By Jason Bhan, MD, Bill Paquin, Chris Hayes, Sundeep Bhan, Michael Cheyne
[Bonus: Link to the free Lab Data RFP Template is below]
In today’s data-driven healthcare landscape, access to lab data, particularly oncology and genomics information, is no longer just a competitive advantage - it’s a strategic imperative. Despite this, many life sciences companies grapple with fragmented data ecosystems, redundant purchases, and ineffective licensing strategies that hinder their return on investment.
Without a coordinated and informed approach to lab data licensing, teams often purchase overlapping datasets from various vendors, receive incomplete or poorly structured information, and experience significant integration delays. This directly leads to poor time-to-value, creates data governance confusion, and ultimately results in suboptimal insights for critical clinical development and commercialization efforts.
As the volume, complexity, and value of lab data continues to surge, the need for a streamlined and standardized approach to data acquisition becomes paramount. We have to ask ourselves: Should we continue to navigate the complexity of contracting with dozens of different labs, each with unique formats, ontologies, and update cycles? Or is it time to streamline the entire process, cutting through bottlenecks in procurement, data engineering, and legal review, to enable faster, more consistent access to the right data?
At Prognos Health, we don’t just understand lab data - we have been shaping its future. For the past decade, we’ve partnered with hundreds of labs and leading life sciences organizations to transform raw lab data into a precise, actionable, and seamlessly integrated asset. Drawing on this experience, we’ve distilled seven best practices for licensing high-value lab data that deliver unwavering scientific rigor and powerful commercial impact.
1. Prioritize Access to Data Sourced Directly from Labs for Unmatched Quality
For lab, genomics, and oncology data, the source defines its value. Prioritize partners with direct lab relationships to guarantee faster, more complete, and reliable data access. Beware of aggregators whose broad offerings can lead to duplicate and incomplete records.
Crucially, demand transparency: know exactly how data is sourced, the proportion from direct lab ties versus aggregators, and that labs are aware of data licensing. Strong data provenance is non-negotiable; it's essential for compliance, data integrity, detailed annotations, consistent updates, and ensuring stable, long-term access.
2. Standardization is Non-Negotiable
Genomic and oncology datasets are notoriously complex. The use of standard ontologies, such as LOINC for lab tests, HGVS for variant nomenclature, and HPO for phenotype mapping, is critical to make data usable across platforms and analytics pipelines. Lab-proprietary codes and free-text variant interpretations must be carefully mapped to standardized structures to enable meaningful comparisons across labs and patient cohorts.
3. Structure Unstructured Data at Scale
In oncology and genomics, ensure partners extract biomarker results (e.g., PD-L1, EGFR, BRCA) from unstructured reports, normalize terms like “pathogenic” or “VUS,” and structure germline test results with attributes like zygosity, transcript ID, and inheritance patterns. Bonus if the data supports granular JSON-style querying.
A significant portion of valuable genetic insights lives in unstructured text, such as variant interpretations, zygosity notations, and test requisitions. Sophisticated NLP pipelines should be employed to extract key features, including:
This structured extraction enables granular querying and AI/ML readiness.
4. Demand Granularity and Longitudinal Completeness
Real-time or daily refreshes are essential in many commercial and clinical applications. Ask what percent of the dataset is refreshed daily and the time lag between a test result and data delivery.
Oncology and genomics insights rely on a complete patient picture. Licensing agreements should require:
5. Ensure Flexibility in Delivery and Use
Ensure the data partner supports cohort-building based on clinical thresholds, diagnostic patterns, or genetic mutations, not just test presence. This requires domain expertise and curated logic to define complex patient conditions from lab results.
A best-in-class lab data provider should support delivery via API, cloud integration, and custom query tools, enabling seamless integration into CRM, RWD platforms, and ML pipelines. The data should be formatted for immediate use in downstream applications, including alerts, cohort building, predictive modeling, and biomarker discovery.
6. Protect Patient Privacy—Without Compromising Utility
Given the identifiability of genetic data, rigorous de-identification methods must be in place. Look for providers who:
7. Vet for Quality, Not Just Quantity
Ask for a copy of the vendor’s standard lab data schema, including field-level definitions, data types, and optional vs. required fields. This ensures alignment between what’s promised and what will be delivered.
Finally, data quality must be demonstrable. Require vendors to provide:
Pro Tip:
Use our free Lab Data RFP Template to standardize your licensing process, ensure vendors meet high-quality thresholds, and reduce risk of buying incomplete or duplicative data.
To learn more about the lab data ecosystem go to https://prognoshealth.com/lab-data-ecosystem
At Prognos Health, we have seen firsthand that not all lab data is created equal. When licensed strategically and responsibly, lab data, especially genomics and oncology data, can unlock transformative insights across drug development, clinical research, and patient care.
For more on how Prognos Health helps life sciences companies access high-quality, real-world lab data, visit www.prognoshealth.com or contact us at marketing@prognoshealth.com