Open AccessStatus Update on Data Required to Build a Learning Health System
Author(s) -
Monica M. Bertagnolli,
Britta L. Anderson,
Kelly J. Norsworthy,
Steven Piantadosi,
Andre Quina,
Richard L. Schilsky,
Robert S. Miller,
Sean Khozin
Publication year - 2020
Publication title -
journal of clinical oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.482
H-Index - 548
eISSN - 1527-7755
pISSN - 0732-183X
DOI - 10.1200/jco.19.03094
Subject(s) - health records , medicine , meaningful use , electronic health record , health care , quality (philosophy) , data science , data quality , medical emergency , medical education , computer science , operations management , philosophy , epistemology , economics , economic growth , metric (unit)
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.