If a patient’s blood pressure begins dropping, respiratory rate increases and mental status subtly changes. Individually, the warning signs may not appear alarming. Together, they could indicate the beginning of clinical deterioration.
AI could recognize those patterns before a patient’s condition worsens. However, Vanessa Riley, a nurse practitioner and founder of the Global Nursing AI Alliance, says recognizing a problem and providing care are two different responsibilities.
“Someone still has to determine whether that signal is clinically meaningful,” she said during the “How to Implement AI in Nursing Workflows,” session at the 2026 HealthStack Conference.
“Someone has to assess the patient,” she continued. “Someone has to decide what happens next, and someone has to be made accountable when the technology is wrong.”
Where AI Fits Into Nursing Practice
A typical nursing shift involves medication administration, patient assessments, discharge education, family communication and extensive documentation. AI offers opportunities to reduce administrative burdens. However, not every workflow problem requires a technological solution.
“Sometimes the answer is redesigning the workflow,” Riley shared. “Sometimes it’s removing unnecessary documentation, and sometimes it’s changing policy.”
Her presentation identified four areas where AI is entering nursing practice: documentation automation, continuous patient monitoring, clinical decision support and workforce optimization. Ambient documentation systems can capture patient encounters and generate draft clinical notes, potentially reducing time spent charting. However, AI-generated information can appear accurate while containing errors, leaving clinicians responsible for reviewing and correcting it.
The challenge is determining whether automation actually saves time or simply replaces manual documentation with another responsibility.
“The goal is not more documentation,” Riley said. “The goal is accurate, clinically useful documentation with less unnecessary work.”
Continuous monitoring offers another application. Predictive models can analyze changes in vital signs, laboratory results and electronic health records to identify patients at risk of deterioration.
Clinical decision support tools can provide risk scores, flag potential medication concerns and prompt nurses to follow established care protocols, allowing clinicians to review relevant information while making treatment decisions.
The objective is to provide actionable information without replacing nurses’ responsibility to assess patients and determine appropriate care.
When AI Predictions Lead To Better Outcomes
Research involving automated patient monitoring illustrates why identifying risk is only one part of effective clinical care.
“The algorithm detected risk, nursing converted risk into clinical surveillance. The clinical team converted surveillance into action,” Riley said. “That distinction is critical. Prediction alone does not improve outcomes. Prediction has to lead to recognition, interpretation, and appropriate clinical action.”
A 2022 study published in Nature Medicine examined the Targeted Real-time Early Warning System (TREWS), a machine learning-based sepsis alert system that monitored 590,736 patients across five hospitals.
Researchers focused on 6,877 patients with sepsis whose alerts appeared before antibiotic treatment began. After adjusting for patient presentation and severity, those whose alerts were confirmed by a provider within three hours had a 3.3-percentage-point lower in-hospital mortality rate, along with less organ failure and shorter hospital stays, compared with patients whose alerts were not confirmed within that period.
Because the research was observational, the findings established an association rather than proving that the technology itself caused the improvements. The study reinforced a central point of Riley’s presentation: Early warnings become valuable when clinicians recognize them, evaluate the patient and respond appropriately.
“A highly accurate model placed into a poorly designed workflow can still produce a poor clinical intervention,” Riley said.
Workforce optimization presents another opportunity. Two nurses assigned the same number of patients may face significantly different workloads because of patient acuity, mobility limitations, isolation precautions and care needs.
AI-powered staffing systems could help hospitals account for those differences rather than relying primarily on patient counts.
“If we define the problem as, how few nurses can we schedule, we have framed the problem incorrectly,” Riley said. “The better question is, how accurately can we match nursing capacity to patient need?”
From Pilot Programs To Clinical Practice
Successful implementation requires more than purchasing software.
Riley’s five-step framework begins with identifying a measurable nursing problem, followed by collaborating with frontline nurses, informatics professionals and patients to design appropriate workflows.
Health systems should then test AI in a single unit with predefined safety limits and procedures allowing clinicians to override automated recommendations. Results should be evaluated before the technology is expanded into routine practice.
Riley identified four measures for evaluating implementation: time recovered, documentation accuracy, patient outcomes and nurse experience.
Those measures help determine whether a tool is reducing administrative burdens and improving care or simply introducing another task into an already demanding nursing shift. The framework also calls for continued monitoring after deployment to identify emerging risks and changes in system performance.
Protecting Patients And Clinical Judgment
Patient safety, equity and ethics were central to Riley’s recommendations.
Her presentation called for validating AI systems using local patient data, monitoring performance across race, language, age and clinical units, and establishing procedures for responding when technology fails.
Nurses must also retain the authority to override automated recommendations, while patients should be informed when AI contributes to their care.
“We should distinguish between computational capability and clinical authority,” Riley said. “AI may outperform humans at particular pattern recognition or information processing tasks. That does not automatically confer authority to determine what should happen to a patient.”
The distinction reinforces the importance of evaluating automated recommendations alongside patient histories, clinical observations and individual needs.
For Riley, the measure of successful AI implementation is not how much technology a hospital adopts, but whether nurses gain more time to care for patients. A slide from Riley’s presentation captured that distinction.
“AI doesn’t replace nursing judgment,” it read. “It gives the nurse more of the day in which to use it.”
