95. doi: 10.1038/clpt.2011.371. Epub 2012 May 16.
citation: Delaney JT, Ramirez AH, Bowton E, Pulley JM, Basford MA, Schildcrout JS, Shi Y, Zink R, Oetjens M, Xu H, Cleator JH, Jahangir E, Ritchie MD, Masys DR, Roden DM, Crawford DC, Denny JC. Predicting clopidogrel response using DNA samples linked to an electronic health record. Clin Pharmacol Ther. 2012 Feb;91(2):257-63. doi: 10.1038/clpt.2011.221. Epub 2011 Dec 21.
citation: Karnes JH, Van Driest S, Bowton EA, Weeke PE, Mosley JD, Peterson JF, Denny JC, Roden DM. Using systems approaches to address challenges for clinical implementation of pharmacogenomics. Wiley Interdiscip Rev Syst Biol Med. 2014 Mar-Apr;6(2):125-35. doi: 10.1002/wsbm.1255. Epub 2013 Dec 6.
citation: Peterson JF, Bowton E, Field JR, Beller M, Mitchell J, Schildcrout J, Gregg W, Johnson K, Jirjis JN, Roden DM, Pulley JM, Denny JC. Electronic health record design and implementation for pharmacogenomics: a local perspective. Genet Med. 2013 Oct;15(10):833-41. doi: 10.1038/gim.2013.109. Epub 2013 Sep 5.
citation: Schildcrout JS, Denny JC, Bowton E, Gregg W, Pulley JM, Basford MA, Cowan JD, Xu H, Ramirez AH, Crawford DC, Ritchie MD, Peterson JF, Masys DR, Wilke RA, Roden DM. Optimizing drug outcomes through pharmacogenetics: a case for preemptive genotyping. Clin Pharmacol Ther. 2012 Aug;92(2):235-42. doi: 10.1038/clpt.2012.66. Epub 2012 Jun 27.
citation: Van Driest SL, Shi Y, Bowton EA, Schildcrout JS, Peterson JF, Pulley J, Denny JC, Roden DM. Clinically actionable genotypes among 10,000 patients with preemptive pharmacogenomic testing. Clin Pharmacol Ther. 2014 Apr;95(4):423-31. doi: 10.1038/clpt.2013.229. Epub 2013 Nov 19.
citation: Harris PA, Scott KW, Lebo L, Hassan N, Lightner C, Pulley J. ResearchMatch: a national registry to recruit volunteers for clinical research. Acad Med. 2012 Jan;87(1):66-73. doi: 10.1097/ACM.0b013e31823ab7d2.
citation: Danciu I, Cowan JD, Basford M, Wang X, Saip A, Osgood S, Shirey-Rice J, Kirby J, Harris PA. Secondary use of clinical data: the Vanderbilt approach. J Biomed Inform. 2014 Dec;52:28-35. doi: 10.1016/j.jbi.2014.02.003. Epub 2014 Feb 14.
citation: Rosenbloom ST, Harris P, Pulley J, Basford M, Grant J, DuBuisson A, Rothman RL. The Mid-South clinical Data Research Network. J Am Med Inform Assoc. 2014 Jul-Aug;21(4):627-32. doi: 10.1136/amiajnl-2014-002745. Epub 2014 May 12.
citation: Byrne DW, Biaggioni I, Bernard GR, Helmer TT, Boone LR, Pulley JM, Edwards T, Dittus RS. Clinical and translational research studios: a multidisciplinary internal support program. Acad Med. 2012 Aug;87(8):1052-9. doi: 10.1097/ACM.0b013e31825d29d4.
citation: Bernard GR, Harris PA, Pulley JM, Benjamin DK, Michael J, Ford DE, Hanley DF, Selker HP, Wilkins CH. A collaborative, academic approach to optimizing the national clinical research infrastructure: The first year of the Trial Innovation Network. J Clin Transl Sci. 2018 Aug;2(4):187-192. doi: 10.1017/cts.2018.319. Epub 2018 Nov 27.
citation: Joosten YA, Israel TL, Williams NA, Boone LR, Schlundt DG, Mouton CP, Dittus RS, Bernard GR, Wilkins CH. Community Engagement Studios: A Structured Approach to Obtaining Meaningful Input From Stakeholders to Inform Research. Acad Med. 2015 Dec;90(12):1646-50. doi: 10.1097/ACM.0000000000000794.
citation: Pulley JM, Harris PA, Yarbrough T, Swafford J, Edwards T, Bernard GR. An informatics-based tool to assist researchers in initiating research at an academic medical center: Vanderbilt Customized Action Plan. Acad Med. 2010 Jan;85(1):164-8. doi: 10.1097/ACM.0b013e3181c481bf.
citation: Kirby JC, Speltz P, Rasmussen LV, Basford M, Gottesman O, Peissig PL, Pacheco JA, Tromp G, Pathak J, Carrell DS, Ellis SB, Lingren T, Thompson WK, Savova G, Haines J, Roden DM, Harris PA, Denny JC. PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability. J Am Med Inform Assoc. 2016 Nov;23(6):1046-1052. doi: 10.1093/jamia/ocv202. Epub 2016 Mar 28.
citation: Mo H, Thompson WK, Rasmussen LV, Pacheco JA, Jiang G, Kiefer R, Zhu Q, Xu J, Montague E, Carrell DS, Lingren T, Mentch FD, Ni Y, Wehbe FH, Peissig PL, Tromp G, Larson EB, Chute CG, Pathak J, Denny JC, Speltz P, Kho AN, Jarvik GP, Bejan CA, Williams MS, Borthwick K, Kitchner TE, Roden DM, Harris PA. Desiderata for computable representations of electronic health records-driven phenotype algorithms. J Am Med Inform Assoc. 2015 Nov;22(6):1220-30. doi: 10.1093/jamia/ocv112. Epub 2015 Sep 5.
citation: Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007 Sep;81(3):559-75. doi: 10.1086/519795. Epub 2007 Jul 25.
citation: Goldstein JI, Crenshaw A, Carey J, Grant GB, Maguire J, Fromer M, O'Dushlaine C, Moran JL, Chambert K, Stevens C; Swedish Schizophrenia Consortium; ARRA Autism Sequencing Consortium; Sklar P, Hultman CM, Purcell S, McCarroll SA, Sullivan PF, Daly MJ, Neale BM. zCall: a rare variant caller for array-based genotyping: genetics and population analysis. Bioinformatics. 2012 Oct 1;28(19):2543-5. doi: 10.1093/bioinformatics/bts479. Epub 2012 Jul 27.
citation: Korn JM, Kuruvilla FG, McCarroll SA, Wysoker A, Nemesh J, Cawley S, Hubbell E, Veitch J, Collins PJ, Darvishi K, Lee C, Nizzari MM, Gabriel SB, Purcell S, Daly MJ, Altshuler D. Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs. Nat Genet. 2008 Oct;40(10):1253-60. doi: 10.1038/ng.237. Epub 2008 Sep 7.
citation: Wilkins CH, Mapes BM, Jerome RN, Villalta-Gil V, Pulley JM, Harris PA. Understanding What Information Is Valued By Research Participants, And Why. Health Aff (Millwood). 2019 Mar;38(3):399-407. doi: 10.1377/hlthaff.2018.05046.
citation: Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, Natarajan P, Lander ES, Lubitz SA, Ellinor PT, Kathiresan S. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018 Sep;50(9):1219-1224. doi: 10.1038/s41588-018-0183-z. Epub 2018 Aug 13.
citation: Arnett DK, Blumenthal RS, Albert MA, Buroker AB, Goldberger ZD, Hahn EJ, Himmelfarb CD, Khera A, Lloyd-Jones D, McEvoy JW, Michos ED, Miedema MD, Munoz D, Smith SC Jr, Virani SS, Williams KA Sr, Yeboah J, Ziaeian B. 2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol. 2019 Sep 10;74(10):1376-1414. doi: 10.1016/j.jacc.2019.03.009. Epub 2019 Mar 17.
citation: Kanis JA, Johnell O, Oden A, Johansson H, McCloskey E. FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos Int. 2008 Apr;19(4):385-97. doi: 10.1007/s00198-007-0543-5. Epub 2008 Feb 22.
citation: Khera AV, Emdin CA, Drake I, Natarajan P, Bick AG, Cook NR, Chasman DI, Baber U, Mehran R, Rader DJ, Fuster V, Boerwinkle E, Melander O, Orho-Melander M, Ridker PM, Kathiresan S. Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease. N Engl J Med. 2016 Dec 15;375(24):2349-2358. doi: 10.1056/NEJMoa1605086. Epub 2016 Nov 13.
citation: Mega JL, Stitziel NO, Smith JG, Chasman DI, Caulfield M, Devlin JJ, Nordio F, Hyde C, Cannon CP, Sacks F, Poulter N, Sever P, Ridker PM, Braunwald E, Melander O, Kathiresan S, Sabatine MS. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet. 2015 Jun 6;385(9984):2264-2271. doi: 10.1016/S0140-6736(14)61730-X. Epub 2015 Mar 4.
citation: Mostafavi H, Harpak A, Agarwal I, Conley D, Pritchard JK, Przeworski M. Variable prediction accuracy of polygenic scores within an ancestry group. Elife. 2020 Jan 30;9:e48376. doi: 10.7554/eLife.48376.
citation: Khera AV, Chaffin M, Zekavat SM, Collins RL, Roselli C, Natarajan P, Lichtman JH, D'Onofrio G, Mattera J, Dreyer R, Spertus JA, Taylor KD, Psaty BM, Rich SS, Post W, Gupta N, Gabriel S, Lander E, Ida Chen YD, Talkowski ME, Rotter JI, Krumholz HM, Kathiresan S. Whole-Genome Sequencing to Characterize Monogenic and Polygenic Contributions in Patients Hospitalized With Early-Onset Myocardial Infarction. Circulation. 2019 Mar 26;139(13):1593-1602. doi: 10.1161/CIRCULATIONAHA.118.035658.
citation: Weigl K, Chang-Claude J, Knebel P, Hsu L, Hoffmeister M, Brenner H. Strongly enhanced colorectal cancer risk stratification by combining family history and genetic risk score. Clin Epidemiol. 2018 Jan 19;10:143-152. doi: 10.2147/CLEP.S145636. eCollection 2018.
citation: Murray MF, Giovanni MA, Klinger E, George E, Marinacci L, Getty G, Brawarsky P, Rocha B, Orav EJ, Bates DW, Haas JS. Comparing electronic health record portals to obtain patient-entered family health history in primary care. J Gen Intern Med. 2013 Dec;28(12):1558-64. doi: 10.1007/s11606-013-2442-0. Epub 2013 Apr 16.
citation: Guttmacher AE, Collins FS, Carmona RH. The family history--more important than ever. N Engl J Med. 2004 Nov 25;351(22):2333-6. doi: 10.1056/NEJMsb042979. No abstract available.
citation: Krist AH, Beasley JW, Crosson JC, Kibbe DC, Klinkman MS, Lehmann CU, Fox CH, Mitchell JM, Mold JW, Pace WD, Peterson KA, Phillips RL, Post R, Puro J, Raddock M, Simkus R, Waldren SE. Electronic health record functionality needed to better support primary care. J Am Med Inform Assoc. 2014 Sep-Oct;21(5):764-71. doi: 10.1136/amiajnl-2013-002229. Epub 2014 Jan 15.
citation: Hulse NC, Ranade-Kharkar P, Post H, Wood GM, Williams MS, Haug PJ. Development and early usage patterns of a consumer-facing family health history tool. AMIA Annu Symp Proc. 2011;2011:578-87. Epub 2011 Oct 22.
citation: Denny JC, Crawford DC, Ritchie MD, Bielinski SJ, Basford MA, Bradford Y, Chai HS, Bastarache L, Zuvich R, Peissig P, Carrell D, Ramirez AH, Pathak J, Wilke RA, Rasmussen L, Wang X, Pacheco JA, Kho AN, Hayes MG, Weston N, Matsumoto M, Kopp PA, Newton KM, Jarvik GP, Li R, Manolio TA, Kullo IJ, Chute CG, Chisholm RL, Larson EB, McCarty CA, Masys DR, Roden DM, de Andrade M. Variants near FOXE1 are associated with hypothyroidism and other thyroid conditions: using electronic medical records for genome- and phenome-wide studies. Am J Hum Genet. 2011 Oct 7;89(4):529-42. doi: 10.1016/j.ajhg.2011.09.008.
citation: Denny JC, Ritchie MD, Basford MA, Pulley JM, Bastarache L, Brown-Gentry K, Wang D, Masys DR, Roden DM, Crawford DC. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics. 2010 May 1;26(9):1205-10. doi: 10.1093/bioinformatics/btq126. Epub 2010 Mar 24.
citation: Denny JC, Bastarache L, Ritchie MD, Carroll RJ, Zink R, Mosley JD, Field JR, Pulley JM, Ramirez AH, Bowton E, Basford MA, Carrell DS, Peissig PL, Kho AN, Pacheco JA, Rasmussen LV, Crosslin DR, Crane PK, Pathak J, Bielinski SJ, Pendergrass SA, Xu H, Hindorff LA, Li R, Manolio TA, Chute CG, Chisholm RL, Larson EB, Jarvik GP, Brilliant MH, McCarty CA, Kullo IJ, Haines JL, Crawford DC, Masys DR, Roden DM. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol. 2013 Dec;31(12):1102-10. doi: 10.1038/nbt.2749.
citation: Ritchie MD, Denny JC, Zuvich RL, Crawford DC, Schildcrout JS, Bastarache L, Ramirez AH, Mosley JD, Pulley JM, Basford MA, Bradford Y, Rasmussen LV, Pathak J, Chute CG, Kullo IJ, McCarty CA, Chisholm RL, Kho AN, Carlson CS, Larson EB, Jarvik GP, Sotoodehnia N; Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) QRS Group; Manolio TA, Li R, Masys DR, Haines JL, Roden DM. Genome- and phenome-wide analyses of cardiac conduction identifies markers of arrhythmia risk. Circulation. 2013 Apr 2;127(13):1377-85. doi: 10.1161/CIRCULATIONAHA.112.000604. Epub 2013 Mar 5.
citation: Simonti CN, Vernot B, Bastarache L, Bottinger E, Carrell DS, Chisholm RL, Crosslin DR, Hebbring SJ, Jarvik GP, Kullo IJ, Li R, Pathak J, Ritchie MD, Roden DM, Verma SS, Tromp G, Prato JD, Bush WS, Akey JM, Denny JC, Capra JA. The phenotypic legacy of admixture between modern humans and Neandertals. Science. 2016 Feb 12;351(6274):737-41. doi: 10.1126/science.aad2149.
citation: Rasmussen-Torvik LJ, Stallings SC, Gordon AS, Almoguera B, Basford MA, Bielinski SJ, Brautbar A, Brilliant MH, Carrell DS, Connolly JJ, Crosslin DR, Doheny KF, Gallego CJ, Gottesman O, Kim DS, Leppig KA, Li R, Lin S, Manzi S, Mejia AR, Pacheco JA, Pan V, Pathak J, Perry CL, Peterson JF, Prows CA, Ralston J, Rasmussen LV, Ritchie MD, Sadhasivam S, Scott SA, Smith M, Vega A, Vinks AA, Volpi S, Wolf WA, Bottinger E, Chisholm RL, Chute CG, Haines JL, Harley JB, Keating B, Holm IA, Kullo IJ, Jarvik GP, Larson EB, Manolio T, McCarty CA, Nickerson DA, Scherer SE, Williams MS, Roden DM, Denny JC. Design and anticipated outcomes of the eMERGE-PGx project: a multicenter pilot for preemptive pharmacogenomics in electronic health record systems. Clin Pharmacol Ther. 2014 Oct;96(4):482-9. doi: 10.1038/clpt.2014.137. Epub 2014 Jun 24.
citation: Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, McLeod L, Delacqua G, Delacqua F, Kirby J, Duda SN; REDCap Consortium. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform. 2019 Jul;95:103208. doi: 10.1016/j.jbi.2019.103208. Epub 2019 May 9.
citation: Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009 Apr;42(2):377-81. doi: 10.1016/j.jbi.2008.08.010. Epub 2008 Sep 30.
citation: Wu RR, Himmel TL, Buchanan AH, Powell KP, Hauser ER, Ginsburg GS, Henrich VC, Orlando LA. Quality of family history collection with use of a patient facing family history assessment tool. BMC Fam Pract. 2014 Feb 13;15:31. doi: 10.1186/1471-2296-15-31.
citation: Beadles CA, Ryanne Wu R, Himmel T, Buchanan AH, Powell KP, Hauser E, Henrich VC, Ginsburg GS, Orlando LA. Providing patient education: impact on quantity and quality of family health history collection. Fam Cancer. 2014 Jun;13(2):325-32. doi: 10.1007/s10689-014-9701-z.
citation: Wu RR, Orlando LA, Himmel TL, Buchanan AH, Powell KP, Hauser ER, Agbaje AB, Henrich VC, Ginsburg GS. Patient and primary care provider experience using a family health history collection, risk stratification, and clinical decision support tool: a type 2 hybrid controlled implementation-effectiveness trial. BMC Fam Pract. 2013 Aug 6;14:111. doi: 10.1186/1471-2296-14-111.
citation: O'Neill SM, Rubinstein WS, Wang C, Yoon PW, Acheson LS, Rothrock N, Starzyk EJ, Beaumont JL, Galliher JM, Ruffin MT 4th; Family Healthware Impact Trial group. Familial risk for common diseases in primary care: the Family Healthware Impact Trial. Am J Prev Med. 2009 Jun;36(6):506-14. doi: 10.1016/j.amepre.2009.03.002.
citation: Hripcsak G, Shang N, Peissig PL, Rasmussen LV, Liu C, Benoit B, Carroll RJ, Carrell DS, Denny JC, Dikilitas O, Gainer VS, Howell KM, Klann JG, Kullo IJ, Lingren T, Mentch FD, Murphy SN, Natarajan K, Pacheco JA, Wei WQ, Wiley K, Weng C. Facilitating phenotype transfer using a common data model. J Biomed Inform. 2019 Aug;96:103253. doi: 10.1016/j.jbi.2019.103253. Epub 2019 Jul 17.
citation: Nguyen XT, Quaden RM, Song RJ, Ho YL, Honerlaw J, Whitbourne S, DuVall SL, Deen J, Pyarajan S, Moser J, Huang GD, Muralidhar S, Concato J, Tsao PS, O'Donnell CJ, Wilson PWF, Djousse L, Gagnon DR, Gaziano JM, Cho K. Baseline Characterization and Annual Trends of Body Mass Index for a Mega-Biobank Cohort of US Veterans 2011-2017. J Health Res Rev Dev Ctries. 2018;5(2):98-107.
citation: Li M, Scaiano M, El Emam K, Malin BA. Efficient Active Learning for Electronic Medical Record De-identification. AMIA Jt Summits Transl Sci Proc. 2019 May 6;2019:462-471. eCollection 2019.
citation: Liu Y, Wan Z, Xia W, Kantarcioglu M, Vorobeychik Y, Clayton EW, Kho A, Carrell D, Malin BA. Detecting the Presence of an Individual in Phenotypic Summary Data. AMIA Annu Symp Proc. 2018 Dec 5;2018:760-769. eCollection 2018.
citation: Xia W, Wan Z, Yin Z, Gaupp J, Liu Y, Clayton EW, Kantarcioglu M, Vorobeychik Y, Malin BA. It's all in the timing: calibrating temporal penalties for biomedical data sharing. J Am Med Inform Assoc. 2018 Jan 1;25(1):25-31. doi: 10.1093/jamia/ocx101.
citation: Prasser F, Gaupp J, Wan Z, Xia W, Vorobeychik Y, Kantarcioglu M, Kuhn K, Malin B. An Open Source Tool for Game Theoretic Health Data De-Identification. AMIA Annu Symp Proc. 2018 Apr 16;2017:1430-1439. eCollection 2017.
citation: Heatherly R, Rasmussen LV, Peissig PL, Pacheco JA, Harris P, Denny JC, Malin BA. A multi-institution evaluation of clinical profile anonymization. J Am Med Inform Assoc. 2016 Apr;23(e1):e131-7. doi: 10.1093/jamia/ocv154. Epub 2015 Nov 13.
citation: El Emam K, Rodgers S, Malin B. Anonymising and sharing individual patient data. BMJ. 2015 Mar 20;350:h1139. doi: 10.1136/bmj.h1139.
citation: Xia W, Heatherly R, Ding X, Li J, Malin BA. R-U policy frontiers for health data de-identification. J Am Med Inform Assoc. 2015 Sep;22(5):1029-41. doi: 10.1093/jamia/ocv004. Epub 2015 Apr 24.
citation: Wan Z, Vorobeychik Y, Xia W, Clayton EW, Kantarcioglu M, Ganta R, Heatherly R, Malin BA. A game theoretic framework for analyzing re-identification risk. PLoS One. 2015 Mar 25;10(3):e0120592. doi: 10.1371/journal.pone.0120592. eCollection 2015.
citation: Loh PR, Danecek P, Palamara PF, Fuchsberger C, A Reshef Y, K Finucane H, Schoenherr S, Forer L, McCarthy S, Abecasis GR, Durbin R, L Price A. Reference-based phasing using the Haplotype Reference Consortium panel. Nat Genet. 2016 Nov;48(11):1443-1448. doi: 10.1038/ng.3679. Epub 2016 Oct 3.
citation: Campion TR Jr, Sholle ET, Davila MA Jr. Generalizable Middleware to Support Use of REDCap Dynamic Data Pull for Integrating Clinical and Research Data. AMIA Jt Summits Transl Sci Proc. 2017 Jul 26;2017:76-81. eCollection 2017.
citation: Sperber NR, Carpenter JS, Cavallari LH, J Damschroder L, Cooper-DeHoff RM, Denny JC, Ginsburg GS, Guan Y, Horowitz CR, Levy KD, Levy MA, Madden EB, Matheny ME, Pollin TI, Pratt VM, Rosenman M, Voils CI, W Weitzel K, Wilke RA, Ryanne Wu R, Orlando LA. Challenges and strategies for implementing genomic services in diverse settings: experiences from the Implementing GeNomics In pracTicE (IGNITE) network. BMC Med Genomics. 2017 May 22;10(1):35. doi: 10.1186/s12920-017-0273-2.