Noah Nasser is the CEO of datma (formerly Omics Data Automation), a number one provider of federated Real-World Data platforms and related tools for evaluation and visualization. datma’s mission is to empower healthcare organizations to optimize their data assets, drive innovation, and improve patient outcomes through advanced data storage, AI-enabled data harmonization, and federated query and workflow technologies. Headquartered in Oregon, the corporate is on the forefront of reworking how healthcare data is shared, monetized, and applied, enabling secure collaboration between data custodians and data consumers.
Are you able to explain how datma.FED utilizes AI to revolutionize healthcare data sharing and evaluation?
datma.FED integrates AI-driven analytical tools to enable secure query execution across our federated network. Its advanced algorithms facilitate the extraction, aggregation, and delivery of de-identified, shareable datasets- allowing data consumers comparable to pharmaceutical corporations and research organizations to extract insights while ensuring full compliance and privacy standards.
By automating complex data queries, datma.FED accelerates access to high-quality, ready-to-use real-world data. This empowers data custodians comparable to health systems and molecular labs to take part in collaborative research efforts while maintaining full control over their data assets.
What are the important thing challenges datma solves for molecular labs and health systems?
datma.FED solves several critical challenges for molecular labs and health systems, including:
- Data Monetization: Enables continuous revenue generation from underutilized healthcare data while allowing data custodians to retain full ownership and control.
- Data Privacy & Security: Keeps sensitive data secure by ensuring it never leaves the information custodian’s environment through a privacy-first federated model.
- Data Compliance Risks: Minimizes the regulatory risks with audit-ready data access controls and full compliance tracking.
- Data Preparation and Business Development: datma takes on the hassle of knowledge preparation to make sure data readiness while connecting data custodians with research and pharma partners.
How does datma ensure data privacy and compliance while enabling secure collaboration between data custodians and data consumers?
datma.FED employs a federated network model, which keeps data securely inside each custodian’s environment while enabling privacy-first collaboration with data consumers. Data goes through a multi-step process: it’s anonymized, filtered for accessibility, and designated as shareable based on custodian-defined permissions. datma then processes external queries without transferring raw data, aggregating only approved, de-identified data fields. Cell-size restrictions prevent re-identification. Every data interaction is auditable and compliant with regulatory standards like HIPAA.
What sets datma.FED aside from other data platforms when it comes to scalability and usefulness?
datma.FED is designed to scale seamlessly through its federated architecture and automatic data readiness features. Its design allows for seamless integration of multimodal healthcare data from multiple sources. The platform’s automated data readiness features – including data labeling and standardization – streamline data preparation and reduce manual effort. By ensuring that data is query-ready and compliant from the beginning, datma.FED enables large-scale, privacy-first data sharing, making it highly scalable and intuitive for research and real-world data applications.
How does the datma.FED platform facilitate the mixing of multimodal healthcare data across silos?
datma.FED facilitates the mixing of multimodal healthcare data across silos through certainly one of its components, datma.BASE. datma.BASE is a comprehensive framework built on proprietary data stores, containers, and APIs. At scale, its advanced capabilities enable the ingestion, aggregation, and harmonization of diverse healthcare data types (EHR, Omics, Images, and Pathology). By breaking down data silos, datma.BASE transforms fragmented datasets into unified, actionable insights.
How does datma’s technology contribute to bridging data gaps in pharmaceutical research and drug development?
datma.FED helps fill critical data gaps for pharmaceutical research and market access strategies. By providing high-quality, ready-to-use real-world data (RWD) with granularity and longitudinal depth, datma.FED enables pharma corporations to make more data-driven decisions. Its secure infrastructure ensures that data stays accessible without compromising privacy or security, supporting comprehensive insights needed for discoveries.
How does datma empower healthcare organizations to monetize their data while maintaining ethical and regulatory standards?
datma enables healthcare organizations to monetize their data by making a secure data-sharing ecosystem where healthcare organizations retain full ownership and control. Through its federated network, data custodians determine what data is accessible and shareable while keeping sensitive information securely inside their very own infrastructure. Comprehensive audit trails, role-based permissions, and regulatory compliance features be certain that all data-sharing activities adhere to moral standards and privacy regulations. This approach allows healthcare organizations to generate recent revenue streams while safeguarding patient privacy and maintaining trust.
What trends in AI and healthcare data do you foresee having the most important impact in the subsequent five years?
AI in healthcare, is tempered by concerns for privacy, security and limited only by data quality. AI already empowers us to deliver truly personalized medicine in oncology but has only scratched the surface of what is feasible. By analyzing vast amounts of multimodal patient data, including genomics, imaging, and biomarker data in context with medical history, demographic and lifestyle aspects, we are going to tailor treatment plans and therapies to individual needs. This results in improved patient outcomes and, ultimately, to reduced healthcare costs. Coupling these tools with distant patient monitoring and patient-reported outcomes will enable early disease detection and improve adherence to treatment plans. Nevertheless, the critical lynchpin in all of this are deep, contextual data sources which might be sufficiently diverse.
Moreover, AI will likely be key in providing advanced access to personalized care. I see a job for AI models in simplifying payer and billing logistics, streamlining burdensome paperwork and ensuring access and equity across the population. Currently, LLM’s have shown some limitations on this application; recent publications have identified their shortcomings concerning medical coding. Clearly, these barriers may be overcome with higher, deeper, and more complete training data.
Finally, AI will proceed accelerating the pace of medical research. AI can discover novel drug targets by analyzing massive datasets, spanning imaging, multi-omic, and other approaches, optimizing clinical trial design, and accelerating drug discovery. Federated learning, a privacy-preserving AI technique, allows institutions to collaborate on research without sharing sensitive patient data, unlocking the potential of collaborative research. Recent advances in causal inference and generative AI, specifically, portend significant advancements in discovery from basic biology to applied therapeutics.
What’s your long-term vision for datma’s impact on healthcare systems and the broader industry?
At datma, we’re focused on constructing a future where higher data drive personalized, accessible, and efficient healthcare. By uniting complex datasets through federated learning, we’re empowering clinicians and researchers to deal with complex healthcare challenges and to unlock recent medical breakthroughs. Our federated, real-world data marketplace, datma.FED, is step one towards realizing this vision.
Imagine a future for healthcare where researchers leverage and analyze vast amounts of patient data, from genomics, imaging, and medical history to lifestyle aspects, to tailor next-generation therapeutics with exquisite patient focus. At the identical time, clinicians can utilize AI to offer the proper care at the proper time with minimal administrative burden. datma’s federated approach accelerates this vision by unlocking the ability of complex, secure medical data. By repeatedly expanding our dataset and launching modern tools like datma.WHY and datma.360, we’re driving earlier disease detection, improved therapies, and higher patient outcomes.
Our vision extends beyond individual patients. datma’s commitment to federated learning unlocks the ability of collaborative research, allowing institutions to research massive datasets without compromising patient privacy. This unleashes a wave of discovery, from identifying novel drug targets to optimizing clinical trials. By leveraging AI’s analytical prowess and causal inference capabilities, we are able to speed up medical research and convey life-saving treatments to patients faster. We’re committed to leading the best way in making this future a reality.
