The Changing Role of the Genetic Counsellor in the Genomics Era

Kohut, Kelly; Limb, Sharne; Crawford, Gillian

doi:10.1007/s40142-019-00163-w

Cited by 12 publications

(15 citation statements)

References 86 publications

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“…Similarly, NLP can be used to make health information more accessible by translating educational materials into other languages or by converting medical terms to their lay definitions [45]. AI-based chatbots have already been deployed to augment the capabilities of genetic counselors to meet rising demands on their time generated by the rapidly expanding volume of clinical and direct-to-consumer genetic testing [46]. In addition, NLP approaches to EHR analysis can overcome the high dimensionality, sparseness, incompleteness, biases, and other confounding factors present in EHR data.…”

Section: Natural Language Processingmentioning

confidence: 99%

Artificial intelligence in clinical and genomic diagnostics

2019

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Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing units (GPUs) that power their training, have led to a recent and rapidly increasing interest in medical AI applications. In clinical diagnostics, AI-based computer vision approaches are poised to revolutionize image-based diagnostics, while other AI subtypes have begun to show similar promise in various diagnostic modalities. In some areas, such as clinical genomics, a specific type of AI algorithm known as deep learning is used to process large and complex genomic datasets. In this review, we first summarize the main classes of problems that AI systems are well suited to solve and describe the clinical diagnostic tasks that benefit from these solutions. Next, we focus on emerging methods for specific tasks in clinical genomics, including variant calling, genome annotation and variant classification, and phenotype-to-genotype correspondence. Finally, we end with a discussion on the future potential of AI in individualized medicine applications, especially for risk prediction in common complex diseases, and the challenges, limitations, and biases that must be carefully addressed for the successful deployment of AI in medical applications, particularly those utilizing human genetics and genomics data.

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Section: Natural Language Processingmentioning

confidence: 99%

Artificial intelligence in clinical and genomic diagnostics

2019

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“…Psychological aspects of testing and risk communication are largely managed within genetic counselling, with referrals to psychologists where indicated. In mainstreaming genomic counselling within oncology, it will be vital to train other health professionals in not only how to explain genomic test results but also how to identify patients needing additional psychosocial support . This is a transformative time for professionals working within genomics and oncology, with a need for close collaborative working between genetic counsellors, geneticists, oncologists, clinical nurse specialists, and other healthcare professionals to coordinate cross‐disciplinary care and maximise benefits for patients as well as their family networks.…”

Section: What Has Been the “Traditional” Approach To Genomic Testing?mentioning

confidence: 99%

“…However, issues such as pre-test consent and appropriateness of risk-reducing surgery raise difficult challenges for patients and healthcare professionals alike. 7,8 Cancer patients have reported feeling generally positive about acquiring information during the diagnostic genetic testing process.…”

Section: Wgs: Challenges Of Secondary Findingsmentioning

confidence: 99%

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The New Genomics Era: Integration of genomics into mainstream oncology and implications for psycho‐oncological care

2020

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“…The number of certified genetic counselors in the United States has more than doubled over the past 10 years, and the scope of practice has expanded since the profession’s inception to include more roles in clinical, research, government, non‐profit, and industry settings (National Society of Genetic Counselors (NSGC), 2020). As the era of genomic medicine and precision health continues to evolve, the roles of genetic counselors will continue to expand and diversify (Bamshad et al, 2018) while the core skill set will remain unique and in‐demand across practice settings (Kohut et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Genetic counselor roles in the undiagnosed diseases network research study: Clinical care, collaboration, and curation

Kohler

Kelley

Boyd

et al. 2021

Journal of Genetic Counseling

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Genetic counselors (GCs) are increasingly filling important positions on research study teams, but there is limited literature describing the roles of GCs in these settings. GCs on the Undiagnosed Diseases Network (UDN) study team serve in a variety of roles across the research network and provide an opportunity to better understand genetic counselor roles in research. To quantitatively characterize the tasks regularly performed and professional fulfillment derived from these tasks, two surveys were administered to UDN GCs in a stepwise fashion. Responses from the first, free-response survey elicited the scope of tasks which informed development of a second structured, multiple-select survey. In survey 2, respondents were asked to select which roles they performed. Across 19 respondents, roles in survey 2 received a total of 947 selections averaging approximately 10 selections per role. When asked to indicate what roles they performed, respondent selected a mean of 50 roles (range 22-70). Survey 2 data were analyzed via thematic coding of responses and hierarchical cluster analysis to identify patterns in responses. From the thematic analysis, 20 non-overlapping codes emerged in seven categories: clinical interaction and care, communication, curation, leadership, participant management, research, and team management. Three themes emerged from the categories that represented the roles of GCs in the UDN: clinical care, collaboration, and curation. Cluster analyses showed that responses were more similar among individuals at the same institution than between institutions. This study highlights the ways GCs apply their unique skill set in the context of a clinical translational research network. Additionally, findings from this study reinforce the wide applicability of core skills that are part of genetic counseling training. Clinical literacy, genomics expertise and analysis, interpersonal, psychosocial and counseling skills, education, professional practice skills, and an understanding of research processes make genetic counselors well suited for such roles and poised to positively impact research experiences and outcomes for participants.

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The Changing Role of the Genetic Counsellor in the Genomics Era

Cited by 12 publications

References 86 publications

Artificial intelligence in clinical and genomic diagnostics

Artificial intelligence in clinical and genomic diagnostics

The New Genomics Era: Integration of genomics into mainstream oncology and implications for psycho‐oncological care

Genetic counselor roles in the undiagnosed diseases network research study: Clinical care, collaboration, and curation

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