Status Update on Data Required to Build a Learning Health System

Abstract

Wide adoption of electronic health records (EHRs) has raised the expectation that data obtained during routine clinical care, termed "real-world" data, will be accumulated across health care systems and analyzed on a large scale to produce improvements in patient outcomes and the use of health care resources. To facilitate a learning health system, EHRs must contain clinically meaningful structured data elements that can be readily exchanged, and the data must be of adequate quality to draw valid inferences. At the present time, the majority of EHR content is unstructured and locked into proprietary systems that pose significant challenges to conducting accurate analyses of many clinical outcomes. This article details the current state of data obtained at the point of care and describes the changes necessary to use the EHR to build a learning health system.

FIG 1.

Conceptual model diagram of mCODE (Minimal Common Oncology Data Elements) version 0.9.1 currently available as an open-source data specification to promote oncology electronic health record data interoperability. CBC, complete blood count; CMP, comprehensive metabolic panel; ECOG, Eastern Cooperative Oncology Group; HGVS, Human Genome Variation Society.