A Human-Centered Approach to Nursing Workforce Stability
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- The U.S. healthcare industry faces a projected nursing shortage of 10% by 2027, a deficit of roughly 350,000 nurses.
- SSM Health partnered with Atalan to develop a system that identifies signs of workplace stress months before a resignation occurs.
- The technology evaluates administrative data to help hospital leaders start early, supportive conversations with staff members.
- Clinical managers worked directly with developers to ensure the dashboard reflects the real-world experiences of frontline workers.
- The initiative focuses on providing customized support to individuals rather than applying a single standardized program.
Table of Contents
- Shifting to Early Intervention
- Co-Designing Tools with Clinical Staff
- Governing Artificial Intelligence on the Frontlines
- The Operational Gap in Traditional Nurse Retention Platforms
- Closing Thoughts
Shifting to Early Intervention
The U.S. nursing shortage is expected to reach 10% by 2027, a deficit of roughly 350,000 nurses nationwide [1, 3]. To address high turnover, SSM Health is using a new nurse retention platform from Atalan to identify workplace stress early [1]. The 24-hospital system will start preventative conversations before clinical staff resign [1, 2].
SSM Health has used Atalan’s workforce intelligence platform for physicians and advanced practice clinicians since 2023. The multi-state, 24-hospital system was an early collaborator on Atalan for Nursing, working with Atalan to shape the platform and build in the perspectives of nurses and nurse leaders throughout development [1].
Quick Take & Core Data
- The national nurse turnover rate averages 16.4% annually [1].
- Approximately 40% of current practicing nurses intend to leave the workforce by 2029 [1].
- The Atalan platform identifies turnover risk up to 12 months in advance with over 75% predictive accuracy [1].
Traditional hospital workforce planning relies on delayed information. Tiffany Chan, MA, CEO and Founder of Atalan, explained that organizations typically work with data that arrives after problems are visible. Chan noted, “By the time burnout appears in a survey, someone resigns, or a manager notices a pattern, the opportunity to prevent that departure may already be slipping away.”
The new system uses machine learning, a type of computer analysis, to connect operational, staffing, and scheduling data. This allows clinical leaders to recognize where staff members need help months in advance.
Jennifer Luer, DNP, RN, System Director of Employee Safety & Wellbeing at SSM Health, stated that leaders use these early indicators to engage with team members compassionately. Luer said, “Rather than applying a one-size-fits-all solution, effective interventions are individualized.”
Dr. Heather Schmidt, System Medical Director of Employee Well-being at SSM Health, described this methodology as a “Predict-Prevent-Protect” approach.
Co-Designing Tools with Clinical Staff
SSM Health incorporated direct feedback from nurses during the dashboard development process. Julie Winegar, RN, BSN, Director of RN Clinical Workforce Planning, said the collaboration with nurses was intentional. Winegar stated, “We have been intentional to co-design the dashboard to ensure this is created with us and not to us.”
By allowing the nursing staff to influence the tool, managers want to improve workplace culture. Winegar added, “Our hope is that the nurses feel seen, heard, and cared for in a way that allows them to feel safe and able to provide the patient care they imagined going into medicine.”
Luer noted that objective data reveals stress patterns, but the greatest value comes from combining those numbers with actual clinical experiences. Luer explained, “These approaches should be developed in partnership with nurses, ensuring their voices help shape both the insights and the actions that follow.”
Governing Artificial Intelligence on the Frontlines
Adding computer models to a human-centered profession often meets resistance. To build trust, SSM Health implemented a strict governance process to ensure the system handles employee information securely [1].
Schmidt explained that the technology provides an extra data point to support human judgment. Schmidt noted, “In healthcare, we already use predictive models to help identify patients at risk for conditions like sepsis, falls, or readmission.”
Luer added that the data supports conversations. The tool does not make decisions on behalf of hospital leaders. Luer said, “When people understand how insights are developed and see that they align with real-world experiences, they are more likely to view the information as a tool for support rather than evaluation.”
The Operational Gap in Traditional Nurse Retention Platforms
Hospitals traditionally gauge workforce health using annual engagement surveys or exit interviews. Chan explained that these tools only explain why a nurse has already left a position. The Atalan system attempts to fill this research gap by identifying who needs support before they consider resigning.
A current limitation is that the rollout is in its early stages. Schmidt noted that it is “too early to tell” which specific operational drivers are the most frequent causes of burnout based on the new data analysis.
Despite the early deployment phase, SSM Health prioritizes immediate support for staff members experiencing pressure. Schmidt said that providing managers with the right resources allows them to “focus less on administrative burden and more on how we can see, address, understand, and ease the strain of those on their team.”
Closing Thoughts
Using advanced workforce analytics changes how hospital systems manage clinical staffing levels. Chan stated that success requires organizations to treat retention with the same rigor applied to patient safety or financial performance. Chan added, “Instead of asking why experienced nurses left, I hope leaders will be asking what they did to help those nurses stay.”
By connecting administrative signals to human outreach, SSM Health managers can deliver highly customized support. Winegar stated, “Each situation will be unique and the individual customization of the interventions will be thoughtfully determined together.”
Learn more about Atalan and SSM Health below:
https://www.atalantech.com/
https://www.ssmhealth.com/home
References
[1] Atalan. (2026, June 25). Atalan launches nurse retention platform designed to identify risk up to 12 months in advance. https://www.atalantech.com/atalan-for-nursing/
[2] SSM Health. (2026). SSM Health clinical services and home. https://www.ssmhealth.com/home
[3] American Organization for Nursing Leadership. (2024). HRSA predicts nursing shortage through 2037. https://www.aonl.org/news/HRSA-predicts-nursing-shortage-through-2037
