Artificial intelligence (AI) is poised to become the standard of care. Although often viewed as experimental, its impact on triage and clinical workflows has been validated through numerous clinical studies throughout academic institutions worldwide. In this blog post, we’re going to explore how AI is used in healthcare, and how AI-powered healthcare enables clinicians across service lines to benefit in some surprising ways.
The infographic below provides a quick overview of some of the crucial terms to know when discussing healthcare AI:
The Real Role of AI in Healthcare
While the clinical world still grasps the full extent of AI’s capabilities, common misconceptions linger as to what healthcare AI can mean. Is it automated call centers? Is my physician going to be robotic? The reality is that the role of AI in healthcare isn’t intended to replace radiologists or other physicians but to augment them and to help health systems bring their full potential to fruition.
Consider the slew of hurdles health systems are faced with today, ranging from overwhelming patient volumes to staffing shortages. These challenges put immense pressure on healthcare facilities, making it clear that AI’s role is crucial in alleviating these burdens and improving overall efficiency and patient care.
Current Healthcare Challenges
- Workforce challenges – physician burnout and shortages
- Financial challenges
- Patient safety and quality
- Access to care
- Patient satisfaction
- Physician-hospital relationships
- Technology
- Population health management
- Reorganization – mergers and acquisitions and partnerships
While the use of AI technology in healthcare won’t alleviate each of these challenges in their entirety, AI does help health systems do more with fewer resources. A connected system of intelligence brings together disparate devices and platforms, which makes it uniquely positioned to address ongoing structural and cultural challenges by acting as a clinical partner and health system optimization tool. But let’s start with the clinical application of AI.
Top Three Ways AI in Healthcare Is Improving Patient Outcomes
By helping health systems accomplish more with fewer resources, the application of AI in healthcare helps with:
1. Reduced Hospital Length of Stay
A shortened length of stay (LOS) isn’t simply about getting patients out of beds and making room for others; it indicates that patients are receiving the right treatment at the right time and experiencing outcomes that enable them to go back to their life outside the hospital. In fact, a study conducted at Cedars-Sinai Medical Center found that implementing AI triage software led to a reduction in LoS by 11.9% for intracranial hemorrhage (ICH) and 26.3% for pulmonary embolism (PE).
2. Enhanced Disease Awareness
Implementing AI in healthcare has benefits beyond triaging. When implemented system-wide, it can even increase specialist awareness of patients in need. By connecting specialists, such as those that work together on the Pulmonary Embolism Response Team (PERT), AI can connect specialists and empower them to act more quickly on more patients, eliminating the needless game of phone tag that ensues when a pulmonary embolism patient surfaces. One retrospective study from Yale New Haven Hospital found that the combination of AI-triggered notifications and triaging resulted in a 40% increase in advanced therapy at their spoke facility.
3. Expedited Treatment
AI in healthcare continually impacts outcomes in ways that may not be expected. Take patient treatment times, for example. What started as a tool to help radiologists triage patients, according to this study, amounted to an “approximately one-hour improvement in the median time to emergency department presentation and over a 13-hour difference in the median time to anticoagulant prescription retrieval” for patients with incidental pulmonary embolism findings. The study further confirms that workflow changes, such as AI-enabled worklist reprioritization for radiologists, led to more timely follow-up care for patients with incidental PE (iPE) findings on CT scans, a process that highlights the role of AI in improving radiology workflows, as explored in this blog on AI use in CT scans.”
How AI Technology is Streamlining Clinical Decision Support
The improvement in clinical outcomes for patient wouldn’t e possible without significant advancements in clinical operations — and a critical component of this progress is the evolution of AI-powered clinical decision support systems.
These systems enhance decision-making at the point of care, reduce variability in treatment and streamline workflows, ultimately contributing to better patient outcomes. Here are a few key highlights:
- Faster analysis and prioritization of scans, leading to quicker diagnosis
- Increased sensitivity and specificity, leading to better accuracy
- Improved workflow and reading time, translating to quicker throughput
Artificial intelligence is rapidly emerging as a transformative force in healthcare, moving from experimental to essential, with its impact validated across clinical studies. Rather than replacing physicians, AI is designed to augment clinical decision-making, streamline operations, and improve patient outcomes.
In the face of persistent healthcare challenges — such as workforce shortages, rising costs, and care variability — AI-powered systems offer meaningful solutions.
These include reducing hospital length of stay, enhancing disease awareness, expediting treatment, and optimizing clinical workflows. By enabling faster, more accurate diagnostics and decision-making, AI serves as both a clinical partner and a health system optimization tool, ultimately helping healthcare providers do more with fewer resources.
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