The Impact of AI on Equitable Staffing: Preliminary Report
- ANA California Staff
- Nov 5
- 13 min read
Authored by: Dr. Jethrone Role DNP, RN, LHIT, ANA\California Advocacy Institute Fellow 2025
Executive Summary
Healthcare systems face increasing complexity, and traditional staffing methods often result in inequities, bias, and dissatisfaction among nurses. Manual processes in hiring, scheduling, and patient assignments create perceptions of unfairness that undermine morale and patient care quality. These perceptions are explained by organizational justice theory, which evaluates fairness through four dimensions: distributive justice (fair distribution of resources), procedural justice (fairness in decision-making processes), interpersonal justice (respectful treatment), and informational justice (transparent communication). Current practices often fail in these areas, leading to frustration and burnout.
Artificial Intelligence (AI) offers a transformative solution by enabling data-driven decisions that promote equity, transparency, and efficiency. AI can streamline hiring by matching candidates to roles based on skills, experience, and preferences while providing timely feedback to applicants. In scheduling, AI tools can analyze workforce needs, predict shortages, and recommend optimized schedules that balance organizational requirements with individual preferences. For patient assignments, AI can assess acuity and workload data to ensure fair distribution and reduce burnout.
However, implementing AI in staffing requires more than technology—it demands ethical oversight grounded in organizational justice principles. Distributive justice must be ensured by designing AI systems that allocate shifts and workloads fairly without bias. Procedural justice requires transparent and consistent algorithms, allowing nurses to understand how decisions are made. Interpersonal justice should guide the respectful and non-intrusive use of AI in performance monitoring or feedback. Informational justice mandates clear communication about AI-driven decisions and the ability for staff to challenge or appeal them.
Frameworks such as BE FAIR provide guidance for eliminating algorithmic bias and ensuring fairness throughout the AI lifecycle. Safeguards must address data integrity, system limitations, and inclusive design, while transparency in AI decision-making builds trust among nurses. Involving nursing staff in the evaluation and deployment of AI tools ensures that solutions align with frontline realities and foster acceptance.
By integrating AI responsibly and embedding organizational justice into its governance, healthcare organizations can reduce administrative burden, improve nurse satisfaction, and enhance patient outcomes. Equitable staffing supported by AI is not only a technological advancement but a strategic imperative for creating a fair, sustainable, and high-performing healthcare workforce.
Summary of Findings
Population, Intervention, Comparison, and Outcome (PICO)
P (Population): In nurses working in healthcare settings who experience staffing and scheduling challenges,
I (Intervention): Does the implementation of AI-driven staffing tools that incorporate organizational justice principles
C (Comparison): Compared to traditional/manual staffing and scheduling practices
O (Outcome): Lead to improved perceptions of fairness, increased nurse satisfaction, and more equitable workload distribution?
Research Keywords
AI and Nursing Workload
DEI in AI
AI and Hiring Bias
AI and Interview Bias
AI in Recruitment
DEI in AI
Theoretical Framework for DEI
AI Framework for Nursing
Staffing and Scheduling
Preliminary Findings
Artificial intelligence (AI) is rapidly transforming workforce management across industries, and in healthcare - particularly nursing - it offers promising solutions to long-standing challenges in equitable staffing.
Equitable staffing refers to the fair distribution of work, opportunities, and resources among employees, ensuring that patient care needs are met without compromising staff well-being. As AI becomes more embedded in staffing systems, its impact must be evaluated not only in terms of efficiency but also through the lens of Organizational Justice, which encompasses distributive, procedural, interpersonal, and informational fairness.
AI has shown strong potential to support distributive justice by aligning staffing decisions with actual workload demands. Hunstein and Fiebig (2024) demonstrated that AI models using patient health indicators - such as the Self-Care Index, fatigue, and pain intensity - can accurately predict nursing workload. This allows for staffing that reflects real-time patient needs, reducing inequities in task distribution. Similarly, Sandow and Bowie (2024) described a logistics engine that uses historical and projected data to proactively schedule nurses, improving roster predictability and reducing managerial burden. These innovations help ensure that nurses are neither overburdened nor underutilized, promoting fairness in workload allocation.
In broader workforce contexts, AI-driven hiring strategies also aim to promote fair outcomes. Mangal (2023) reviewed techniques such as blind resume screening and predictive analytics, which can help organizations identify qualified candidates more equitably. However, Drage and Mackereth (2022) cautioned that attempts to “strip” race and gender from AI systems often ignore systemic inequalities, potentially reinforcing discrimination. These findings underscore the need for AI systems to be designed with equity - not just neutrality - in mind.
Procedural justice emphasizes the fairness of the processes used to make decisions. In staffing, this means ensuring that AI systems are transparent, inclusive, and accountable. Cary Jr et al. (2025) introduced the BE FAIR framework, which empowers nurses to lead ethical AI implementation. This includes involving frontline staff in AI governance, auditing algorithms for bias, and ensuring that decision-making processes are understandable and justifiable.
Kelan (2024) expanded on this by introducing the concept of “algorithmic inclusion,” which calls for inclusive data, design, and decision-making in AI-supported hiring. These principles are essential for healthcare organizations, where staffing decisions directly impact patient outcomes and employee well-being. Without inclusive development and oversight, AI systems risk perpetuating existing inequities.
Interpersonal justice focuses on the quality of interactions and the respect shown to individuals. AI tools must be designed and deployed in ways that respect user experience and avoid impersonal or intrusive evaluations. Mirowska and Mesnet (2022) found that candidates prefer human involvement in AI assessments, citing concerns about empathy and fairness. In nursing, Rony et al. (2024b) emphasized how AI can support work-life balance by enabling flexible scheduling and remote monitoring, but cautioned that technology should complement—not replace—human judgment and compassion.
Informational justice involves the fairness of the information provided to employees about decisions that affect them. Transparency in how AI systems function, how data is used, and how decisions are made is critical. Abdelhalim et al. (2024) proposed a DEI safeguard framework for chatbots, emphasizing input, design, and functional protections to ensure fairness and inclusion. In staffing, this translates to clear communication about how AI tools operate, what data they use, and how employees can challenge or appeal decisions.
O’Connor et al. (2023) highlighted that many AI applications in healthcare lack real-world validation and transparency, which can erode trust and hinder adoption. Organizations must prioritize clear, consistent communication and provide avenues for feedback and redress.
AI offers transformative opportunities to advance equitable staffing in nursing and beyond. By aligning AI implementation with the principles of Organizational Justice—distributive, procedural, interpersonal, and informational—organizations can ensure that staffing decisions are fair, transparent, and respectful. Nurse leaders, HR professionals, and AI developers must collaborate to design systems that promote equity, empower employees, and safeguard ethical standards. When thoughtfully applied, AI can serve as a powerful ally in building a more just and resilient workforce.
Impact of AI in Nursing
The complexity of healthcare continues to challenge healthcare consumers and providers. Problems related to healthcare resources pose barriers to accessible care, effective healthcare operations, and healthy working environments. Emerging technologies such as Artificial Intelligence (AI) and Telemedicine are utilized to resolve healthcare delivery issues. These technologies enhance daily healthcare operations but also promote equitable access to care.
Nurses, the largest and most trusted workforce in healthcare, experience workforce challenges leading to dissatisfaction and a negative perception that processes are unjust, biased, and inequitable. Staffing and scheduling are among the areas which nurses express dissatisfaction. Improving staffing and scheduling practices by incorporating flexibility, autonomy, and equity can improve nurse satisfaction (Stimpfel et al, 2025). Traditional ways of staffing are ineffective because there are multiple factors that management must consider in achieving equitable staffing. Relying on manual staffing methods can lead to biased staffing and scheduling decisions. Nursing leaders should leverage data and decision-making tools to ensure nurse satisfaction and equitable staffing.
Defining Equitable Staffing
Equitable staffing refers to the fair and just distribution of workforce resources to ensure that employees have balanced workloads, access to opportunities, and an inclusive work environment.
According to organizational justice theory (Wiseman & Stillwell, 2022), individuals’ perception of events, actions, or decisions within an organization adhere to a standard of fairness.
These perceptions are classified into four categories and differentiated by how fairness is evaluated by employees:
Distributive Justice refers to the employee’s judgment on how resources or benefits are distributed. In the context of equitable staffing, a nurse may ask, "Do I get my preferred schedule like everyone else?"
Procedural Justice refers to the perception of employees whether the decision-making is fair. In the context of equitable staffing, a nurse may ask, "Was my manager biased when she made the schedule?"
Interpersonal Justice refers to how employees are treated with dignity, respect, and politeness by decision-makers or authority figures. In the context of equitable staffing, a nurse may ask, "Did my charge nurse treat me with respect when she made the patient assignment?"
Informational Justice refers to the employee’s perceived fairness of the communication and explanations provided. In the context of equitable staffing, the nurse may ask, "Was I given an honest explanation when my schedule was changed?"
There are three major components in staffing where nurses experience unfair and unjust practices:
Hiring refers to the process of a nurse applying for a job and being hired for the job.
Scheduling & Staffing refers to the time a nurse schedules to work and shows up to work.
Patient Assignment refers to the time a nurse is assigned a patient workload.
The following are common challenges that nurses experience in current staffing practices:
Hiring
Alex, a job-seeking nurse, encountered challenges such as complex job descriptions, lack of feedback from employers, and the sense that some organizations were biased against their experience.
Jordan, a nurse manager, received too many applicants through the hiring system and didn’t have time to review them all. They focused strictly on experience to fill the open position as quickly as possible.
Scheduling & Staffing
Alex, RN, faced numerous challenges in creating their schedule, which often conflicted with personal responsibilities and family commitments. It felt unfair that some coworkers consistently received preferred shifts.
Jordan, a nurse manager, balanced the schedule based on the known preferences of their team. They anticipated that Taylor, RN, might be unavailable on Wednesdays, so they moved Alex to that spot—one of many competing considerations.
Patient Assignment
Alex noticed they were often assigned patients with the highest acuity and workload. They felt dissatisfied that others were able to take breaks while they rarely could due to their assignments.
It was a busy shift for Casey, the charge nurse, who was managing multiple procedures and emergencies on the unit. They needed to make assignments quickly and, knowing Alex was highly skilled, assigned them the ROSC patient.
Impact Scenarios
Through these examples, the staff did not experience equitable staffing and organizational justice. Staffing data are too large for nursing leaders to ingest and use to make their own staffing decisions. Nurses must be supported with tools that help them make data-driven decisions. Healthcare leaders must consider integrating emerging technologies in staffing practices.
The transformative impact of AI in nursing can improve nursing practices by reducing administrative burden, providing decision support, and shaping the future of healthcare (Rony et al, 2024). AI can assist nurses through data-driven tools that promote equitable staffing, such as patient acuity-based staffing, skill & experience-based assignments, fair workload distribution, diversity and inclusion in hiring and scheduling, and even geographic and socioeconomic equity in staffing.
The use of AI tools for staffing can positively impact the nursing experience. Here is the picture if AI is used in staffing practice:
Hiring
Alex searched for RN jobs based on their preferences and received recommendations with comparative summaries of wages, benefits, and other factors. They also received feedback on the status of their applications.
Jordan, a nurse manager, received a list of applicants that included comparative summaries of experience, skills, education, cultural fit, and other key factors important to them when selecting the right candidate.
Scheduling & Staffing
Alex entered their schedule profile and received recommendations for shifts that best matched their preferences. They reviewed the options, approved the shifts that worked, and made necessary adjustments.
Jordan, a nurse manager, had visibility into staffing needs for the next six weeks. The AI system analyzed potentially short shifts and provided recommendations on available resources, considering both skill levels and cost effectiveness.
Patient Assignment
Alex felt that their workload was well balanced and noticed that the team’s skills and experience were evenly distributed. They were also recognized for the quality of their work and patient care.
Casey, the charge nurse, was busy assisting the unit and didn’t have time to review every chart for assignments. An AI tool generated a summary of patient workloads and acuity levels, offering assignment recommendations to support fair distribution.
Considerations to Ensure Equitable Staffing with AI
Organizational Justice Theory in AI
The use of tools powered by automation and AI clearly can optimize our staffing practices to exercise equitable staffing. Nursing leaders must evaluate their existing staffing practices to ensure organizational justice is met.
AI tools in staffing can support nurse leaders to make data driven decisions that support equitable staffing. The implementation of AI tools should be thoroughly evaluated to ensure that fairness, transparency, and compliance with regulatory requirements (Mangal, 2023). The American Nurses Association (2022) stated that justice, fairness, and equity must be included in the oversight of AI use in nursing practice. Nurses should be informed of the impact of AI in nursing processes and patient outcomes.
Organizational justice, as a foundation to evaluate equitable staffing practices, must be utilized to evaluate AI tools. Nursing stakeholders must be involved in evaluating AI tools prior to adoption. Here are things to consider in evaluating AI tools for equitable staffing using organizational justice.
Distributive Justice
Ensuring Equitable AI Decisions: AI should fairly distribute resources, opportunities, and rewards, such as assigning workloads without bias or favoritism.
Procedural Justice
Transparent & Consistent AI Decision-Making: Employees should understand how AI-driven decisions are made.
Interpersonal Justice
Ethical AI in Employee Interactions: AI tools used for feedback, evaluations, or performance monitoring should be respectful and non-intrusive.
Informational Justice
Clear Explanations for AI Decisions: Employees should be informed about how AI-based recommendations or decisions are made and be given the ability to challenge them.
Safeguards
Safeguards must be in place in implementing AI tools for equitable staffing. Nurse leaders must put a process in place that ensures involvement of nurses in implementing AI tools and ensuring that it will perform according to set guardrails. Here is a framework used for AI chatbots development (Abdelhalim, 2024) that can be used by nursing in designing and implementing AI tools for equitable staffing.
Input Safeguards
Ensuring that data used in the AI tool is reviewed by all key stakeholders.
Functional Safeguards
Ensuring that users are aware of the limitations of the AI tool.
Design Safeguards
Ensuring that all key stakeholders have input on the design of the AI tool.
BE FAIR Framework
Research on health equity aimed at eliminating biased and unjust AI algorithms have resulted in the development of frameworks that guide nurses in the design and deployment of AI in nursing practice. The Bias Elimination for Fair AI in Healthcare (BE FAIR) framework is a strategic approach that equips nurses with skills for AI governance. This framework includes the algorithm life cycle stages of AI accompanied by BE FAIR action items that guides nurses on appropriate steps to mitigate bias (Cary Jr, 2025). This framework can be used in ensuring AI tools used in staffing are equitable.
Conclusion
Equitable staffing is essential to nurse satisfaction, retention, and the delivery of safe, high-quality care. Yet, current staffing practices often fall short due to the complexity of data and the limitations of manual decision-making. Artificial Intelligence (AI) offers a transformative opportunity to address these challenges by enabling data-driven, transparent, and fair staffing decisions across hiring, scheduling, and patient assignment.
To realize this potential, nurses must be empowered to actively participate in the selection, design, and implementation of AI tools. Their lived experiences and insights are essential to ensuring that AI systems reflect the realities of nursing practice and promote organizational justice—distributive, procedural, interpersonal, and informational. Safeguards and frameworks, such as the BE FAIR model, must be adopted to mitigate bias and uphold equity throughout the AI lifecycle.
Healthcare organizations and nursing leaders must foster transparency, collaboration, and continuous evaluation.
Key initiatives to support equitable staffing include:
Development of Nurse-Centered Assessment Tools: Create tools that gather feedback from nurses on staffing practices, perceived fairness, and workload distribution. These tools should be integrated into daily workflows and used to inform staffing decisions.
Evaluation of Staffing Systems: Conduct regular audits of hiring, scheduling, and patient assignment systems to assess whether they support equitable staffing. Include metrics such as schedule fairness, workload balance, and transparency of decision-making.
Educational Webinars and Workshops: Organize recurring webinars featuring subject matter experts in nursing leadership, AI ethics, health equity, and technology. These sessions should address current staffing challenges, showcase AI solutions, and provide training on interpreting and using AI-generated insights.
Outcome Measurement Frameworks: Establish clear metrics to evaluate the impact of AI on staffing equity. Suggested indicators include nurse engagement scores, turnover rates, schedule satisfaction, and perceived fairness in patient assignments.
Collaborations with Technology Vendors: Partner with tech companies to co-design staffing platforms that embed equity principles. Ensure that AI systems used for hiring, scheduling, and patient assignment are transparent, customizable, and validated by frontline nurses.
Safeguard Implementation Across AI Lifecycle: Apply input, functional, and design safeguards to ensure AI tools are inclusive, explainable, and aligned with nursing values. Include mechanisms for nurses to challenge or appeal AI-generated decisions.
Continuous Validation and Research: Use data collected from these initiatives to support ongoing research into AI and equitable staffing. This research should inform policy development, system improvements, and future innovations in staffing practices.
By integrating AI responsibly and inclusively, the nursing profession can move toward a future where staffing practices are not only efficient but also equitable—ensuring that every nurse is supported, respected, and fairly treated.
References
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