When AI Screens Out Care: How Applicant Tracking Systems Undermine Equitable Hiring in Nursing
By: Sarah K. Wells MSN RN CEN CNL
The Problem with Tech in Hiring
Applicant Tracking System (ATS) software—now commonly enhanced with artificial intelligence—was designed to streamline hiring. In nursing, where workforce shortages are real and urgent, that efficiency sounds like a win. But there’s a harder truth: these systems can quietly reinforce inequities and filter out strong, qualified nurse candidates before a human ever reviews their application.
ATS platforms prioritize keyword matching, formatting, and predictive ranking. That means candidates who don’t “speak the algorithm’s language” are less likely to move forward—even if they are clinically excellent. New graduates, internationally educated nurses, first-generation college graduates, and people of color, among other candidate groups with diverse backgrounds, can be especially vulnerable. Their experience may be rich, but if it’s not translated into the exact phrasing the system expects, it can be overlooked.
AI integrations add another layer of concern. These tools are often trained on historical hiring data, which can reflect longstanding biases in healthcare. If past hiring favored certain schools, career paths, or demographics, AI can unintentionally replicate those patterns at scale—under the guise of objectivity.
Digital Literacy
There’s also a digital literacy gap. Not every nurse has equal access to coaching on ATS optimization, professional resume writing, or LinkedIn strategies. Nurses working multiple jobs, those from under-resourced backgrounds, or those re-entering the workforce may be disproportionately disadvantaged by systems that reward polish over potential.
The Impact
The result? A hiring process that can exclude diverse talent at the front door - before interviews, before skills assessments, before human connection.
What is the solution?
Healthcare organizations have a responsibility to examine this. Equitable hiring isn’t just about intention; it’s about process. That means auditing ATS tools for bias, reevaluating rigid keyword filters, incorporating human review earlier in the process, and educating applicants on how to navigate these systems.
Because in a profession built on advocacy and equity, our hiring practices should reflect the same values.
New Thing Nurse is addressing the problem.
Sarah K. Wells, the founder of New Thing Nurse, is serving as a 2026 ANA-California Advocacy Institute Fellow, focusing on AI and equitable staffing. Working with Dr. Adrienne McIntyre and Dr. Sotera Delos Santos, Sarah is conducting a survey that explores how AI tools are used in hiring, staffing, scheduling, and patient assignment decisions across healthcare organizations. Insights will help inform policy recommendations that support responsible AI use, strengthen nursing oversight, and promote equitable workforce practices.
Sarah and her colleagues are working to contribute to a framework and policy recommendations that address the use of AI in equitable staffing models, grounded in organizational justice and fairness, and will ultimately co-host a panel discussion with nursing and healthcare leaders to share insights and facilitate conversation on equitable staffing models supported by AI in late 2026.
About the Author: Sarah K. Wells, MSN, RN, CEN, CNL is an experienced nurse career strategist dedicated to helping nurses and nurse practitioners of all experience levels and specialties achieve success in their nursing and NP journeys. Sarah founded New Thing Nurse to help provide support and guidance to the nursing community in a simple and direct format. Sarah’s vision is to foster a more supportive and fulfilled nursing world that spreads throughout healthcare and beyond.
Sarah is serving as a 2026 Advocacy Fellow with ANA-California, focusing on AI and equitable nurse staffing. Learn more about the 2026 ANA-California Advocacy Fellowships.
New Thing Nurse helps the nursing and NP community thrive in their careers! Join us on IG or Facebook @newthingnurse 🩺