New, critical, investigative, and qualitative report analyzes the observations and learnings across the complex components of biomedical data warehousing, querying, and collaboration.
Palo Alto, CA, May 13, 2021 – enlightenbio LLC, today announced the publication of its Warehousing Clinical & Genomics Biomedical Data – A Needs & Challenges Analysis Report. This critical, investigative, and qualitative report analyzes the observations and learnings across the complex components of biomedical data warehousing, querying, and collaboration to further clinical applications via population studies, data querying, and validation of research findings. This report benefits commercial entities developing solutions to address the ever growing biomedical data needs of this expanding industry.
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 data warehousing sector.
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Download Table of Contents to learn more about the report specifics.
Medical organizations understand the benefit of being empowered by data-driven approaches to reduce operational costs and time and to provide researchers and clinicians the necessary tools to decipher critical research data and data for clinical-decision making. Innovative technological advancements have allowed us to sequence and uncover mutational events at an unprecedented scale, while facilitating linking genomic data to high quality clinical data and diagnosis. Implementation of clinical genomics applications and enterprise-wide clinical data warehouses are fundamental for successful genomic medicine programs. As such, leading medical organizations have established precision medicine programs that support personalized patient treatment. 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 whole exome sequencing are well adopted, the 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. Essential to overcoming these challenges are scalable, fully automated analysis and knowledge extraction solutions incorporating rich annotation information. With massive quantities of next-generation sequencing (NGS) data (linked to 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 hindered 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.
To create our robust analysis, our research focused on these questions:
- What are the unmet needs and challenges of medical organizations/clinical end users in relation to biomedical (clinical and genomics) data implementations; and
- What innovations of emerging technologies are required for successful adoption and acceleration of clinical genomics data warehouse-based applications?
- End user interviews: Conducted to understand medical industry and clinical end users’ needs and challenges, cloud solution preferences, and challenges with clinical data and integrating and communicating findings via an electronic healthcare system with the physician and the patient.
- Twenty-one (21) end user interviews from eighteen (18) medical research organizations provided insights into unmet data needs and challenges
- Deep level analysis:
- Researched optimal genomic data generation, data flow, and intelligence data platform requirements that support the interplay between clinical research and clinical genomics.
Clinical interviews detailed challenges associated with creating a workflow that incorporates a clinical data warehouse connecting clinical research with clinical diagnostics and vice versa. Efficient clinical decision-making and reporting findings between clinical research and the clinic to optimize clinical outcome and patient treatment stem from this important workflow.
Figure 1: Mapping the organizations of twenty-one (21) end users interviewed to the Clinical Genomics Workflow.
The 47-page Warehousing Clinical & Genomics Data – A Challenges & Needs Analysis Report contains 8 Figures and 17 Tables.
This release represents a major 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.