Combine technology and practical interventions to reduce burnout.

This month’s article about burnout by Jocelyn Perez, MSN, RN, PCCN (see page 23) reminded me of a column I wrote a couple of years ago about the use of artificial intelligence (AI) to detect signs of staff burnout. Unfortunately, although 2 years have passed since I wrote that column, burnout remains a significant issue within nursing. As Perez notes, almost 18% of newly graduated nurses, citing stress and lack of staffing, leave the profession within 1 year. In addition, 69% of young nurses (under 25 years) report experiencing burnout.

Perez outlines a practical approach to applying mindfulness in the workplace as a strategy for addressing burnout. Knowing that staff burnout exists within an organization and identifying its root cause also offers opportunities for mitigation. As I described in my previous article, AI can help detect signs of staff burn­out using a range of methods and tools.

For example, data sources that healthcare organizations already use every day can analyze patterns of overtime, frequency of sick leave, and other workload-related data. Subtle trends in some performance indicators—such as increased error rates, reduced patient interaction time, or frequent tardiness—also may suggest burnout.

In addition, combining AI with employee engagement and satisfaction surveys can provide a window into stress levels. For instance, natural language processing can help analyze open-ended survey responses for signs of stress or discontent within a nursing unit.

Many hospital human resource (HR) departments have personnel who scan social media looking for various issues. With the appropriate ethical considerations and permissions in place, they can use AI to monitor and analyze public social media activity of staff for indications of burnout. In addition, text analytics and sentiment analysis applied to written or spoken communication can help gauge emotional tone, which also may indicate stress or burnout.

Individual nurses can use their wearable devices as sources of information. Biofeedback from smartwatches or similar devices can provide data on stress levels, sleep quality, and other health metrics, which nurses can then use to self-correct before issues become serious problems.

Researchers are training machine learning models to predict burnout based on workload, hours worked, and personal well-being metrics. This predictive analytics approach may prove one of the most valuable strategies for identifying the drivers of burnout and implementing interventions before it occurs.

The use of AI comes with some caveats, including data privacy. Whatever technology an organization uses to help detect and address burnout must comply with privacy laws and ethical guidelines. Organizations also want to work collaboratively with their information technology and HR personnel as well as with staff and leaders to ensure whatever approach they take meets the needs of the workers they want to support.

When burnout is detected among staff members, organization leaders must look for effective and practical interventions that nurses can apply in the workplace. The approach Perez and her team took at their organization offers promise and can easily be applied elsewhere. They even offer a budget justification—a useful tool for acquiring stakeholder buy-in. Ultimately, ethically applying the appropriate technologies and interventions can help organizations detect areas of concern, address them, and support nurses as they manage workplace stress.

American Nurse Journal. 2025; 20(3). Doi: 10.51256/ANJ032504

Lillee Gelinas, DNP, RN, CPPS, FAAN

Editor-in-Chief