Dark Data Understanding for Extracting Real World Evidence
Real world data (RWD) and real world evidence (RWE) are playing an increasing role in health care decisions as they provide an understanding of the value of a medical product beyond the controlled settings of clinical studies and clearly circumscribed subpopulations.
In the era of value-based care, the effort of players in the health care system is shifting from a primary focus on regulatory approval by agencies such as FDA to striving for a comprehensive assessment of the added value of a medical product in terms of patient outcomes.
Real-world evidence can help to identify which patients will get the most value from a therapy, based on their genetic, social and lifestyle footprint that is typically not captured in clinical trials. In particular, RWE has the potential to deliver a more comprehensive picture and deeper understanding of the safety, effectiveness and economics of a drug product. Ultimately, it might yield insights that contribute to improving medical products based on unmet needs
Real World Evidence can help to
- monitor postmarket safety and adverse events
- contribute to understand the value of a drug, for patients, HCPs, insurances and regulatory agencies
- generate data to support coverage decisions and to develop guidelines and decision support tools for use in clinical practice
- support the design of clinical trials
However, real-world evidence is typically not readily available, but is hidden in diverse datasets including:
- Electronic health records (EHRs)
- Lab Data: Genomic Data, Tissue Pathology, Lab Test, etc.
- Medical claim data, Insurane Company Data
- Social Media and Patient Networks (e.g. Twitter, Blogs. PatientsLike Me)
- HCP Interview Data
- Patient-reported outcomes
- Health-monitoring devices
Most of these datasets are unstructured, containing large amounts of unanalyzed raw text that needs to be analyzed, structured and homogenized to make it accessible for analyses and distill the „evidence” out of them. This requires methods for natural language processing that are able to extract key insights.
Semalytix excells in this respect, having substantial experience in analyzing unstructured data in the healthcare domain to generate insights that allow a deeper understanding of:
- Self-reported patient value: the value of a drug for patients as self-reported on various platforms including experience with adverse events and how they dealt with them
- HCP assessment of level of evidence: the assessment of HCPs of their experience with drugs, reporting on their level of trust, assessment of evidence, problems, lessons learned, etc.
- Data on special populations: the performance of drug products on special subpopulations that are not considered in studies, e.g. having mixed conditions or had several unsuccessful therapies
- Patient histories: fostering an understanding of which drugs are typically taken in which phase of a disease, together with which other drugs, etc.
The technology stack developed by Semalytix puts evidence contained in data sources as mentioned above at your direct disposal using easy-to-understand visual analytics.