PREDICTING PERFORMANCE OF CLINICAL TRIAL FACILITATORS USING PATIENT CLAIMS AND HISTORICAL DATA

• Main Authors: TALAMAS, Francisco Xavier, MANYAKOV, Nikolay Vladimirovitch, KIP, Geoffrey Jerome, VERSTRAETE, Hans Roeland Geert Wim
• Format: Patent
• Language: English; French; German
• Published: 21.08.2024
• Subjects: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; PHYSICS

A clinical trial site evaluation system applies a machine learning technique to predict recruitment performance of a candidate clinical trial facilitator (such as a clinical trial site or a clinical trial investigator) for a clinical trial based on patient claims data or other data associated with the candidate clinical trial facilitator. In a training phase, a training system trains the machine learning model based on historical recruitment data associated with historical clinical trials and patient claims data (or other data) associated with the clinical trial facilitators associated with those trials. In a prediction phase, the machine learning model is applied to claims data (or other data) associated with candidate clinical trial facilitators to predict recruitment performance.