Using AI to unlock Real-World Evidence in immunotherapy
Real-world evidence (RWE) is becoming a valuable addition to randomized controlled clinical trials (RCT) to better understand the mechanisms and outcomes of cancer treatments. However, most real-world data (RWD) is locked in a non-uniform patient reporting system by hospitals and healthcare professionals.
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Cancer therapies often have more than one indication for their application. To better understand the clinical performance and outcomes of these therapies, it is important to determine the distribution of indications for which a given treatment is prescribed. However, to answer what this distribution is in a real-life setting, there is a need to gain insights from unstructured data such as Electronic Health Records (HER).
The study aimed to gain granular insights into the use of cancer treatments in clinical practice:
- To identify and quantify the patient population treated with the selected cancer therapeutic
- To quantify the patient population according to predefined indications for the selected cancer therapeutic
- To determine the patient population receiving the therapeutic as first-line or second-line treatment
Cancer therapies often have more than one indication for their application. To better understand the clinical performance and outcomes of these therapies, it is important to determine the distribution of indications for which a given treatment is prescribed. However, to answer what this distribution is in a real-life setting, there is a need to gain insights from unstructured data such as Electronic Health Records (HER).
The study aimed to gain granular insights into the use of cancer treatments in clinical practice:
- To identify and quantify the patient population treated with the selected cancer therapeutic
- To quantify the patient population according to predefined indications for the selected cancer therapeutic
- To determine the patient population receiving the therapeutic as first-line or second-line treatment
