Effect of genetics clinical decision support tools on health-care providers’ decision making: a mixed-methods systematic review

Abstract

Purpose

Patient care involving genetics is challenging for nongenetics health-care providers. Clinical decision support (CDS) tools are a potential solution because they provide patient-specific risk assessments and/or management recommendations. This systematic review synthesized evidence on whether using CDS tools resulted in appropriate changes in genetics-related patient management made by nongenetics health-care providers.

Methods

A comprehensive search in MEDLINE, Embase, and CINAHL yielded 2,239 unique articles. Two independent reviewers screened abstracts and full texts for quantitative, qualitative, and mixed-methods articles on management changes by nongenetics clinicians using a CDS tool as part of patient care. Effect sizes were calculated for quantitative studies and all articles were analyzed together using narrative synthesis. Twenty articles were included.

Results

In 12/16 quantitative studies, CDS tools slightly increased appropriate changes in management, but study design appeared to affect the statistical significance of the effect. The qualitative data in the four remaining studies reaffirmed that CDS tools facilitated management decisions but raised questions about their effect on patient outcomes.

Conclusion

Our review assessed clinical utility of CDS tools, finding that they slightly increase appropriate management changes by nongenetics providers. Future studies on CDS tools should explicitly evaluate decision making and patient outcomes.

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Fig. 1: PRISMA flowchart.

Data availability

Data relevant to this systematic review is available upon request.

References

  1. 1.

    Mikat-Stevens, N. A., Larson, I. A. & Tarini, B. A. Primary-care providers’ perceived barriers to integration of genetics services: a systematic review of the literature. Genet. Med. 17, 169–176, https://doi.org/10.1038/gim.2014.101 (2015).

    Article  PubMed  Google Scholar 

  2. 2.

    Carroll, J.C., Allanson, J. & Morrison, S. et al. Informing integration of genomic medicine into primary care: an assessment of current practice, attitudes, and desired resources. Front. Genet. 10, 1189, https://doi.org/10.3389/fgene.2019.01189 (2019).

  3. 3.

    Krier, J. B., Kalia, S. S. & Green, R. C. Genomic sequencing in clinical practice: applications, challenges, and opportunities. Dialogues Clin. Neurosci. 18, 299–312 (2016).

    Article  Google Scholar 

  4. 4.

    Berberich, A. J., Ho, R. & Hegele, R. A. Whole genome sequencing in the clinic: empowerment or too much information? CMAJ. 190, E124–E125, https://doi.org/10.1503/cmaj.180076 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Overby, C. L., Kohane, I. & Kannry, J. L. et al. Opportunities for genomic clinical decision support interventions. Genet. Med. 15, 817–823, https://doi.org/10.1038/gim.2013.128 (2013).

    Article  PubMed  Google Scholar 

  6. 6.

    Sim, I. et al. Clinical decision support systems for the practice of evidence-based medicine. J. Am. Med. Inform. Assoc. 8, 527–534, http://www.ncbi.nlm.nih.gov/pubmed/11687560 (2001).

    CAS  Article  Google Scholar 

  7. 7.

    Grosse, S. D. & Khoury, M. J. What is the clinical utility of genetic testing? Genet. Med. 8, 448–450, https://doi.org/10.1097/01.gim.0000227935.26763.c6 (2006).

    Article  PubMed  Google Scholar 

  8. 8.

    Welch, B. M. & Kawamoto, K. Clinical decision support for genetically guided personalized medicine: a systematic review. J. Am. Med. Inform. Assoc. 20, 388–400, https://doi.org/10.1136/amiajnl-2012-000892 (2013).

    Article  PubMed  Google Scholar 

  9. 9.

    Bombard, Y., Bach, P. B. & Offit, K. Translating genomics in cancer care. J. Natl. Compr. Canc. Netw. 11, 1343–1353, http://www.ncbi.nlm.nih.gov/pubmed/24225968 (2013).

    CAS  Article  Google Scholar 

  10. 10.

    Lizarondo. L. et al. Mixed methods systematic reviews. In: Aromataris E, Munn Z, eds. Joanna Briggs Institute Reviewer’s Manual. The Joanna Briggs Institute; Adelaide, Australia; 2019. Accessed May 19, 2020. https://reviewersmanual.joannabriggs.org/.

  11. 11.

    Hong, Q. N., Pluye, P., Bujold, M. & Wassef, M. Convergent and sequential synthesis designs: implications for conducting and reporting systematic reviews of qualitative and quantitative evidence. Syst. Rev. 6, 61, https://doi.org/10.1186/s13643-017-0454-2 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Moher, D., Liberati, A., Tetzlaff, J. & Altman, D. G. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 339, 332–336, https://doi.org/10.1136/bmj.b2535 (2009).

    Article  Google Scholar 

  13. 13.

    Ouzzani, M., Hammady, H., Fedorowicz, Z. & Elmagarmid, A. Rayyan—a web and mobile app for systematic reviews. Syst. Rev. 5, 210, https://doi.org/10.1186/s13643-016-0384-4 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Vassy, J. L., Bates, D. W. & Murray, M. F. Appropriateness: a key to enabling the use of genomics in clinical practice? Am. J. Med. 129, 551–553, https://doi.org/10.1016/j.amjmed.2016.02.010 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Mirzazadeh, A., Malekinejad, M. & Kahn, J. G. Relative risk reduction is useful metric to standardize effect size for public heath interventions for translational research. J. Clin. Epidemiol. 68, 317–323, https://doi.org/10.1016/j.jclinepi.2014.11.013 (2015).

    Article  PubMed  Google Scholar 

  16. 16.

    Azzopardi, D. Group comparison calculator. http://www.neoweb.org.uk/Additions/compare.htm (2019).

  17. 17.

    Andrade, C. Understanding relative risk, odds ratio and related terms: as simple as it can get. J. Clin. Psychiatry. 76, e857–e861 (2015).

    Article  Google Scholar 

  18. 18.

    Borenstein M, Hedges LV, Higgins JPT, Rothstein HR When does it make sense to perform a meta-analysis? In: Introduction to Meta-Analysis. John Wiley and Sons; Chichester, West Sussex, United Kingdom; 2009:357-64. https://doi.org/10.1002/9780470743386.

  19. 19.

    Lockwood, C. et al. Chapter 2: Systematic reviews of qualitative evidence. In: Aromataris E, Munn Z, eds. Joanna Brigg’s Institute Reviewer’s Manual. The Joanna Briggs Institute; Adelaide, Australia; 2017. Accessed May 21, 2020. https://wiki.joannabriggs.org/display/MANUAL/Chapter+2%3A+Systematic+reviews+of+qualitative+evidence.

  20. 20.

    Tufanaru C, Munn Z, Aromataris E, Campbell JHL Chapter 3: Systematic reviews of effectiveness. In: Aromataris E, Munn Z, eds. Joanna Briggs Institute Reviewer’s Manual. The Joanna Briggs Institute; Adelaide, Australia; 2017. Accessed April 25, 2019. https://reviewersmanual.joannabriggs.org/.

  21. 21.

    Balshem, H. et al. Finding Grey Literature Evidence and Assessing for Outcome and Analysis Reporting Biases When Comparing Medical Interventions: AHRQ and the Effective Health Care Program Methods Guide for Comparative Effectiveness Reviews.; Rockville, Maryland, United States; 2013. Accessed October 14, 2020. www.effectivehealthcare.ahrq.gov/reports/final.cfm.

  22. 22.

    Blasco-Fontecilla, H. Clinical utility of pharmacogenetic testing in children and adolescents with severe mental disorders. J. Neural Transm. (Vienna). 126, 101–107, https://doi.org/10.1007/s00702-018-1882-4 (2019).

    Article  PubMed  Google Scholar 

  23. 23.

    Borden, B. A. et al. Assessment of provider-perceived barriers to clinical use of pharmacogenomics during participation in an institutional implementation study. Pharmacogenet. Genomics 29, 31–38, https://doi.org/10.1097/FPC.0000000000000362 (2019).

    CAS  Article  PubMed  Google Scholar 

  24. 24.

    Edelman, E. A. et al. Implementation of an electronic genomic and family health history tool in primary prenatal care. Am. J. Med. Genet. C. 166, 34–44 (2014).

  25. 25.

    Michalopoulos, S. N. et al. Influence of a genomic classifier on post-operative treatment decisions in high-risk prostate cancer patients: results from the PRO-ACT study. Curr. Med. Res. Opin. 30, 1547–1556, https://doi.org/10.1185/03007995.2014.919908 (2014).

    CAS  Article  PubMed  Google Scholar 

  26. 26.

    Petzel, S. V., Vogel, R. I., McNiel, J., Leininger, A., Argenta, P. A. & Geller, M. A. Improving referral for genetic risk assessment in ovarian cancer using an electronic medical record system. Int. J. Gynecol. Cancer 24, 1003–1009, https://doi.org/10.1097/IGC.0000000000000148 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Badani, K. et al. Impact of a genomic classifier of metastatic risk on postoperative treatment recommendations for prostate cancer patients: A report from the DECIDE study group. Oncotarget. 4, 600–609, https://doi.org/10.18632/oncotarget.918 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Baer, H. J. et al. Use of a web-based risk appraisal tool for assessing family history and lifestyle factors in primary care. J. Gen. Intern. Med. 28, 817–824, https://doi.org/10.1007/s11606-013-2338-z (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Hall-Flavin, D. K. et al. Utility of integrated pharmacogenomic testing to support the treatment of major depressive disorder in a psychiatric outpatient setting. Pharmacogenet. Genomics 23, 535–548, https://doi.org/10.1097/FPC.0b013e3283649b9a (2013).

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    Scheuner, M. T. et al. A cancer genetics toolkit improves access to genetic services through documentation and use of the family history by primary-care clinicians. Genet. Med. 16, 60–69, https://doi.org/10.1038/gim.2013.75 (2014).

    Article  PubMed  Google Scholar 

  31. 31.

    Emery, J. et al. The GRAIDS Trial: a cluster randomised controlled trial of computer decision support for the management of familial cancer risk in primary care. Br. J. Cancer 97, 486–493, https://doi.org/10.1038/sj.bjc.6603897 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Wilson, B. J. et al. Cluster randomized trial of a multifaceted primary care decision-support intervention for inherited breast cancer risk. Fam. Pract. 23, 537–544, https://doi.org/10.1093/fampra/cml026 (2006).

    Article  PubMed  Google Scholar 

  33. 33.

    Tural, C. et al. Clinical utility of HIV-1 genotyping and expert advice: the Havana trial. AIDS. 16, 209–218, https://doi.org/10.1097/00002030-200201250-00010 (2002).

    Article  PubMed  Google Scholar 

  34. 34.

    Kim, K., Magness, J. W., Nelson, R., Baron, V. & Brixner, D. I. Clinical utility of pharmacogenetic testing and a clinical decision support tool to enhance the identification of drug therapy problems through medication therapy management in polypharmacy patients. J. Manag. Care Spec. Pharm. 24, 1251 (2018).

    Google Scholar 

  35. 35.

    O’Donnell, P. H. K. et al. Pharmacogenomics-based point-of-care clinical decision support significantly alters drug prescribing. Clin. Pharmacol. Ther. 102, 859–869, https://doi.org/10.1002/cpt.709 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Brixner, D. A. et al. The effect of pharmacogenetic profiling with a clinical decision support tool on healthcare resource utilization and estimated costs in the elderly exposed to polypharmacy. J. Med. Econ. 19, 213–228, https://doi.org/10.3111/13696998.2015.1110160 (2016).

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    Klinkenberg-Ramirez, S. et al. Evaluation: a qualitative pilot study of novel information technology infrastructure to communicate genetic variant updates. Appl. Clin. Inform. 7, 461–476, https://doi.org/10.4338/ACI-2015-11-RA-0162 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Unertl, K. M., Field, J. R., Price, L. & Peterson, J. F. Clinician perspectives on using pharmacogenomics in clinical practice. Per. Med. 12, 339–347, https://doi.org/10.2217/PME.15.10 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Doerr, M., Edelman, E., Gabitzsch, E., Eng, C. & Teng, K. Formative evaluation of clinician experience with integrating family history-based clinical decision support into clinical practice. J. Pers. Med. 4, 115–136, https://doi.org/10.3390/jpm4020115 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Orlando, L. A. et al. Clinical utility of a Web-enabled risk-assessment and clinical decision support program. Genet. Med. 18, 1020–1028, https://doi.org/10.1038/gim.2015.210 (2016).

    Article  PubMed  Google Scholar 

  41. 41.

    Singh, A. B. Improved antidepressant remission in major depression via a pharmacokinetic pathway polygene pharmacogenetic report. Clin. Psychopharmacol. Neurosci. 13, 150–156, https://doi.org/10.9758/cpn.2015.13.2.150 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Gottesman, O. et al. The CLIPMERGE PGx program: clinical implementation of personalized medicine through electronic health records and genomics-pharmacogenomics. Clin. Pharmacol. Ther. 94, 214–217, https://doi.org/10.1038/clpt.2013.72 (2013).

  43. 43.

    Kawamoto, K., Houlihan, C. A., Balas, E. A. & Lobach, D. F. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 330, 765, https://doi.org/10.1136/bmj.38398.500764.8F (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N. & Kroeker, K. I. An overview of clinical decision support systems: benefits, risks, and strategies for success. npj Digit. Med. 3, 1–10, https://doi.org/10.1038/s41746-020-0221-y (2020).

    Article  Google Scholar 

  45. 45.

    Williams, M. S. et al. Genomic information for clinicians in the electronic Health record: lessons learned from the Clinical Genome Resource Project and the Electronic Medical Records and Genomics Network. Front. Genet. 10, 1059, https://doi.org/10.3389/fgene.2019.01059 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This project is partially funded by an Early Career Award from the Ontario Ministry of Research and Innovation (ER17-13-045). A.S. was supported by an Ontario Graduate Scholarship from the University of Toronto. Y.B. was supported by a New Investigator Award from the Canadian Institutes of Health Research (CIHR). We thank Andrea Tricco, Lusine Abrahamyan, and Petros Pechlivanoglou for their advice and feedback over the course of this study.

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Conceptualization: A.S., Y.B., J.C.C. Formal analysis: A.S., Y.B., J.C.C. Funding acquisition: Y.B., A.S. Investigation: A.S., E.U., L.E.O., C.M., S.S. Methodology: A.S., Y.B., J.C.C. Project administration: A.S., Y.B., J.C.C. Supervision: Y.B., J.C.C. Visualization: A.S. Writing—original draft: A.S., Y.B., J.C.C. Writing—review & editing: A.S., Y.B., J.C.C., E.U., L.E.O., C.M., S.S.

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Correspondence to Yvonne Bombard PhD.

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Sebastian, A., Carroll, J.C., Oldfield, L.E. et al. Effect of genetics clinical decision support tools on health-care providers’ decision making: a mixed-methods systematic review. Genet Med (2021). https://doi.org/10.1038/s41436-020-01045-1

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