Artificial Intelligence in Emergency Medicine

Introduction

In the fast-paced and high-stakes environment of a crowded emergency department (ED), timely and informed decision-making is crucial for providing effective patient care. Various decision points, spanning from the prehospital phase to the final disposition, demand careful consideration. Recent advancements in artificial intelligence (AI) have paved the way for innovative solutions to enhance decision support in emergency care. This article explores the impact of AI-based clinical decision support (CDS) on different phases of the ED journey and examines the challenges and opportunities associated with its implementation.

What Are the Artificial Intelligence-Based Clinical Decision Support Points During the Emergency Department?

The ED journey can be divided into distinct phases: prehospital, triage, investigation, intervention, and final disposition. Each phase involves critical decision-making, and the effectiveness of patient care depends on the accuracy and promptness of these decisions.

• Prehospital Decision-Making: In the time before reaching the hospital, decisions are typically made based on the experience of the on-site medical staff and the established protocols of the local emergency medical service. However, making these decisions becomes challenging when faced with limited resources and incomplete patient information. Artificial Intelligence Clinical Decision Support (AI-CDS) has been introduced to enhance the accuracy of initial diagnoses using prehospital information and data. Additionally, AI-CDS can assist in dispatching higher-level medical providers when certain contexts or critical conditions are identified during conversations between callers and bystanders. A previous study attempted to predict hospital admission using machine learning based on prehospital data, providing prehospital providers with insights into patient prognosis after Emergency Department (ED) care. To enhance stroke diagnosis, natural language processing techniques were applied to prehospital paramedic reports. Another study successfully predicted early sepsis using prehospital data.

• Triage Optimization: Countries and organizations have their systems in place to prioritize and provide early care for critical patients and efficiently distribute resources. Incorrectly assessing the severity of a patient's condition during triage can lead to a loss of resources or the worsening of critically ill patients due to delayed care. In the field of triage, various AI-based systems and new AI methods have been introduced. Efforts have been made to predict outcomes such as mortality or hospital admission, including the development of models for specific clinical conditions like sepsis. However, there are limited studies demonstrating the prospective use of AI for triage research or implementation. It is crucial to explore ways to align emergency department (ED) triage with the allocation of resources, including staff, beds, and investigative procedures.

• Investigation and Intervention: The investigation phase involves the prescription and interpretation of diagnostic tests, while the intervention phase includes medical interventions to improve patient conditions. AI can assist in the allocation of investigatory and treatment options, optimizing the use of resources. Some research has focused on predicting the need for specific diagnostic tests, such as computed tomography, in the evaluation of diseases. AI predictions can also influence the decision for selected interventions. However, the majority of studies have centered around the interpretation of diagnostic tests in relation to final diagnosis and outcome.

• Final Disposition: The final disposition phase involves the decision for further management, such as intensive care unit admission or the discharge of a patient. AI algorithms can assist in predicting patient-specific dispositions and the likelihood of readmission or return after discharge. These predictive models provide valuable insights for physicians to make informed decisions about patient care.

What Are the Challenges in Implementing AI-CDS in the Emergency Department?

While the potential benefits of AI-CDS in the ED are substantial, several challenges must be addressed for successful implementation:

• Data Quality and Collection: One major challenge lies in the quality of data used to train and operate AI algorithms. Unlike data from intensive care units, ED data collection is hindered by rapid patient turnover and a complex environment. Efforts should be made to encourage data collection and ensure the maintenance of data quality to enhance the effectiveness of AI-CDS.

• Explainability of AI Algorithms: The "black box" nature of AI algorithms poses a challenge in understanding the decision-making process. To overcome this, explainable AI has been introduced, incorporating modified scoring systems or displaying feature importance. Explainable AI is crucial in emergency medicine, where transparency in decision-making is essential for gaining the trust of healthcare professionals.

• Regulatory Hurdles: The healthcare field is strictly regulated, posing a significant barrier to the timely implementation of AI-CDS. Randomized controlled trials, a standard strategy for analyzing the regulatory process, are often challenging or impractical for AI-CDS. Overcoming these regulatory hurdles requires collaborative efforts from the ED, information technology, and data science.

What Are the Opportunities and Future Prospects?

Despite the challenges, the integration of AI-CDS in the ED presents numerous opportunities for improving patient care and operational efficiency:

• Continuous Development and Evaluation: Continuous efforts in the development and evaluation of AI-CDS models are essential. Addressing data quality concerns, refining algorithms, and conducting prospective research will contribute to the successful integration of AI into the ED workflow.

• Collaborative Decision-Making: AI should be viewed as a tool for collaborative decision-making rather than a replacement for human judgment. The combination of AI insights and clinical expertise can lead to more informed and effective decisions, ultimately benefiting patient outcomes.

• Implementation Strategies: Efforts should be directed towards developing practical implementation strategies for AI-CDS in the ED. Collaboration between healthcare professionals, IT experts, and data scientists is crucial for overcoming regulatory hurdles and ensuring the seamless integration of AI technologies.

• Training and Education: Healthcare professionals need to be adequately trained in utilizing AI-CDS tools. Incorporating AI education into medical training programs can enhance the acceptance and proficiency of these technologies among healthcare providers.

Conclusion

The integration of AI-based clinical decision support in the emergency department holds great promise for optimizing patient care and resource allocation. From the prehospital phase to the final disposition, AI can enhance decision-making at crucial points in the ED journey. However, overcoming challenges related to data quality, explainability, and regulatory hurdles is essential for successful implementation. With continuous development, collaboration, and education, AI has the potential to revolutionize emergency care, providing healthcare professionals with valuable insights and improving patient outcomes.