Earlier this week, representatives from multiple hospitals and health systems had an important opportunity at the Capitol to speak directly with key lawmakers about the value of artificial intelligence (AI) tools and the dangers of severely curbing these tested and valuable supports for health care workers.
The advocacy event, organized by CHA, came at a critical juncture in this year’s legislative session. A handful of bills that would severely curb the use of longstanding hospital tools that clinicians use to support diagnosis, medication management, and note-taking have passed out of Appropriations committees and are now headed for floor votes. Consider, for example, AB 2575 (Ortega, D-Hayward), a CNA-sponsored bill that would impose such onerous disclosure requirements that hospitals would effectively be banned from using certain technologies that streamline documentation, allowing clinicians to spend less time on paperwork and more time with patients.
Many, many thanks to those hospital leaders who took time from their busy schedules to speak about how AI is being used in hospitals to assist clinicians, improve patient safety, enhance care delivery, and, yes, save lives. Legislators need to continue to hear your voices and how your focus on patient care and experience is driving the use of AI; CHA developed an issue brief for members’ use in these conversations.
With predictive analytics and early warning systems — such as sepsis detection tools — clinicians are able to identify patterns that may signal patient deterioration or infection sooner than traditional methods so they can intervene earlier. In some studies, AI was shown to improve detection of certain cancers by as much as 90%. And language access and health-literacy tools are helping reduce barriers for patients with limited English proficiency. At the same time, AI is helping reduce clinician burnout and improve patients’ experience.
Some other ways AI supports better health outcomes, greater patient satisfaction, and improved working conditions for clinicians:
- Stroke identification and triage — AI-powered imaging analysis identifies large vessel occlusions in CT angiography scans within minutes, automatically alerting stroke teams and enabling faster intervention.
- Medication safety — Clinical decision support systems screen medication orders for drug interactions, allergies, dosing errors, and contraindications.
- Cancer screening — AI-assisted imaging tools help radiologists identify suspicious findings in mammograms, chest CT scans, and pathology slides that might otherwise be missed.
- Deterioration prediction — Early warning scores powered by machine learning identify patients at risk of rapid clinical deterioration or cardiac arrest, or those needing transfer to an intensive care unit, enabling proactive intervention by rapid response teams.
- Readmission risk reduction — Predictive models identify patients at elevated risk of 30-day readmission, allowing care teams to coordinate transitional care resources — such as home health visits, medication reconciliation, and follow-up appointments — for those who need them most.
- Health equity — AI tools are increasingly used to identify disparities in care delivery, find patients who may be falling through care gaps, and ensure that social determinants of health are considered in care planning.
As we continue to set the record straight on the use of AI in health care, policymakers should recognize that hospitals are already using AI in a careful, transparent, and clinician-led manner to improve patient care and support an overstretched workforce.
Any legislative and regulatory approaches must preserve flexibility for innovation and efficiency while reinforcing existing safeguards that no one disputes — such as human oversight, patient privacy, and equity protections.
