1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Biomedical Data Science
  4. Volume 3, 2020
  5. Article

Annual Review of Biomedical Data Science

Volume 3, 2020

Biomedical Data Science and Informatics Challenges to Implementing Pharmacogenomics with Electronic Health Records

Abstract

Pharmacogenomic information must be incorporated into electronic health records (EHRs) with clinical decision support in order to fully realize its potential to improve drug therapy. Supported by various clinical knowledge resources, pharmacogenomic workflows have been implemented in several healthcare systems. Little standardization exists across these efforts, however, which limits scalability both within and across clinical sites. Limitations in information standards, knowledge management, and the capabilities of modern EHRs remain challenges for the widespread use of pharmacogenomics in the clinic, but ongoing efforts are addressing these challenges. Although much work remains to use pharmacogenomic information more effectively within clinical systems, the experiences of pioneering sites and lessons learned from those programs may be instructive for other clinical areas beyond genomics. We present a vision of what can be achieved as informatics and data science converge to enable further adoption of pharmacogenomics in the clinic.

Keyword(s): clinical decision support, electronic health records, pharmacogenomics, standards, transfusion
Loading

Article metrics loading...

/content/journals/10.1146/annurev-biodatasci-020320-093614
2020-07-20
2024-05-05
Loading full text...

Full text loading...

/deliver/fulltext/biodatasci/3/1/annurev-biodatasci-020320-093614.html?itemId=/content/journals/10.1146/annurev-biodatasci-020320-093614&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Relling MV, Evans WE. 2015. Pharmacogenomics in the clinic. Nature 526:7573343–50
     [Google Scholar]
  2. 2. 
    Volpi S, Bult CJ, Chisholm RL, Deverka PA, Ginsburg GS et al. 2018. Research directions in the clinical implementation of pharmacogenomics: an overview of US programs and projects. Clin. Pharmacol. Ther. 103:5778–86
     [Google Scholar]
  3. 3. 
    Levy KD, Blake K, Fletcher-Hoppe C, Franciosi J, Goto D et al. 2019. Opportunities to implement a sustainable genomic medicine program: lessons learned from the IGNITE Network. Genet. Med. 21:3743–47
     [Google Scholar]
  4. 4. 
    Schildcrout JS, Denny JC, Bowton E, Gregg W, Pulley JM et al. 2012. Optimizing drug outcomes through pharmacogenetics: a case for preemptive genotyping. Clin. Pharmacol. Ther. 92:2235–42
     [Google Scholar]
  5. 5. 
    Dunnenberger HM, Crews KR, Hoffman JM, Caudle KE, Broeckel U et al. 2015. Preemptive clinical pharmacogenetics implementation: current programs in five US medical centers. Annu. Rev. Pharmacol. Toxicol. 55:89–106
     [Google Scholar]
  6. 6. 
    Van Driest SL, Shi Y, Bowton EA, Schildcrout JS, Peterson JF et al. 2014. Clinically actionable genotypes among 10,000 patients with preemptive pharmacogenomic testing. Clin. Pharmacol. Ther. 95:4423–31
     [Google Scholar]
  7. 7. 
    Samwald M, Xu H, Blagec K, Empey PE, Malone DC et al. 2016. Incidence of exposure of patients in the United States to multiple drugs for which pharmacogenomic guidelines are available. PLOS ONE 11:10e0164972
     [Google Scholar]
  8. 8. 
    Bush WS, Crosslin DR, Owusu-Obeng A, Wallace J, Almoguera B et al. 2016. Genetic variation among 82 pharmacogenes: the PGRNseq data from the eMERGE network. Clin. Pharmacol. Ther. 100:2160–69
     [Google Scholar]
  9. 9. 
    Roden DM, Van Driest SL, Mosley JD, Wells QS, Robinson JR et al. 2018. Benefit of preemptive pharmacogenetic information on clinical outcome. Clin. Pharmacol. Ther. 103:5787–94
     [Google Scholar]
  10. 10. 
    Bousman CA, Zierhut H, Müller DJ 2019. Navigating the labyrinth of pharmacogenetic testing: a guide to test selection. Clin. Pharmacol. Ther. 106:2309–12
     [Google Scholar]
  11. 11. 
    Vo TT, Bell GC, Owusu Obeng A, Hicks JK, Dunnenberger HM 2017. Pharmacogenomics implementation: considerations for selecting a reference laboratory. Pharmacotherapy 37:91014–22
     [Google Scholar]
  12. 12. 
    Owusu-Obeng A, Weitzel KW, Hatton RC, Staley BJ, Ashton J et al. 2014. Emerging roles for pharmacists in clinical implementation of pharmacogenomics. Pharmacotherapy 34:101102–12
     [Google Scholar]
  13. 13. 
    Bell GC, Crews KR, Wilkinson MR, Haidar CE, Hicks JK et al. 2014. Development and use of active clinical decision support for preemptive pharmacogenomics. J. Am. Med. Inform. Assoc. 21:e1e93–99
     [Google Scholar]
  14. 14. 
    Peterson JF, Bowton E, Field JR, Beller M, Mitchell J et al. 2013. Electronic health record design and implementation for pharmacogenomics: a local perspective. Genet. Med. 15:10833–41
     [Google Scholar]
  15. 15. 
    ASHP (Am. Soc. Health-System Pharm.) 2015. ASHP statement on the pharmacist's role in clinical pharmacogenomics. Am. J. Health Syst. Pharm. 72:7579–81
     [Google Scholar]
  16. 16. 
    Nickola TJ, Green JS, Harralson AF, O'Brien TJ 2012. The current and future state of pharmacogenomics medical education in the USA. Pharmacogenomics 13:121419–25
     [Google Scholar]
  17. 17. 
    Weitzel KW, Aquilante CL, Johnson S, Kisor DF, Empey PE 2016. Educational strategies to enable expansion of pharmacogenomics-based care. Am. J. Health Syst. Pharm. 73:231986–98
     [Google Scholar]
  18. 18. 
    Caudle KE, Gammal RS, Whirl-Carrillo M, Hoffman JM, Relling MV, Klein TE 2016. Evidence and resources to implement pharmacogenetic knowledge for precision medicine. Am. J. Health Syst. Pharm. 73:231977–85
     [Google Scholar]
  19. 19. 
    Huddart R, Sangkuhl K, Whirl-Carrillo M, Klein TE 2019. Are randomized controlled trials necessary to establish the value of implementing pharmacogenomics in the clinic. ? Clin. Pharmacol. Ther. 106:2284–86
     [Google Scholar]
  20. 20. 
    Gaedigk A. 2019. Pharmacogenetics: chasing perfection. Clin. Pharmacol. Ther. 106:2265–70
     [Google Scholar]
  21. 21. 
    Relling MV, Altman RB, Goetz MP, Evans WE 2010. Clinical implementation of pharmacogenomics: overcoming genetic exceptionalism. Lancet Oncol 11:6507–9
     [Google Scholar]
  22. 22. 
    Relling MV. 2019. An end to pharmacogenetic exceptionalism. J. Am. Coll. Clin. Pharm. 2:3202–3
     [Google Scholar]
  23. 23. 
    Lavertu A, McInnes G, Daneshjou R, Whirl-Carrillo M, Klein TE, Altman RB 2018. Pharmacogenomics and big genomic data: from lab to clinic and back again. Hum. Mol. Genet. 27:R1R72–78
     [Google Scholar]
  24. 24. 
    Gottesman O, Kuivaniemi H, Tromp G, Faucett WA, Li R et al. 2013. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet. Med. 15:10761–71
     [Google Scholar]
  25. 25. 
    Rasmussen-Torvik LJ, Stallings SC, Gordon AS, Almoguera B, Basford MA et al. 2014. Design and anticipated outcomes of the eMERGE-PGx project: a multicenter pilot for preemptive pharmacogenomics in electronic health record systems. Clin. Pharmacol. Ther. 96:4482–89
     [Google Scholar]
  26. 26. 
    Weitzel KW, Alexander M, Bernhardt BA, Calman N, Carey DJ et al. 2016. The IGNITE network: a model for genomic medicine implementation and research. BMC Med. Genom. 9:1
     [Google Scholar]
  27. 27. 
    Cavallari LH, Beitelshees AL, Blake KV, Dressler LG, Duarte JD et al. 2017. The IGNITE Pharmacogenetics Working Group: an opportunity for building evidence with pharmacogenetic implementation in a real-world setting. Clin. Transl. Sci. 10:3143–46
     [Google Scholar]
  28. 28. 
    O'Donnell PH, Wadhwa N, Danahey K, Borden BA, Lee SM et al. 2017. Pharmacogenomics-based point-of-care clinical decision support significantly alters drug prescribing. Clin. Pharmacol. Ther. 102:5859–69
     [Google Scholar]
  29. 29. 
    Evans WE, Crews KR, Pui C-H 2013. A health-care system perspective on implementing genomic medicine: pediatric acute lymphoblastic leukemia as a paradigm. Clin. Pharmacol. Ther. 94:2224–29
     [Google Scholar]
  30. 30. 
    Mathias PC, Hendrix N, Wang W-J, Keyloun K, Khelifi M et al. 2017. Characterizing pharmacogenomic-guided medication use with a clinical data repository. Clin. Pharmacol. Ther. 102:2340–48
     [Google Scholar]
  31. 31. 
    Hinderer M, Boeker M, Wagner SA, Lablans M, Newe S et al. 2017. Integrating clinical decision support systems for pharmacogenomic testing into clinical routine—a scoping review of designs of user-system interactions in recent system development. BMC Med. Inform. Decis. Mak. 17:181
     [Google Scholar]
  32. 32. 
    Hicks JK, Crews KR, Hoffman JM, Kornegay NM, Wilkinson MR et al. 2012. A clinician-driven automated system for integration of pharmacogenetic interpretations into an electronic medical record. Clin. Pharmacol. Ther. 92:5563–66
     [Google Scholar]
  33. 33. 
    Hoffman JM, Dunnenberger HM, Hicks JK, Caudle KE, Whirl Carrillo M et al. 2016. Developing knowledge resources to support precision medicine: principles from the Clinical Pharmacogenetics Implementation Consortium (CPIC). J. Am. Med. Inform. Assoc. 23:4796–801
     [Google Scholar]
  34. 34. 
    Hicks JK, Stowe D, Willner MA, Wai M, Daly T et al. 2016. Implementation of clinical pharmacogenomics within a large health system: from electronic health record decision support to consultation services. Pharmacotherapy 36:8940–48
     [Google Scholar]
  35. 35. 
    Pulley JM, Denny JC, Peterson JF, Bernard GR, Vnencak-Jones CL et al. 2012. Operational implementation of prospective genotyping for personalized medicine: the design of the Vanderbilt PREDICT project. Clin. Pharmacol. Ther. 92:187–95
     [Google Scholar]
  36. 36. 
    Weitzel KW, Elsey AR, Langaee TY, Burkley B, Nessl DR et al. 2014. Clinical pharmacogenetics implementation: approaches, successes, and challenges. Am. J. Med. Genet. C 166:156–67
     [Google Scholar]
  37. 37. 
    Hoffman JM, Haidar CE, Wilkinson MR 2014. PG4KDS: a model for the clinical implementation of pre‐emptive pharmacogenetics. Am. J. Med. Genet. C 166:145–55
     [Google Scholar]
  38. 38. 
    Bielinski SJ, Olson JE, Pathak J, Weinshilboum RM, Wang L et al. 2014. Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time—using genomic data to individualize treatment protocol. Mayo Clin. Proc. 89:125–33
     [Google Scholar]
  39. 39. 
    Goldspiel BR, Flegel WA, DiPatrizio G, Sissung T, Adams SD et al. 2014. Integrating pharmacogenetic information and clinical decision support into the electronic health record. J. Am. Med. Inform. Assoc. 21:3522–28
     [Google Scholar]
  40. 40. 
    Manzi SF, Fusaro VA, Chadwick L, Brownstein C, Clinton C et al. 2017. Creating a scalable clinical pharmacogenomics service with automated interpretation and medical record result integration—experience from a pediatric tertiary care facility. J. Am. Med. Inform. Assoc. 24:174–80
     [Google Scholar]
  41. 41. 
    van der Wouden CH, Bank PCD, Özokcu K, Swen JJ, Guchelaar H-J 2019. Pharmacist-initiated pre-emptive pharmacogenetic panel testing with clinical decision support in primary care: record of PGx results and real-world impact. Genes 10:6416
     [Google Scholar]
  42. 42. 
    Blagec K, Koopmann R, Crommentuijn-van Rhenen M, Holsappel I, van der Wouden CH et al. 2018. Implementing pharmacogenomics decision support across seven European countries: The Ubiquitous Pharmacogenomics (U-PGx) project. J. Am. Med. Inform. Assoc. 25:7893–98
     [Google Scholar]
  43. 43. 
    Caraballo PJ, Hodge LS, Bielinski SJ, Stewart AK, Farrugia G et al. 2017. Multidisciplinary model to implement pharmacogenomics at the point of care. Genet. Med. 19:4421–29
     [Google Scholar]
  44. 44. 
    Caraballo PJ, Bielinski SJ, St. Sauver JL, Weinshilboum RM 2017. Electronic medical record-integrated pharmacogenomics and related clinical decision support concepts. Clin. Pharmacol. Ther. 102:2254–64
     [Google Scholar]
  45. 45. 
    Roncato R, Dal Cin L, Mezzalira S, Comello F, De Mattia E et al. 2019. FARMAPRICE: a pharmacogenetic clinical decision support system for precise and cost-effective therapy. Genes 10:4276
     [Google Scholar]
  46. 46. 
    Danahey K, Borden BA, Furner B, Yukman P, Hussain S et al. 2017. Simplifying the use of pharmacogenomics in clinical practice: building the genomic prescribing system. J. Biomed. Inform. 75:110–21
     [Google Scholar]
  47. 47. 
    Rasmussen LV, Smith ME, Almaraz F, Persell SD, Rasmussen-Torvik LJ et al. 2019. An ancillary genomics system to support the return of pharmacogenomic results. J. Am. Med. Inform. Assoc. 26:4306–10
     [Google Scholar]
  48. 48. 
    Gottesman O, Scott SA, Ellis SB, Overby CL, Ludtke A et al. 2013. The CLIPMERGE PGx Program: clinical implementation of personalized medicine through electronic health records and genomics-pharmacogenomics. Clin. Pharmacol. Ther. 94:2214–17
     [Google Scholar]
  49. 49. 
    Overby CL, Devine EB, Abernethy N, McCune JS, Tarczy-Hornoch P 2015. Making pharmacogenomic-based prescribing alerts more effective: a scenario-based pilot study with physicians. J. Biomed. Inform. 55:249–59
     [Google Scholar]
  50. 50. 
    Devine EB, Lee C-J, Overby CL, Abernethy N, McCune J et al. 2014. Usability evaluation of pharmacogenomics clinical decision support aids and clinical knowledge resources in a computerized provider order entry system: a mixed methods approach. Int. J. Med. Inform. 83:7473–83
     [Google Scholar]
  51. 51. 
    Melton BL, Zillich AJ, Saleem J, Russ AL, Tisdale JE, Overholser BR 2016. Iterative development and evaluation of a pharmacogenomic-guided clinical decision support system for warfarin dosing. Appl. Clin. Inform. 7:41088–106
     [Google Scholar]
  52. 52. 
    Nguyen KA, Patel H, Haggstrom DA, Zillich AJ, Imperiale TF, Russ AL 2019. Utilizing a user-centered approach to develop and assess pharmacogenomic clinical decision support for thiopurine methyltransferase. BMC Med. Inform. Decis. Mak. 19:1194
     [Google Scholar]
  53. 53. 
    Collins T, Power K, McCallie D Jr., Owings R 2019. Finding a place for pharmacogenetics in the electronic health record. Clin. Pharmacol. Ther. 106:2295–97
     [Google Scholar]
  54. 54. 
    Bielinski SJ, St. Sauver JL, Olson JE, Larson NB, Black JL et al. 2020. Cohort profile: the right drug, right dose, right time: using genomic data to individualize treatment protocol (RIGHT protocol). Int. J. Epidemiol. 49:23–24k
     [Google Scholar]
  55. 55. 
    Ji Y, Skierka JM, Blommel JH, Moore BE, VanCuyk DL et al. 2016. Preemptive pharmacogenomic testing for precision medicine: a comprehensive analysis of five actionable pharmacogenomic genes using next-generation DNA sequencing and a customized CYP2D6 genotyping cascade. J. Mol. Diagn. 18:3438–45
     [Google Scholar]
  56. 56. 
    Farrugia G, Weinshilboum RM. 2013. Challenges in implementing genomic medicine: the Mayo Clinic Center for Individualized Medicine. Clin. Pharmacol. Ther. 94:2204–6
     [Google Scholar]
  57. 57. 
    Crews KR, Cross SJ, McCormick JN, Baker DK, Molinelli AR et al. 2011. Development and implementation of a pharmacist-managed clinical pharmacogenetics service. Am. J. Health Syst. Pharm. 68:2143–50
     [Google Scholar]
  58. 58. 
    Gammal RS, Crews KR, Haidar CE, Hoffman JM, Baker DK et al. 2016. Pharmacogenetics for safe codeine use in sickle cell disease. Pediatrics 138:1e20153479
     [Google Scholar]
  59. 59. 
    Haidar CE, Relling MV, Hoffman JM 2019. Preemptively precise: returning and updating pharmacogenetic test results to realize the benefits of preemptive testing. Clin. Pharmacol. Ther. 106:5942–44
     [Google Scholar]
  60. 60. 
    Cavallari LH, Weitzel KW, Elsey AR, Liu X, Mosley SA et al. 2017. Institutional profile: University of Florida Health Personalized Medicine Program. Pharmacogenomics 18:5421–26
     [Google Scholar]
  61. 61. 
    Smith DM, Weitzel KW, Elsey AR, Langaee T, Gong Y et al. 2019. CYP2D6-guided opioid therapy improves pain control in CYP2D6 intermediate and poor metabolizers: a pragmatic clinical trial. Genet. Med. 21:81842–50
     [Google Scholar]
  62. 62. 
    Cicali EJ, Weitzel KW, Elsey AR, Orlando FA, Vinson M et al. 2019. Challenges and lessons learned from clinical pharmacogenetic implementation of multiple gene–drug pairs across ambulatory care settings. Gen. Med. 21:2264–74
     [Google Scholar]
  63. 63. 
    Cecchin E, Roncato R, Guchelaar HJ, Toffoli G 2017. Ubiquitous Pharmacogenomics (U-PGx): The time for implementation is now. An Horizon2020 Program to drive pharmacogenomics into clinical practice. Curr. Pharm. Biotechnol. 18:3204–9
     [Google Scholar]
  64. 64. 
    Starren J, Williams MS, Bottinger EP 2013. Crossing the omic chasm: a time for omic ancillary systems. JAMA 309:121237–38
     [Google Scholar]
  65. 65. 
    Ury AG. 2013. Storing and interpreting genomic information in widely deployed electronic health record systems. Genet. Med. 15:10779–85
     [Google Scholar]
  66. 66. 
    Marsolo K, Spooner SA. 2013. Clinical genomics in the world of the electronic health record. Genet. Med. 15:10786–91
     [Google Scholar]
  67. 67. 
    Moriyama T, Nishii R, Perez-Andreu V, Yang W, Klussmann FA et al. 2016. NUDT15 polymorphisms alter thiopurine metabolism and hematopoietic toxicity. Nat. Genet. 48:4367–73
     [Google Scholar]
  68. 68. 
    Yang JJ, Landier W, Yang W, Liu C, Hageman L et al. 2015. Inherited NUDT15 variant is a genetic determinant of mercaptopurine intolerance in children with acute lymphoblastic leukemia. J. Clin. Oncol. 33:111235–42
     [Google Scholar]
  69. 69. 
    Relling MV, Schwab M, Whirl-Carrillo M, Suarez-Kurtz G, Pui C-H et al. 2019. Clinical Pharmacogenetics Implementation Consortium guideline for thiopurine dosing based on TPMT and NUDT15 genotypes: 2018 update. Clin. Pharmacol. Ther. 105:51095–105
     [Google Scholar]
  70. 70. 
    Moriyama B, Obeng AO, Barbarino J, Penzak SR, Henning SA et al. 2017. Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for CYP2C19 and voriconazole therapy. Clin. Pharmacol. Ther. 102:145–51
     [Google Scholar]
  71. 71. 
    Hicks JK, Dunnenberger HM, Gumpper KF, Haidar CE, Hoffman JM 2016. Integrating pharmacogenomics into electronic health records with clinical decision support. Am. J. Health Syst. Pharm. 73:231967–76
     [Google Scholar]
  72. 72. 
    Whirl-Carrillo M, McDonagh EM, Hebert JM, Gong L, Sangkuhl K et al. 2012. Pharmacogenomics knowledge for personalized medicine. Clin. Pharmacol. Ther. 92:4414–17
     [Google Scholar]
  73. 73. 
    Relling MV, Klein TE. 2011. CPIC: Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenomics Research Network. Clin. Pharmacol. Ther. 89:3464–67
     [Google Scholar]
  74. 74. 
    Relling MV, Klein TE, Gammal RS, Whirl-Carrillo M, Hoffman JM, Caudle KE 2019. The Clinical Pharmacogenetics Implementation Consortium: 10 years later. Clin. Pharmacol. Ther. 107:171–75
     [Google Scholar]
  75. 75. 
    Gaedigk A, Sangkuhl K, Whirl-Carrillo M, Twist GP, Klein TE et al. 2019. The evolution of PharmVar. Clin. Pharmacol. Ther. 105:129–32
     [Google Scholar]
  76. 76. 
    Gaedigk A, Ingelman-Sundberg M, Miller NA, Leeder JS, Whirl-Carrillo M et al. 2018. The Pharmacogene Variation (PharmVar) Consortium: incorporation of The Human Cytochrome P450 (CYP) Allele Nomenclature Database. Clin. Pharmacol. Ther. 103:3399–401
     [Google Scholar]
  77. 77. 
    Caudle KE, Klein TE, Hoffman JM, Muller DJ, Whirl-Carrillo M et al. 2014. Incorporation of pharmacogenomics into routine clinical practice: the Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline development process. Curr. Drug Metab. 15:2209–17
     [Google Scholar]
  78. 78. 
    Rehm HL, Berg JS, Brooks LD, Bustamante CD, Evans JP et al. 2015. ClinGen—the clinical genome resource. N. Engl. J. Med. 372:2235–42
     [Google Scholar]
  79. 79. 
    Overby CL, Heale B, Aronson S, Cherry JM, Dwight S et al. 2016. Providing access to genomic variant knowledge in a healthcare setting: a vision for the ClinGen electronic health records workgroup. Clin. Pharmacol. Ther. 99:2157–60
     [Google Scholar]
  80. 80. 
    Caudle KE, Keeling NJ, Klein TE, Whirl-Carrillo M, Pratt VM, Hoffman JM 2018. Standardization can accelerate the adoption of pharmacogenomics: current status and the path forward. Pharmacogenomics 19:10847–60
     [Google Scholar]
  81. 81. 
    Herr TM, Peterson JF, Rasmussen LV, Caraballo PJ, Peissig PL, Starren JB 2019. Pharmacogenomic clinical decision support design and multi-site process outcomes analysis in the eMERGE Network. J. Am. Med. Inform. Assoc. 26:2143–48
     [Google Scholar]
  82. 82. 
    Luzum JA, Pakyz RE, Elsey AR, Haidar CE, Peterson JF et al. 2017. The Pharmacogenomics Research Network Translational Pharmacogenetics Program: outcomes and metrics of pharmacogenetic implementations across diverse healthcare systems. Clin. Pharmacol. Ther. 102:3502–10
     [Google Scholar]
  83. 83. 
    Kalman LV, Agúndez J, Appell ML, Black JL, Bell GC et al. 2016. Pharmacogenetic allele nomenclature: international workgroup recommendations for test result reporting. Clin. Pharmacol. Ther. 99:2172–85
     [Google Scholar]
  84. 84. 
    Moyer AM, Rohrer Vitek CR, Giri J, Caraballo PJ 2017. Challenges in ordering and interpreting pharmacogenomic tests in clinical practice. Am. J. Med. 130:121342–44
     [Google Scholar]
  85. 85. 
    Fan M, Bousman CA. 2019. Commercial pharmacogenetic tests in psychiatry: Do they facilitate the implementation of pharmacogenetic dosing guidelines. ? Pharmacopsychiatry In press
    [Google Scholar]
  86. 86. 
    Hoshitsuki K, Crews KR, Yang W, Smith CA, Hankins JS et al. 2019. Challenges in clinical implementation of CYP2D6 genotyping: choice of variants to test affects phenotype determination. Genet. Med. 22:232–33
     [Google Scholar]
  87. 87. 
    Pratt VM, Del Tredici AL, Hachad H, Ji Y, Kalman LV et al. 2018. Recommendations for clinical CYP2C19 genotyping allele selection: a report of the Association for Molecular Pathology. J. Mol. Diagn. 20:3269–76
     [Google Scholar]
  88. 88. 
    Pratt VM, Cavallari LH, Del Tredici AL, Hachad H, Ji Y et al. 2019. Recommendations for clinical CYP2C9 genotyping allele selection: a joint recommendation of the Association for Molecular Pathology and College of American Pathologists. J. Mol. Diagn. 21:5746–55
     [Google Scholar]
  89. 89. 
    Caudle KE, Dunnenberger HM, Freimuth RR, Peterson JF, Burlison JD et al. 2017. Standardizing terms for clinical pharmacogenetic test results: consensus terms from the Clinical Pharmacogenetics Implementation Consortium (CPIC). Genet. Med. 19:2215–23
     [Google Scholar]
  90. 90. 
    Dolin RH, Boxwala A, Shalaby J 2018. A pharmacogenomics clinical decision support service based on FHIR and CDS hooks. Methods Inf. Med. 57:S 02e115–23
     [Google Scholar]
  91. 91. 
    Klein TE, Ritchie MD. 2018. PharmCAT: a pharmacogenomics clinical annotation tool. Clin. Pharmacol. Ther. 104:119–22
     [Google Scholar]
  92. 92. 
    Sangkuhl K, Whirl-Carrillo M, Whaley RM, Woon M, Lavertu A et al. 2019. Pharmacogenomics clinical annotation tool (PharmCAT). Clin. Pharmacol. Ther. 104:119–22
     [Google Scholar]
  93. 93. 
    Martin MA, Hoffman JM, Freimuth RR, Klein TE, Dong BJ et al. 2014. Clinical pharmacogenetics implementation consortium guidelines for HLA-B genotype and abacavir dosing: 2014 update. Clin. Pharmacol. Ther. 95:5499–500
     [Google Scholar]
  94. 94. 
    Johnson JA, Caudle KE, Gong L, Whirl-Carrillo M, Stein CM et al. 2017. Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for pharmacogenetics-guided warfarin dosing: 2017 update. Clin. Pharmacol. Ther. 102:3397–404
     [Google Scholar]
  95. 95. 
    Caudle KE, Rettie AE, Whirl-Carrillo M, Smith LH, Mintzer S et al. 2014. Clinical pharmacogenetics implementation consortium guidelines for CYP2C9 and HLA-B genotypes and phenytoin dosing. Clin. Pharmacol. Ther. 96:5542–48
     [Google Scholar]
  96. 96. 
    Kuperman GJ, Reichley RM, Bailey TC 2006. Using commercial knowledge bases for clinical decision support: opportunities, hurdles, and recommendations. J. Am. Med. Inform. Assoc. 13:4369–71
     [Google Scholar]
  97. 97. 
    Wilkinson MD, Dumontier M, Aalbersberg IJJ, Appleton G, Axton M et al. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3:160018
     [Google Scholar]
  98. 98. 
    Hingle S. 2016. Electronic health records: an unfulfilled promise and a call to action. Ann. Intern. Med. 165:11818–19
     [Google Scholar]
  99. 99. 
    Downing NL, Bates DW, Longhurst CA 2018. Physician burnout in the electronic health record era: Are we ignoring the real cause. ? Ann. Intern. Med. 169:150–51
     [Google Scholar]
  100. 100. 
    Ashton M. 2018. Getting rid of stupid stuff. N. Engl. J. Med. 379:191789–91
     [Google Scholar]
  101. 101. 
    Ray JM, Ratwani RM, Sinsky CA, Frankel RM, Friedberg MW et al. 2019. Six habits of highly successful health information technology: powerful strategies for design and implementation. J. Am. Med. Inform. Assoc. 26:101109–14
     [Google Scholar]
  102. 102. 
    Masys DR, Jarvik GP, Abernethy NF, Anderson NR, Papanicolaou GJ et al. 2012. Technical desiderata for the integration of genomic data into electronic health records. J. Biomed. Inform. 45:3419–22
     [Google Scholar]
  103. 103. 
    Welch BM, Eilbeck K, Del Fiol G, Meyer LJ, Kawamoto K 2014. Technical desiderata for the integration of genomic data with clinical decision support. J. Biomed. Inform. 51:3–7
     [Google Scholar]
  104. 104. 
    Hoffman MA. 2007. The genome-enabled electronic medical record. J. Biomed. Inform. 40:144–46
     [Google Scholar]
  105. 105. 
    Manolio TA, Rowley R, Williams MS, Roden D, Ginsburg GS et al. 2019. Opportunities, resources, and techniques for implementing genomics in clinical care. Lancet 394:10197511–20
     [Google Scholar]
  106. 106. 
    Shirts BH, Salama JS, Aronson SJ, Chung WK, Gray SW et al. 2015. CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record. J. Am. Med. Inform. Assoc. 22:61231–42
     [Google Scholar]
  107. 107. 
    van der Sijs H, Aarts J, Vulto A, Berg M 2006. Overriding of drug safety alerts in computerized physician order entry. J. Am. Med. Inform. Assoc. 13:2138–47
     [Google Scholar]
  108. 108. 
    Carspecken CW, Sharek PJ, Longhurst C, Pageler NM 2013. A clinical case of electronic health record drug alert fatigue: consequences for patient outcome. Pediatrics 131:6e1970–73
     [Google Scholar]
  109. 109. 
    Topaz M, Seger DL, Slight SP, Goss F, Lai K et al. 2016. Rising drug allergy alert overrides in electronic health records: an observational retrospective study of a decade of experience. J. Am. Med. Inform. Assoc. 23:3601–8
     [Google Scholar]
  110. 110. 
    Nanji KC, Slight SP, Seger DL, Cho I, Fiskio JM et al. 2014. Overrides of medication-related clinical decision support alerts in outpatients. J. Am. Med. Inform. Assoc. 21:3487–91
     [Google Scholar]
  111. 111. 
    Kawamanto K, Flynn MC, Kukhareva P, El Halta D, Hess R et al. 2018. A pragmatic guide to establishing clinical decision support governance and addressing decision support fatigue: a case study. AMIA Annu. Symp. Proc. 2018:624–33
     [Google Scholar]
  112. 112. 
    Daniels CC, Burlison JD, Baker DK, Robertson J, Sablauer A et al. 2019. Optimizing drug-drug interaction alerts using a multidimensional approach. Pediatrics 143:3e20174111
     [Google Scholar]
  113. 113. 
    Payne TH, Hines LE, Chan RC, Hartman S, Kapusnik-Uner J et al. 2015. Recommendations to improve the usability of drug-drug interaction clinical decision support alerts. J. Am. Med. Inform. Assoc. 22:61243–50
     [Google Scholar]
  114. 114. 
    McDaniel RB, Burlison JD, Baker DK, Hasan M, Robertson J et al. 2016. Alert dwell time: introduction of a measure to evaluate interruptive clinical decision support alerts. J. Am. Med. Inform. Assoc. 23:e1e138–41
     [Google Scholar]
  115. 115. 
    McCoy AB, Waitman LR, Lewis JB, Wright JA, Choma DP et al. 2012. A framework for evaluating the appropriateness of clinical decision support alerts and responses. J. Am. Med. Inform. Assoc. 19:3346–52
     [Google Scholar]
  116. 116. 
    Ubanyionwu S, Formea CM, Anderson B, Wix K, Dierkhising R, Caraballo PJ 2018. Evaluation of prescriber responses to pharmacogenomics clinical decision support for thiopurine S-methyltransferase testing. Am. J. Health Syst. Pharm. 75:4191–98
     [Google Scholar]
  117. 117. 
    Bernstam EV, Smith JW, Johnson TR 2010. What is biomedical informatics. ? J. Biomed. Inform. 43:1104–10
     [Google Scholar]
  118. 118. 
    Kulikowski CA, Shortliffe EH, Currie LM, Elkin PL, Hunter LE et al. 2012. AMIA Board white paper: definition of biomedical informatics and specification of core competencies for graduate education in the discipline. J. Am. Med. Inform. Assoc. 19:6931–38
     [Google Scholar]
  119. 119. 
    Birney E, Vamathevan J, Goodhand P 2017. Genomics in healthcare: GA4GH looks to 2022. bioRxiv 203554. https://doi.org/10.1101/203554
    [Crossref] [Google Scholar]
  120. 120. 
    Inst. Med 2015. Genomics-Enabled Learning Health Care Systems: Gathering and Using Genomic Information to Improve Patient Care and Research: Workshop Summary Washington, DC: Natl. Acad. Press
  121. 121. 
    Williams MS, Buchanan AH, Davis FD, Faucett WA, Hallquist MLG et al. 2018. Patient-centered precision health in a learning health care system: Geisinger's genomic medicine experience. Health Aff 37:5757–64
     [Google Scholar]
  122. 122. 
    Garratty G, Dzik W, Issitt PD, Lublin DM, Reid ME, Zelinski T 2000. Terminology for blood group antigens and genes-historical origins and guidelines in the new millennium. Transfusion 40:4477–89
     [Google Scholar]
  123. 123. 
    ICCBBA (Int. Counc. Common. Blood Bank. Autom.) 2018. What is ISBT 128? Media Resour., ICCCBBA https://www.iccbba.org/home/isbt-128-basics/what-is-isbt-128
  124. 124. 
    Straff Tiehan A 2018. Laboratory informatics systems in the blood bank. Modern Blood Banking and Transfusion Practices D Harmening 590–606 Philadelphia: F.A. Davis. , 7th ed..
     [Google Scholar]
  125. 125. 
    van Gammeren AJ, van den Bos AG, Som N, Veldhoven C, Vossen RCRM, Folman CC 2019. A national transfusion register of irregular antibodies and cross (X)-match problems: TRIX, a 10-year analysis. Transfusion 59:82559–66
     [Google Scholar]
  126. 126. 
    CDC (Cent. Dis. Control Prev.) 2001. Development of community- and state-based immunization registries. CDC response to a report from the National Vaccine Advisory Committee. MMWR Recomm. Rep. 50:RR–17
     [Google Scholar]
  127. 127. 
    Hinman AR, Ross DA. 2010. Immunization registries can be building blocks for national health information systems. Health Aff 29:4676–82
     [Google Scholar]
  128. 128. 
    Linkins RW, Feikema SM. 1998. Immunization registries: the cornerstone of childhood immunization in the 21st century. Pediatr. Ann. 27:6349–54
     [Google Scholar]
  129. 129. 
    Ozdemir V, Williams-Jones B, Glatt SJ, Tsuang MT, Lohr JB, Reist C 2006. Shifting emphasis from pharmacogenomics to theragnostics. Nat. Biotechnol. 24:8942–46
     [Google Scholar]
  130. 130. 
    Pene F, Courtine E, Cariou A, Mira J-P 2009. Toward theragnostics. Crit. Care Med. 37:1 Suppl.S50–58
     [Google Scholar]
  131. 131. 
    Smit E. 2014. Antiviral resistance testing. Curr. Opin. Infect. Dis. 27:6566–72
     [Google Scholar]
  132. 132. 
    Syal K, Mo M, Yu H, Iriya R, Jing W et al. 2017. Current and emerging techniques for antibiotic susceptibility tests. Theranostics 7:71795–805
     [Google Scholar]
  133. 133. 
    Dixon BE, Simonaitis L, Goldberg HS, Paterno MD, Schaeffer M et al. 2013. A pilot study of distributed knowledge management and clinical decision support in the cloud. Artif. Intell. Med. 59:145–53
     [Google Scholar]
  134. 134. 
    Oh S, Cha J, Ji M, Kang H, Kim S et al. 2015. Architecture design of healthcare software-as-a-service platform for cloud-based clinical decision support service. Healthc. Inform. Res. 21:2102–10
     [Google Scholar]
/content/journals/10.1146/annurev-biodatasci-020320-093614
Loading
Biomedical Data Science and Informatics Challenges to Implementing Pharmacogenomics with Electronic Health Records
Annual Review of Biomedical Data Science 3, 289 (2020); https://doi.org/10.1146/annurev-biodatasci-020320-093614
/content/journals/10.1146/annurev-biodatasci-020320-093614
/content/journals/10.1146/annurev-biodatasci-020320-093614
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error