Market Access for Targeted Therapy in NSCLC
A pharmaceutical company with a targeted therapy for non-small cell lung cancer (NSCLC) was facing competitive pressures from market-leading alternatives. Despite strong clinical data, the company was losing patients to competitor therapies at key decision points along the patient journey.
Evidence Generation for Targeted Therapy in Advanced Breast Cancer
A global pharmaceutical company with a targeted therapy for advanced breast cancer that demonstrated strong clinical efficacy sought to generate real-world evidence to support inclusion in NCCN clinical guidelines- a key driver for physician adoption and payer reimbursement.
Enabling Clinical Trial Recruitment & Internal Research
A large regional health system sought to expand its precision medicine research by launching investigator-led clinical studies and collaborating with external research partners. However, they faced challenges in making their clinical, genomics, and imaging data readily unusable for research studies.
Scaling Research Infrastructure to Attract Funding & Multi-Site Studies
A mid-sized academic medical center (500-900 beds) with a growing research program had extensive clinical, genomic, and imaging datasets and wanted to leverage these assets for external collaborations, grant funding, and multi-site research studies.
Monetizing Molecular Lab Data for Financial Sustainability
By implementing datma.FED, the lab securely monetized its molecular and pathology data while keeping it securely within their own environment. datma.BASE, integrated into datma.FED, automated data readiness, handling everything from extracting unstructured pathology data to mapping source data into a common model. This ensured the lab’s data was ready for external queries with minimal internal effort.
Why AI in Precision Medicine Needs Federated Data Infrastructure
When new targeted therapies are introduced into routine clinical care, adoption is rarely uniform. Even with strong clinical data, some therapies experience slower uptake across sites, regions, or patient groups.
datma Introduces New Pathology Data Labeling System for its Flagship Federated Real World Data Platform, Expanding the Use of Pathology Imaging for Precision Medicine Drug Discovery and Development
datma, a leading provider of federated Real-World Data platform and related analytical tools, today announced its patent-protected pathology data labeling system will be part of its datma.FED platform, improving the preparation and usability of pathology data for federated analysis.
How Health Systems Can Extract Full Value from Their Healthcare Data Without Taking on Additional Work
While the potential of healthcare data is widely recognized, turning that potential into something actionable has proven harder than expected, especially for institutions that lack time, resources, or infrastructure to support large-scale data initiatives.
Empowering Molecular Tumor Boards
Providers are challenged by the sheer breadth and depth of the information required to make good treatment decisions in modern oncology practice, and the lack of an integrated solution for managing, presenting and analyzing multi-modal data presents barriers to both providers and to improved patient outcomes.
Approaches to Addressing Barriers in Adopting Genomic Testing and Targeted Therapy in Oncology
To effectively utilize genomic testing and targeted therapies, the oncologist needs to correlate a patient's demographics and co-morbidities, evidence of cancer as observed in radiology studies and cause of cancer as observed in pathology and genomic tests.
Multimodal, Federation Ready, Integrative Framework for Biomedical Data
The datma.BASE biomedical information management platform is an integrated solution to the storage, integration and analysis challenges facing modern biomedical researchers. Utilizing highly optimized data storage technologies and compute management systems, datma.BASE allows researchers to ingest, access, analyze and even securely collaborate via federation with other researchers over large, integrated omics, imaging, and phenotypic/clinical data sets.
Healthcare Data Monetization: The Path to Innovation and Increased Revenue
Healthcare data is a powerful asset, with the potential to transform patient care and drive breakthroughs in personalized medicine, drug discovery, and disease prevention. Data monetization offers data custodians (Health Systems, Biobanks, Labs etc.) the opportunity to tap into this potential, generating new revenue streams while supporting advancements in medical research and innovation.
How UPenn Researchers Predicted Heart Transplant Rejection datma.BASE
At dātma, we have been privileged to support researchers in identifying new biomarkers to predict heart transplant rejection. These technologies play a crucial role in advancing medicine through the development of Machine Learning and AI models.
Navigating Challenges: Barriers to Adopting Genomic Testing and Targeted Therapy in Oncology
The adoption of genomic testing and targeted therapies in the clinical setting has not kept up with these innovations. Explore the system and oncologist specific barriers to adopting genomic testing and targeted therapies along with potential solutions to address these challenges.
Visualization and UX Challenges in Utilizing Complex Multimodal Data
Producing usable visualizations and user-friendly UX for complex multimodal data presents hurdles as large as data management itself, especially when diverse users with varied use cases access the same data for vastly different tasks.
Revolutionizing Healthcare with Federated Learning and Artificial Intelligence
The healthcare industry is on the brink of a transformative era, ready to personalize treatment plans based on individual genetic, environmental, and lifestyle factors. This monumental shift is driven by the convergence of several enabling factors: high-throughput sequencing and imaging technologies, the widespread adoption of electronic health records (EHRs), and the maturation of sophisticated analytics and data science frameworks.
Managing Large Molecular Data Sets
Integrating large and complex molecular biomedical datasets at scales useful for research and clinical applications poses significant challenges. This is especially true for genetic and genomic data, which are commonly used in clinical practice.