Clinical genomics in the EHR

Most U.S. cancer centers receive molecular results the same way they received them in 2010: as PDF attachments faxed back from a reference lab. The genomic information lives outside the EHR, invisible to decision support, invisible to trial-matching algorithms, invisible to outcomes research. The data exists; it just doesn't travel where it needs to go. Dr. Osterman leads the Clinical Genomics Workstream at Vanderbilt-Ingram Cancer Center and has spent the better part of a decade pulling that data into structured EHR resources where it can actually be used.

Being early. In 2019 only one U.S. institution had integrated genomic results from a reference laboratory directly into Epic. Vanderbilt was the eighth. Under Dr. Osterman's leadership, that integration shipped, the pipeline matured, and the corpus grew rapidly: by the end of 2021 there were roughly twelve thousand tumor genomic reports living as structured data inside the Vanderbilt EHR. Today Vanderbilt holds more structured genomic data in its electronic health record than any other institution in the United States. The rollout was profiled in Healthcare Innovation (2021) as Vanderbilt prepared to deploy the Epic Genomics Module, again in Healthcare Innovation (2022) once the downstream benefits became visible, and in Discoveries in Medicine (2023).

The backload. Going forward with structured genomic ingestion is half the problem. The other half is the historical record - thousands of patients already on therapy, with molecular reports already sitting in the EHR as PDFs or scanned attachments, none of it useful to decision support until it is restructured. Dr. Osterman's team designed a backload pipeline that converts that historical genomic data into standard HL7-formatted resources inside Epic, using the same ingestion path as new incoming reports. The result: research cohorts, trial-matching algorithms, and tumor-board prep all see a unified structured corpus across the patient's entire history, not just from cut-over forward. The full upstream workflow is documented in the NCCN abstract BIO23-019: Precision Oncology - Integrating Structured Genomic Data Into the Electronic Health Record (Vento & Osterman, 2023).

Sharing the work with the broader community. Two keynote-scale talks pulled the implementation work into national view. At the American College of Medical Genetics annual meeting in March 2022, Dr. Osterman delivered Data to Knowledge to Wisdom: Improving Care Through Creative Use of Information Systems - framing what becomes possible when genomic results stop being attachments and start being structured clinical signal. Five months later, at the August 2022 Epic User Group Meeting (UGM) - the largest annual gathering of Epic-using health systems - he presented Precision Medicine: Using Structured Genomic Data in Clinical Decision Support, walking other institutions through the build details so they could replicate it locally. The MyCancerGenome integration that powers the clinical interpretation layer was written up the year before (Holt et al., JCO CCI, 2021).

Showing the standard on top - with students. Structured data inside one institution's EHR is necessary but not sufficient: research collaborators outside Vanderbilt still can't get to it without an interoperable surface. Dr. Osterman led a team of undergraduate Vanderbilt computer-science students through the first mCODE Genomics Pilot Project at Vanderbilt University Medical Center (Li et al., JCO Clinical Cancer Informatics, 2024) - a working FHIR-on-Azure pipeline that lifts the structured genomic data out of the EHR and into mCODE-conformant resources downstream applications can consume. The pilot doubled as an early-career training experience: undergraduates learning cloud development, FHIR, and oncology data shaping while building something other implementers could reuse.

What it enables. Structured genomic data unlocks several adjacent decision surfaces, each of which has its own published track: trial-matching at scale (Jain et al., JCO CCI, 2021) showed what could be matched and what couldn't using NGS-result data alone; the molecular tumor board framework (Jain et al., The Oncologist, 2021) showed how to govern the human-review side around the structured data. Industry collaborations build on the same foundation - Tempus AI on reference-laboratory integration and multi-omic discovery, nference on research-data infrastructure (formalized in a 2023 agreement), GE HealthCare on immunotherapy outcome prediction, and Microsoft on Azure-based FHIR infrastructure. None could have happened the same way without the integration work first.

The through-line: build the boring infrastructure first; the research, the AI, and the partnerships compound on top of it. See also cancer data standards (mCODE) for the standards layer that makes this data portable beyond Vanderbilt, and AI in oncology for what becomes possible once both the structure and the standards are in place.