# Oncology Knowledge Rapid Alerts: Integrating biomarker-driven clinical decision support for therapy selection at point-of-care

> **NIH NIH R21** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2022 · $449,971

## Abstract

PROJECT SUMMARY/ABSTRACT
Precision cancer medicine uses patient molecular test results to guide cancer therapy selection. An increasing
number of cancer therapies are now targeted to specific molecular alterations, and tumor molecular
testing is now standard of care. A significant obstacle in advancing precision cancer therapy is the rapid pace
of change in this field—making it challenging for oncologists to stay up to date. The long-term goal of this
project is to integrate clinical decision support (CDS) for precision cancer therapy selection directly into
the electronic health record (EHR). Ultimately, we will create an integrated CDS solution within the EHR that
includes best practice alerts to oncologists and integration of CDS into the molecular testing result
report. The objective of this R21 is to develop a CDS algorithm to match molecular test results to targeted
therapy assertions from a widely used precision oncology knowledgebase, deliver an open-source web service
application programming interface (API) that can support vendor-agnostic integration with any EHR that uses
the new genomic data standards, and provide proof-of-concept, point-of-care CDS in our local EHR
environment. We will accomplish this through the following specific aims:
Aim 1. Develop an open-source algorithm and API for computing and storing biomarker-driven CDS
from the My Cancer Genome (MCG) knowledgebase. We will develop an algorithm that will compute
targeted therapy options for a patient’s tumor histology and molecular profile from the data stored in the
MCG knowledgebase. We will build a web service API that receives molecular testing data, calls the MCG
knowledgebase, runs the CDS algorithm, and outputs the computed CDS in a format that can be stored for
rapid retrieval and that is compatible with EHRs using the new genomic data standards.
Aim 2. Develop methods for integration of biomarker-driven CDS into the EHR as human-readable
statements. In this aim, we will build API functions to receive communications from the EHR when an
oncologist views tumor test results, retrieve cached CDS, and send CDS to the EHR. Using non-small cell lung
cancer as a proof-of-concept, our initial use cases will provide integration of CDS with the molecular results
report and best practice alerts to oncologists.
This project will provide tools to match patient molecular test results to appropriate targeted therapies. At the
conclusion of this project, an oncologist will be able to view CDS when they access patient molecular results in
the EHR. This point-of-care CDS will facilitate delivery of the right information to the right person at the right
time to maximize impact on clinical care.

## Key facts

- **NIH application ID:** 10526824
- **Project number:** 1R21CA274545-01
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Christine M Micheel
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $449,971
- **Award type:** 1
- **Project period:** 2022-09-19 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10526824

## Citation

> US National Institutes of Health, RePORTER application 10526824, Oncology Knowledge Rapid Alerts: Integrating biomarker-driven clinical decision support for therapy selection at point-of-care (1R21CA274545-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10526824. Licensed CC0.

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