Dataset Information

Integrating Germline Genetics Into Precision Oncology Practice in the Veterans Health Administration: Challenges and Opportunities.

ABSTRACT:

Objectives

The advent of germline testing as a standard-of-care practice for certain tumor types and patients presents unique opportunities and challenges for the field of precision oncology. This article describes strategies to address workforce capacity, organizational structure, and genetics education needs within the US Department of Veterans Affairs (VA) with the expectation that these approaches may be applicable to other health care systems.

Observations

Germline information can have health, reproductive, and psychosocial implications for veterans and their family members, which can pose challenges when delivering germline information in the setting of cancer care. Additional challenges include the complexity inherent in the interpretation of germline information, the national shortage of genetics professionals, limited awareness and knowledge about genetic principles among many clinicians, and organizational barriers, such as the inability to order genetic tests and receive results in the electronic health record. These challenges demand thoughtful implementation planning at the health care system level to develop sustainable strategies for the delivery of high-quality genetic services in precision oncology practice.

Conclusions

The VA is uniquely positioned to address the integration of germline genetic testing into precision oncology practice due to its outsized role in treating veterans with cancer, training the health care workforce, and developing, testing, and implementing innovative models of clinical care.

SUBMITTER: Scheuner MT

PROVIDER: S-EPMC7473729 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Publications

Integrating Germline Genetics Into Precision Oncology Practice in the Veterans Health Administration: Challenges and Opportunities.

Scheuner Maren T MT Myrie Kenute K Peredo Jane J Hoffman-Hogg Lori L Lundquist Margaret M Guerra Stephanie L SL Ball Douglas D

Federal practitioner : for the health care professionals of the VA, DoD, and PHS 20200801 Suppl 4

<h4>Objectives</h4>The advent of germline testing as a standard-of-care practice for certain tumor types and patients presents unique opportunities and challenges for the field of precision oncology. This article describes strategies to address workforce capacity, organizational structure, and genetics education needs within the US Department of Veterans Affairs (VA) with the expectation that these approaches may be applicable to other health care systems.<h4>Observations</h4>Germline informatio ...[more]

PMID: 32908356

Similar Datasets

Project description:BackgroundThe ability of machine learning (ML) to process and learn from large quantities of heterogeneous patient data is gaining attention in the precision oncology community. Some remarkable developments have taken place in the domain of image classification tasks in areas such as digital pathology and diagnostic radiology. The application of ML approaches to the analysis of DNA data, including tumor-derived genomic profiles, microRNAs, and cancer epigenetic signatures, while relatively more recent, has demonstrated some utility in identifying driver variants and molecular signatures with possible prognostic and therapeutic applications.MethodsWe conducted semi-structured interviews with academic and clinical experts to capture the status quo, challenges, opportunities, ethical implications, and future directions.ResultsOur participants agreed that machine learning in precision oncology is in infant stages, with clinical integration still rare. Overall, participants equated ongoing developments with better clinical workflows and improved treatment decisions for more cancer patients. They underscored the ability of machine learning to tackle the dynamic nature of cancer, break down the complexity of molecular data, and support decision-making. Our participants emphasized obstacles related to molecular data access, clinical utility, and guidelines. The availability of reliable and well-curated data to train and validate machine learning algorithms and integrate multiple data sources were described as constraints yet necessary for future clinical implementation. Frequently mentioned ethical challenges included privacy risks, equity, explainability, trust, and incidental findings, with privacy being the most polarizing. While participants recognized the issue of hype surrounding machine learning in precision oncology, they agreed that, in an assistive role, it represents the future of precision oncology.ConclusionsGiven the unique nature of medical AI, our findings highlight the field's potential and remaining challenges. ML will continue to advance cancer research and provide opportunities for patient-centric, personalized, and efficient precision oncology. Yet, the field must move beyond hype and toward concrete efforts to overcome key obstacles, such as ensuring access to molecular data, establishing clinical utility, developing guidelines and regulations, and meaningfully addressing ethical challenges.

Dataset Information

Integrating Germline Genetics Into Precision Oncology Practice in the Veterans Health Administration: Challenges and Opportunities.

Objectives

Observations

Conclusions

Publications

Integrating Germline Genetics Into Precision Oncology Practice in the Veterans Health Administration: Challenges and Opportunities.

Similar Datasets

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