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M. Berger
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P3.03 - Poster Session/ Treatment of Locoregional Disease – NSCLC (ID 214)
- Event: WCLC 2015
- Type: Poster
- Track: Treatment of Locoregional Disease – NSCLC
- Presentations: 1
- Moderators:
- Coordinates: 9/09/2015, 09:30 - 17:00, Exhibit Hall (Hall B+C)
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P3.03-032 - MD Anderson Oncology Expert Advisor™: A Cognitive Clinical Decision Support Tool for Evidence-Based Multi-Disciplinary Lung Cancer Care (ID 3039)
09:30 - 09:30 | Author(s): M. Berger
- Abstract
Background:
The majority of patients diagnosed with non-small cell lung cancer (NSCLC) receive care in the community setting with limited access to multidisciplinary management common in tertiary care centers. The availability of genomics allows tailored treatments for patients; and with novel, rapidly emerging therapeutic options, it is challenging for busy clinicians to maintain familiarity with current therapy recommendations. Therefore, to empower practicing oncologists in community settings to offer the optimal management at the first intervention, we have developed the MD Anderson Oncology Expert Advisor™ (OEA) application for multi-disciplinary management of lung cancer patients. As the first multi-disciplinary solution for providing comprehensive management of lung cancer, the objective of OEA™ Lung is to leverage cognitive analytics on vast and ever evolving clinical care information and patient big data to disseminate knowledge and expertise, thus enabling physicians to provide evidence-based care and management tailored for the individual patient, similar to consulting an expert. Further, we aimed to create a system for sharing knowledge from more experienced experts to provide care pathways and management recommendations for physicians globally.
Methods:
Using cognitive computing, our cancer center partnered with IBM Watson to develop an expert system designed to provide physicians with the tools needed to process high-volume patient and medical information and to stay up-to-date with the latest treatment and management options, so that they can make the best evidence-based treatment decisions for their lung cancer patients. The OEA™ application for lung was built upon core capabilities of the OEA™ applications for leukemia and molecular/targeted therapies. Experts in multiple disciplines including thoracic surgery, medical oncology, and radiation oncology met regularly to design and provide specialized input to the IBM technical team in an agile development cycle. This system was powered to utilize both structured and unstructured data from validated sources; to thoroughly evaluate and stage patients; and to offer eligible clinical trials and personalized therapeutic options. In addition to delivering evidence-based, weighted therapy recommendations, OEA™ Lung provides care pathways for management of toxicities for each treatment modality (surgery, radiation, and medical oncology).
Results:
The OEA™ Lung application supports three core functions: 1) dynamic patient summary assimilating complete (structured and unstructured) data to show demographics, labs, genotype, treatment history, and previous treatment responses; 2) weighted evidence-based, multimodality treatment options, with recommendations based on literature support which is provided, along with screening for relevant trials; 3) care pathway advisories, to manage treatment related toxicities for each modality. Further, this product improves quality of care by optimizing outcomes with access to trials and care pathways.
Conclusion:
The OEA™ application for lung is a cognitive expert system designed to assimilate multidisciplinary recommendations for care and management of lung cancer patients based on current consensus guidelines and expert recommendations from a quaternary referral cancer center to the community practice setting. By democratizing knowledge from our specialty cancer center, we have taken steps toward achieving an important goal of ending cancer for all, by providing global access to optimal cancer care for patients with this disease. Further evaluation of outcomes following implementation are warranted.
