Augmented Intelligence to Identify Patients With Advanced Heart Failure in an Integrated Health System

• Published in: JACC. Advances (Online) Vol. 1; no. 4; p. 100123
• Main Authors: Cheema, Baljash, Mutharasan, R Kannan, Sharma, Aditya, Jacobs, Maia, Powers, Kaleigh, Lehrer, Susan, Wehbe, Firas H, Ronald, Jason, Pifer, Lindsay, Rich, Jonathan D, Ghafourian, Kambiz, Tibrewala, Anjan, McCarthy, Patrick, Luo, Yuan, Pham, Duc T, Wilcox, Jane E, Ahmad, Faraz S
• Format: Journal Article
• Language: English
• Published: United States 01.10.2022
• Subjects: integrated healthcare system; augmented intelligence; machine learning; advanced heart failure; electronic health record; artificial intelligence
• ISSN: 2772-963X; 2772-963X
• DOI: 10.1016/j.jacadv.2022.100123

Timely referral for specialist evaluation in patients with advanced heart failure (HF) is a Class 1 recommendation. However, the transition from stage C HF to advanced or stage D HF often goes undetected in routine care, resulting in delayed referral and higher mortality rates. The authors sought to develop an augmented intelligence-enabled workflow using machine learning to identify patients with stage D HF and streamline referral. We extracted data on HF patients with encounters from January 1, 2007, to November 30, 2020, from a HF registry within a regional, integrated health system. We created an ensemble machine learning model to predict stage C or stage D HF and integrated the results within the electronic health record. In a retrospective data set of 14,846 patients, the model had a good positive predictive value (60%) and low sensitivity (25%) for identifying stage D HF in a 100-person, physician-reviewed, holdout test set. During prospective implementation of the workflow from April 1, 2021, to February 15, 2022, 416 patients were reviewed by a clinical coordinator, with agreement between the model and the coordinator in 50.3% of stage D predictions. Twenty-four patients have been scheduled for evaluation in a HF clinic, 4 patients started an evaluation for advanced therapies, and 1 patient received a left ventricular assist device. An augmented intelligence-enabled workflow was integrated into clinical operations to identify patients with advanced HF. Endeavors such as this require a multidisciplinary team with experience in design thinking, informatics, quality improvement, operations, and health information technology, as well as dedicated resources to monitor and improve performance over time.