The Relationship Between Vedolizumab Therapeutic Drug Monitoring, Biomarkers of Inflammation, and Clinical Outcomes in Inflammatory Bowel Disease in the Real-World Setting

Background Despite widespread use of therapeutic drug monitoring to guide anti-TNF biologic prescribing in IBD, its role for other biologic classes remains unclear. The present study aimed to assess the relationship between early vedolizumab trough concentrations (VTC) and real-world outcomes in inflammatory bowel disease (IBD). Methods Individuals with IBD enrolled in the Takeda Canada Patient […]

Automating Access to Real-World Evidence

Background Real-world evidence is important in regulatory and funding decisions. Manual data extraction from electronic health records (EHR) is time-consuming. Automated extraction using natural language processing and artificial intelligence may facilitate this process. We compared manual and automated data collection from EHR of patients with advanced lung cancer. Methods Previously, we extracted data using an […]

Real-World Data Curation to Transform Medical Investigation: Technology to Reimagine the Economics of Evidence Gathering and Support Regulatory Decision-Making

The rapid emergence of ‘big data’ is driving the need for rapid, accurate, cost-effective collection, distillation, and analysis of previously inaccessible data, unleashing unimaginable discovery across every sector, including the field of scientific investigation. This data availability, when combined with Artificial Intelligence (AI) and machine learning, has fueled a bold vision for healthcare. There is […]

Using Real World Data to Transform the Canadian Healthcare System

Purpose Health data holds the promise of improved treatments and better patient outcomes, but is fragmented, not interoperable and inaccessible. If we could transform our healthcare system to enable meaningful data translation at scale, the system would benefit significantly by being able to act on insights, ultimately benefiting patients. For example, as over 90% of […]

Using AI to Improve Precision Medicine: Real-World Impact of Biomarker Testing in Advanced Lung Cancer

Background Advances in targeted therapy and immunotherapy for lung cancer improves patient outcomes but requires molecular testing of cancer samples. Successful biomarker testing depends on many factors; quality improvement initiatives require access to real-world data. Natural Language Processing (NLP) and Artificial Intelligence (AI) technology automate data abstraction from unstructured electronic health records (EHR), eliminating the […]

Real World Outcomes of Advanced NSCLC Patients with Liver Metastases

Background Patients with advanced lung cancer represent a heterogenous population with varying patterns of metastasis. Those with liver metastases may represent a unique cohort with differential response to therapy, including immunotherapy in NSCLC. Novel Natural Language Processing (NLP) and Artificial Intelligence (AI) technology enables automated extraction of real-world data to examine these populations at greater […]

Real World Evidence of the Impact of Immunotherapy in Patients with Advanced Lung Cancer

Background PD-1 axis inhibitors have become a standard treatment modality in the management of advanced lung cancer. Novel Natural Language Processing (NLP) and Artificial Intelligence (AI) technology enables automated extraction of real-world data at greater scale than current manual chart abstraction processes, which can be used to further explore the impact of these agents in […]

Generating Real-World Evidence: Using Automated Data Extraction to Replace Manual Chart Review

Background: Real world evidence is a valuable resource to help guide clinical care beyond evidence generated from clinical trials, for example safety and effectiveness of novel treatments in special populations. Administrative databases often lack sufficient clinical detail to address gaps in the improvement of patient management and quality of care. Detailed clinical data collection and […]

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