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How Verana Health Uses the Databricks Lakehouse to Democratize Data and Deploy AI for Medical Innovation

by Lawrence Whittle

This blog post is in collaboration with Lawrence Whittle (Chief Commercial Officer) at Verana Health.

 

Across industries, data scientists spend up to 80% of their time trying to properly prepare and cleanse datasets for data mining and artificial intelligence (AI). For clinical researchers, life science analysts, and healthcare professionals, this challenge is amplified by the added regulatory burdens around healthcare data, requiring patient data to be anonymized while still providing demographic and population information necessary to correct for bias. The data challenge in healthcare is exacerbated by the fact that up to 80% of the data is unstructured.

This is why Verana Health came into existence. In partnership with three leading medical societies, we have built an exclusive, real-world data network of more than 20,000 healthcare clinicians, approximately 90 million de-identified patients and more than 500 million patient visits. By providing high-quality, curated datasets (Qdata®), ready for exploration by researchers and data scientists, we can help clinicians and life sciences companies accelerate medical innovation.

Our Verana Health customers and partners utilize these datasets to help identify trial patients, understand population-level impact of public health policy decisions, and monitor the safety and treatment patterns of patients receiving their drugs. When you consider that the average drug discovery process takes about a decade and costs about $1-2 billion per drug, accelerating the process by even a single month could translate into tens of millions of dollars in savings or accelerated revenue. The real difference between Verana Health's Qdata offerings, compared to the general data market, is quality. Quality is defined across multiple dimensions such as cohort size, longitudinal nature (~10 years of data), and most importantly, depth of variables that are a direct result of our approach to harvest previously untapped variables from unstructured data that has historically been locked in clinical notes and images.

So, how do we turn all of that data into insights? We use the Databricks Lakehouse to ingest, process, and organize our petabyte-scale data warehouse of health information.

How Verana Health runs on the Databricks lakehouse

The Databricks and Verana Health collaboration is a critical element for the provision of high-quality datasets to the life sciences and clinician marketplace. The integrated solutions normalize and curate petabytes of health information across three therapeutic areas of neurology, ophthalmology and urology. This enables Verana Health to leverage the data for clinical trial optimization, real-world evidence studies, population health analytics, and publication of Merit-based Incentive Payment System quality measures for Centers for Medicare and Medicaid Services (CMS) reporting.

To start, we pull the data into the Lakehouse from our exclusive network of specialty medical society partners using purpose-built data connectors to ensure patient confidentiality (1). We then leverage Delta Lake's multi-hop architecture (bronze, silver, gold) to progressively cleanse, prepare, and organize the data for downstream usage (2).

  • Raw data is ingested as bronze tables.
  • Each source clinician might use different formats or schema for their electronic health records, so data is normalized, cleansed, and organized in silver. Further transformations, such as de-identification, are applied for the gold tables (2). Natural language processing can also be applied at this stage to, for example, convert free-form clinician notes into usable variables.
  • These gold tables are now ready to be shared with our customers as fully cleansed and prepped data products for use in analytics and AI (3).

Databricks Lakehouse

With the Databricks Unity Catalog, we are able to centralize access control, auditing, lineage, and data discovery across Databricks workspaces. Specifically, we can define and control access down to the table, column, and row level — ensuring the right data is shared with a researcher without requiring him/her to filter through large chunks of unnecessary data. This has saved enormous amounts of time and compute costs.

How Verana Health helps scientists collaborate better and faster

Our Verana Health data scientists utilize Databricks notebooks for interactive exploration as well as code development. An easy-to-use web interface allows them to work in preferred languages such as SQL, Python, and R (even within the same notebook). Results can be placed directly into dashboards and in-line visualizations, as well as exported to external tools such as Tableau and Google Docs. Notebooks and code are easily managed with source control (git), separate from data and results.

Complex analyses can be created leveraging workflows, which allows our Verana Health data scientists to orchestrate complex calculations by connecting individual analyses and code. Workflows can then be run manually, automatically triggered by arrival of new data, or on a schedule. Complete results, execution time metrics, and messages are accessible during and after runs. This saves scientists significant time, compared to running complex calculations interactively.

Behind the scenes, Databricks provides rich features for performance tuning and cost optimization. These include a natively compiled Apache Spark implementation (Photon acceleration), which allows analyses to run up to 20% faster; and non-interactive job clusters, which can be used within workflows for additional 20% performance gain. Other key features include Delta tables, which allow our data scientists to assemble very large datasets incrementally–and extract versions by date or tag. This supports fully reproducible results without the cost and complexity of managing multiple copies.

Maximizing real-world data for healthcare insights

Verana Health is at the forefront of digital health, leveraging its extensive real-world data network and strategic collaborations to revolutionize healthcare. With Databricks, Verana Health is able to maximize the value of its vast amounts of data, enabling the delivery of high-quality datasets and empowering researchers, analysts, and clinicians.

Through Databricks' advanced capabilities, Verana Health can efficiently analyze and explore complex health information, collaborate seamlessly, and generate valuable insights. The integration of Databricks, with Verana Health's platform, enhances our ability to optimize clinical trials, conduct real-world evidence studies, drive population health analytics, and support CMS reporting. By combining cutting-edge technology with deep expertise, Verana Health and Databricks are driving innovation and propelling the healthcare industry forward.


Frequently asked questions

What is Verana Health and what problem does it solve?

Verana Health is a healthcare data company that partners with three leading medical societies to build a real-world data network of more than 20,000 clinicians, approximately 90 million de-identified patients, and more than 500 million patient visits. It turns this data into curated datasets (Qdata®) that researchers and life sciences companies use to identify trial patients, assess the population-level impact of public health policy, and monitor drug safety and treatment patterns. This addresses a core industry challenge: clinical researchers must clean and prepare data despite heavy regulatory burdens and the fact that up to 80% of healthcare data is unstructured.

What makes Verana Health's Qdata® offerings different from other healthcare data products?

Qdata® stands out for its quality, defined by cohort size, longitudinal depth of about ten years of data, and the breadth of variables extracted from previously untapped unstructured sources like clinical notes and images. Verana Health's datasets cover three therapeutic areas—neurology, ophthalmology, and urology—normalized and curated at petabyte scale. This depth supports use cases such as clinical trial optimization, real-world evidence studies, population health analytics, and CMS quality-measure reporting.

How does Verana Health process data using the Databricks Lakehouse?

Verana Health ingests data from its exclusive network of specialty medical society partners through purpose-built data connectors that protect patient confidentiality, then uses Delta Lake's multi-hop architecture to refine it in three stages. Raw data lands in bronze tables; it is then normalized, cleansed, and organized in silver tables to account for differing clinician record formats; finally, de-identification and natural language processing (for example, converting free-form clinician notes into usable variables) are applied to produce gold tables ready for customer use in analytics and AI.

How does Unity Catalog support Verana Health's data governance needs?

Unity Catalog lets Verana Health centralize access control, auditing, lineage, and data discovery across Databricks workspaces. It allows the team to define and control access down to the table, column, and row level, so researchers receive only the data relevant to them rather than sifting through unnecessary information. This granular governance has saved Verana Health significant time and compute costs.

What performance and collaboration benefits has Verana Health gained from Databricks features like Photon?

Verana Health's data scientists use Databricks notebooks to write code interactively in SQL, Python, and R within the same notebook, with results feeding directly into dashboards or exported to tools like Tableau and Google Docs. On the performance side, Photon acceleration—a natively compiled Apache Spark implementation—lets analyses run up to 20% faster, and non-interactive job clusters used within workflows provide an additional 20% performance gain. Delta tables further let scientists assemble large datasets incrementally and pull versions by date or tag, supporting reproducible results without maintaining multiple data copies.

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