Background Precision oncology increasingly utilizes molecular profiling of tumors to determine treatment decisions with targeted therapeutics. The molecular profiling data is valuable in the treatment of individual patients as well as for multiple secondary uses. Objective To automatically parse, categorize, and aggregate clinical molecular profile data generated during cancer care as well as use this data to address multiple secondary use cases. Methods A system to parse, categorize and aggregate molecular profile data was created. A naÿve Bayesian classifier categorized results according to clinical groups. The accuracy of these systems were validated against a published expertly-curated subset of molecular profiling data. Results Following one year of operation, 819 samples have been accurately parsed and categorized to generate a data repository of 10,620 genetic variants. The database has been used for operational, clinical trial, and discovery science research. Conclusions A real-time database of molecular profiling data is a pragmatic solution to several knowledge management problems in the practice and science of precision oncology.

Oncology practice increasingly requires the use of molecular profiling of tumors to inform the use of targeted therapeutics. However, many oncologists use third-party laboratories to perform tumor genomic testing, and these laboratories may not have electronic interfaces with the provider's electronic medical record (EMR) system. The resultant reporting mechanisms, such as plain-paper faxing, can reduce report fidelity, slow down reporting procedures for a physician's practice, and make reports less accessible. Vanderbilt University Medical Center and its genomic laboratory testing partner have collaborated to create an automated electronic reporting system that incorporates genetic testing results directly into the clinical EMR. This system was iteratively tested, and causes of failure were discovered and addressed. Most errors were attributable to data entry or typographical errors that made reports unable to be linked to the correct patient in the EMR. By providing direct feedback to providers, we were able to significantly decrease the rate of transmission errors (from 6.29% to 3.84%; P , .001). The results and lessons of 1 year of using the system and transmitting 832 tumor genomic testing reports are reported.

156 Background: The future of oncology will increasingly utilize information about tumor genomics. A growing number of tumor subtypes require genomic information as standard of care to inform treatment decisions. Clinical decision support (CDS) systems are able to improve healthcare and ensure best practices, but rely on machine-readable data. Typically, 3rd party genotyping results are returned in PDF reports that are not computable for standard presentation, CDS, or research purposes. Proposed oncology CDS platforms (such as CancerLinQ) will require computable tumor genomic information. Methods: A 3rd party CLIA-certified laboratory performed targeted exome tumor sequencing for a subset of cancer patients seen at Vanderbilt University (VUMC). The variants considered “actionable” were packaged into a standardized extensible markup language (XML) message and transmitted securely to a clinical server on a daily basis; the full PDF report was also transferred electronically. These messages were automatically received and parsed to match patients and display mutation data in their electronic medical record (EMR) for clinicians. This information was also placed into the VUMC Research Derivative (see Danciu et al. J Biomed Inform 2014 for more details). Results: The XML message, including specimen metadata, was successfully created by the 3rd party lab and received by VUMC after an iterative testing process was conducted. Clinicians were notified automatically when results were ready, within their EMR workflow. Because the primary purpose of the test is clinical, variants of unknown significance were not included in the XML transfer (these were however available in the PDF report). Conclusions: To our knowledge, this is the first demonstration of the automated incorporation of tumor genotype information from a 3rd party lab into a clinical EMR. Electronic transfer of genotype data enables rapid consumption of results by clinicians within their standard workflow, passive CDS (e.g. pointers to external knowledge bases), active CDS (e.g. standard and clinical trial treatment recommendations), and secondary use: all activities that improve the quality of cancer care.

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