Highlights
- •Machine learning improves identification of heart failure patients over automated approaches that rely on a few structured variables.
- •Improvement in identification may be related to use of free text from clinical notes.
- •Initial screening with the use of a machine learning algorithm can reduce time needed for providers to perform chart review, thus improving efficiency of care.
Abstract
Background
Interventions to reduce readmissions after acute heart failure hospitalization require
early identification of patients. The purpose of this study was to develop and test
accuracies of various approaches to identify patients with acute decompensated heart
failure (ADHF) with the use of data derived from the electronic health record.
Methods and Results
We included 37,229 hospitalizations of adult patients at a single hospital during
2013–2015. We developed 4 algorithms to identify hospitalization with a principal
discharge diagnosis of ADHF: 1) presence of 1 of 3 clinical characteristics, 2) logistic
regression of 31 structured data elements, 3) machine learning with unstructured data,
and 4) machine learning with the use of both structured and unstructured data. In
data validation, algorithm 1 had a sensitivity of 0.98 and positive predictive value
(PPV) of 0.14 for ADHF. Algorithm 2 had an area under the receiver operating characteristic
curve (AUC) of 0.96, and both machine learning algorithms had AUCs of 0.99. Based
on a brief survey of 3 providers who perform chart review for ADHF, we estimated that
providers spent 8.6 minutes per chart review; using this this parameter, we estimated
that providers would spend 61.4, 57.3, 28.7, and 25.3 minutes on secondary chart review
for each case of ADHF if initial screening were done with algorithms 1, 2, 3, and
4, respectively.
Conclusions
Machine learning algorithms with unstructured notes had the best performance for identification
of ADHF and can improve provider efficiency for delivery of quality improvement interventions.
Key Words
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Article info
Publication history
Published online: September 05, 2017
Accepted:
August 25,
2017
Received in revised form:
August 16,
2017
Received:
February 20,
2017
Footnotes
Funding: This work was supported by the Agency for Healthcare Research and Quality, grant K08HS23683.
Identification
Copyright
© 2017 Elsevier Inc. All rights reserved.
