For localized information and support, would you like to switch to your country-specific website for {0}?
Key takeaways
Clinical decision support (CDS) tools can support cardiovascular disease (CVD) care, but to gain acceptance digital CDS tools must prove their worth by demonstrating multi-dimensional value
The primary barriers to adopting cardiology algorithms stem from existing logistical and workforce-related bottlenecks
The future of tools like chest pain algorithms depends on a symbiotic partnership between the algorithm and the physician
In the high-stakes corridors of modern cardiology, a new kind of consultant is beginning to make its presence felt. It doesn’t wear a white coat or carry a stethoscope; instead, it lives within the lines of code that pulse through hospital servers. Cardiovascular disease remains the leading cause of death globally, a stark reality that has sent researchers and clinicians on a search for tools that can catch the first flickers of a failing heart before it stops.1,2 Chest pain algorithms offer great promise in this direction, as clinical decision-support tools (CDS) are no longer futuristic concepts, but the next frontier in the effort to personalize patient care.3
Unfortunately, while the technology is ready, according to a recently convened panel of experts, the "clinical ecosystem" is not yet fully prepared to embrace it.4 Establishing a digital solution, such as a chest pain algorithm as a life-saving clinical standard, is often faced with structural, psychological, and regulatory hurdles.
The panel was made up of cardiology experts from leading institutions including: Christopher Baugh (Harvard), C. Michael Gibson (Harvard), Evangelos Giannitsis (Univ. of Heidelberg), James Januzzi (Harvard), Cynthia Papendick (Univ. of Adelaide), Hans-Peter Brunner-La Rocca (Maastricht Univ.), and Lori B. Daniels (UC San Diego).
Cardiology algorithms
Currently, digital tools are already proving their worth in the practice of cardiology. For instance, Medtronic’s AccuRhythm AI algorithm has been shown to reduce false atrial fibrillation alerts by over 74%, significantly easing the burden of clinical review for doctors monitoring implanted devices.5-7 Other tools, like the CoDE-ACS and CoDE-HF models, use machine learning to synthesize complex data—age, heart rate, and protein levels—to predict the probability of a heart attack or heart failure with remarkable accuracy.8-10
These cardiology algorithms act as a sophisticated safety net. In the high-demand environment of an emergency department, where physicians must rapidly decide who can safely go home and who needs a high-cost admission, tools like the ARTEMIS algorithm can help "rule out" myocardial infarctions more efficiently than traditional guideline-recommended strategies.11-14 The promise is clear: a more consistent, data-driven level of care that persists even outside of normal operating hours or in resource-limited community clinics.15-18
The human component
If the algorithms are sound and evidence-based, why aren't these tools everywhere? Dr. Baugh and his colleagues suggest that the greatest barrier isn't the technology itself, but the "workflow" into which it must fit.4 Healthcare professionals are already battling a crisis of burnout, with symptoms affecting nearly 50% of physicians.19-21 Any tool that adds to the administrative "work burden", even if it promises better patient outcomes, is likely to face rejection from the front lines.
There is also the phenomenon of "alert fatigue," where doctors become desensitized to a constant barrage of digital notifications. If a digital tool is too "noisy," providing low-priority or irrelevant alerts, it risks being ignored entirely; studies have shown that clinicians override drug safety alerts between 77% and 90% of the time.22-24 For an algorithm to be adopted, it must be intuitive and seamless. Above all, as this panel emphasized, it is still important to leave the clinician in control of the final decision.4
The three currencies of value
To convince the various gatekeepers of the medical world, a digital tool must prove its worth in three different "currencies":
Clinical value: For doctors on the panel like Dr. Giannitsis or Dr. Morrow, the evidence must show improved patient outcomes or closer adherence to gold-standard guidelines. Robust trials are needed to ensure algorithms don't "overfit" their data, becoming less accurate as medical standards evolve.25-28
Economic value: For hospital administrators, a tool must demonstrate a quantifiable return on investment. One estimate suggests that using AI for colorectal cancer genotyping could save $400 million in the U.S. alone.29-31
Human value: For the patient, digital tools offer empowerment and personalization. When patients understand the logic behind a recommendation, they are more likely to engage with their treatment.32-34
Regulating cardiology algorithms
Even a perfectly designed tool faces the wall of regulation. In Europe, medical AI must navigate the AI Act and General Data Protection Regulation (GDPR).35-37 The risk is not theoretical; one study cited by the panel found that an algorithm could re-identify over 85% of adults in a dataset even after their names had been removed, simply by analyzing physical activity patterns.38-40 Furthermore, regulators like the US Food and Drug Administration (FDA) are still grappling with how to handle "adaptive" AI—algorithms that continue to learn and change after they have been approved.35-37
The path forward for chest pain algorithms
The consensus among the expert panel is that the future of cardiology depends on a "local committee" approach to implementation. Hospitals need multidisciplinary teams that include doctors, IT technicians, and administrators to oversee the deployment of CDS tools, ensuring they are scalable, secure, and genuinely helpful.4
In the end, academia, industry, and regulators must move in lockstep. We are moving toward a world where the physician and the algorithm work in a symbiotic partnership, but that partnership requires a foundation of trust that can only be built through rigorous evidence and thoughtful design.
To understand this transition, one might think of digital tools like chest pain algorithms not as a replacement for the driver, but as a high-tech navigation system in a storm. The navigator can process thousands of data points about the road ahead that the driver cannot see, but it is still the driver who must keep their hands on the wheel and decide when to turn. For these experts, the goal of the algorithm is not to take over the journey, but to ensure that everyone arrives safely at their destination.
To access deeper insights from the panel, read the article published in Critical Pathways in Cardiology.
Contributors
Chris Baugh, MD, MBA, FACEP
Associate Professor of Emergency Medicine at Harvard Medical School
Prof Baugh is an Associate Professor of Emergency Medicine at Harvard Medical School, USA, and recently completed a 5-year term as the Vice Chair of Clinical Affairs for the Department of Emergency Medicine at Brigham and Women’s Hospital. He attended the University of Pennsylvania School of Medicine and obtained his MBA from the Wharton School. He trained in Emergency Medicine at Brigham and Women’s and Massachusetts General Hospitals. He previously served as the Medical Director of both the Brigham’s Urgent Care Center and Department of Emergency Medicine and as Chair of the Observation Medicine Section of the American College of Emergency Physicians. He recently joined the Board of Trustees of the Emergency Medicine Foundation.
His clinical interests are in emergency medicine, cardiology, oncology, healthcare finance, healthcare policy, observation medicine, clinical operations, and quality and safety. He has co-authored over 160 publications in his field and has published on the clinical and administrative aspects of observation care in the New England Journal of Medicine, Health Affairs, JAMA Open, Annals of Emergency Medicine, and Academic Emergency Medicine.
C. Michael Gibson, MS, MD
Interventional Cardiologist, researcher, and educator
C. Michael Gibson is a Professor of Medicine at Harvard Medical School, Chief Executive Officer of the non-profit Baim Institute for Clinical Research and PERFUSE, and an Interventional Cardiologist from Beth Israel Deaconess Medical Center. Professor Gibson invented measures of coronary blood flow that are widely used today including the Thrombolysis in Myocardial Infarction (TIMI) frame count and the TIMI myocardial perfusion grade. A prodigious speaker, he has published over 700 peer-reviewed papers.
Prof. Evangelos Giannitsis, MD, PhD — Cardiologist working at the University Hospital of Heidelberg, Germany
Prof Giannitsis is a senior physician in Cardiology at the University of Heidelberg, where he also leads the Cardiac Biomarker Research Group. His research focuses on cardiac biomarkers, particularly troponins, and their integration with cardiac MRI phenotypes. He currently chairs the certification committee for Chest Pain Units for the German Society of Cardiology. Prof Giannitsis is an Assistant Editor of Clinical Research in Cardiology, an active member of the ESC, and has co-authored over 600 publications.
James Januzzi, MD
Professor of Medicine at Harvard Medical School
Prof James Januzzi is currently the Adoph Hutter Professor of Medicine at Harvard Medical School and Chief Scientific Officer and Gibson Chair at the Baim Institute for Clinical Research. He performed a residency in internal medicine at the Brigham and Women’s Hospital, followed by a fellowship in cardiology and cardiac ultrasound at the Massachusetts General Hospital. He joined the staff at Massachusetts General Hospital in 2000 and joined the Baim Institute in 2014. He is a clinician, teacher, experienced clinical researcher and clinical trialist.
Prof Januzzi’s research work has contributed greatly to the understanding of cardiac biomarker testing, and his studies have set international standards for use in diagnosis, prognosis, and management of patients suffering from heart failure and acute coronary syndromes. He has chaired or participated in numerous committees for trials focused on drug development for heart failure, coronary artery disease and diabetes mellitus.
Prof Januzzi is among the world’s top 1% most cited researchers, having published more than 900 manuscripts, book chapters, and review articles.
Cynthia Papendick, MD
Emergency Physician & Associate Professor of Emergency Medicine, University of Adelaide’s Medical School
Cynthia Papendick is an Associate Professor at the University of Adelaide Medical School in Australia and an Emergency Physician at the Royal Adelaide Hospital, focusing her work and research on improving outcomes and resource utilization for patients presenting to the emergency department with chest pain, particularly those with suspected acute coronary syndrome.
Hans-Peter Brunner La-Roca, MD
Vice Chairman of the Department of Cardiology, Maastricht University Medical Centre
Prof Brunner-La Rocca is Director of the Heart Failure Clinic and Vice Chairman of the Department of Cardiology at Maastricht University Medical Centre, University of Maastricht, The Netherlands. He completed his medical degree at the University of Zürich, training in both internal medicine and cardiology, before attaining Senior Cardiology positions in Zürich and Basel, Switzerland. He has been awarded fellowships by the European Society of Cardiology and European Heart Failure Association.
During his career as a cardiologist, his research has focused on using biomarkers and eHealth to
individualise therapy and management of patients with chronic heart failure (CHF). Notably, he coordinates the large IHI-supported project iCARE4CVD, which aims to personalize medicine in cardiovascular diseases using biomarkers. He serves as Principal Investigator and national coordinator for multiple randomized controlled trials. He has co-authored over 400 peer-reviewed publications in his field and serves as a reviewer for several high-impact journals.
Lori B. Daniels, M.D., FACC
Cardiologist, Professor of Cardiovascular Medicine and Epidemiology, University of California San Diego
Lori B. Daniels Lori B. Daniels is a Cardiologist, Director of the Cardiovascular Intensive Care Unit, and Professor of Cardiovascular Medicine and Epidemiology at the University of California in San Diego. She has co-authored more than 200 publications in her field, focusing on the use of biomarkers to assess cardiovascular risk in a variety of populations.
Explore articles from our community
References
Di Cesare M, et al. The Heart of the World. Global Heart. 2024 Jan 25;19(1):11.
Almansouri NE, et al. Early diagnosis of cardiovascular diseases in the era of artificial intelligence: An in-depth review. Cureus. 2024 Mar 19;16(3):e55869.
Rossello X, et al. Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme. Eur J Prev Cardiol. 2019 Sep;26(14):1534-1544.
Baugh CW, et al. Considerations for the adoption of digital algorithms and cardiovascular decision-support tools in clinical practice. Crit Pathw Cardiol. 2026 Jan 15; online ahead of print.
Jablonski AM, et al. The use of algorithms in assessing and managing persistent pain in older adults. Am J Nurs. 2011 Mar;111(3):34-43.
Radtke PA, et al. B-AB24-04 Artificial intelligence enables dramatic reduction of false atrial fibrillation alerts from insertable cardiac monitors. Heart Rhythm. 2021 Aug;18(8):S47.
de Koning E, et al. AI algorithm to predict acute coronary syndrome in prehospital cardiac care: Retrospective cohort study. JMIR Cardio. 2023 Oct 31;7:e51375.
Zellweger MJ, et al. A new non-invasive diagnostic tool in coronary artery disease: Artificial intelligence as an essential element of predictive, preventive, and personalized medicine. EPMA J. 2018 Aug 16;9(3):235-247.
Doudesis D, et al. Machine learning for diagnosis of myocardial infarction using cardiac troponin concentrations. Nat Med. 2023 May;29(5):1201-1210.
Lee KK, et al. Development and validation of a decision support tool for the diagnosis of acute heart failure: Systematic review, meta-analysis, and modelling study. BMJ. 2022 Jun 13;377:e068424.
Emakhu J, et al. Acute coronary syndrome prediction in emergency care: A machine learning approach. Comput Methods Programs Biomed. 2022 Oct;225: 107080.
Neumann JT, et al. Personalized diagnosis in suspected myocardial infarction. Clin Res Cardiol. 2023 Sep;112(9):1288-1301.
Toprak B, et al. Diagnostic accuracy of a machine learning algorithm using point-of-care high-sensitivity cardiac troponin I for rapid rule-out of myocardial infarction: A retrospective study. Lancet Digit Health. 2024 Oct;6(10):e729-e738.
Eurlings C, et al. Use of artificial intelligence to assess the risk of coronary artery disease without additional (non-invasive) testing: Validation in a low-risk to intermediate-risk outpatient clinic cohort. BMJ Open. 2022 Sep 26;12(9):e055170.
Miller DD and Brown EW. Artificial intelligence in medical practice: the question to the answer? Am J Med. 2018 Feb;131(2):129-133.
Grote T and Berens P. On the ethics of algorithmic decision-making in healthcare. J Med Ethics 2020;46:205-211.
Bollestad M, et al. A randomized controlled trial of a diagnostic algorithm for symptoms of uncomplicated cystitis at an out-of-hours service. Scand J Prim Health Care 2015 Jun;33(2):57-64.
Gruber K. Is the future of medical diagnosis in computer algorithms? Lancet Digit Health. 2019 May;1(1):e15-e16.
West CP, et al. Physician burnout: Contributors, consequences and solutions. J Intern Med. 2018 Jun;283(6):516-529.
Bourji H, et al. Evaluating the alarm fatigue and its associated factors among clinicians in critical care units. EJ-CLINICMED. 2020 Dec;1(1);1-10.
Khanna NN, et al. Economics of artificial intelligence in healthcare: Diagnosis vs. treatment. Healthcare (Basel). 2022 Dec;10(12):2493.
Al-Abri R and Al-Balushi A. Patient satisfaction survey as a tool towards quality improvement. Oman Med J. 2014 Jan;29(1):3-7.
Madanian S, et al. Patients' perspectives on digital health tools. PEC Innov. 2023 May;2:100171.
Wan PK, et al. Reducing alert fatigue by sharing low-level alerts with patients and enhancing collaborative decision making using blockchain technology: scoping review and proposed framework (MedAlert). J Med Internet Res 2020;22:e22013.
Zellweger MJ, et al. A new non-invasive diagnostic tool in coronary artery disease: Artificial intelligence as an essential element of predictive, preventive, and personalized medicine. EPMA J. 2018 Aug; 9(3):235-247.
Labovitz DL, et al. Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke 2017 May;48(5):1416-1419.
Han R, et al. Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review. Lancet Digit Health. 2024 May;6(5):e367-e373.
Finlayson SG, et al. The clinician and dataset shift in artificial intelligence. N Engl J Med 2021 Jul;385(3):283-286.
Kacew AJ, et al. Artificial intelligence can cut costs while maintaining accuracy in colorectal cancer genotyping. Front Oncol. 2021 Jun;11:630953.
Rakers MM, et al. Perceived barriers and facilitators of structural reimbursement for remote patient monitoring, an exploratory qualitative study. Health Policy Technol. 2022 Dec;12(2):100718.
Jongsma KR, et al. Why we should not mistake accuracy of medical AI for efficiency. NPJ Digit Med. 2024 Mar;7(1):57.
Islam SMS, et al. Can digital health help improve medication adherence in cardiovascular disease? Expert Rev Med Devices. 2024 Dec;21(12):1071-1075.
Bucher A. The patient experience of the future is personalized: using technology to scale an N of 1 approach. J Patient Exp. 2023 Apr;10:23743735231167975.
Soellner M, and Koenigstorfer J. Compliance with medical recommendations depending on the use of artificial intelligence as a diagnostic method. BMC Med Inform Decis Mak. 2021 Aug;21(1):236.
U.S. Food and Drug Administration. Artificial intelligence & medical products: how CBER, CDER, CDRH, and OCP are working together. [Internet; cited 2026 Jan 20]. Available from: https://www.fda.gov/media/177030/download?attachment.
Medicines & Healthcare products Regulatory Agency. Software and AI as a medical device change programme roadmap. [Internet; cited 2026 Jan 20]. Available from: https://www.gov.uk/government/publications/software-and-ai-as-a-medical-device-change-programme/software-and-ai-as-a-medical-device-change-programme-roadmap.
Onitiu D, et al. How AI challenges the medical device regulation: patient safety, benefits, and intended uses. J Law Biosci. 2024 Apr; lsae007.
Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics. 2021 Sep;22(1):122.
Na L, et al. Feasibility of reidentifying individuals in large national physical activity data sets from which protected health information has been removed with use of machine learning. JAMA Netw Open. 2018 Dec;1(8):e186040.
Kwon JM, et al. Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography. Scand J Trauma Resusc Emerg Med. 2020 Oct;28(1):98.