Open AccessThe Promise of Big Data and Digital Solutions in Building a Cardiovascular Learning System: Opportunities and Barriers
Author(s) -
Makoto Mori,
Rohan Khera,
Zhenqiu Lin,
Joseph S. Ross,
Wade L. Schulz,
Harlan M. Krumholz
Publication year - 2020
Publication title -
methodist debakey cardiovascular journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.552
H-Index - 23
eISSN - 1947-6094
pISSN - 1947-6108
DOI - 10.14797/mdcj-16-3-212
Subject(s) - medicine , big data , analytics , health care , data science , scale (ratio) , knowledge management , computer science , data mining , physics , quantum mechanics , economics , economic growth
The learning health system is a conceptual model for continuous learning and knowledge generation rooted in the daily practice of medicine. While companies such as Google and Amazon use dynamic learning systems that learn iteratively through every customer interaction, this efficiency has not materialized on a comparable scale in health systems. An ideal learning health system would learn from every patient interaction to benefit the care for the next patient. Notable advances include the greater use of data generated in the course of clinical care, Common Data Models, and advanced analytics. However, many remaining barriers limit the most effective use of large and growing health care data assets. In this review, we explore the accomplishments, opportunities, and barriers to realizing the learning health system.