Systematic Review of Clinical Decision Support Systems for Prehospital Acute Coronary Syndrome Identification

Integrated Hands-Free Electronic Patient Care Report (ePCR) Charting (IHeC): Designing the Architecture

The nature of paramedic workloads typically results in incomplete or lack of patient care reports on patient handover to emergency department staff. Patient information gaps can increase emergency department staff's workload, cause care delays, and increase risks of adverse events. An integrated hands-free electronic patient care report (ePCR) could eliminate this gap. We conducted an environmental scan of the available literature on technologies to improve paramedic documentation and current advanced paramedic charting systems. Two technologies, speech recognition documentation and live telemetry sharing systems, were identified as potential improvements. A theoretical architecture for an integrated hands-free ePCR charting (IHeC) system was developed by combining these technologies. The ePCR could be completed and available upon patient arrival to the hospital using speech recognition and vital sign sharing technology. The IHeC system could solve the problem of patient information gaps and provide a platform for more advanced integration of paramedic services.

Navigating AI in Cardiology: A Scoping Review of Integration through Clinical Decision Support Systems for Acute Coronary Syndrome

BACKGROUND

The integration of AI in diagnosing and managing ACS shows increasing promise, yet challenges remain in translating AI-CDSS into clinical practice. This study evaluates the advancements and limitations of AI for ACS over the past three years, purpose of understanding the scope, limitations, and potential of AI-CDSS in ACS.

MATERIALS AND METHODS

We conducted a systematic review of recent literature, adhering to guidelines for systematic reviews. We applied QUADAS-2 and PROBAST tools for quality assessment, focusing on biases in study designs. Ten studies about AI-CDSS in ACS management underwent critical analysis, emphasizing the strength of their research methods and the thoroughness of their prospective validation to ensure theoretical integrity and practical reliability.

RESULTS

Our research reveals that while discourse around AI-CDSS in ACS management intensifies, obstacles hinder efficacy in practical settings. These challenges include biases in tests and unrepresentative patient selection, pointing to the need for rigorous and inclusive samples. The lack of sufficient external and prospective validation in studies also raises concerns clinical utility of AI-CDSS. The result is the gap between the potential benefits of AI-CDSS and the actual impact of improving diagnostic accuracy and outcomes for ACS limitations identified.

CONCLUSIONS

While AI-CDSS shows promise for improving diagnostic accuracy, treatment efficacy, and workflows in ACS, this study highlights the imperative to enhance model validation, including prospective validation, and address lingering diagnostic gaps. Improving study design and mitigating biases remain crucial for the acceptance and effectiveness of AI-CDSS in acute cardiac care settings.

Enhancing prehospital decision-making: exploring user needs and design considerations for clinical decision support systems

BACKGROUND

In prehospital emergency care, providers face significant challenges in making informed decisions due to factors such as limited cognitive support, high-stress environments, and lack of experience with certain patient conditions. Effective Clinical Decision Support Systems (CDSS) have great potential to alleviate these challenges. However, such systems have not yet been widely adopted in real-world practice and have been found to cause workflow disruptions and usability issues. Therefore, it is critical to investigate how to design CDSS that meet the needs of prehospital providers while accounting for the unique characteristics of prehospital workflows.

METHODS

We conducted semi-structured interviews with 20 prehospital providers recruited from four Emergency Medical Services (EMS) agencies in an urban area in the northeastern U.S. The interviews focused on the decision-making challenges faced by prehospital providers, their technological needs for decision support, and key considerations for the design and implementation of a CDSS that can seamlessly integrate into prehospital care workflows. The data were analyzed using content analysis to identify common themes.

RESULTS

Our qualitative study identified several challenges in prehospital decision-making, including limited access to diagnostic tools, insufficient experience with certain critical patient conditions, and a lack of cognitive support. Participants highlighted several desired features to make CDSS more effective in the dynamic, hands-busy, and cognitively demanding prehospital context, such as automatic prompts for possible patient conditions and treatment options, alerts for critical patient safety events, AI-powered medication identification, and easy retrieval of protocols using hands-free methods (e.g., voice commands). Key considerations for successful CDSS adoption included balancing the frequency and urgency of alerts to reduce alarm fatigue and workflow disruptions, facilitating real-time data collection and documentation to enable decision generation, and ensuring trust and accountability while preventing over-reliance when using CDSS.

CONCLUSION

This study provides empirical insights into the challenges and user needs in prehospital decision-making and offers practical and system design implications for addressing these issues.

Enhancing Prehospital Decision-Making: Exploring User Needs and Design Considerations for Clinical Decision Support Systems

Background

In prehospital emergency care, providers face significant challenges in making informed decisions due to factors such as limited cognitive support, high-stress environments, and lack of experience with certain patient conditions. Effective Clinical Decision Support Systems (CDSS) have great potential to alleviate these challenges. However, such systems have not yet been widely adopted in real-world practice and have found to cause workflow disruptions and usability issues. Therefore, it is critical to investigate how to design CDSS that meet the needs of prehospital providers while accounting for the unique characteristics of prehospital workflows.

Methods

We conducted semi-structured interviews with 20 prehospital providers recruited from four emergency medical services (EMS) agencies in an urban area in the northeastern U.S. The interviews focused on the decision-making challenges faced by prehospital providers, their technological needs for decision support, and key considerations for the design and implementation of a CDSS that can seamlessly integrate into prehospital care workflows. The data were analyzed using content analysis to identify common themes.

Results

Our qualitative study identified several challenges in prehospital decision-making, including limited access to diagnostic tools, insufficient experience with certain critical patient conditions, and a lack of cognitive support. Participants highlighted several desired features to make CDSS more effective in the dynamic, hands-busy, and cognitively demanding prehospital context, such as automatic prompts for possible patient conditions and treatment options, alerts for critical patient safety events, AI-powered medication identification, and easy retrieval of protocols and guidelines using voice commands. Key considerations for successful CDSS adoption included prioritizing alerts to reduce alert fatigue and workflow disruptions, facilitating real-time data collection and documentation to enable decision generation, and ensuring trust and accountability while preventing over-reliance when using CDSS.

Conclusion

This study provides empirical insights into the challenges prehospital providers face and offers design recommendations for developing CDSS solutions that align with prehospital workflows.

Characterizing chest pain in patients with acute coronary syndrome at Vietnam National Heart Institute: a case-control study

OBJECTIVE

This study aimed to distinguish chest pain characteristics between patients with and without acute coronary syndrome (ACS) at Vietnam National Heart Institute.

METHODS

A case-control study using a structured chest pain assessment questionnaire was performed to examine pain characteristics.

RESULTS

Smoking, a history of heart attack, and a family history of cardiovascular disease were associated with increased ACS-related chest pain risk. Patients without ACS more frequently reported left or central chest pain, mild discomfort, pain triggered by activity, and relief with rest or nitroglycerin. ACS-related chest pain was more often characterized by pain radiating to the back, a sensation of tightness or severe discomfort, gradual intensity increase, occurrence at rest or with minimal exertion, and accompanying sweating. No significant sex differences were found in ACS-related chest pain symptoms.

CONCLUSIONS

Targeted assessment of chest pain features-such as pain radiation, pressure sensation, symptom escalation, duration, activity triggers, and relief factors-could improve public awareness and support the development of educational resources on ACS and non-ACS symptoms.

Prediction of emergency department presentations for acute coronary syndrome using a machine learning approach

The relationship between weather and acute coronary syndrome (ACS) incidence has been the subject of considerable research, with varying conclusions. Harnessing machine learning techniques, our study explores the relationship between meteorological factors and ACS presentations in the emergency department (ED), offering insights into seasonal variations and inter-day fluctuations to optimize patient care and resource allocation. A retrospective cohort analysis was conducted, encompassing ACS presentations to Dutch EDs from 2010 to 2017. Temporal patterns were analyzed using heat-maps and time series plots. Multivariable linear regression (MLR) and Random Forest (RF) regression models were employed to forecast daily ACS presentations with prediction horizons of one, three, seven, and thirty days. Model performance was assessed using the coefficient of determination (R²), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The study included 214,953 ACS presentations, predominantly unstable angina (UA) (94,272; 44%), non-ST-elevated myocardial infarction (NSTEMI) (78,963; 37%), and ST-elevated myocardial infarction (STEMI) (41,718; 19%). A decline in daily ACS admissions over time was observed, with notable inter-day (estimated median difference: 41 (95%CI = 37-43, p = < 0.001) and seasonal variations (estimated median difference: 9 (95%CI 6-12, p = < 0.001). Both MLR and RF models demonstrated similar predictive capabilities, with MLR slightly outperforming RF. The models showed moderate explanatory power for ACS incidence (adjusted R² = 0.66; MAE (MAPE): 7.8 (11%)), with varying performance across subdiagnoses. Prediction of UA incidence resulted in the best-explained variability (adjusted R² = 0.80; MAE (MAPE): 5.3 (19.1%)), followed by NSTEMI and STEMI diagnoses. All models maintained consistent performance over extended prediction horizons. Our findings indicate that ACS presentation exhibits distinctive seasonal changes and inter-day differences, with marked reductions in incidence during the summer months and a distinct peak prevalence on Mondays. The predictive performance of our model was moderate. Nonetheless, we obtained good explanatory power for UA presentations. Our model emerges as a potentially valuable supplementary tool to enhance ED resource allocation or future predictive models predicting ACS incidence in the ED.

[Digitalization and clinical decision tools]

Digitalization in cardiovascular emergencies is rapidly evolving, analogous to the development in medicine, driven by the increasingly broader availability of digital structures and improved networks, electronic health records and the interconnectivity of systems. The potential use of digital health in patients with acute chest pain starts even in the prehospital phase with the transmission of a digital electrocardiogram (ECG) as well as telemedical support and digital emergency management, which facilitate optimization of the rescue pathways and reduce critical time intervals. The increasing dissemination and acceptance of guideline apps and clinical decision support tools as well as integrated calculators and electronic scores are anticipated to improve guideline adherence, translating into a better quality of treatment and improved outcomes. Implementation of artificial intelligence to support image analysis and also the prediction of coronary artery stenosis requiring interventional treatment or impending cardiovascular events, such as heart attacks or death, have an enormous potential especially as conventional instruments frequently yield suboptimal results; however, there are barriers to the rapid dissemination of corresponding decision aids, such as the regulatory rules related to approval as a medical product, data protection issues and other legal liability aspects, which must be considered.

The Safety INdEx of Prehospital On Scene Triage (SINEPOST) study: The development and validation of a risk prediction model to support ambulance clinical transport decisions on-scene

One of the main problems currently facing the delivery of safe and effective emergency care is excess demand, which causes congestion at different time points in a patient’s journey. The modern case-mix of prehospital patients is broad and complex, diverging from the traditional ‘time critical accident and emergency’ patients. It now includes many low-acuity patients and those with social care and mental health needs. In the ambulance service, transport decisions are the hardest to make and paramedics decide to take more patients to the ED than would have a clinical benefit. As such, this study asked the following research questions: In adult patients attending the ED by ambulance, can prehospital information predict an avoidable attendance? What is the simulated transportability of the model derived from the primary outcome? A linked dataset of 101,522 ambulance service and ED ambulance incidents linked to their respective ED care record from the whole of Yorkshire between 1st July 2019 and 29th February 2020 was used as the sample for this study. A machine learning method known as XGBoost was applied to the data in a novel way called Internal-External Cross Validation (IECV) to build the model. The results showed great discrimination with a C-statistic of 0.81 (95%CI 0.79–0.83) and excellent calibration with an O:E ratio was 0.995 (95% CI 0.97–1.03), with the most important variables being a patient’s mobility, their physiological observations and clinical impression with psychiatric problems, allergic reactions, cardiac chest pain, head injury, non-traumatic back pain, and minor cuts and bruising being the most important. This study has successfully developed a decision-support model that can be transformed into a tool that could help paramedics make better transport decisions on scene, known as the SINEPOST model. It is accurate, and spatially validated across multiple geographies including rural, urban, and coastal. It is a fair algorithm that does not discriminate new patients based on their age, gender, ethnicity, or decile of deprivation. It can be embedded into an electronic Patient Care Record system and automatically calculate the probability that a patient will have an avoidable attendance at the ED, if they were transported. This manuscript complies with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement (Moons KGM, 2015).

Prehospital diagnostic algorithm for acute coronary syndrome using machine learning: a prospective observational study

Rapid and precise prehospital recognition of acute coronary syndrome (ACS) is key to improving clinical outcomes. The aim of this study was to investigate a predictive power for predicting ACS using the machine learning-based prehospital algorithm. We conducted a multicenter observational prospective study that included 10 participating facilities in an urban area of Japan. The data from consecutive adult patients, identified by emergency medical service personnel with suspected ACS, were analyzed. In this study, we used nested cross-validation to evaluate the predictive performance of the model. The primary outcomes were binary classification models for ACS prediction based on the nine machine learning algorithms. The voting classifier model for ACS using 43 features had the highest area under the receiver operating curve (AUC) (0.861 [95% CI 0.775–0.832]) in the test score. After validating the accuracy of the model using the external cohort, we repeated the analysis with a limited number of selected features. The performance of the algorithms using 17 features remained high AUC (voting classifier, 0.864 [95% CI 0.830–0.898], support vector machine (radial basis function), 0.864 [95% CI 0.829–0.887]) in the test score. We found that the machine learning-based prehospital algorithms showed a high predictive power for predicting ACS.

Using latent class analysis to identify clinical features of patients with occlusive myocardial infarction: Preangiogram prediction remains difficult

Background

Treatment decisions in myocardial infarction (MI) are currently stratified by ST elevation (ST‐elevation myocardial infarction [STEMI]) or lack of ST elevation (non‐ST elevation myocardial infarction [NSTEMI]) on the electrocardiogram. This arose from the assumption that ST elevation indicated acute coronary artery occlusion (OMI). However, one‐quarter of all NSTEMI cases are an OMI, and have a higher mortality. The purpose of this study was to identify features that could help identify OMI.

Methods

Prospectively collected data from patients undergoing percutaneous coronary intervention (PCI) was analyzed. Data included presentation characteristics, comorbidities, treatments, and outcomes. Latent class analysis was undertaken, to determine patterns of presentation and history associated with OMI.

Results

A total of 1412 patients underwent PCI for acute MI, and 263 were diagnosed as OMI. Compared to nonocclusive MI, OMI patients are more likely to have fewer comorbidities but no difference in cerebrovascular disease and increased acute mortality (4.2% vs. 1.1%; p < .001). Of OMI, 29.5% had delays to their treatment such as immediate reperfusion therapy. With latent class analysis, while clusters of similar patients are observed in the data set, the data available did not usefully identify patients with OMI compared to non‐OMI.

Conclusion

Features between OMI and STEMI are broadly very similar. However, there was no difference in age and risk of cerebrovascular disease in the OMI/non‐OMI group. There are no reliable characteristics therefore for identifying OMI versus non‐OMI. Delays to treatment also suggest that OMI patients are still missing out on optimal treatment. An alternative strategy is required to improve the identification of OMI patients.

Prediction of acute neurovascular syndromes with prehospital clinical features witnessed by bystanders

BACKGROUND

The prompt recognition of an acute neurovascular syndrome by the patient or a bystander witnessing the event can directly influence outcome. We aimed to study the predictive value of the medical history and clinical features recognized by the patients' bystanders to preclassify acute stroke syndromes in prehospital settings.

METHODS

We analyzed 369 patients: 209 (56.6%) with acute ischemic stroke (AIS), 107 (29.0%) with intracerebral hemorrhage (ICH), and 53 (14.4%) with subarachnoid hemorrhage (SAH). All patients had neuroimaging as diagnostic gold standard. We constructed clinical prediction rules (CPRs) with features recognized by the bystanders witnessing the stroke onset to classify the acute neurovascular syndromes before final arrival to the emergency room (ER).

RESULTS

In all, 83.2% cases were referred from other centers, and only 16.8% (17.2% in AIS, 15% in ICH, and 18.9% in SAH) had direct ER arrival. The time to first assessment in ≤ 3 h occurred in 72.4% (73.7%, 73.8%, and 64.2%, respectively), and final ER arrival in ≤ 3 h occurred in 26.8% (32.1%, 15.9%, and 28.3%, respectively). Clinical features referred by witnesses had low positive predictive values (PPVs) for stroke type prediction. Language or speech disorder + focal motor deficit showed 63.3% PPV, and 77.0% negative predictive value (NPV) for predicting AIS. Focal motor deficit + history of hypertension had 35.9% PPV and 78.8% NPV for ICH. Headache alone had 27.9% PPV and 95.3% NPV for SAH. In multivariate analyses, seizures, focal motor deficit, and hypertension increased the probability of a time to first assessment in ≤ 3 h, while obesity was inversely associated. Final ER arrival was determined by age and a direct ER arrival without previous referrals.

CONCLUSION

CPRs constructed with the witnesses' narrative had only adequate NPVs in the prehospital classification of acute neurovascular syndromes, before neuroimaging confirmation.