e18635 Background: With almost 85% of patients treated outside NCI-designated cancer centers, providing comprehensive, state-of-the-art cancer care to millions of patients in the U.S. remains a significant challenge. We aimed to combine cutting edge AI based technology of DLVTB with high value-added services to provide evidence-based care management recommendations and implementation support to the patients and physicians in the community. To further demonstrate the feasibility, reproducibility, scalability, and benefits of DLVTB, we evaluated key outcomes from incorporating DLVTB in a cohort of 35 patients with advanced colorectal adenocarcinoma (CRC). Methods: Our core-enabling technology is a deep learning based Natural Language Understanding Engine that employs (1) natural language processing for medical text digestion and structuring, (2) decision trees and multilayer perception models to produce evidence-based treatment protocols, and (3) natural language based report generation. The technology platform is combined with human support from oncology sub-specialists to deliver a comprehensive Interpretive Report with a prioritized list of recommendations for each patient. These recommendations are operationalized by a case management team to ensure care implementation and monitoring of outcomes. Results: Thirty-five patients with CRC were referred for incorporation of DLVTB into clinical practice. Median age of patients was 57 years with 68.6% males. About 88.6% of the patients were Stage IV and 82.9% were treated by community practice oncologists. Median time since diagnosis was 17 months (1-73 months). Overall, DLVTB-cohort demonstrated an increase in median Overall Survival of 12 months per patient in comparison with historical cohorts. More specifically, DLVTB recommended initial and/or additional biomarker testing for 71% of the patients with further precision oncology-guided treatment recommendation (e.g. an EGFR inhibitor) for 80% of the patients. 63% of the patients were eligible for at least one clinical trial. 58% of the trials identified were within proximity (≤50 miles) of the patient’s primary residence. Thus, DLVTB was able to identify a trial that did not require extensive travel, provided eligibility and actionability closer to the point-of-care. 14% of the DLVTB evaluated patients subsequently enrolled in the recommended clinical trial, far surpassing the national average (3%). Moreover, DLVTB recommended treatments achieved on average savings of $39,194 per patient which is 35% of the average drug cost per patient. Conclusions: Our results demonstrate the feasibility and benefits of incorporating DLVTB into clinical practice. The utilization of DLVTB as a clinical trial enrollment tool and an engine for development of pathways resulting in improved clinical outcomes, in a cost-effective, and innovative care delivery model.