Medical data is generally messy and complicated. Electronic health records (EHR) are often sparse or incomplete. EHR is frequently presented in different and inconsistent units; in some cases, computer errors and lab tests even generate false results.
This white paper gives healthcare systems and professionals, physicians, CMIOs, Chief Quality Officers, population health managers, and payors valuable insights on the challenges inherent in making sense of big medical data, and why machine learning is a valuable tool for doing this.
They will come away with better understanding of how difficult and rewarding it is to have develop healthcare algorithms to find patient-specific risk trajectories. They will discover early care opportunities that this can offer to make a difference in the lives of patients and their families.
Highlights of the report include:
Read the white paper today.