Excelra can build custom epidemiology models to assess the incidence and prevalence of disease. Covariate patient characteristics can help in trial design and benchmark controlled RCTs against complex real-world clinical context.
In the era of personalized medicine, the objective is to stratify the eligible treatment population to improve efficacy and minimize adverse events. By mining EMRs we can predict the subset of patients who truly benefit from an expensive intervention, helping payers manage their cost exposure.
DISCERNING PATTERNS OF HEALTH AND DISEASE
As we move towards an era of personalized medicine, stratification of the eligible population for improved efficacy, and minimized adverse events is critical. By mining literature, as well as real-world data (RWD) sets like surveillance data, claims data, electronic medical records (EMRs), etc., we can predict the subset of patients who truly benefit from an intervention, helping our clients to develop strong value arguments for targeted patient populations, and helping payers manage their cost exposure. Excelra can further stratify the patient population using “-omics” data sets, to look beyond the traditional “one-size-fits-all” approach for treatment, and move towards personalized therapies.