A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods
Background: Artificial intelligence (AI) enabled algorithms can detect or predict cardiovascular conditions using electrocardiogram (ECG) data. Clinical studies have evaluated ECG-AI algorithms, including a recent single-center study which evaluated outcomes when clinicians were provided with ECG-AI...
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Elsevier
2025-06-01
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| Series: | American Heart Journal Plus |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266660222500031X |
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| author | Francisco Lopez-Jimenez Heather M. Alger Zachi I. Attia Barbara Barry Ranee Chatterjee Rowena Dolor Paul A. Friedman Stephen J. Greene Jason Greenwood Vinay Gundurao Sarah Hackett Prerak Jain Anja Kinaszczuk Ketan Mehta Jason O'Grady Ambarish Pandey Christopher Pullins Arjun R. Puranik Mohan Krishna Ranganathan David Rushlow Mark Stampehl Vinayak Subramanian Kitzner Vassor Xuan Zhu Samir Awasthi |
| author_facet | Francisco Lopez-Jimenez Heather M. Alger Zachi I. Attia Barbara Barry Ranee Chatterjee Rowena Dolor Paul A. Friedman Stephen J. Greene Jason Greenwood Vinay Gundurao Sarah Hackett Prerak Jain Anja Kinaszczuk Ketan Mehta Jason O'Grady Ambarish Pandey Christopher Pullins Arjun R. Puranik Mohan Krishna Ranganathan David Rushlow Mark Stampehl Vinayak Subramanian Kitzner Vassor Xuan Zhu Samir Awasthi |
| author_sort | Francisco Lopez-Jimenez |
| collection | DOAJ |
| description | Background: Artificial intelligence (AI) enabled algorithms can detect or predict cardiovascular conditions using electrocardiogram (ECG) data. Clinical studies have evaluated ECG-AI algorithms, including a recent single-center study which evaluated outcomes when clinicians were provided with ECG-AI results. A Multicenter Pragmatic IMplementation Study of ECG-AI-Based Clinical Decision Support Software to Identify Low LVEF (AIM ECG-AI) will evaluate clinical impacts of clinical decision support software (CDSS) integrated within the electronic health record (EHR) to provide point-of-care ECG-AI results to clinicians during routine outpatient care. Methods: AIM ECG-AI is a multicenter, cluster-randomized trial recruiting and randomizing clinicians to receive access to the CDSS (intervention) or provide usual care. Clinicians are recruited from 5 geographically distinct health systems and clustered at the care team level. AIM ECG-AI will evaluate clinical care provided during >32,000 eligible clinical encounters with adult patients with no history of low LVEF and who have a digital ECG documented within the health system's EHR, with 90 day follow up. Results: Study data includes clinician surveys, study software metrics, and EHR data as a read-out for clinician decision-making. AIM ECG-AI will evaluate detection of left ventricular ejection fraction ≤40 % by echocardiography, with exploratory endpoints. Subgroup analyses will evaluate the health system, clinician, and patient-level characteristics associated with outcomes (NCT05867407). Conclusion: AIM ECG-AI is the first multisite clinical evaluation of an EHR-integrated, point-of-care CDSS to provide ECG-AI results in the clinical workflow. The findings will provide valuable insights for clinically focused software design to bring AI into routine clinical practice. |
| format | Article |
| id | doaj-art-f851bf9b51e3401a94fb9e9f5c0a2521 |
| institution | OA Journals |
| issn | 2666-6022 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | American Heart Journal Plus |
| spelling | doaj-art-f851bf9b51e3401a94fb9e9f5c0a25212025-08-20T02:28:20ZengElsevierAmerican Heart Journal Plus2666-60222025-06-015410052810.1016/j.ahjo.2025.100528A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methodsFrancisco Lopez-Jimenez0Heather M. Alger1Zachi I. Attia2Barbara Barry3Ranee Chatterjee4Rowena Dolor5Paul A. Friedman6Stephen J. Greene7Jason Greenwood8Vinay Gundurao9Sarah Hackett10Prerak Jain11Anja Kinaszczuk12Ketan Mehta13Jason O'Grady14Ambarish Pandey15Christopher Pullins16Arjun R. Puranik17Mohan Krishna Ranganathan18David Rushlow19Mark Stampehl20Vinayak Subramanian21Kitzner Vassor22Xuan Zhu23Samir Awasthi24Mayo Clinic, Rochester, MN, United States of AmericaAnumana, Inc., Cambridge, MA, United States of America; nference, Inc., Cambridge, MA, United States of America and Bengaluru, India; Corresponding author at: Anumana, Inc., Cambridge, MA, United States of AmericaMayo Clinic, Rochester, MN, United States of AmericaMayo Clinic, Rochester, MN, United States of AmericaDuke University School of Medicine, Durham, NC, United States of AmericaDuke University School of Medicine, Durham, NC, United States of AmericaMayo Clinic, Rochester, MN, United States of AmericaDuke University School of Medicine, Durham, NC, United States of America; Duke Clinical Research Institute, United States of AmericaMayo Clinic, Rochester, MN, United States of AmericaAnumana, Inc., Cambridge, MA, United States of America; nference, Inc., Cambridge, MA, United States of America and Bengaluru, IndiaAnumana, Inc., Cambridge, MA, United States of America; nference, Inc., Cambridge, MA, United States of America and Bengaluru, IndiaAnumana, Inc., Cambridge, MA, United States of America; nference, Inc., Cambridge, MA, United States of America and Bengaluru, IndiaMayo Clinic, Jacksonville, FL, United States of AmericaAnumana, Inc., Cambridge, MA, United States of America; nference, Inc., Cambridge, MA, United States of America and Bengaluru, IndiaMayo Clinic, Rochester, MN, United States of AmericaUT Southwestern Medical Center, Dallas, TX, United States of AmericaMayo Clinic Arrowhead, AZ, United States of AmericaAnumana, Inc., Cambridge, MA, United States of America; nference, Inc., Cambridge, MA, United States of America and Bengaluru, IndiaAnumana, Inc., Cambridge, MA, United States of America; nference, Inc., Cambridge, MA, United States of America and Bengaluru, IndiaMayo Clinic, Rochester, MN, United States of AmericaNovartis Pharmaceuticals Corp, East Hanover, NJ, United States of AmericaUT Southwestern Medical Center, Dallas, TX, United States of AmericaAnumana, Inc., Cambridge, MA, United States of America; nference, Inc., Cambridge, MA, United States of America and Bengaluru, IndiaMayo Clinic, Rochester, MN, United States of AmericaAnumana, Inc., Cambridge, MA, United States of America; nference, Inc., Cambridge, MA, United States of America and Bengaluru, IndiaBackground: Artificial intelligence (AI) enabled algorithms can detect or predict cardiovascular conditions using electrocardiogram (ECG) data. Clinical studies have evaluated ECG-AI algorithms, including a recent single-center study which evaluated outcomes when clinicians were provided with ECG-AI results. A Multicenter Pragmatic IMplementation Study of ECG-AI-Based Clinical Decision Support Software to Identify Low LVEF (AIM ECG-AI) will evaluate clinical impacts of clinical decision support software (CDSS) integrated within the electronic health record (EHR) to provide point-of-care ECG-AI results to clinicians during routine outpatient care. Methods: AIM ECG-AI is a multicenter, cluster-randomized trial recruiting and randomizing clinicians to receive access to the CDSS (intervention) or provide usual care. Clinicians are recruited from 5 geographically distinct health systems and clustered at the care team level. AIM ECG-AI will evaluate clinical care provided during >32,000 eligible clinical encounters with adult patients with no history of low LVEF and who have a digital ECG documented within the health system's EHR, with 90 day follow up. Results: Study data includes clinician surveys, study software metrics, and EHR data as a read-out for clinician decision-making. AIM ECG-AI will evaluate detection of left ventricular ejection fraction ≤40 % by echocardiography, with exploratory endpoints. Subgroup analyses will evaluate the health system, clinician, and patient-level characteristics associated with outcomes (NCT05867407). Conclusion: AIM ECG-AI is the first multisite clinical evaluation of an EHR-integrated, point-of-care CDSS to provide ECG-AI results in the clinical workflow. The findings will provide valuable insights for clinically focused software design to bring AI into routine clinical practice.http://www.sciencedirect.com/science/article/pii/S266660222500031XElectrocardiogramArtificial intelligenceLeft ventricular ejection fractionHeart failureClinical decision supportBest practice alerts |
| spellingShingle | Francisco Lopez-Jimenez Heather M. Alger Zachi I. Attia Barbara Barry Ranee Chatterjee Rowena Dolor Paul A. Friedman Stephen J. Greene Jason Greenwood Vinay Gundurao Sarah Hackett Prerak Jain Anja Kinaszczuk Ketan Mehta Jason O'Grady Ambarish Pandey Christopher Pullins Arjun R. Puranik Mohan Krishna Ranganathan David Rushlow Mark Stampehl Vinayak Subramanian Kitzner Vassor Xuan Zhu Samir Awasthi A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods American Heart Journal Plus Electrocardiogram Artificial intelligence Left ventricular ejection fraction Heart failure Clinical decision support Best practice alerts |
| title | A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods |
| title_full | A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods |
| title_fullStr | A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods |
| title_full_unstemmed | A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods |
| title_short | A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods |
| title_sort | multicenter pragmatic implementation study of ai ecg based clinical decision support software to identify low lvef clinical trial design and methods |
| topic | Electrocardiogram Artificial intelligence Left ventricular ejection fraction Heart failure Clinical decision support Best practice alerts |
| url | http://www.sciencedirect.com/science/article/pii/S266660222500031X |
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