Abstract

Aims

Worsening heart failure (WHF) events occurring in non‐inpatient settings are becoming increasingly recognized, with implications for prognostication. We evaluate the performance of a natural language processing (NLP)‐based approach compared with traditional diagnostic coding for non‐inpatient clinical encounters and left ventricular ejection fraction (LVEF).

Methods and results

We compared characteristics for encounters that did vs. did not meet WHF criteria, stratified by care setting [i.e. emergency department (ED) and observation stay]. Overall, 8407 (22%) encounters met NLP‐based criteria for WHF (3909 ED visits and 4498 observation stays). The use of an NLP‐derived definition adjudicated 3983 (12%) of non‐primary HF diagnoses as meeting consensus definitions for WHF. The most common diagnosis indicated in these encounters was dyspnoea. Results were primarily driven by observation stays, in which 2205 (23%) encounters with a secondary HF diagnosis met the WHF definition by NLP.

Conclusions

The use of standard claims‐based adjudication for primary diagnosis in the non‐inpatient setting may lead to misclassification of WHF events in the ED and overestimate observation stays. Primary diagnoses alone may underestimate the burden of WHF in non‐hospitalized settings.

Details

Title
Applying natural language processing to identify emergency department and observation encounters for worsening heart failure
Author
Hamilton, Steven A. 1   VIAFID ORCID Logo  ; Ambrosy, Andrew P. 2 ; Parikh, Rishi V. 1 ; Tan, Thida C. 1 ; Fitzpatrick, Jesse K. 3 ; Avula, Harshith R. 4 ; Sandhu, Alexander T. 5 ; Ku, Ivy A. 1 ; Go, Alan S. 6 ; Sax, Dana 7 ; Bhatt, Ankeet S. 8 

1  Department of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, CA, USA 
2  Department of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, CA, USA, Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA, Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, USA 
3  Department of Cardiology, Kaiser Permanente Santa Clara Medical Center, Santa Clara, CA, USA 
4  Department of Cardiology, Kaiser Permanente Walnut Creek Medical Center, Walnut Creek, CA, USA 
5  Division of Cardiology and the Cardiovascular Institute, Department of Medicine, Stanford University, Stanford, CA, USA, Palo Alto Veterans Affairs Healthcare System, Palo Alto, CA, USA 
6  Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA, Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, USA, Department of Epidemiology, Biostatistics and Medicine, University of California, San Francisco, San Francisco, CA, USA, Department of Medicine, Stanford University, Palo Alto, CA, USA 
7  Department of Emergency Medicine, Kaiser Permanente Oakland Medical Center, Oakland, CA, USA 
8  Department of Cardiology, Kaiser Permanente San Francisco Medical Center, San Francisco, CA, USA, Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA, Department of Medicine, Stanford University School of Medicine, Palo Alto, CA, USA 
Pages
2542-2545
Section
Original Article
Publication year
2024
Publication date
Oct 1, 2024
Publisher
Oxford University Press
e-ISSN
20555822
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1002/ehf2.14829
ProQuest document ID
3109516075
Copyright
© 2024. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.