Background: Delirium is an acute confusional state, associated with morbidity and mortality in diverse medically ill populations. Delirium is preventable and treatable when diagnosed but the diagnosis is often missed. This important and difficult diagnosis is an attractive candidate for computer-aided decision support if it can be reliably identified at scale.
Objective: Here, using an electronic health record-based case definition of delirium, we characterize incidence of this highly morbid condition in 2 large academic medical centers.
Methods: Using the electronic health record of 2 large New England academic medical centers, we calculated and compared the rate of the diagnosis of delirium using a range of administrative and discharge summary text-based case definitions over an 8-year period.
Results: Depending on case definitions, the overall delirium rate ranged from 2.0-5.4% of 809,512 admissions identified. The identified rate of delirium increased between 2005 and 2013, such that by the final year of the study, one of the two sites reported delirium in 7.0% of cases. The concordance between case definitions was low; only half of the cases identified by text analysis were captured by administrative data.
Conclusion: Delirium may be better captured by composite outcomes, including both administrative claims data and elements drawn from unstructured data sources. That the rate of delirium observed in this study is far lower than the current literature estimates suggests that further work on case definitions, identification, and documented diagnosis is required.
Keywords: delirium; electronic health records; epidemiology; predictive modeling..
Copyright © 2017 The Academy of Psychosomatic Medicine. Published by Elsevier Inc. All rights reserved.