Advancing CVD Management with Digital Triage Tools in the emergency room

• Clinical decision support tools (CDSTs) can significantly improve diagnostic accuracy and streamline workflows in the emergency department. By integrating these tools healthcare professionals are assisted in making timely and precise decisions, leading to better patient outcomes, reduced diagnostic errors and more efficient use of healthcare resources^1,2
• CDSTs can support the shift towards personalized medicine by tailoring recommendations to individual patient characteristics. Using high-quality robust data, machine learning algorithms and real-time data analytics, digital triage tools enable the early identification of either high- or low-risk patients and the delivery of personalized treatment plans. This also reduces healthcare costs by preventing potential complications and unnecessary clinical interventions
• Future advancements, including the integration with wearable devices, genomic data, and telemedicine, hold promise for further enhancing the capabilities and impact of CDSTs in cardiovascular disease (CVD) patient management

Cardiovascular disease (CVD) encompasses a range of conditions, including coronary artery disease, heart failure, arrhythmias, and stroke. According to the World Health Organization (WHO), CVD accounts for approximately 17.9 million deaths annually, representing 31% of all global deaths.³ The broad presentation of CVD often requires immediate and precise intervention to improve patient outcomes. About 20% of emergency room visits are preventable, with half being for cardiovascular conditions.⁴ The complexity of diagnosing and managing CVD, particularly in the emergency room, necessitates advanced digital triage tools to support clinical decision making and reduce emergency room visits.

Clinical decision support tools (CDSTs) are computer-based systems designed to assist healthcare professionals during emergency room triage and follow-up. They integrate patient data, clinical guidelines, and predictive algorithms to provide tailored and specific treatment recommendations. These tools can enhance diagnostic accuracy, streamline workflows, support timely interventions, and improve adherence to guidelines, ultimately leading to better patient outcomes.^1,2 However, challenges to their implementation include poor familiarisation of physicians with technology, integration difficulties with electronic health records, and bandwidth issues within the digital health infrastructure.

One of the primary applications of CDSTs in CVD management is during chest pain triage to enhance diagnostic accuracy. For example, CDSTs can analyse high-sensitivity troponin (hs-troponin) levels along with other clinical parameters to stratify a patient’s risk of CVD and guide further testing or treatment.^5,6

The implementation of electronic health records integrated with CDSTs can also improve the efficiency of patient care by reducing the time spent on documentation.⁷

Professor Christopher Baugh and Professor Evangelos Giannitsis highlight the significant role of digital triage tools in the emergency room. They discuss that crowding, staffing shortages, and high patient throughput are the biggest challenges in the emergency room. CDSTs can aid in the accurate classification and triage of patients, potentially addressing these challenges

Several digital technologies have been developed for CVD management within the emergency room. Notable examples include the HEART score and the Global Registry of Acute Coronary Events (GRACE) score, both of which aid in risk stratification and have been shown to improve the efficiency of care.^8-11

The HEART score is a widely used interactive tool for risk stratification in patients with chest pain. It incorporates five elements: patient history, ECG, age, risk factors and troponin levels and categorises patients into low, intermediate, and high-risk groups, guiding further diagnostic and therapeutic decisions.⁸ Studies show that its use can reduce unnecessary admissions and improve the efficiency of care in the emergency room.⁹

The GRACE score is used to predict the risk of death and AMI in patients with acute coronary syndrome (ACS) by integrating clinical variables such as age, heart rate, systolic blood pressure, and creatinine levels.¹⁰ The use of the GRACE score in the emergency room has been associated with an improved risk prediction of mortality in patients with ACS and can help identify high risk patients who may benefit from more aggressive treatment and close monitoring.^10,11

Recent advancements in artificial intelligence (AI) have also led to machine learning algorithms that provide high accuracy in predicting patient outcomes and guiding treatment plans. Machine learning algorithms can also be used to predict the risk of adverse events, such as heart failure exacerbations, allowing for early intervention and improved patient outcomes.¹² Integrating AI in emergency medicine allows machine learning algorithms to rapidly analyze complex clinical data, such as serial ECGs, to predict adverse cardiovascular outcomes and assist clinicians at the bedside.¹³

Clinical decision support tools offer numerous benefits, including improved diagnostic accuracy, enhanced patient outcomes, and reduced healthcare costs. By integrating multiple data sources and applying evidence-based algorithms quickly, CDSTs reduce the likelihood of diagnostic errors and ensure that patients receive appropriate care promptly.¹ Studies demonstrate that CDSTs in managing acute coronary syndrome (ACS) can reduce mortality and morbidity rates and support personalized care by tailoring recommendations to individual patient characteristics.^12,14

While CDSTs provide broad benefits, challenges remain, including data quality and completeness, user acceptance, and ethical and legal considerations. Accurate data input and seamless integration with existing electronic health record systems are crucial for successful implementation. Overcoming resistance to change and ensuring healthcare professionals are adequately trained are essential for the adoption of CDSTs. Professor Giannitsis suggests there is poor familiarisation with digital devices in a very heterogeneous healthcare landscape. Additionally, addressing bandwidth issues within the digital health infrastructure is necessary for the widespread use of these tools.

"There is a lot of excitement over what new technology in the emergency room could bring to complement the clinician, to make him or her more efficient and safer in their practice and to take them away from the computer and to the bedside where they want to be spending their time,” said Prof. Baugh. Advancements in technology and data science offer new opportunities for their use in emergency rooms and broader clinical practice. The integration of CDSTs with wearable devices and remote monitoring systems can provide continuous data on patients' cardiovascular health, enabling early detection and proactive management.¹⁵ Furthermore, the use of CDSTs in clinical research can enhance patient recruitment, retention, and the development of new clinical guidelines.

CDSTs have the potential to revolutionise CVD management in the emergency room by improving diagnostic accuracy, streamlining workflows, and supporting timely interventions. Addressing challenges related to data quality, user acceptance, and ethical considerations is essential for their successful implementation. Ongoing advancements in technology hold promise for further enhancing personalized medicine and clinical research. By embracing CDSTs and integrating them into routine emergency room practices, healthcare professionals can significantly improve patient outcomes and quality of life for those with CVD.