Jefferson Einstein, part of the Jefferson Health health system, recently implemented artificial intelligence applications to address challenges in managing acute pulmonary embolism cases. The rising imaging volumes and manual workflows at Jefferson Einstein were limiting the speed and efficiency of identifying and triaging PE cases, especially in critical situations. Recognizing the potential of AI to streamline workflows and enhance patient care, the healthcare system decided to explore AI solutions.
Dr. Avi Sharma, the director and associate chair of AI at Jefferson Einstein, highlighted the organization’s commitment to innovation and staying at the forefront of advancements. The technology from health IT vendor Aidoc was chosen to optimize the pulmonary embolism response team (PERT) workflows. Aidoc’s AI platform flagged suspected PEs in real-time, enabling proactive identification of potential cases and notifying the PERT through a mobile application. This streamlined process facilitated timely treatment and rapid mobilization of the multidisciplinary team for urgent interventions.
The implementation of AI technology revolutionized how Jefferson Einstein manages PE cases. The PERT now receives notifications through mobile alerts, allowing interventional radiologists to assess patient imaging and lab results remotely. Real-time updates ensure seamless collaboration across departments, significantly reducing delays in treatment. The AI system has also been incorporated into educational efforts, providing training for radiology residents to familiarize them with the tools early on.
The results of implementing AI at Jefferson Einstein have been promising. The PERT intervention rates increased by 73.8%, demonstrating the efficiency of AI in triaging critical cases and ensuring high-risk PE patients receive timely care. Additionally, there was a reduction in time to treatment, with the alert system significantly decreasing overall exam-to-needle time for patients with acute pulmonary embolism. The efficiency in radiology workflows has also improved, with measurable gains in critical result communication turnaround times.
Dr. Sharma’s advice for other hospitals and health systems considering AI is to focus on identifying pain points in workflows and selecting AI systems that seamlessly integrate with existing systems. Building a culture of trust and curiosity, engaging early adopters, and sharing success stories are essential steps in adopting AI technology. Ongoing education, particularly for radiologists in training, is crucial for the future of AI-enabled healthcare.
In conclusion, AI is a long-term investment in patient care that raises the standard of care across the board. By leveraging AI technology, healthcare organizations can ensure they deliver the highest level of care to every patient.
