New variant selection and report feature in the LifeOmic Oncology solution
Written by Simon Silitonga, Samantha Yeager, Shawn Zhu
Edited by Steven Bray, Melissa Webb
Cancer diagnosis and treatment is complex. It involves various medical experts, including oncologists, researchers, pharmacists, primary care physicians, and others, all working together to best serve their patients. One modern practice in the field is for many experts to meet in a Molecular Tumor Board (MTB) and discuss potential treatment recommendations for their cancer patients based on the genetic variants found in those patients and their cancers.
Despite recent technological advances in the fields of genomic sequencing and precision medicine, there are gaps in the end-to-end processes these experts must use to analyze and record critical data. One such gap exists between the data the MTB has at its disposal and the notes and recommendations they would like to make. With little better options, many clinicians use ad hoc, locally saved spreadsheets or even pen and paper notes. This produces a myriad of problems. Local files can be lost, or may be hard to share among an entire care team. Hand typed or handwritten notes can contain typos that could lead to confusion. Even when tumor board notes are successfully added to the electronic medical record, they are not at all searchable for one patient, let alone across a whole cohort of similar patients, making it all but impossible to see improvement or patterns over time or between patients. Overall, this leads to headaches for hardworking clinicians and many lost opportunities to give patients faster and more effective care.
In this day and age, we can do better. At LifeOmic, we’re challenging the status quo with our Oncology solution, and part of that involves closing this gap for MTBs.
The guiding principles of our solution are:
- Interoperability with external services
- Seamless simplicity of use throughout the tumor board review process
- Data accessibility for searching and continuous learning
In this article, we will showcase our new variant selection and report feature of our LifeOmic Oncology solution that will greatly improve the MTB recommendation and reporting process.
This new feature allows clinicians to quickly generate molecular tumor board reports using clinical and genomic data aggregated together in the LifeOmic Platform and publish them to an EHR system through the LifeOmic SMART-on-FHIR connection.
In the LifeOmic Platform, the clinical team can search through the list of actionable molecular findings from all sequencing tests in one place. They can easily select one or more variants of interest to add to a collaborative report, which automatically draws from this variant list. In this example, an oncologist selects variants of the ATR and MAP3K1 genes.
Once the variants are selected, the MTB can review the findings and work together to add recommendations directly into the report. Recommendations can include clinical trials, drugs, or other actions that may help treat the cancer in this specific patient. In this example, the MTB is recommending specific clinical trials.
The report can then be saved and sent to an EHR. Here’s what the published report looks like in the Cerner EHR system. The template for these published reports is highly configurable and can be changed to fit the needs of any specific MTB.
With interoperability in mind, this new feature was designed to follow the Genomics Reporting Implementation Guide to represent the genetic data in the FHIR specification. This allows for the data to be used consistently, across tools, to further many clinical and research use cases. For example, clinicians can integrate MTB results to trigger specific care pathway tools, or researchers could compare outcomes of various patient cohorts based on their reported variants and associated recommendations.
This new variant selection workflow solves many problems clinicians currently face. Future improvements will also speed the trial matching and drug recommendations processes, allowing more data consistency and searchability within the tool. This will allow for powerful new functionality, such as identifying variant treatment patterns across patients and having access to past recommendations to easily recommend the same to new patients. As more variant, recommendation, and outcome data is stored, it can also begin to serve as critical data points for machine learning models.
Overall, we aim to save clinicians time and energy, which they can then apply to problems that better deserve their knowledge and experience. These combined tools will help improve patient outcomes by bridging the technology gap between precision medicine practices and tools, improving treatment efficacy and providing more opportunity for clinical trials placement. We hope these tools make precision medicine more accessible for a more positive outcome for everyone in the oncology process. If you are in this space, we would love to hear from you!
