Enhancing clinical decision-making with digital innovation

• Clinical decision-making algorithms and digital pathways are crucial for standardizing care, but real-world challenges often complicate their implementation
• There is significant potential for artificial intelligence to enhance diagnostic accuracy, particularly in electro-cardiogram interpretation and biomarker analysis
• Future tools should aim to integrate diverse data sources in real-time to improve efficiency and complement clinical judgment to transform cardiology and emergency care practices

In a world where healthcare systems are trying to adapt to the latest technologies available, key factors to success depend on the adaptation of new tools—such as digital clinical decision-making algorithms, artificial intelligence-driven diagnostic systems, and advanced biomarker testing—to clinical practice. These innovative tools are crucial in emergency medicine and they support rapid, accurate triage and diagnosis of critical conditions.¹

In the third episode of the podcast series Cardio Insights, Prof. Cynthia Papendick, an Emergency Physician at the Royal Adelaide Hospital, and Professor Lori B. Daniels, a Cardiologist from the University of California San Diego, share their perspectives on the evolving role of digital solutions and clinical decision-making algorithms in cardiology and emergency medicine.
Their discussion highlights key themes that reflect the challenges, opportunities, and future directions for integrating technology into clinical practice.

Both Prof. Papendick and Prof. Daniels emphasize the importance of clinical decision-making algorithms in standardizing care for patients presenting with chest pain. Prof. Papendick highlights the utility of tools like the HEART score and high-sensitivity troponin pathways in Australia, which help to ensure evidence-based approaches across diverse emergency department teams.² She notes, “Having these pathways allows people to maintain a homogeneous and evidence-based approach to common cardinal presentations, for example, chest pain.” Similarly, Prof. Daniels observed how digital pathways, digital systems that manage a patient’s healthcare journey,  were vital when high-sensitivity troponins were introduced in the United States, ensuring consistency and safety despite initial unfamiliarity with the test.³

However, both experts acknowledge the limitations of current practices. For example, real-world challenges, such as delays in sample collection or deviations from strict timelines (e.g., one-hour testing windows), often complicate adherence to digital pathways. Digital algorithms could address these gaps by dynamically adjusting calculations based on real-time data, thereby improving accuracy and efficiency.¹

The conversation also shed light on disparities in the adoption of advanced diagnostic tools like high-sensitivity troponins across health systems. In the U.S., for instance, not all hospitals have transitioned to these tests, creating inconsistencies in patient care.⁴ Prof. Daniels points out that digital algorithms could bridge this gap by helping to standardize data interpretation across different testing methods and settings.⁵

Prof. Papendick further underscores how disparities extend to interpreting complex clinical scenarios. For example, distinguishing between type 1 and type 2 myocardial infarctions often requires nuanced consideration of patient-specific factors like renal function or gastrointestinal bleeding.⁶ She emphasizes that “you can have a raised troponin in a patient who has a gastrointestinal bleed, and they’re actually having a type two myocardial infarction.”

Digital tools capable of integrating multiple data points—such as prior test results or comorbidities—could enhance diagnostic precision and reduce variability across institutions.⁷

Prof. Daniels expressed optimism about integrating AI into electrocardiogram (ECG) interpretation, particularly for detecting subtle changes indicative of acute myocardial infarction (AMI).  She commented that “there’s already interpretation of electrocardiograms, and if you could merge that with biomarkers, I think that could be extremely useful.”

Current computer-generated ECG interpretations often fall short, requiring manual review by specialists—a process ripe for improvement through AI-driven insights.8 Prof. Papendick echoes this sentiment, advocating for AI tools that could provide more reliable ECG interpretations while contextualizing findings within broader clinical scenarios. An example of this could be, integrating biomarkers with AI-analyzed ECG data to support more comprehensive assessments of chest pain in patients.

Despite their enthusiasm for digital solutions, both experts acknowledged significant barriers to implementation. Prof. Daniels noted that demonstrating improvements—such as reduced mortality or hospitalizations—is a high bar for new technologies. Instead, initial metrics like efficiency gains or resource utilization may be more attainable benchmarks for evaluating digital tools. She observed, “From a hospital-wide perspective, if you can make a system more efficient and even save 30 minutes or an hour, that may not be a ‘wow’ for physicians, but for the hospital system as a whole, it can really be important.”

Prof. Papendick added that emergency departments often default to admitting patients as a precautionary measure, and that digital algorithms capable of holistically assessing patient data could help identify cases where admission is unnecessary, optimizing resource use.⁹

When asked about their wishes for future digital solutions, both experts envision tools that integrate diverse data sources to guide clinical decision-making more effectively. For Prof. Papendick, this includes better ECG interpretation algorithms that account for subtle abnormalities often overlooked by current systems. For Prof. Daniels, the focus is on heart failure diagnostics—specifically tools that incorporate factors like age, comorbidities, and prior biomarker levels to distinguish acute and chronic conditions.

Looking ahead, both agree that AI-driven solutions must be implemented thoughtfully to ensure they complement rather than replace clinical judgment. By merging advanced analytics with clinician expertise, these technologies have the potential to transform cardiology and emergency medicine.

Cardio Insights is a podcast hosted by Mathieu Chaffard, featuring thought leaders from leading institutions across the globe to explore the potential of digital solutions in cardiovascular and emergency medicine.