Advancing the Calculation of Nurse Turnover Costs: A Methodological Approach Using the RETAIN Framework

Abstract

Background:

Nurse turnover has reached a crossroads, inflating labor costs and threatening organizational stability. Persistent vacancies and reliance on contract labor continue to strain healthcare systems. This paper introduces the RETAIN framework to quantify turnover expenses and guide evidence-based investment in nursing.

Methods:

The RETAIN framework follows a structured, four-phase methodology. First, the nursing turnover continuum was process-mapped to capture every touch point and cost associated with turnover. Second, a cost accounting structure was applied to categorize both direct and indirect expenditures. Third, staffing back-fill strategies were analyzed to assess financial trade-offs. Finally, all variables were integrated into a modeling dashboard that enables dynamic scenario testing using modifiable inputs. This approach supports workforce advocacy through financial analysis.

Conclusion:

The RETAIN framework provides a comprehensive methodology for quantifying nurse turnover costs. In part two, the framework is applied to a multi-hospital population to identify granular cost data and outcomes of interest.

Nurse turnover has reached an urgent tipping point, creating financial and operational challenges that directly affect both patient care and organizational stability. While the overall nurse workforce has rebounded following pandemic-driven shortages, many returning nurses continue to choose nonhospital roles, leaving acute care environments with significant staffing pressures (Auerbach et al., 2024). A national review of employment patterns shows that turnover remains pervasive and is strongly influenced by factors like staffing practices and leadership quality, which push up labor costs, risk care disruptions, and underscores the need for precise, contemporary cost data (Jones et al., 2024). For nurse leaders and policymakers, quantifying these expenses is essential to building effective retention strategies, securing adequate funding, and ensuring a stable nursing workforce in an increasingly volatile workforce environment.

Background

Turnover: Retention and Recruitment

Healthcare organizations are grappling with unprecedented financial challenges related to the recruitment and retention of nurses (American Hospital Association, n.d.; Li et al., 2023). The turnover rate of nurses is at its highest in recent memory, surpassing any trends observed in the last ten years (Bae, 2023; American Hospital Association, n.d; American Hospital Association, 2021; Kovner et al., 2014). From 2012 to the pre-pandemic era, nursing turnover rates remained steady at 13% to 17% (NSI Nursing Solutions, Inc., 2017; NSI Nursing Solutions, Inc., 2022; NSI Nursing Solutions, Inc., 2023; Aiken et al., 2023; Jones et al., 2024; Kovner et al., 2014). By 2020, turnover rates had risen sharply to 18.7%, before skyrocketing to 27.1% in 2021 NSI Nursing Solutions, Inc., 2017; NSI Nursing Solutions, Inc., 2022; NSI Nursing Solutions, Inc., 2023).

The nursing profession is also experiencing recruitment pressures due to an aging workforce and the increasing needs of the patient populations they serve (U.S. Department of Health and Human Services, 2019.; American Association of Colleges of Nursing, n.d.; U.S. Census Bureau, n.d.; Zhang et al., 2018). According to the U.S. Census Bureau, the aging U.S. population is expected to grow from 54 million in 2019 to 81 million by 2040, which will intensify the demand for healthcare services (U.S. Census Bureau, n.d.). Concurrently, the nursing workforce is witnessing an age-related uptick in retirements and job vacancies, exacerbating the existing nursing shortage (American Association of Colleges of Nursing, n.d.; Zhang et al., 2018). Nurses at the opposite end of the spectrum face less favorable work and health-related outcomes than their more tenured colleagues (Bae, 2023). A study by EPIC Systems highlighted a demographic shift, with a significant reduction of 19.5% in median tenure to 2.78 years and a 55.5% spike in shifts covered by nurses with under a year of experience (2021 to 2022), emphasizing the urgent need for more robust retention programs (Epic Research, 2022). The simultaneous aging of both patient and nurse populations, as well as the significant evidence of increased turnover in nurses with minimal tenure, creates a supply and demand challenge (Smiley et al., 2023; National Academies of Sciences, Engineering, and Medicine, 2021; Epic Research, 2022). As the need for nursing care swells, the number of available nurses to provide care for this growing elderly population is dwindling (Smiley et al., 2023; National Academies of Sciences, Engineering, and Medicine, 2021; Zhang et al., 2018). The surge in departures and difficulties with recruitment caused a profound shift in the nursing landscape, resulting in greater nurse labor costs due to the need to hire more expensive contract nurses.

Contract Labor

In response to the evolving healthcare landscape shaped by COVID-19, a significant portion of the nursing workforce transitioned to contract roles due to job flexibility or higher compensation associated with these positions (Hansen & Tuttas, 2022; American Hospital Association, n.d.). This trend not only reshaped the composition of the nursing workforce but underscored the changing priorities and adaptive strategies of the nursing workforce during a global health crisis (Hansen & Tuttas, 2022; American Hospital Association, n.d.). Historically, hospital systems utilized supplemental workforce resources or contract nurses to fill temporary workforce openings caused by events such as seasonal surges in patients or newly opened units (Hansen & Tuttas, 2022). Before the COVID-19 pandemic, it was known that the overuse of contract nurses was linked to poorer patient outcomes, such as increased rates of mortality, falls, and medication errors (Bae et al., 2010; Dall’Ora et al., 2020). However, as the COVID-19 pandemic spread, more contract nurses were employed, resulting in a change in the nursing workforce composition and a substantial economic impact (American Hospital Association, n.d.). According to the American Hospital Association, there was a surge in contract nurse job postings, noting a 120% increase from 2019 to 2022 that corresponded with an increase in contract nurse hours worked (American Hospital Association, n.d.). Simultaneously, according to the Bureau of Labor Statistics, approximately 47,000 nurses were employed through temporary contract work in 2019 (U.S. Bureau of Labor Statistics, n.d.-a). This figure climbed by 42% in 2021, when around 67,000 nurses were employed through temporary contract labor (U.S. Bureau of Labor Statistics, n.d.-b).

By the end of 2021, hospitals saw their overall expenses grow by 20% compared to 2019 (Kaufman Hall, 2022). This surge in costs, along with escalating inflation, took a toll on healthcare organizations that typically operate on thin margins, leading to a median dip of 3.8% in their operating margins by the end of 2021 relative to pre-pandemic figures (Kaufman Hall, 2022). The shift in the nursing workforce has led to considerable strain, with many nurses transitioning from conventional staff roles to contract nursing positions or exiting the profession altogether (Kaufman Hall, 2022). Nursing labor cost increases during COVID-19 contributed to $54 billion in hospital net income losses across the U.S. in 2021 (American Hospital Association, n.d.; American Hospital Association, 2021; Beauvais et al., 2023). Labor costs in hospitals rose by 19.1% from 2019 to 2021, with contract nurses comprising 38.6% of total nursing costs by January 2022, a significant rise from 4.7% in January 2019 (American Hospital Association, n.d.). This increase in the volume of contract nursing has highlighted the critical need for thoughtful workforce management and proactive investment in nursing staff (American Hospital Association, n.d.; Epic Research, 2022; Beauvais et al., 2023). Most importantly, the cost of this contract labor diverts funding away from investments in core nursing staff, undermining retention efforts and fueling a cycle of low morale, high turnover, and ongoing workforce instability (Aiken et al., 2023; Yang & Mason, 2022).

To break the cycle of turnover and reliance on contract labor, healthcare systems are proposing structural solutions, such as the implementation of an internal travel nurse team. To address concerns related to price gouging by contract nurse agencies, several prominent hospital networks, including Mayo Clinic, Emory Healthcare, and the University of Pittsburgh Medical Center, have taken the proactive step of establishing an internal travel nurse team (University of Pittsburgh Medical Center, n.d.; Mayo Clinic, n.d.; Emory Healthcare, n.d.). Internal travel nurse teams offer temporary assignments within the same healthcare organization to attract and retain skilled clinicians, address staffing shortages, and reduce reliance on costly external agencies, while providing higher pay, benefits, and flexible scheduling (University of Pittsburgh Medical Center, n.d.; Mayo Clinic, n.d.; Emory Healthcare, n.d.). Unlike float pools, internal travel teams are designed to mirror external travel nurse roles, offering assignment-based contracts, tailored onboarding, and enhanced compensation and benefits. Moreover, for large healthcare organizations that span multiple states, these internal travel teams offer nurses valuable flexibility in their assignment locations (Coombs, 2023).

Turnover Cost: Past and Present

A number of studies between 1990 and 2006 estimated per-nurse turnover costs, which ranged from $11,700 to $31,500, using methodologies such as descriptive statistical analyses, cost accrual curves, and learning curve models (Waldman et al., 2004; Wise, 1990). However, Jones and colleagues were pioneers and the first to rigorously identify the hidden cost of nurse turnover (Jones, 2004, 2005, 2008). Jones continued to explore the financial implications of nursing workforce labor costs through the Nursing Turnover Cost Calculation Methodology; which has been acknowledged as the gold standard since 2004 (Jones, 2004, 2005). The findings from this research showed an estimated cost of $62,100 to $67,100 per nurse turnover (Jones, 2004, 2005). This model was further updated to account for Consumer Price Index inflation (Jones, 2008), and although there have been several narrative and systematic reviews on the model, there is a lack of recent data-based outcomes to reference (Bae, 2022; Li & Jones, 2013).

A comprehensive and up-to-date accounting of the financial impact of workforce trends, such as turnover, vacancies, contract employment, and the use of internal travel teams within the nursing workforce, has yet to be fully documented. Furthermore, the costs associated with workforce composition changes have not been thoroughly examined in the past two decades, when this angle of advocacy was first popularized. That said, during the past five years, the healthcare sector has been profoundly disrupted by the COVID-19 pandemic, accelerating extreme changes affecting the nursing workforce, which many argue experienced the greatest burden of instability (Aiken et al., 2023). With nurse labor representing the largest cost line item, it is essential for healthcare organizations to understand the factors driving these expenses amid ongoing financial strain (American Hospital Association, n.d.; Li et al., 2023).

Therefore, this two-part publication aims, first and foremost, to outline a methodology for evaluating nurse turnover costs in the post-COVID era, utilizing process mapping and cost accounting through the use of the RETAIN framework. This innovative approach takes into consideration the variability in contract labor utilization and the ongoing impact of interventions like internal travel teams, which gained traction during the pandemic and remain pertinent. In the subsequent part, the RETAIN framework will be applied to a large sample of medical-surgical units across six hospitals to quantify the costs associated with nurse turnover. Additionally, both parts of the publication will explore the concept of a tangible modeling dashboard tailored for healthcare executives. This dashboard, designed to incorporate user input and model cost savings, provides data-driven evidence to support executives in advocating for budgetary adjustments pertaining to their nursing workforce.

Theoretical Underpinning

This study is grounded in Covell’s (2008) Theory of Nursing Intellectual Capital, adapted through the operational lens of the RETAIN Framework. Covell’s theory defines nursing intellectual capital as comprising two essential components: human capital and structural capital. Nursing human capital refers to the clinical expertise, institutional knowledge, and unit-specific familiarity that individual nurses acquire over time. These competencies are critical for maintaining operational efficiency and preventing costly disruptions associated with nurse turnover. In parallel, nursing structural capital includes the systems, staffing processes, and organizational infrastructures that support the preservation, transfer, and strategic deployment of that expertise (Covell, 2008). Together, these forms of capital are central to the functioning, stability, and financial performance of healthcare organizations as seen in Figure 1.

Figure 1.

Integrated RETAIN Framework Grounded in Nursing Intellectual Capital Theory

The RETAIN Framework operationalizes this theory by linking the assessment of nursing workforce data to targeted investment decisions that address root causes of instability. The process begins with assessment, where the RETAIN Framework analyzes operational and financial indicators—such as turnover rates, vacancy duration, contract labor reliance, absenteeism, and associated costs, to identify performance gaps that erode institutional stability. These insights inform the diagnosis and planning phase, where underlying drivers, such as core staff hourly rate, contract labor hourly rate, turnover rate, time to fill, and percentage of contract labor utilization, are analyzed and translated into a financially grounded Workforce Investment Strategy that targets the specific inefficiencies identified in the assessment (Covell, 2008).

Depending on the diagnosis, investment is directed into Nursing Human Capital through mechanisms such as retention bonuses, internal career mobility pathways, or compensation restructuring. Alternatively, investment may focus on Nursing Structural Capital, including the development of cross-training systems, internal float pools, or flexible scheduling protocols. Each investment is purposefully aligned with a diagnosed gap and designed to yield measurable improvements in operational performance and cost containment (Covell, 2008). Finally, the Evaluation phase measures the impact of these interventions using both conventional workforce outcomes and, when appropriate, a reapplication of the RETAIN Framework to assess progress, recalibrate strategies, and reinforce continuous improvement. This cyclical process ensures that investments are not only reactive but part of an ongoing strategy to strengthen nursing capital and improve financial and organizational outcomes.

Depending on the diagnosis, investment may be directed toward either Nursing Human Capital or Nursing Structural Capital (Covell, 2008). Investments in human capital may include retention bonuses, competitive compensation packages, or internal mobility programs that preserve institutional knowledge and reduce the loss of experienced staff. Structural capital investments, by contrast, may focus on strengthening recruitment pipelines, implementing flexible staffing models, or enhancing scheduling systems to better absorb the operational disruptions caused by turnover. Each investment is strategically aligned with the diagnosed gap and is designed to produce measurable improvements in operational performance and cost containment. The Evaluation phase measures the impact of these interventions using both conventional workforce outcomes and, when appropriate, a reapplication of the RETAIN Framework to assess progress, recalibrate strategies, and reinforce continuous improvement. This cyclical process ensures that investments are not only reactive but part of an ongoing strategy to strengthen nursing capital and improve financial and organizational outcomes.

By adapting Covell’s (2008) intellectual capital theory through a financial and operational framework, this study provides a strategic, data-driven model for workforce investment. Nurse labor is positioned not as a fixed cost, but as a dynamic asset—subject to erosion, recovery, and deliberate growth through financially accountable planning.

Methodology

The RETAIN Framework

The RETAIN framework (“Retention Evaluation and Turnover Analysis for the Investment in Nursing”) for calculating the cost of nurse turnover comprises a comprehensive series of steps, including data collection methodology, cost accounting variables, and visualization tools, culminating in a tangible modeling dashboard. The goal of this framework is to assist healthcare organizations, especially nurse leaders, in transitioning from a state of insufficient financial workforce data to being well-informed with tangible outcomes. This enables them to effectively advocate for investment in their nursing workforce at the C-suite and board level.

The RETAIN framework includes a list of stakeholders and data sources required for data collection. In addition, the framework includes a list of specific data points to be collected from each stakeholder broken down to the most granular detail. While every hospital system is structured differently and pulls certain levels of granularity in its data, this list is meant to be broad enough to adapt it to every hospital system. For example, some hospital systems collect individual-level productivity metrics, whereas others collect unit-level data. Once a list of cost categories has been defined, the most efficient methodology for data collection will be described. These data sources will be a combination of objective backend human resources and finance datasets, as well as subjective data collected from stakeholders, such as nurse managers and nurse executives. Once data collection is complete, the analysis will begin using the RETAIN framework by organizing and presenting granular variable data for the per-nurse cost of turnover. Cost sub-categories will be added and collapsed into cost category totals. Additionally, back-fill variations will also be calculated, which will be added to create a final per-nurse cost of turnover. These data will be fed into a dashboard, which allows for modeling capabilities. The RETAIN dashboard will allow leaders to modify variables based on goals or tangible outcome targets; it will then output cost savings and visualize the data in a digestible format for workforce advocacy.

Data Collection

Data collection for the RETAIN framework is tailored to the unique environment of each hospital system and is influenced by the specific human resources and financial software in use, as well as the underlying cost-accounting methodologies (e.g., Strata’s EPSI software). The framework leverages a continuum of data-collection methods, ranging from objective data extracted from internal databases, which ensures validity, reliability, and supports automation, to subjective data gathered through interviews and surveys. While objective data forms the backbone of the dataset, capturing nuanced insights that are not available from internal records requires a qualitative approach.

A comprehensive stakeholder engagement strategy is integral to the data collection process for the RETAIN framework. Our approach involved a granular examination of the data requirements, engaging a significant number of stakeholders as detailed in Table 1. This thorough engagement ensures that the necessary information is obtained to fully operationalize the RETAIN framework. Although different hospital systems may use varying titles for analogous roles, the underlying functions and responsibilities remain consistent, facilitating a broad-based approach to data collection. While it is not always necessary to interact with every stakeholder listed, a wider scope of engagement can streamline the process and enhance the quality and completeness of the collected data.

Table 1.

Stakeholder Roles and Responsibilities in the RETAIN Framework

Stakeholder / Department	Sample Job Titles	Key Roles and Responsibilities	Examples of Data Provided
Nurses	Registered Nurse (RN), Staff Nurse, Clinical RN	Deliver direct patient care; may provide exit feedback if leaving	N/A
Nurse Management	Nurse Manager, Assistant Nurse Manager, Staffing Analyst	Oversee unit operations; manage staffing and schedule adjustments	Coverage strategies during vacancies (e.g., overtime, contract labor)
Human Resources (HR)	Chief Human Resources Officer, HR Manager, HR Data Analyst	Manage recruitment, benefits, and personnel records; track turnover data	Time-to-fill rates, separation records, background check costs, onboarding fees
Onboarding and Education	Onboarding Specialist, Orientation Coordinator, Unit Educator	Develop orientation programs; coordinate preceptors	Orientation materials cost, preceptor hours
Finance and Budget	Chief Financial Officer, Budget Analyst, Financial Data Specialist	Develop and monitor budget allocations; track salaries, benefits, and labor expenditures	Salary and benefits data, contract labor rates, internal travel nurse rates, cost of orientation allowances
Talent Acquisition	Recruiter, Talent Acquisition Specialist, TA Analyst	Execute recruitment campaigns and hiring processes	Advertising expenses for vacant positions, sign-on bonus data
Marketing and Advertising	Marketing Specialist, Corporate Communications, Marketing Analyst	Support recruitment with targeted marketing and advertising	Media placement costs for nurse recruitment (e.g., online ads, print campaigns)
Operations	Chief Operating Officer, Director of Operations, Operations Analyst	Maintain hospital-wide operational efficiency; coordinate resource allocation	Lost productivity estimates during vacancies, operational metrics to quantify staffing gaps
Nurse Executives	Chief Nurse Executive/Officer or VP of Nursing	Provide strategic leadership on nursing policies; approve financial decisions	Data access and structure information
Clinical Nurse Specialists	Clinical Nurse Specialist	Guide evidence-based practice and specialized training	Additional orientation or specialized training costs for new hires

In our implementation, qualitative insights were gathered through one-on-one interviews and open-ended survey questions with nurse managers. Four in-depth interviews focused on identifying key workflows and estimating the time and cost associated with specific turnover-related touchpoints. These interviews enabled detailed process mapping and helped uncover hidden labor and administrative costs. An additional six nurse managers completed open-ended surveys with the same questions, serving as a confirmatory step to validate the consistency and duration of tasks identified in the interviews. This dual-method approach was instrumental in identifying cost drivers, validating assumptions, and shaping the structure of the RETAIN framework—particularly in areas where objective data alone could not capture operational nuance.

To ensure consistency, data discrepancies were minimal and were resolved or validated through consultations with nurse leaders and operational managers to confirm accuracy and contextual relevance. Additionally, maintaining parity throughout the data collection process is essential for ensuring accuracy and comparability. Differences in unit types, nurse experience levels, and hospital locations can significantly influence key variables and potentially skew results. For example, merging data from a medical-surgical unit with that from an intensive care unit may compromise validity due to the latter’s longer onboarding processes and higher pay scales. By clearly distinguishing among these factors, the RETAIN framework enhances both the rigor and reliability of its nurse turnover cost calculations.

Turnover Continuum

To ensure the validity and reliability of the final cost calculations for nurse turnover, we have thoroughly examined the nurse turnover process. We used the concept of process mapping to gain an extensive knowledge of all the granular details related to nurse turnover. Process mapping entails creating a visual diagram depicting the sequence of activities, decisions, and outputs in a business process. This method is useful because it necessitates a thorough examination of all details within a process, in this case, nurse turnover. The nurse turnover process has a wide-ranging impact on the healthcare system, making this methodology critical for keeping track of all relevant details. Finalizing the process map involved significant interactions with stakeholders throughout the healthcare system, from nurse managers to the C-suite.

Once the process of nurse turnover was thoroughly examined, the RETAIN Nurse Turnover Continuum was created. The RETAIN Nurse Turnover Continuum defines the scope of our measurement at a high level. Our turnover measurement will begin 90 days before the departure of the first fully productive core staff nurse (Nurse A) and will continue until the newly hired nurse (Nurse B) who replaces the departing nurse achieves the status of a fully productive core staff nurse, as shown in Figure 2. This method allows us to capture the entire process as we move through the employment cycle from the departing nurse to the nurse who succeeds them. For example, according to existing reports, healthcare organizations typically observe a decrease in Nurse A’s productivity immediately before their departure (Jones, 2004, 2005). Once Nurse A separates or turns over, a cascade of events ensues. Nurse A should ideally be replaced or back-filled as soon as possible, either by a contract nurse or an internal travel nurse. Back-filling in human resources is the process of replacing an employee who has transitioned to a different position, taken leave, resigned, or has been terminated (Society for Human Resource Management n.d.). In nursing, it is essential to promptly back-fill positions left vacant to sustain the consistent demand for patient care and ensure the healthcare organization maintains a safe staffing level for delivering quality healthcare. Simultaneously, various departments, such as human resources, marketing, and talent acquisition, would begin behind-the-scenes efforts to recruit Nurse B. The duration of the necessary back-fill by a contract nurse or an internal travel nurse is determined by the “time to fill,” or in weeks, between the job posting and Nurse B’s start of orientation. Once Nurse B has been identified and onboarded, they begin the orientation process, during which they remain unproductive, whether in the classroom or on the unit working with a preceptor, until they can take on a full patient caseload. When Nurse B is capable of managing a full patient caseload, the process is complete.

Figure 2.

The RETAIN Framework Turnover Continuum

Categories/subcategories

Each step of the turnover process was deconstructed through a detailed process mapping exercise, which revealed distinct cost categories and their corresponding subcategories, as illustrated in Figure 3. For example, a category focused on onboarding may include a subcategory for expenses related to raw materials, such as printing and programming identification badges for new nurses. This level of detail allows for precise analysis of individual activities and interactions. The resulting data equips executives with credible insights to guide strategic decisions, prioritize actions, and allocate resources with confidence.

Figure 3.

Cost Categories and Subcategories in the RETAIN Framework

Data Analysis

The RETAIN Framework offers a structured, multi-phase methodology for quantifying the financial consequences of nurse turnover through the integration, synthesis, and analysis of workforce and cost data. This process unfolds in three analytic phases: the consolidation and cleaning of data inputs, detailed cost subcategory analysis, and evaluation of staffing strategies used to back-fill vacancies. The first phase involves compiling data from a range of institutional sources, including human resources systems, financial records, and structured leadership interviews, into a single, coherent dataset. Ensuring internal consistency across disparate sources is essential; discrepancies are identified and resolved through a systematic cleaning protocol designed to preserve analytic validity. The resulting dataset captures turnover-related activity at a high level of resolution, providing a reliable foundation for subsequent cost analysis. In the second phase, the framework enables the identification and quantification of individual cost components associated with turnover. These include but are not limited to, pre-separation labor utilization, administrative separation tasks, recruitment and hiring expenditures, onboarding processes, and orientation programming.

Each component is calculated independently using raw input variables and then aggregated to generate an overall cost estimate per nurse turnover. For example, the cost implications of using contract labor compared to deploying internal travel nurses can be assessed in precise financial terms, allowing organizations to evaluate the relative efficiency of different staffing options. Finally, the third phase focuses on the analysis of back-fill strategies. Healthcare organizations frequently use a mix of contract nurses, internal float pools, and overtime to address staffing shortfalls. Each approach carries distinct cost structures and operational trade-offs. Contract labor often enables rapid deployment but comes with higher hourly rates and agency fees. Internal travel programs, while requiring greater upfront investment in onboarding and support, may offer long-term cost advantages and more stable patient care delivery. The RETAIN Framework supports a comparative evaluation of these strategies, enabling leadership to optimize workforce configuration based on both cost and continuity considerations.

To contextualize these financial drivers within a broader organizational strategy, Figure 4 introduces a value driver tree framework that articulates the mechanisms by which nurse turnover generates economic strain. Turnover-related costs are disaggregated into discrete value drivers, classified as either direct, such as separation, recruitment, and back-fill costs, or indirect, encompassing downstream effects on team cohesion, care quality, morale, and institutional reputation. While the indirect consequences are less readily monetized, they are no less central to understanding the full scope of turnover’s impact on system performance. What distinguishes this framework is its integration of cost drivers with corresponding operational levers—defined as domains within the healthcare system that can be adjusted to mitigate costs and improve retention outcomes. These levers, displayed on the right side of Figure 4, span workforce design, professional development infrastructure, organizational culture, and human resource efficiency. By linking areas of financial vulnerability to sites of managerial control, the RETAIN Framework facilitates a shift from retrospective cost accounting to proactive, value-based decision-making.

Figure 4.

Mapping Nurse Turnover Value Drivers to Organizational Operational Levers

Supplement 1 provides the detailed variable structure used to operationalize each cost driver. Inputs such as unscheduled leave days prior to departure, wage differentials between staffing models, and administrative onboarding costs were identified during the process mapping phase of the framework. Taken together, the value driver tree and supplemental variable set establish a transparent and replicable framework for translating workforce instability into quantifiable financial metrics. Once the values or costs associated with each variable are collected, a composite per-nurse turnover cost can be calculated. These individual costs are then summed to derive an aggregate total turnover cost for the unit or organization. Ultimately, this approach offers more than a method for calculating turnover costs; it serves as a strategic planning tool. By identifying the relationship between specific cost drivers and modifiable operational levers, the RETAIN Framework equips health systems with the analytic foundation necessary to justify and prioritize investments in nursing workforce stability.

Cost Savings Modeling

Once per-nurse and total turnover costs are established, the subsequent cost-savings modeling becomes relatively straightforward. Our approach involves constructing basic models centered around high-level strategic goals defined by healthcare leaders, incorporating both operational variables (such as salary and turnover rate) and human resource metrics (such as time to fill vacancies). For example, if a chief nurse sets an objective to reduce turnover by 5% across the system and a chief human resources officer aims to halve the time to fill open positions, these targeted adjustments can be directly applied to the turnover cost data to generate cost-savings projections. This method not only furnishes compelling evidence to secure additional resources but also provides a data-driven rationale for investing in the nursing workforce.

More sophisticated modeling techniques extend this approach by comparing historical data with current performance metrics. For instance, a mean reversion model can be employed to contrast pre–COVID–19 averages with present-day figures, thereby quantifying the potential savings if key metrics were to revert to earlier baselines. Such analyses empower healthcare leaders to advocate for initiatives designed to restore or even exceed previous performance levels, ultimately guiding strategic decisions that enhance both financial and operational outcomes.

Dashboard

The final step of the RETAIN framework is creating an executive dashboard that consolidates all previous outputs—cost categories, subcategories, total cost, and cost-saving models. This dashboard, built-in platforms like Microsoft Excel, Power BI, or Tableau, allows real-time updates and modeling by adjusting five key variables: core staff hourly rate, contract labor hourly rate, turnover rate, time to fill, and percentage of contract labor utilization. These five variables were identified during the process mapping phase and selected because they represent key levers that leaders can use to diagnose issues and make adjustments to improve financial performance and critical operation metrics.

The dashboard offers two main advantages for healthcare leaders. First, it enables goal setting and predictive modeling; users can simulate changes in these variables to gauge the potential impact on total nurse turnover costs. For instance, raising core hourly rates or reducing contract labor usage can diminish per-nurse turnover expenses. Second, leaders can automate the dashboard to capture reoccurring data pulls from back-end databases, which facilitates continuous monitoring and helps determine whether interventions are yielding improvements. Although confounding variables and seasonal trends can affect results, the extended use of the dashboard strengthens confidence in observed patterns and supports more informed decision-making.

Limitations

By focusing exclusively on experienced, non-new graduate medical-surgical nurses, this framework produces a conservative cost estimate, excluding higher-cost populations such as new graduates and specialty unit nurses. This targeted scope establishes a foundational baseline for future iterations that will incorporate greater granularity across nurse experience levels, unit types, and separation pathways.

This approach enables consistency and comparability across healthcare systems, while acknowledging that future versions of the framework should incorporate greater flexibility to reflect the diverse realities of turnover. Although this iteration does not capture the full range of departure types or contextual nuances, it is intentionally designed as a foundational framework—one that can be refined over time as data availability, analytic capacity, and operational needs evolve.

Conclusion

The RETAIN framework offers a rigorous, theory-based approach to quantifying the financial impact of nurse turnover within the ever-changing healthcare landscape. By uniting concepts from the Deliberative Nursing Process and Human Capital theories, the framework thoroughly examines the costs of turnover—capturing both contract labor and internal travel team expenses—and informs strategic, evidence-driven decisions about workforce investment.

In the next paper, the RETAIN framework is applied to a retrospective analysis of 60 medical-surgical units across seven hospitals in a large academic medical center. Crucially, the insights from this investigation enabled healthcare leaders to make data-driven decisions that, in this specific healthcare system, led directly to investments in nurse compensation and sparked broader organizational efforts aimed at bolstering workforce stability.