How integrating AI and clinical decision support systems can help in the ER
By Bill Siwicki —Deployment of artificial intelligence for point-of-care clinical decision support is in its nascency. Despite the media attention and proliferation of AI studies, translation to clinical practice is rare. Little evidence exists on best practices for deployment, particularly in emergency medicine.
Emergency medicine serves as the frontline of healthcare and the integration of AI and clinical decision support at this critical care point has the potential to revolutionize the way care is delivered, affecting numerous downstream processes, said Andrew Taylor, associate professor of emergency medicine, director of emergency department clinical informatics and associate director of informatics and data science research at Yale University School of Medicine.
Combining AI and CDS
Taylor will be speaking on this subject at the HIMSS24 Global Conference & Exhibition in an educational session titled “Deploying Artificial Intelligence for Clinical Decision Support in Emergency Medicine.”
“In the ED, where quick and accurate decision-making is critical, AI-CDS tools can significantly streamline processes, improve patient outcomes and optimize the use of resources,” he explained. “However, this is a complex environment with many variables – from patient demographics to symptom presentation. Therefore, the deployment of AI tools must be carried out with meticulous planning and sensitivity to the unique stressors and workflow of the ED.
“Throughout this session at HIMSS24, we will explore various applications of AI-CDS in the ED including triage, patient disposition, diagnosis and risk assessment,” he continued. “We will also maintain a focus on a guiding philosophy: AI in medicine must grow as an organic extension of human empathy and care, not as a detached technological force.”
The human elements of healthcare
Taylor’s approach emphasizes the creation of AI systems that are technically advanced yet seamlessly integrated with the human elements of healthcare.
“It’s about cultivating AI tools that support clinicians rather than replace them, ensuring that technology is a means to enhance the human-centric care that lies at the heart of medicine,” he said.
Session attendees should leave with a deep understanding of AI applications and workflow integration and stakeholder engagement, Taylor stated.
“Regarding AI applications, our discussion will delve into the ways AI-CDS facilitates rapid and precise triage, which is just one facet of its broader capabilities,” he explained. “By swiftly analyzing complex patient data, AI algorithms can accurately assess the severity of a patient’s condition, ensuring timely and appropriate medical attention.
“This advanced triage process is not the only benefit; AI-CDS extends its utility to encompass risk assessment, aiding in the prediction of patient outcomes and contributing to more informed decision-making regarding patient disposition – whether that involves ICU admission, an inpatient bed or discharge to home care,” he continued.
Moreover, AI-CDS systems are instrumental in enhancing diagnostic accuracy, which is crucial in the high-stakes environment of the emergency department, he added.
“By integrating these varied functions, AI-CDS supports a more nuanced and efficient allocation of emergency department resources and fosters improved patient outcomes through its multifaceted support of clinical decisions,” Taylor noted.
Acceptance and integration
Regarding workflow integration and stakeholder engagement, the success of AI-CDS hinges not just on the sophistication of the technology but also on the acceptance and integration of these systems by those who are directly impacted by their use, he said.
“Clinicians, healthcare staff and patients are the cornerstone stakeholders whose insights, expertise and experiences drive the development of AI solutions that are ethically conscious, transparent and free of bias,” he said. “By actively involving these stakeholders, AI tools can be crafted to address the nuanced demands of healthcare delivery, ensuring that such innovations serve as a supportive extension of human care.
“This process of engagement is critical in fostering AI systems that are not only technologically advanced but also resonate with the core values of healthcare – compassion, privacy and equity,” he continued. “It’s through this collaborative approach that we can develop AI-CDS tools that respect the delicate human aspects of healthcare, ensuring that these systems are perceived as allies in clinical decision-making rather than as impersonal or disruptive forces.”
A robust infrastructure
On another front, an additional takeaway from this HIMSS24 session will be the importance of establishing a robust infrastructure for AI-CDS deployment and long-term utilization, Taylor revealed.
“The efficacy of these systems relies on their ability to blend into the existing clinical processes, enhancing rather than complicating the decision-making pathway,” he explained. “AI-CDS tools should therefore be designed with user experience at the forefront, ensuring they are user-friendly, intuitive and provide actionable insights that align with the clinicians’ thought processes.
“Moreover, the infrastructure supporting the deployment of AI-CDS must be robust and adaptable, capable of evolving with the changing landscape of clinical data and healthcare practices,” he continued. “The deployment strategy should include the implementation of machine learning operations, known as MLOps, which is pivotal in the monitoring, maintenance and continuous improvement of AI applications.”
This framework ensures that AI-CDS tools remain effective, secure and relevant over the long term, maintaining compliance with stringent data security standards and adapting to the dynamic environment of emergency medicine, he added.
Enhancing patient care
“By building a resilient infrastructure that accounts for the life cycle management of AI tools, we enable these systems to become enduring assets in medicine, continually enhancing patient care while navigating the complexities and ever-changing demands of the healthcare landscape,” he said.
“It is this meticulous attention to operational infrastructure and the cultivation of a symbiotic relationship between AI-CDS tools and clinical workflows that will drive the success and sustainability of AI in emergency care settings,” he concluded.
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