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6508 Background: Electronic decision support is increasingly prevalent in clinical practice. Traditional tools map guidelines into an interactive platform. An alternative method builds on experience-based learning. Methods: Memorial Sloan-Kettering (MSK), IBM and WellPoint teamed to develop IBM Watson – a cognitive computing system leveraging natural language processing (NLP), machine learning (ML) and massive parallel processing – to help inform clinical decision making. We made a prototype for lung cancers using manufactured and anonymized patient cases. We configured this tool to read medical language and extract specific attributes from each case to identify appropriate treatment options benchmarked against MSK expertise, anonymized patient cases and published evidence. Treatment options reflect consensus guidelines and MSK best practices where guidelines are not granular enough to match treatments to unique patients. Analysis and building accuracy is ongoing and iterative. Results: 420 manufactured and 525 anonymized patient cases trained the initial models. Early results show accuracy improvement in NLP and ML in identifying treatment options (Table). All treatment plans were guideline adherent. A proportion of cases showed the need to incorporate tailored treatment plans reflecting MSK’s practice beyond guidelines – e.g. 11% of cases required addressing a site of critical metastasis before initiating guideline supported treatment. Conclusions: IBM Watson is extracting information from free text medical records that supports building ML models to assist in selecting treatments for persons with lung cancers. This tool can select treatment options from consensus guidelines, and, through ML, it will identify personalized treatment plans. Training is ongoing to improve individualized decision making and optimize the web-based tool that connects with IBM Watson. [Table: see text]