Extracting physician group intelligence from electronic health records to support evidence based medicine

• Published in: PloS one Vol. 8; no. 5; p. e64933
• Main Authors: Weber, Griffin M, Kohane, Isaac S
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 29-05-2013; Public Library of Science (PLoS)
• Subjects: Biology; Clinical decision making; Clinical Laboratory Techniques - standards; Clinical medicine; Communication; Computer Science; Crowdsourcing; Datasets; Decision Making; Diagnostic Tests, Routine - standards; Electronic health records; Electronic Health Records - statistics & numerical data; Electronic medical records; Evidence-Based Medicine - standards; Health care; Hospitals; Humans; Informatics; Intelligence; Intervals; Laboratories; Laboratory tests; Medical personnel; Medicine; Patients; Pediatrics; Physician-Patient Relations; Physicians; Physicians - psychology; Physicians - standards; Practice Patterns, Physicians' - standards; Studies; Subpopulations; Womens health; United States > US; Massachusetts
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0064933

Evidence-based medicine employs expert opinion and clinical data to inform clinical decision making. The objective of this study is to determine whether it is possible to complement these sources of evidence with information about physician "group intelligence" that exists in electronic health records. Specifically, we measured laboratory test "repeat intervals", defined as the amount of time it takes for a physician to repeat a test that was previously ordered for the same patient. Our assumption is that while the result of a test is a direct measure of one marker of a patient's health, the physician's decision to order the test is based on multiple factors including past experience, available treatment options, and information about the patient that might not be coded in the electronic health record. By examining repeat intervals in aggregate over large numbers of patients, we show that it is possible to 1) determine what laboratory test results physicians consider "normal", 2) identify subpopulations of patients that deviate from the norm, and 3) identify situations where laboratory tests are over-ordered. We used laboratory tests as just one example of how physician group intelligence can be used to support evidence based medicine in a way that is automated and continually updated.