Challenge the odds with artificial intelligence
Using natural language processing and the analysis of unstructured and structured data to analyze both patient records and trial inclusion/exclusion criteria, clinical trial recruitment with AI enables oncologists to quickly review a list of potential trials for every patient, while supporting the clinical trial office in reaching enrollment numbers.
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Using natural language processing to understand both structured and unstructured patient data
Automating manual steps to help increase the speed of trial identification
Helping find viable trial options for each patient
Enabling a boost in clinical trial enrollment to meet recruitment goals and support the advancement of cancer treatments
Learning and improving over time
Increasing efficiency through connections across multidimensional, siloed sources
of adults with cancer participate in clinical trials
of US clinical trials fail to meet recruitment timelines
cancer research trials falter due to low patient recruitment
IBM Clinical Development is a cloud EDC that supports your studies with integrated Medical Coding, ePRO and more.
1 National Cancer Clinical Trials System for the 21st Century: Reinvigorating the NCI Cooperative Group Program,” ncbi.nlm.nih.gov/books/NBK220370.
2 Daniel Garrun, "Clinical trial delays: America’s patient recruitment dilemma," Drug Development-Technology, July 18, 2012, https://www.drugdevelopment-technology.com/features/featureclinical-trial-patient-recruitment/.
3 Go RS, Meyer CM, et al., Journal of Clinical Oncology 2010, 28:15s (suppl; abstr 6069).
4 T. Haddad, J. Helgeson, et.al., “Impact of a cognitive computing clinical trial matching system in an ambulatory oncology practice,” Presentation at ASCO 2018.