At edenceHealth we believe that data can save lives, but only if we turn them into insights. Therefore, we create and implement custom statistical models that unlock a whole range of new opportunities, not only in optimization of business processes, but most importantly, also in improvement of patient care. More specific, we do both patient-level prediction and population-level estimation.
At the patient-level causal effect contrasts what happened to the exposed patient, with what would have happened had the exposure not occurred or had a different exposure occurred. Such models give the opportunity to the integration of a much more personalized medical care, moving beyond the status quo in healthcare.
At the population level, the objective is to produce a high-quality estimate of a causal effect. Use-cases for population-level effect estimation include treatment selection, safety surveillance, and comparative effectiveness.