In 2024, the average turnover rate for registered nurses that worked in hospitals across the United States stood at **** percent. This was lower than the turnover rate of **** percent in 2022. According to this survey, the percentage of registered nurses (RN) that left hospitals in 2023 ranged from roughly ** percent to nearly ** percent, depending on the discipline. The highest RN turnover was found among Telemetry nurses. On the other hand, RN turnover was the lowest in Pediatrics.

In 2024, the average turnover rate of all registered nurses (RNs) in U.S. hospitals stood at **** percent. The percentage of employees leaving hospitals has decreased since 2021 and for the first time it stands at a lower percentage than in 2020. At the same time, the turnover rate of all hospital staff was **** percent. For RNs who were full or part-time employees, turnover was consistently lower.

Nurse shortages are substantial and increasing, making nurse retention critically important. This study is part of a project that aims to lay the foundation for developing a tool to help hospitals identify potential retention issues at an early stage and select appropriate interventions. The first study within this project was an integrative review, which showed that although many studies have investigated factors associated with nurse retention or turnover, the focus has primarily been on individual and organizational factors, with less attention paid to team-level factors. However, it is expected that team factors—such as team climate, leadership, teamwork, and communication—also play a significant role in nurse retention. A second observation from the literature is that many analyses assume that individual-level observations are independent of team-level observations, whereas it is likely that individual and team-level factors are interrelated, and that the team context influences the retention of individual nurses. This implies that factors should be assessed in conjunction with one another. There is a need for insight into which interrelated factors play a role within nursing teams and how these influence retention and turnover. Although not all relevant factors are routinely recorded, hospitals collect substantial data on their employees that can be used to investigate these relationships. This study protocol describes a retrospective cohort study that will use routinely collected hospital data to explore how individual and team-level factors are associated with nurse turnover, defined as both leaving the hospital and moving to another team or position within the same hospital.

The turnover rate of nurses in skilled nursing facilities (SNFs) in the U.S. varies by the quality rating of the facility. Among facilities with just a one-star rating, turnover stood as high as **** percent in Q1 2024. Among facilities with a five-star rating, turnover was almost ** percent lower, yet still high at, **** percent.

This umbrella review aimed to identify structures that healthcare systems have put in place to stabilize, support, and provide for the sustained enhancement of the nursing workforce, as well as highlight outstanding knowledge gaps.

Following screening, we selected 69 studies for inclusion, the majority of which investigated interventions classified as implementation strategies (n=60) or delivery arrangements (n=8) under EPOC taxonomy. We identified heterogeneity in terms of the types of structures identified across reviews and the methods used for their evaluation.

The findings of this umbrella review suggest that there is no one-size-fits-all approach for supporting the nursing workforce; rather, a multi-level, multi-pronged approach may be more appropriate to collectively have the most impactful outcomes. Limitations of this review include the phenomenon of interest (structures) being difficult to define and the inability to capture recently published primary sources. Future studies should incorporate rigorous implementation and evaluation plans, focusing on the long-term impacts of such strategies, and prioritize the dissemination of learnings.

ObjectiveThe objective of this study was to develop an evaluation system for the allocation of hospital nursing human resources in hospitals in the context of the Healthy China initiative.AimsThe evaluation system aims to provide a foundation and recommendations for optimizing the allocation of hospital nursing human resources by providing data-driven insights to guide staffing decisions. These recommendations are designed to enhance patient safety, reduce adverse events, and improve overall nursing care quality while supporting the sustainable development of healthcare systems. The system also seeks to enhance nursing human resource management by enabling managers to allocate resources more effectively based on clinical workload, patient needs, and nursing competencies. The primary objectives of this optimization process include reducing nurse workload, improving job satisfaction, and decreasing turnover rates, all of which facilitate improved patient outcomes.MethodsThe evaluation index pool and questionnaire for the index system consultation were developed using a literature review and semi-structured expert interviews. Considering the need for detailed information on the number of experts and the criteria for indicator deletion, we clarified that the study included 26 experts. These experts were selected based on their extensive experience and professional background in nursing management. The criteria for indicator deletion included a variation coefficient of >0.25 and expert consensus on the relevance and importance of the indicators. We conducted two rounds of expert consultations using the Delphi method to screen the evaluation indexes. Subsequently, the weights of the indexes were calculated using the analytic hierarchy process (AHP).ResultsA nursing human resource staffing evaluation system was established, comprising 3 primary indexes, 7 secondary indexes, and 25 tertiary indexes.ConclusionThe findings of this study provide empirical evidence and specific recommendations for evaluating, guiding, incentivizing, and improving nursing human resource management practices in China. This study is the first structured Delphi-and AHP-based staffing evaluation system in a Chinese hospital setting. By integrating expert consensus and hierarchical analysis, we have developed a novel framework tailored to China’s healthcare needs. This framework significantly contributes to the integrated development of nursing human resource management. Based on the results, we recommend that hospitals implement the evaluation system to regularly monitor and optimize their nursing staffing levels. Moreover, further research may help explore the long-term effects of this system on patient outcomes and nurse retention rates across different hospital settings. Future studies may also examine the adaptability of the evaluation system to various specialties and the potential need for adjustments in specific contexts.

The 2004 National Nursing Home Survey (NNHS), conducted between August and December of 2004, was reintroduced into the field after a five-year break, during which time the survey was redesigned and expanded to collect many new data items. The 2004 NNHS included a supplemental survey of nursing assistants employed by nursing homes, the National Nursing Assistant Survey (NNAS), which was sponsored by the Office of the Assistant Secretary for Planning and Evaluation (APSE). Nursing assistants were considered eligible to participate in the survey if they 1) provided assistance with activities of daily living (ADLs); 2) were paid to provide those services; 3) were certified (or in the process of certification) to provide Medicare/Medicaid reimbursable services; 4) worked at least 16 hours per week; and 5) were employees of the nursing home and not contract employees. A sample of up to eight nursing assistants was selected from about half of the nursing home sample at the time of the facility interview. The NNAS was administered after the nursing home visit, using a computer-assisted telephone interview (CATI) system.

In 2024, the average cost for U.S. hospitals to make up for one registered nurse who left (i.e. hire and train a new nurse) amounted to ****** U.S. dollars. This has increased by roughly *** percent compared to the previous year, and has been increasing year over year. To reduce such unnecessary cost, hospitals must strive for better retention.

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US Nursing Education Market Size 2025-2029

The US nursing education market size is forecast to increase by USD 161.9 billion at a CAGR of 30% between 2024 and 2029.

US Nursing Education Market is experiencing significant growth, driven by the increasing demand for competency-based learning and the integration of advanced technologies such as Augmented Reality (AR) and Virtual Reality (VR) in nursing education. The shift towards competency-based learning is a response to the evolving healthcare landscape and the need for nurses to possess a higher level of skills and knowledge to provide effective patient care. Furthermore, the use of AR and VR technologies in nursing education offers learning experiences, enabling students to practice complex procedures in a safe and controlled environment. However, the market is not without challenges.
One of the significant challenges is the lack of standardized assessment metrics to measure the effectiveness of nursing education programs. This challenge hampers the ability to evaluate the success of educational initiatives and the readiness of graduates to enter the workforce. To capitalize on the market opportunities and navigate these challenges effectively, companies must focus on developing innovative solutions that address the need for competency-based learning and provide reliable assessment metrics. Additionally, investing in the integration of AR and VR technologies can offer a competitive edge in the market.

What will be the size of the US Nursing Education Market during the forecast period?

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The nursing education market in the US is experiencing significant growth and innovation, driven by the demand for advanced nursing informatics solutions and continuing education units. This trend is reflected in the development of nurse recruitment strategies that leverage telehealth platforms and nursing curriculum tailored to healthcare technology. Nursing salary trends continue to influence the market, as nursing informatics specialists become increasingly essential for effective healthcare data management. Nursing simulation software and nursing career pathways are key components of nursing education trends, providing clinical experience and patient safety initiatives that align with patient-centered care and improved health outcomes.
Accreditation standards and nursing faculty recruitment are also critical areas of focus, as institutions seek to maintain high educational standards and remain competitive. Patient portals, mobile health apps, and nursing education consultants are essential tools for nursing workforce development, enabling professional growth and leadership training. Nursing ethics committees and clinical data analytics further enhance the quality of nursing education and research, ensuring that the nursing profession remains at the forefront of healthcare innovation.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Graduate courses
  Postgraduate courses


End-user

  Hospitals
  Home healthcare services


Program Type

  Associate Degree
  Bachelor's Degree
  Master's Degree
  Doctoral Programs


Delivery Mode

  On-Campus
  Online
  Hybrid


Institution Types

  Universities
  Community Colleges
  Vocational Schools


Geography

  US

By Type Insights

The graduate courses segment is estimated to witness significant growth during the forecast period.

The nursing education market in the US is experiencing significant growth due to the rising enrollment in undergraduate and graduate nursing programs. This trend is driven by the increasing demand for specialized nursing professionals in various fields, such as geriatric nursing, mental health nursing, and critical care nursing. The American Nurses Association and other nursing organizations advocate for continued nursing education as a means of addressing health disparities and improving patient care. E-learning platforms, nursing simulation labs, and clinical skills training are essential components of graduate nursing programs, providing students with the necessary theoretical and practical knowledge.

Nursing informatics, healthcare reform, and patient safety are key areas of focus, with data analytics and clinical decision support playing crucial roles. The nursing workforce is evolving, with an emphasis on nurse retention, nursing leadership, and nursing professional development. Online nursing programs, mobile health, and wearable technology are transforming nursing education, making it more accessible and flexible. Nursing evaluation, nursing diagnosis, and nursing standards are integral parts of nursing education, ensuring that students are prepared for the nursing licens

According to a survey from 2024, certified nursing assistants (CNA) had a turnover rate of over ** percent, making it the highest among all hospital staff in the United States. The second-highest turnover rates were among patient care technicians (PCT), followed by environmental services staff.

This study investigated whether parenting stress, work-family conflict, resilience affect retention intent in Korean nurses returning to work after parental leave.

Overview

The Per Diem Nurse Staffing Market is projected to reach approximately USD 16.4 billion by 2033, growing from USD 8.7 billion in 2023 at a CAGR of 6.5% between 2024 and 2033. The growth is driven by the persistent global shortage of nurses and the need for flexible workforce management. According to the World Health Organization (WHO), the global nursing workforce reached 29.8 million in 2023, yet a deficit of about 4.5 million nurses is projected by 2030. These shortages lead to unstable shift coverage and create opportunities for per-diem staffing solutions across hospitals and healthcare facilities.

Demographic ageing remains a long-term market driver. The United Nations projects that one in six people will be over 65 years old by 2050, while WHO indicates that the global population aged 60 and above will nearly double between 2015 and 2050. Older populations require more inpatient and long-term care, increasing daily fluctuations in patient volumes. These variations heighten the need for short-notice staffing, where per-diem nurses provide rapid and cost-effective coverage. As health systems adapt to ageing demographics, per-diem staffing ensures continuity of care amid unpredictable demand surges.

Government regulations and staffing mandates are further supporting demand. The Centers for Medicare & Medicaid Services (CMS) in the United States established minimum staffing standards for long-term care in 2024. Such regulations require consistent nurse-to-patient ratios, even during peak demand, compelling providers to maintain per-diem pools. Similar regulations in states like California also reinforce the use of flexible staffing models to ensure compliance. In other regions, tighter quality oversight in skilled nursing facilities is prompting administrators to use per-diem nurses to meet staffing adequacy requirements.

Financial pressures are influencing providers to reduce reliance on expensive external agencies and invest in internal per-diem pools. NHS England has reported declines in agency spending due to stricter rules and price caps while maintaining high levels of internal bank staff usage. This transition enables cost control while ensuring service continuity. Additionally, persistent retention challenges, nurse burnout, and high turnover rates continue to fuel temporary staffing requirements. Per-diem nurses serve as a strategic buffer against absenteeism and ongoing recruitment shortages across global healthcare systems.

The U.S. Bureau of Labor Statistics forecasts about 189,100 registered-nurse openings annually between 2024 and 2034, reinforcing structural replacement needs. Growing oversight of staffing quality and the rise of care delivery in post-acute settings further enhance the adoption of flexible workforce models. The market’s sustained expansion reflects its crucial role in maintaining service reliability, regulatory compliance, and cost efficiency. Overall, demographic shifts, labor shortages, and policy reforms are expected to anchor steady growth in the global per-diem nurse staffing industry through 2033.

This dashboard was created by the Neighborhood Nexus team to show nursing workforce supply, demographic and labor statistics to develop appropriate recruitment and retention strategies.

Background: Current human resources planning models in nursing are unreliable and ineffective as they consider volumes, but ignore effects on quality in patient care. The project RN4CAST aims innovative forecasting methods by addressing not only volumes, but quality of nursing staff as well as quality of patient care. Methods/Design: A multi-country, multilevel cross-sectional design is used to obtain important unmeasured factors in forecasting models including how features of hospital work environments impact on nurse recruitment, retention and patient outcomes. In each of the 12 participating European countries, at least 30 general acute hospitals were sampled. Data are gathered via four data sources (nurse, patient and organizational surveys and via routinely collected hospital discharge data). All staff nurses of a random selection of medical and surgical units (at least 2 per hospital) were surveyed. The nurse survey has the purpose to measure the experiences of nurses on their job (e.g. job satisfaction, burnout) as well as to allow the creation of aggregated hospital level measures of staffing and working conditions. The patient survey is organized in a sub-sample of countries and hospitals using a one-day census approach to measure the patient experiences with medical and nursing care. In addition to conducting a patient survey, hospital discharge abstract datasets will be used to calculate additional patient outcomes like in-hospital mortality and failure-to-rescue. Via the organizational survey, information about the organizational profile (e.g. bed size, types of technology available, teaching status) is collected to control the analyses for institutional differences. This information will be linked via common identifiers and the relationships between different aspects of the nursing work environment and patient and nurse outcomes will be studied by using multilevel regression type analyses. These results will be used to simulate the impact of changing different aspects of the nursing work environment on quality of care and satisfaction of the nursing workforce. Discussion: RN4CAST is one of the largest nurse workforce studies ever conducted in Europe, will add to accuracy of forecasting models and generate new approaches to more effective management of nursing resources in Europe.

In 2024, the average staff turnover rate of hospitals in the U.S. stood at **** percent. The percentage of employees leaving hospitals has decreased since the peak of ** percent in 2021. A closer look at turnover reveals that most was among less tenured staff, with the highest rates among certified nursing assistants.

Background: Community factors may affect nurses’ job behavior and decision making. There is a gap in the literature regarding the impact of community satisfaction, family ties, and community preferences on acute care nurses’ turnover intention and job satisfaction. Furthermore, no studies have examined the differences in community satisfaction, community preferences, and family ties among nurses working in rural and urban settings.

Purpose: To identify the impact of family ties, community satisfaction, and community preferences on turnover intention and job satisfaction among acute care nurses working in Ontario’s urban and rural areas.

Methods: Descriptive correlational survey design was used in this study. A targeted stratified sampling technique was used to recruit acute care nurses working in Ontario’s urban and rural areas (N=349) between May 2019 and July 2019. Dillman’s approach was used to guide data collection. Parametric and non-parametric tests were used for data analysis.

Results: A significant association was found between working settings and community preferences. A statistically significant positive relationship between community satisfaction and nurses’ job satisfaction was identified. Furthermore, community satisfaction had a negative impact on turnover intention. Neither community preference nor family ties were significantly associated with turnover intention or job satisfaction.

Conclusion: The study suggests that community satisfaction can influence important nurse work-related outcomes. Future studies should replicate and validate these results in different contexts and cultures. Retaining nurses may be difficult if they are not satisfied with their communities

Nurse Scheduling AI Market Outlook

According to our latest research, the global Nurse Scheduling AI market size in 2024 stands at USD 512.7 million, reflecting robust adoption across healthcare systems worldwide. The market is expanding at a CAGR of 21.4% from 2025 to 2033, driven by the increasing need for efficient workforce management and cost optimization in healthcare. By 2033, the Nurse Scheduling AI market is forecasted to reach USD 3.45 billion, as per our data-driven projections. The surge in demand is attributed to the acute global nursing shortage, rising operational complexities in healthcare facilities, and the urgent need to reduce administrative burdens through automation.

One of the primary growth factors fueling the Nurse Scheduling AI market is the persistent and worsening global nursing shortage. Healthcare providers are under immense pressure to maintain optimal staffing levels while ensuring compliance with labor laws and providing quality patient care. Traditional manual scheduling methods are highly time-consuming, error-prone, and often result in staff dissatisfaction and burnout. The adoption of AI-driven nurse scheduling solutions enables healthcare organizations to automate complex rostering processes, optimize shift allocations, and balance workloads. This leads to improved staff satisfaction, reduced turnover, and enhanced patient outcomes. Additionally, the integration of predictive analytics in AI scheduling tools allows healthcare administrators to anticipate staffing requirements based on patient inflow patterns, further driving market growth.

Another key growth driver is the increasing digitization and modernization of healthcare operations. The shift towards electronic health records (EHRs), telehealth, and digital workforce management systems has created a fertile ground for the implementation of AI-based scheduling platforms. These solutions seamlessly integrate with existing hospital information systems, enabling real-time data exchange and decision-making. The growing emphasis on operational efficiency, cost containment, and regulatory compliance has compelled healthcare providers to invest in advanced scheduling technologies. Furthermore, the COVID-19 pandemic has highlighted the importance of agile and resilient workforce management, accelerating the adoption of Nurse Scheduling AI solutions across hospitals, clinics, and long-term care facilities globally.

The rising focus on improving the work-life balance of nursing staff and enhancing overall healthcare delivery is also propelling the Nurse Scheduling AI market. AI-powered scheduling systems offer features such as self-scheduling, shift swapping, and preference-based rostering, empowering nurses with greater control over their work schedules. This not only boosts employee morale but also reduces absenteeism and enhances retention rates. The ability to quickly adapt to sudden changes in staffing needs, such as during public health emergencies or seasonal surges in patient volume, further underscores the value proposition of AI-driven scheduling. As healthcare organizations strive to create supportive and flexible work environments, the demand for intelligent nurse scheduling solutions is expected to witness sustained growth.

From a regional perspective, North America currently dominates the Nurse Scheduling AI market, accounting for the largest revenue share in 2024. This leadership is underpinned by the presence of advanced healthcare infrastructure, high adoption of digital health technologies, and significant investments in AI-driven workforce management tools. Europe follows closely, driven by increasing healthcare expenditures and stringent labor regulations. The Asia Pacific region is poised for the fastest growth, fueled by rapid healthcare digitization, expanding hospital networks, and rising awareness about the benefits of AI in workforce management. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by healthcare modernization initiatives and the growing need to address staffing inefficiencies.

Component Analysis

The Nurse Scheduling AI market by component is bifurcated into Software and Services. The software segment holds the lion’s share of the market, primarily due to the increasing deployment of robust AI-powered scheduling platforms across healthcare facilities. These software solutions are desig

Lommi M, Caruso R, Conte G, Magon A, Porcelli B, Stievano A, Rocco G, Notarnicola I, Sabatino L, Latina R, De Maria M, Di Simone E, De Benedictis A, Gualandi R, Tartaglini D, Ivziku D. Assessment of the Psychometric Characteristics of the Italian Version of the Nurse Manager Actions Scale. Nurs Rep. 2023 Sep 1;13(3):1185-1202. doi: 10.3390/nursrep13030102. PMID: 37755345; PMCID: PMC10534939.

Abstract

This systematic review and meta-analysis examines the persistent challenges facing rural nursing workforces in Europe and Asia, focusing on recruitment, retention, and practice experiences. Drawing on 20 studies published between 2000 and 2025, the research integrates quantitative and qualitative evidence to identify both shared and region-specific determinants affecting rural nurses.

Quantitative findings reveal that rural nurses in both regions are significantly more likely to express intent to leave their positions compared to urban counterparts. The pooled odds ratio was 1.5 in Europe and 1.7 in Asia. Job satisfaction was notably lower among rural nurses, with standardized mean differences of –0.32 in Europe and –0.28 in Asia. Burnout levels were higher in Asia (SMD = 0.35) than in Europe (SMD = 0.25), and five-year retention rates were 62% in Europe versus 48% in Asia. These disparities highlight systemic issues such as resource limitations, migration pressures, and policy gaps.

Thematic synthesis of qualitative studies identified four overarching challenges: professional isolation, expanded scope of practice, community embeddedness, and the tension between personal sacrifice and professional fulfillment. European studies emphasized policy incentives and intra-EU mobility, while Asian studies highlighted family obligations, international migration, and infrastructure deficits.

Subgroup and meta-regression analyses further revealed that lower-middle-income Asian countries had higher turnover intent, and community nurses experienced greater burnout than hospital-based peers. GDP per capita was inversely associated with turnover intent, underscoring the influence of economic context.

Policy recommendations include enhancing professional development and harmonizing incentives in Europe, and investing in rural infrastructure and family-supportive policies in Asia. Globally, the study advocates for context-sensitive workforce strategies aligned with WHO guidelines and encourages cross-regional learning.

Overall, the review underscores the urgent need for tailored interventions to address rural nursing shortages, balancing universal challenges with region-specific realities to ensure equitable healthcare access across diverse settings.

Turnover in adult nursing OVERVIEW: Content and thematic analysis. (XLSX 26Â kb)