- •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.
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.
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.
To read this article in full you will need to make a payment
Purchase one-time access:Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
One-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:Subscribe to Journal of Cardiac Failure
Already a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
- Most frequent conditions in U.S. hospitals, 2011: statistical brief #162.Healthcare Cost and Utilization Project, Rockville (MD)2013
- 2013 ACCF/ACA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines.J Am Coll Cardiol. 2013; 62: e147-e239
- Readmissions Reduction Program.(Available at)https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.htmlDate accessed: November 23, 2016
- Hospital strategy uptake and reductions in unplanned readmission rates for patients with heart failure: a prospective study.J Gen Intern Med. 2015; 30: 605-611
- National survey of hospital strategies to reduce heart failure readmissions: findings from the Get With the Guidelines–Heart Failure registry.Circ Heart Fail. 2012; 5: 680-687
- Variability in implementation of interventions aimed at reducing readmissions among patients with heart failure: a survey of teaching hospitals.Acad Med. 2016; 91: 522-529
- An administrative claims measure suitable for profiling hospital performance on the basis of 30-day all-cause readmission rates among patients with heart failure.Circ Cardiovasc Qual Outcomes. 2008; 1: 29-37
- ACCF/AHA/AMA-PCPI 2011 performance measures for adults with heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Performance Measures and the American Medical Association-Physician Consortium for Performance Improvement.Circulation. 2012; 125: 2382-2401
- Automated identification and predictive tools to help identify high-risk heart failure patients: pilot evaluation.J Am Med Inform Assoc. 2016; 23: 872-878
- An electronic medical record report improves identification of hospitalized patients with heart failure.J Card Fail. 2015; 22: 402-405
- Comparison of approaches for heart failure case identification from electronic health record data.JAMA Cardiol. 2016; 1: 1014-1020
- Heart failure-associated hospitalizations in the United States.J Am Coll Cardiol. 2013; 61: 1259-1267
- Fact sheet: two-midnight rule.(Available at)https://www.cms.gov/Newsroom/MediaReleaseDatabase/Fact-sheets/2015-Fact-sheets-items/2015-07-01-2.htmlDate accessed: November 23, 2016
- Feature selection, L1 vs L2 regularization, and rotational invariance.(Paper presented at: Proceedings of the Twenty-First International Conference on Machine Learning)2004
- Desiderata for computable representations of electronic health records–driven phenotype algorithms.J Am Med Inform Assoc. 2015; 22: 1220-1230
- A review of approaches to identifying patient phenotype cohorts using electronic health records.J Am Med Inform Assoc. 2014; 21: 221-230
Published online: September 05, 2017
Accepted: August 25, 2017
Received in revised form: August 16, 2017
Received: February 20, 2017
Funding: This work was supported by the Agency for Healthcare Research and Quality, grant K08HS23683.
© 2017 Elsevier Inc. All rights reserved.