New, qualitative report provides an in-depth analysis across the complex, multi-step clinical genomics data process, including genomic data generation and analysis, data flow, and data warehousing.
Palo Alto, CA, February 17, 2021 – enlightenbio LLC, today announced the publication of its Clinical Genomics Report – The Interplay Between Clinical Research & Clinical Diagnostics report. This new, qualitative report provides an in-depth analysis across the complex, multi-step clinical genomics data process (which includes genomic data generation, data flow, and data warehousing), clinical genomics data solution providers, market trends, technologies impacting clinical genomics applications development, clinical genomics adoption challenges, and a detailed challenges & needs analysis as identified in discussions with clinical end users.
The report is unique, in that it is not a predictive market research report, but rather builds on data gathered from many end user interviews combined with an extensive analysis of the clinical genomics sector.
While this report does not intend to provide direct recommendations on commercial offerings, the deep-dive analysis is an insightful review to help clinicians, researchers, commercial entities, and investors choose the best partner for success. Furthermore, this report provides valuable insights into existing implementations at leading medical organizations.
For a limited time only, apply Promo Code 10OFFCLINICALOMICS and get 10% off when purchasing the report – valid until March 31, 2021.
Download Table of Contents to learn more about the report specifics.
Leading medical organizations have established precision medicine programs that support personalized patient treatment. Implementation of clinical genomics applications and enterprise-wide clinical data warehouses are considered the foundation for successful genomic medicine programs. Innovative technological advancements have allowed us to sequence and uncover mutational events at unprecedented scale, while facilitating linking genomic data to high quality clinical data and diagnosis. Medical organizations understand the benefit of being empowered by data-driven approaches to reduce operational costs and time and to provide researchers and clinicians what is necessary to decipher critical research data and data for clinical-decision making. However, technical and scientific limitations still need to be addressed for optimized and universal use of various data sources for both clinical and research purposes.
While data production is no longer a challenge, and targeted panels – and to some extent whole exome sequencing – are well adopted, the expected dramatic rise in whole genome sequencing will result in unforeseeable quantities of data at the clinical level that need to be managed, understood, and communicated. Low-cost sequencing of whole genomes at population scale is already in existence, but not yet widespread in the clinic, as many observed changes at the genome level cannot yet be fully interpreted or explain an existing phenotype. Scalable, fully automated analysis and knowledge extraction solutions incorporating rich annotation information are necessary to overcome these challenges. With massive quantities of NGS data (linked to different clinical and other types of data), artificial intelligence and machine learning are hailed as pivotal solutions to address the data interpretation and knowledge extraction challenges and to advance the clinical application of genomics.
Despite increasing efforts and investments in implementing clinical applications and building data solutions, many organizations are still challenged with the multi-faceted complexities in transforming to become data-driven. Implementations are challenged by ineffective data sharing, scalability and automation issues, non-optimized data generation and data flow approaches, and non-standardized data from numerous sources. Implementing a complex clinical data warehouse presents many challenges starting with the various data sources it needs to support and the tools required to view the clinical information. Ingestion of data of different types and origins with relevant metadata; data transformation, standardization, and cleansing to support the needs of a diverse set of end users in both the clinical and research settings; and varied end users with individual needs and computation capabilities are all important considerations.
Clinical interviews detailed challenges associated with creating a workflow that incorporates a clinical data warehouse connecting clinical research with clinical diagnostics and vice versa. This important workflow leads to efficient clinical decision-making and reporting findings between clinical research and the clinic, which can optimize clinical outcome and patient treatment.
Our “Clinical Genomics Report: The Interplay Between Clinical Research and Clinic Diagnostics” provides an in-depth analysis of differences in product characteristics related to data processing, analysis, knowledge extraction and reporting of findings (including type of content integrated for meaningful extraction), and compliance and security mechanisms. Both clinical end users and commercial companies who require insight into this expanding industry and its providers and products will benefit from our critical, investigative, and qualitative report.
- To create our robust comparison, we researched these questions:
- What are the current implementation choices of data solutions and testing services at leading medical organizations;
- What are the unmet needs and challenges of medical organizations/clinical end users in relation to clinical genomics implementation;
- What are current clinical genomics market trends, and what innovations/technologies impact the adoption of clinical genomics applications;
- Who are the key commercial data solution providers and what solutions/products do they offer;
- Who are the genetic testing service providers and what specific services do they provide; and
- What needs do the genomics data management, process, analysis and interpretation commercial companies address with what product capabilities, and how do they compare across the ecosystem of solution providers?
- End user interviews (N=21): Conducted to understand medical industry and clinical end user’s needs and challenges, commercial solution preferences, and challenges with clinical data and integrating and communicating findings via an electronic healthcare system with the physician and the patient.
- Meta-Data analysis: Performed a deep-dive interrogation of individual software, platform solutions, and genetic testing providers with publicly available information on the WWW [scientific publications, presentations, annual reports, white papers, and use cases].
- Deep level analysis:
- Researched implemented clinical genomics workflows at leading medical organizations (N=14) to support their internal precision medicine efforts.
- Evaluated key commercial software and platform providers (N=18) of clinical genomics solutions, such as scaled data storage and computing solutions, and data analysis and interpretation to understand product focus, capabilities, and the strategy to address end user needs.
- Researched optimal genomic data generation, data flow, and intelligence data platform requirements that support the interplay between clinical research and clinical genomics.
- Evaluated commercial players (N=8) implementing artificial intelligence/machine learning applications for clinical genomics.
- Company/product profiles: Reviewed key companies with comprehensive solutions across the entire Clinical Genomics Workflow, including genetic testing/diagnostics service providers (N=20), their product focus, offered capabilities, and their strategy to address end user needs, and more.
- Key representative input: Interviewed company representatives of established software suppliers to learn about current and future product solutions.
The 280-page Clinical Genomics – The Interplay between Clinical Research and Clinical Diagnostics consists of 13 Figures, 95 Tables, and 20 comprehensive Company Profiles of leading commercial companies across the entire workflow [BC Platforms, Bluebee, Color, Congenica, DNAnexus, Fabric Genomics, Foundation Medicine, Freenome, Genoox, Genuity Science, Google Life Sciences, GRAIL, Helix, Illumina (with BaseSpace), Invitae, PierianDx, QIAGEN, Seven Bridges Genomics, SOPHiA Genetics, and Tempus – includes company metrics, funding sources, product details, founder/executive and board information, additional notes, and company visions].
We are very excited about this major report release that represents a significant milestone for us in our mission to bring up-to-date, concise information with translatable value to our customer community. This report is a wise and impactful investment that will help avoid potentially costly mistakes for customers, provide valuable insight to software and platform providers looking to address unmet user and stakeholder needs, and guide investors significant strategic decisions.