Unstructured narrative patient data, found in real world data sources such as progress notes and correspondence within electronic health records, contains a wealth of information that, if used correctly, can drive improvements in the clinical trial process. Currently, organizations must deploy expensive clinical personnel to manually review this existing rich and abundant resource for clinical trial recruitment
At the Mayo Clinic they are using IBM Watson’s natural language processing (NLP) and data analytics capabilities to sift through millions of pages of clinical trial and patient data for subject recruitment. Also, a study published by Cincinnati Children’s Hospital Medical Center assessing the effectiveness of NLP in clinical trials, reported that the workload was reduced by 92% with a 450% increase in subject screening efficiency.
Clinithink’s CLiX ENRICH for Clinical Trials provides value for subject recruitment and beyond. By accessing rich, unstructured patient data found within electronic medical records (EMRs), case report forms (CRFs), patient reported outcomes (PROs), clinical trial management systems (CTMS), electronic data capture (EDC) systems, trip reports and other clinical documentation, Clinithink’s solution enables a positive impact during several critical points throughout the clinical trials process.
In this video I explain how, when used for Feasibility, Subject Recruitment and Pharmacovigilance, CLiX ENRICH for Clinical Trials provides a distinct data advantage to realize:
- Savings of time and money
- Risk evaluation and reduction
- Increased predictability
- Optimized views of subject trial data