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AI-based Technology Makes A Case For Improving Oncology Clinical Fulfilment (Recently presented at ASCO)

Deep Lens has completed a study highlighting how the proprietary AI identification and matching technology, VIPER, was able to efficiently and effectively improve oncology fulfillment.

Implementing VIPER at a California community cancer center over three months, the platform was able to identify new patients who were candidates for 16 clinical trials.

The platform provided access to electronic medical records and pathology systems (EMR/LIS) of 5,706 surgical pathology patients. A clinical research coordinator was alerted when VIPER found patients who met the protocol requirements of a clinical study and were ready for final approval steps.

The findings were remarkable, with results showing that VIPER was able to triage all 5,706 surgical pathology cases, and identified 1,045 cancer patients who qualified for further inquiry for clinical study enrollment in three weeks.

Further triage based on the inclusion and exclusion criteria found 150 previously unidentified patients were, in fact, suitable for 16 of the 20 complex clinical studies that included 11 different tumor types included in analysis across 12 biomarkers (HER2, HER4, EGFR, MET, RET, MTC, ATM, ALK, ROS1, PD-1, RAS, and MSI high).

The VIPER system only required one novice clinical research coordinator to assist in the process of identifying 150 patients that previously had taken six full time staff members. The VIPER system increased monthly candidate patient catchment for 16 of the 20 studies under investigation, which is approximately 600 patients annually added for final triage for studies being conducted.

All-in-all, this study showcased the potential for this AI-based platform to identify patients who might have been previously missed using traditional clinical study recruitment methods.

The use of one team member to effectively triage participants, instead of the usual six different care team members, illustrated how the technology could be used to free up vital resources and reduce staff burden.

Deep Lens plans to continue scaling this platform to additional institutions and use further studies to validate these findings.

To review the full ASCO Abstract click here

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