A Comprehensive Approach to Clinical Decision Support in the Return of Genome Informed Risk Assessments to Primary Care Pediatricians

Abstract

Background Primary care pediatricians play an important role in genetic testing, including referrals, test ordering, responding to results, assessing risk, treatment, and managing care. As genetic testing rapidly evolves to include new tests identifying patients at risk for certain conditions, alert-based clinical decision support is insufficient in assisting pediatric primary care providers in working with patients, parents, genetics, and other specialties. Supporting pediatricians in the return of these results requires addressing gaps in genetics training and integrating genetics into practice with education, information resources, and specialized tools.

Objectives This study aimed to capture requirements for developing systems and processes to support primary care pediatricians in the return of genome-informed risk assessments.

Methods We performed a requirements analysis to inform the design of clinical decision support tools and processes for pediatric providers of patients who received a genome informed risk assessment, a novel test that combines polygenic risk scores with patient and family histories to deliver a risk assessment for common medical conditions. We developed an interview guide consisting of scenario presentations, questionnaires, and semi-structured questions to elicit provider responses on a broad set of requirements to manage results with patients and caregivers.

Results Twenty providers from 10 primary care pediatric practices within a single health system participated in the study. The findings demonstrated that providers feel responsible to be involved in the process of returning results but require a support system that integrates education, provider and patient information resources, effective communication with genetics, and electronic health record decision support tools that can accommodate a range of clinical scenarios and provider workflow preferences.

Conclusion Supporting providers with the return of genetic testing results such as the genome informed risk assessment requires a comprehensive approach to decision support consisting of education, communication, and a comprehensive and integrated set of electronic health record tools.

Keywords

clinical decision support - requirements analysis and design - workflows and human interactions - genetics - ambulatory care/primary care - pediatrics

• References

• 1 Gottesman O, Kuivaniemi H, Tromp G. et al; eMERGE Network. The electronic medical records and genomics (eMERGE) network: past, present, and future. Genet Med 2013; 15 (10) 761-771
• 2 Terek S, Del Rosario MC, Hain HS. et al. Attitudes among parents towards return of disease-related polygenic risk scores (PRS) for their children. J Pers Med 2022; 12 (12) 1945
• 3 Electronic Medical Records and Genomics. (eMERGE) Network. Genome.gov. September 14, 2022. Accessed April 17, 2023 at: https://www.genome.gov/Funded-Programs-Projects/Electronic-Medical-Records-and-Genomics-Network-eMERGE
• 4 Linder JE, Allworth A, Bland HT. et al; eMERGE Consortium. Returning integrated genomic risk and clinical recommendations: the eMERGE study. Genet Med 2023; 25 (04) 100006
• 5 Lennon NJ, Kottyan LC, Kachulis C. et al; GIANT Consortium, All of Us Research Program. Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations. Nat Med 2024; 30 (02) 480-487
• 6 Hull LE, Gold NB, Armstrong KA. Revisiting the roles of primary care clinicians in genetic medicine. JAMA 2020; 324 (16) 1607-1608
• 7 Haga SB, Kim E, Myers RA, Ginsburg GS. Primary care physicians' knowledge, attitudes, and experience with personal genetic testing. J Pers Med 2019; 9 (02) 29
• 8 Haga SB, Carrig MM, O'Daniel JM. et al. Genomic risk profiling: attitudes and use in personal and clinical care of primary care physicians who offer risk profiling. J Gen Intern Med 2011; 26 (08) 834-840
• 9 Harding B, Webber C, Rühland L. et al. Bridging the gap in genetics: a progressive model for primary to specialist care. BMC Med Educ 2019; 19 (01) 195
• 10 Scott J, Trotter T. Primary care and genetics and genomics. Pediatrics 2013; 132 (Suppl. 03) S231-S237
• 11 Dougherty MJ, Wicklund C, Johansen Taber KA. Challenges and opportunities for genomics education: insights from an institute of medicine roundtable activity. J Contin Educ Health Prof 2016; 36 (01) 82-85
• 12 Klassen TP, Hartling L, Craig JC, Offringa M. Children are not just small adults: the urgent need for high-quality trial evidence in children. PLoS Med 2008; 5 (08) e172
• 13 Chaudhari BP, Manickam K, McBride KL. A pediatric perspective on genomics and prevention in the twenty-first century. Pediatr Res 2020; 87 (02) 338-344
• 14 Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. A roadmap for national action on clinical decision support. J Am Med Inform Assoc 2007; 14 (02) 141-145
• 15 Holden RJ, Carayon P, Gurses AP. et al. SEIPS 2.0: a human factors framework for studying and improving the work of healthcare professionals and patients. Ergonomics 2013; 56 (11) 1669-1686
• 16 Sittig DF, Singh H. A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Qual Saf Health Care 2010; 19 (Suppl 3, Suppl 3): i68-i74
• 17 Gagnon MP, Desmartis M, Labrecque M. et al. Systematic review of factors influencing the adoption of information and communication technologies by healthcare professionals. J Med Syst 2012; 36 (01) 241-277
• 18 Jaspers MWM, Smeulers M, Vermeulen H, Peute LW. Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings. J Am Med Inform Assoc 2011; 18 (03) 327-334
• 19 Kilsdonk E, Peute LW, Jaspers MWM. Factors influencing implementation success of guideline-based clinical decision support systems: a systematic review and gaps analysis. Int J Med Inform 2017; 98: 56-64
• 20 Johnson D, Del Fiol G, Kawamoto K. et al. Genetically guided precision medicine clinical decision support tools: a systematic review. J Am Med Inform Assoc 2024; 31 (05) 1183-1194
• 21 Dolin RH, Shenvi E, Alvarez C. et al. PillHarmonics: an orchestrated pharmacogenetics medication clinical decision support service. Appl Clin Inform 2024; 15 (02) 378-387
• 22 Pennington JW, Karavite DJ, Krause EM, Miller J, Bernhardt BA, Grundmeier RW. Genomic decision support needs in pediatric primary care. J Am Med Inform Assoc 2017; 24 (04) 851-856
• 23 About PeRC. CHOP Research Institute. . Accessed October 27, 2022 at: https://www.research.chop.edu/pediatric-research-consortium/about
• 24 Rasmussen LV, Overby CL, Connolly J. et al. Practical considerations for implementing genomic information resources. Experiences from eMERGE and CSER. Appl Clin Inform 2016; 7 (03) 870-882
• 25 Harris PA, Taylor R, Minor BL. et al; REDCap Consortium. The REDCap consortium: building an international community of software platform partners. J Biomed Inform 2019; 95: 103208
• 26 Tavakol M, Dennick R. Making sense of Cronbach's alpha. Int J Med Educ 2011; 2: 53-55
• 27 Grebe TA, Khushf G, Chen M. et al; ACMG Social, Ethical and Legal Issues Committee. The interface of genomic information with the electronic health record: a points to consider statement of the American College of Medical Genetics and Genomics (ACMG). Genet Med 2020; 22 (09) 1431-1436
• 28 Slomp C, Morris E, Price M, Elliott AM, Austin J. GenCOUNSEL Study. The stepwise process of integrating a genetic counsellor into primary care. Eur J Hum Genet 2022; 30 (07) 772-781
• 29 Connolly JJ, Berner ES, Smith M. et al. Education and electronic medical records and genomics network, challenges, and lessons learned from a large-scale clinical trial using polygenic risk scores. Genet Med 2023; 25 (09) 100906
• 30 Van Dort BA, Zheng WY, Sundar V, Baysari MT. Optimizing clinical decision support alerts in electronic medical records: a systematic review of reported strategies adopted by hospitals. J Am Med Inform Assoc 2021; 28 (01) 177-183
• 31 Prior M, Guerin M, Grimmer-Somers K. The effectiveness of clinical guideline implementation strategies–a synthesis of systematic review findings. J Eval Clin Pract 2008; 14 (05) 888-897
• 32 Wiley K, Findley L, Goldrich M. et al. A research agenda to support the development and implementation of genomics-based clinical informatics tools and resources. J Am Med Inform Assoc 2022; 29 (08) 1342-1349
• 33 Shoenbill K, Fost N, Tachinardi U, Mendonca EA. Genetic data and electronic health records: a discussion of ethical, logistical and technological considerations. J Am Med Inform Assoc 2014; 21 (01) 171-180
• 34 Garcia SJ, Zayas-Cabán T, Freimuth RR. Sync for genes: making clinical genomics available for precision medicine at the point-of-care. Appl Clin Inform 2020; 11 (02) 295-302
• 35 Wasserman RC. The patient record and the rise of the pediatric EHR. Curr Probl Pediatr Adolesc Health Care 2022; 52 (01) 101108
• 36 Menzel MB, Madrigal VN. Genetic testing and screening of children. In: Nortjé N, Bester JC. eds. Pediatric Ethics: Theory and Practice. Springer International Publishing; 2022: 313-328
• 37 Zayas-Cabán T, Chaney KJ, Rogers CC, Denny JC, White PJ. Meeting the challenge: health information technology's essential role in achieving precision medicine. J Am Med Inform Assoc 2021; 28 (06) 1345-1352