Emergency departments (EDs) are under more pressure than ever. Apart from the growing patient volumes, staffing shortages, and increasing expectations to deliver safe, high-quality care, EDs are also a frequent destination for some of the most vulnerable patients, those transferred from skilled nursing facilities (SNFs) and other post-acute facilities.
Too often, SNF patients arrive with incomplete or outdated information, leaving clinicians to make critical decisions with limited context. Frontline teams must then find ways to manage higher demand and complexity without compromising outcomes.
As an emergency physician, I have seen firsthand how this lack of information can slow care, drive up costs, and lead to avoidable hospitalizations. But I have also seen how artificial intelligence (AI) can begin to change that. By filling key information gaps, AI enables emergency clinicians to act more quickly and confidently, improving safety and continuity of care across care settings.
Bridging the information gap
One of the biggest challenges in emergency care involves post-acute transfers. When a patient from an SNF arrives without clear documentation or arrives with a lengthy packet that must be reviewed manually, valuable minutes are lost. Clinicians are forced to repeat tests or delay treatment to understand what brought the patient in.
AI-enabled tools now make it possible to rapidly extract and summarize the most relevant details from the patient’s recent stay, such as vital signs, new diagnoses, changes in condition, or treatments performed in the last 24 hours. Typically within seconds, ER teams can see why the patient was transferred, what has been done, and what is needed next. That structured, real-time insight helps determine whether hospitalization is necessary or whether the patient can be safely stabilized and returned to the facility.
The AI models are built on some of the largest post-acute data sets in North America, providing a comprehensive view of a patient’s history, but one that is also timely, typically up to the minute of their transport. Integrating this intelligence directly into existing ED workflows eliminates the need to log in to separate systems, or sift through the thick pile of forms, reports, and other paper that patients usually arrive with. Teams can immediately act on the information presented within their existing electronic health record screens, supporting faster, evidence-based decisions.
Advancing efficiency and value-based care
When used responsibly, AI can help health systems achieve measurable gains in both efficiency and quality. It can shorten dwell time in the ED, reduce unnecessary admissions, and identify patients at the highest risk of readmission. Targeted care plans developed in the ED can also safely divert frequent utilizers to more appropriate outpatient resources, improving patient flow and conserving capacity for true emergencies.
Equally important, AI can strengthen staff and patient safety. Real-time alerts about behavioral or clinical risks allow teams to prepare before contact with the patient. Such advance notice helps reduce workplace incidents and improve confidence at the bedside. By replacing uncertainty with actionable insights, clinicians experience less stress and greater satisfaction, benefits that ripple through patient care.
The ability to avoid unnecessary admissions and redundant diagnostics also supports the shift toward value-based care. That is because having precise, context-rich information at the point of care enables faster, more confident decision-making, ensuring patients receive the right level of care in the right setting.
This drives both financial sustainability and care continuity, yielding safer patient experiences and more efficient use of resources for providers and payers.
Building a connected continuum
Yet, even with these improvements, hospital clinicians remain cautious about deeply embedding new AI technology into clinical workflows. That is why transparency in data and recommendations is critical. After all, these tools should highlight what matters most, not replace human judgment, and clinicians should be able to trace every insight back to its original documentation.
Likewise, responsible AI frameworks should emphasize fairness, accountability, and privacy. Models should perform equitably across diverse patient populations, while rigorous data governance must ensure patient information is protected. When deployed with these safeguards, AI can enhance clinical confidence rather than cause additional concern.
Ultimately, AI’s promise in the ED extends beyond operational efficiency. We are building a more connected system that bridges acute and post-acute settings by uniting insights across the continuum. The insights empower clinicians with the information they need at the moment they need it, resulting in faster, safer, and more human-centered care.
EDs will continue to face increasing demand and complexity. Yet we are already seeing that the responsible use of AI offers a meaningful opportunity to critical care teams. Moreover, AI can help position EDs everywhere, as central hubs for coordinated, safe, and efficient patient transitions across the continuum of care.
Photo: pablohart, Getty Images
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