Operationalizing the RETAIN Framework: Calculating the Cost of Nurse Turnover in Practice

Abstract

Background:

This Part II study applies the RETAIN Framework to fill a 20-year gap in validated primary evidence on nurse turnover costs and provide contemporary benchmarks for health systems.

Methods:

Through the RETAIN Framework, we employed a bottom-up methodology that ties expenditures to discrete turnover events. Each instance of turnover was analyzed at the event level, allowing us to assign costs directly to the departure, orientation, and backfill of individual nurses. This approach yielded a comprehensive and precise estimate of turnover costs for 1,501 medical–surgical nurses, representing 23% of the health system’s nursing workforce across seven hospitals within a large academic medical center.

Results:

In 2023, 360 nurse turnover instances produced a 24% turnover rate. The time to fill vacancies averaged 21.7 weeks, with 75.5% of backfill hours covered by costly contract labor. In our sample, the per-nurse turnover cost when replaced with contract labor was $85,498, resulting in a total expenditure of $27.9 million.

Discussion:

Findings demonstrate that turnover costs are not fixed but shaped by managerial choices. The RETAIN executive dashboard translated cost drivers into actionable evidence, directly informing a $150 million salary investment added roughly $9,000 annually to nurses’ pay.

Conclusion:

By linking event-level costs to organizational outcomes, the RETAIN Framework establishes turnover reduction as both a financial imperative and a strategic opportunity.

In Part I of this series, Advancing the Calculation of Nurse Turnover Costs: A Methodological Approach Using the RETAIN Framework, we introduced the RETAIN Framework (“Retention Evaluation and Turnover Analysis for the Investment in Nursing”) as a structured methodology for quantifying the financial impact of nurse turnover and linking cost drivers to modifiable operational factors that can be modified to improve retention (Razmpour et al., 2025a). Grounded in Nursing Intellectual Capital Theory, the framework integrates conceptual rigor with practical applicability, enabling both precise measurement and informed decision-making related to turnover cost (Covell, 2008). Our initial paper examined the theoretical foundations, data structure requirements, and analytic approach necessary to capture the discrete costs associated with nurse turnover. It also introduced the concept of modeling the potential effects of alternative staffing strategies (Razmpour et al., 2025a). By establishing a transparent and replicable framework for integrating financial analysis with workforce dynamics, Part I provides the technical and methodological foundation for the applied evaluation in Part II.

Background

Nurse turnover reached a crossroads during the COVID-19 pandemic, creating financial and operational challenges that directly impacted patient care and organizational stability. While turnover rates had historically remained between 13% and 17%, they rose sharply to 18.7% in 2020 and peaked at 27.1% in 2021 (NSI Nursing Solutions, Inc., 2017, 2022, 2023). During the same period, hospital labor costs increased by 19%, with contract nurses accounting for 39% of total nursing labor expenses by early 2022, a significant rise from 5% in 2019 (American Hospital Association, n.d.). These shifts coincided with high volumes of turnover driven by individual factors, such as career and educational advancement, and organizational pressures, including widespread burnout, job dissatisfaction, and declining trust in leadership. Collectively leading hundreds of thousands of nurses leaving their positions annually (Jones et al., 2024). The resulting instability heightened reliance on contract labor, which drove up staffing costs. This era also introduced new cost dynamics, including the emergence of internal travel teams, rising wages and benefit loads, and more resource-intensive recruitment operations, creating cost drivers that have shifted meaningfully over the past two decades (Bureau of Labor Statistics, 2024, 2025; Mayo Clinic, n.d.) At the same time, hospitals now have access to more detailed workforce and financial data through modern human resource and finance systems, creating new opportunities for granular, activity-level cost measurement that were not previously feasible. Despite the shift in the cost environment and the advancements in data infrastructure that now enable more granular measurement, few studies have quantified the financial impact of nurse turnover with the precision required to inform targeted and cost-effective retention strategies. Notably, no primary, validated evidence generation on nurse turnover costs has been conducted in more than two decades, leaving healthcare leaders and policymakers without evidence-based cost benchmarks to guide strategic workforce investments.

Early studies conducted between 1990 and 2006 estimated per-nurse turnover costs ranging from $11,700 to $31,500, employing methods such as cost accrual curves and learning curve models (Waldman et al., 2004; Wise, 1990). Jones (2004, 2005) advanced this foundational work, developing the Nursing Turnover Cost Calculation Methodology (NTCCM), which became the field’s benchmark. The NTCCM estimated turnover costs between $62,100 and $67,100 per-nurse, revealing previously unrecognized cost drivers. This methodology applies a top-down allocation model that aggregates organizational expenses such as advertising, recruiting, hiring, termination, and divides these totals by the number of nurses who left. While this standardized measurement highlighted the substantial financial impact of turnover, its reliance on aggregate organizational costs limits the precision of individual-level estimates. Although highly influential and frequently cited in both narrative and systematic reviews (Bae, 2022; Li & Jones, 2013), the methodology has not been empirically updated, restricting current understanding of how nurse turnover costs have evolved in today’s healthcare environment.

Therefore, this Part II paper aims to apply the RETAIN Framework to a large, real-world nursing sample to validate its theoretical foundations, update estimates of both per-nurse and total turnover cost and establish a direct link between financial outcomes and modifiable organizational drivers. This approach is intended to produce contemporary, generalizable evidence to inform workforce strategy and reinforce the business case for targeted retention interventions.

Methods

Design & Sample

A retrospective, multi-site analysis was conducted within a large academic healthcare system in the southeastern United States, which has a capacity of more than 3,000 licensed beds, with the study sample drawn from 60 medical–surgical units across seven hospitals. The financial impact of nurse turnover, vacancy rates, and associated labor variability during fiscal year 2023 (October 1, 2022, to September 30, 2023) was assessed. The cohort included 1,501 experienced point-of-care registered nurses, who had 360 turnover events. New graduate nurses were excluded due to their extended residency and orientation periods, which differ substantially from the standardized onboarding process used for experienced nurses. Limiting the analysis to medical–surgical nurses allowed for consistent cost assumptions across the sample, thereby enhancing internal validity and the generalizability of findings. Additionally, inclusion of intensive care or specialty units would have compromised the validity of staffing models, orientation length, and compensation structures, potentially altering the precision and validity of cost estimates.

Data Source

The RETAIN Framework combines objective and subjective data to generate precise and contextually grounded turnover cost estimates. Objective data were drawn from human resources systems (e.g., PeopleSoft, Workday), cost-accounting platforms (e.g., Strata), payroll, and scheduling systems to capture turnover logs, time-to-fill metrics, staffing patterns, and backfill utilization. Vacancy costs and contract labor expenditures were derived from agency invoices and related records. To supplement this, qualitative insights were gathered through four in-depth interviews with nurse managers, used to map workflows, estimate of time and costs of turnover-related tasks, and identify hidden administrative and labor burdens. An additional six nurse managers completed open-ended surveys with the same questions to validate these findings. Interviews and surveys with nurse managers were used solely to validate operational time estimates not represented in administrative datasets. This expert-informed approach was essential for identifying cost drivers and revealing operational nuances not captured in administrative data. Any discrepancies were resolved through consultation with nurse leaders and operational managers to ensure both accuracy and contextual relevance.

The RETAIN Framework

The RETAIN Framework, detailed in Part 1 (Razmpour et al., 2025a), is a structured and adaptable methodology designed to quantify the financial impact of nurse turnover while linking cost drivers to modifiable operational levers. Within this structure, the framework identifies key turnover-related activities, assigns corresponding financial inputs, and attributes resulting costs to the specific departure that generated them, producing a cumulative assessment grounded in actual organizational records. In this present study, the RETAIN framework was operationalized using the same core methodological principles outlined in Part I, including event-level mapping of turnover activities, alignment of those activities with financial and operational data, and application of the nurse turnover continuum to define analytic boundaries. The analysis followed standardized steps, including mapping turnover activities across the pre-separation, separation, vacancy, recruitment, and onboarding phases, and linking each to its corresponding financial inputs. Together, these steps provide a transparent structure for capturing the major components of turnover-related costs and generate cost estimates that more accurately reflect the financial impact of nurse turnover on the organization.

Data collection within the RETAIN Framework is standardized using a conservative approach designed to ensure generalizability across diverse U.S. health systems. The process includes stakeholder mapping to capture key phases of the turnover cycle, such as separation costs, the cost and duration of temporary backfill arrangements, and the onboarding and orientation period of new hires. This structured mapping enables the identification of discrete cost categories and subcategories, supporting a more accurate and comprehensive accounting of turnover-related expenses.

The analytic process follows a three-way structure. Incorporating both objective and subjective data inputs, detailed cost components, and an overall per-nurse turnover cost estimate. Backfill strategies, such as contract labor, internal travel teams, and overtime, are assessed based on cost efficiency and their potential impact on continuity of care. Each activity-level cost is calculated independently and aggregated to generate per-nurse and system-level turnover cost estimates, providing a transparent and replicable structure for financial analysis. This analytic sequence parallels the three-phase structure of RETAIN described in Part I while tailoring the variable set to the multihospital sample examined here. Results are integrated into an interactive executive dashboard, enabling exploration of the financial impact of turnover across various operational scenarios. This functionality allows leaders to directly link changes in workforce policies or practices to projected cost savings, supporting data-driven advocacy for investments in nursing workforce stability.

To place this analytic structure in context, RETAIN adds to earlier approaches by providing activity-level detail within each turnover episode. Prior estimates of nurse turnover costs relied primarily on top-down methodologies that produced aggregate, population-level estimates of turnover-related expenses (Jones, 2004, 2005). These approaches advanced the field by quantifying the overall magnitude of turnover costs and highlighted the need for research to examine the operational activities underlying these expenses (Jones, 2004, 2005). In contrast, broader healthcare finance literature has emphasized activity-based costing, which attributes costs to specific activities that consume resources, and time-driven activity-based costing, which further links these activities to the time required to perform them (Kaplan & Anderson, 2004; Keel et al., 2017). The RETAIN Framework builds on this work, applying a bottom-up, event-level methodology in which individual turnover episodes are mapped, the activities within each episode are identified, and costs are attributed only to the operational steps directly associated with the specific departure. This structure produces cost estimates that more precisely reflect the financial consequences of turnover and clarify the operational drivers underlying variation across units and systems.

Results

During the study period, there were 360 voluntary turnover events, resulting in an overall turnover rate of 24% (Table 1). The nurses included in this sample represented approximately 23% of the health system’s total nursing workforce. The average time-to-fill a vacancy was 21.7 weeks. Average all-in hourly wages (salary + benefits) were $56.24 for core staff nurses, $112.60 for contract nurses, $66.22 for internal travel-team nurses, and $77.63 for overtime. To address these vacancies, backfill was covered primarily by contract labor (75.5%), with smaller proportions met through internal travel-team nurses (11.8%) and overtime (12.7%).

Table 1.

Nursing Workforce Composition and Labor Characteristics

Characteristic	Value
Total Sample
Total number of hospitals	7
Total number of units	60
Total med/surg nurses (headcount)	1,501
Nurse turnover events	360
Turnover rate	24%
Average time to fill vacancy	21.7 weeks
Contact Labor Utilization for Backfill	75.5%
Internal Travel Team Utilization for Backfill	11.8%
Overtime Utilization for Backfill	12.7%
Average Hourly Wage ($)
Core med/surg nurses	$56.24
Contract labor	$112.60
Internal travel-team nurse	$66.22
Overtime rate	$77.63

Per-nurse core turnover costs, excluding backfill labor, are presented in Table 2. Outgoing costs averaged $612 per-nurse, including $488 for pre-separation activities and $123 for separation processes. In contrast, hiring and onboarding costs were substantially higher, averaging $37,094 per-nurse. The largest components in this category were orientation ($22,988) and recruitment ($12,494), followed by advertising ($1,075) and onboarding activities ($538). Overall, hiring and onboarding accounted for 98.4% of the total core turnover cost, whereas outgoing costs represented only 1.6%. The total per-nurse core turnover cost was $37,706. While significant, these costs align with operational realities that are largely driven by orientation length and the associated labor and spending within talent acquisition, including premium pay.

Table 2.

Core Nurse Turnover Costs (Per-Nurse)

Cost Category	Per-Nurse ($)	% of Core Cost
Outgoing Costs
Pre-Separation	$488.16	1.3
Separation	$123.37	0.3
Subtotal: Outgoing	$611.53	1.6
Hiring & Onboarding
Advertising	$1,074.74	2.9
Recruitment	$12,493.58	33.1
Onboarding	$538.38	1.4
Orientation	$22,987.70	61.0
Subtotal: Hiring & Onboarding	$37,094.40	98.4
Core Cost Total	$37,705.93	100.0

For Table 3, Panel A presents the incremental backfill costs per-nurse. Each backfill modality includes three cost components: orientation, onboarding, and wage delta between core staff and the backfill modality. Contract labor was the most expensive backfill strategy, averaging $47,792 per-nurse, comprised of $35 for onboarding, $5,234 for orientation, and $42,522 in wage delta. Internal travel team coverage averaged $23,054 per-nurse, including $538 for onboarding, $9,704 for orientation, and $12,811 in wage delta. Overtime was the least costly option, averaging $6,895 per-nurse, entirely attributable to the wage delta, as there were no orientation or onboarding costs. While overtime is less expensive on a per-nurse basis, it may introduce downstream risks such as staff fatigue, burnout, and diminished productivity that are not captured in direct cost estimates.

Table 3.

Incremental Backfill Costs and Combined Turnover Costs by Backfill Strategy

Panel A – Incremental Backfill Costs Per-Nurse	Onboarding ($)		Orientation ($)	Wage Delta ($)	Total Backfill Cost Per-Nurse ($)
Contract Labor	$35.45		$5,233.88	$42,522.27	$47,791.60
Internal Travel Team 1	$538.38		$9,704.33	$12,811.21	$23,053.91
Overtime 2	$0.00		$0.00	$6,894.59	$6,894.59
Panel B – Combined Core + Backfill Turnover Costs by Strategy		Per-Nurse Turnover Cost ($)		Utilization Rate (%)		Total Annual Turnover Cost ($)
Contract Labor		$85,497.52		75.5		$23,238,225.94
Internal Travel Team1		$60,759.84		12.7		$2,777,939.88
Overtime2		$44,600.52		11.8		$1,894,630.09
Weighted Average Per-Nurse Turnover Cost / Total Annual Turnover Cost		$77,529.99		100.0		$27,910,795.91

¹Internal travel team members incur onboarding and orientation costs only for the first deployment ($23,053.91). Subsequent redeployments require only the wage differential, reducing backfill cost to $12,811.21 and total turnover cost to $49,905.61 per-nurse.

²Overtime backfill assumes no onboarding or orientation costs, as existing staff have already completed required training.

Panel B combines the core turnover costs from Table 2 with the incremental backfill costs, reporting both the proportion of turnover covered by each backfill strategy and the resulting total annual turnover cost. When a nurse vacancy occurs, the health system must rely on one of three primary backfill strategies, each carrying a distinct financial impact. Backfill utilization was distributed as follows: 75.5% contract labor, 12.7% internal travel team, and 11.8% overtime. Covering a vacancy with overtime costs approximately $44,601 per-nurse who turns over, while utilizing the internal travel team increases the cost to $60,760. Contract labor was both the most frequently relied upon and the most financially burdensome strategy for filling vacancies, with each nurse turnover event covered by contract labor costing the health system an estimated $85,498. Given its high cost and dominant use, contract labor substantially contributed to the system’s total annual turnover cost of $27.9 million.

Table 4 presents the incremental financial impact of a ±10% change in five modifiable value drivers identified in the RETAIN Framework: core RN hourly wage, contract RN hourly wage, time-to-fill a vacancy, RN turnover rate, and contract labor utilization. These variables were previously established as operational levers with the greatest influence on total turnover cost. Using proportional metrics rather than absolute changes places each driver on a comparable scale, allowing for a more equitable assessment of their relative financial impact while remaining within a range that is both operationally plausible and managerially actionable. A ±10% change in the core RN hourly wage (±$5.26/hour) was associated with a $4,109 change in per-nurse turnover cost and $1.48 million in total annual cost. The same proportional change in contract RN hourly wage (±$11.26/hour) resulted in the largest impact: an $8,864 change in per-nurse cost and a $3.19 million change in total annual turnover cost. Adjusting the average time-to-fill by 2.17 weeks altered per-nurse turnover cost by $2,649 and total cost by $954,221. A 2.4%-point change in the RN turnover rate did not affect the per-nurse cost but changed the number of turnover events, leading to a $2.79 million shift in total annual cost. Finally, increasing or decreasing contract labor utilization by 7.6 percentage points resulted in a $1,868 change in per-nurse cost and a $672,818 change in total annual turnover cost.

Table 4.

Incremental Impact of Change in Key Value Drivers on Per-Nurse and Total Turnover Costs

Modifiable Variable	Incremental Change	Per-Nurse Cost Impact ($)	Total Cost Impact ($)
Core RN Hourly Wage	±10%	$4,109.45	$1,480,386.80
Contract RN Hourly Wage	±10%	$8,863.64	$3,193,038.62
Time-to-Fill Vacancy	±10%	$2,648.85	$954,221.33
RN Turnover Rate	±10%	$01	$2,792,940.46
Contract Labor Utilization 2	±10%	$1,867.69	$672,818.42

¹Changes in the RN Turnover Rate affect total turnover cost by modifying the number of turnover events, while the cost per-nurse remains constant. Under the RETAIN model, a fixed cost is assigned to each turnover event; thus, increasing the turnover rate increases total cost but does not alter the per-nurse estimate.

²Contract labor utilization reflects the percentage of backfill hours filled by external contract nurses. Any reduction from the current state assumes a proportional shift to internal travel team (ITT) coverage.

Figure 1 presents a series of sensitivity analyses evaluating potential cost savings from simultaneous adjustments to two operational variables. Each analysis modeled deviations from baseline operational values reported in Table 1 to quantify how changes in paired workforce levers impact annual turnover-related expenditures.

Figure 1.

Sensitivity Analysis of Key Value Drivers Influencing Nurse Turnover Costs in the RETAIN Framework

¹Contract labor utilization reflects the percentage of backfill hours filled by external contract nurses. Any reduction from the current state assumes a proportional shift to internal travel team coverage.

Time-to-fill and contract labor hourly wage were analyzed together, as both directly influence the magnitude and duration of high-cost backfill. Baseline values of 21.7 weeks to fill a vacancy and $112.60 per hour for contract nurses served as the reference point. Reducing time-to-fill to six weeks yielded savings exceeding $4.3 million, with additional cost reductions when contract rates also declined. In contrast, longer vacancy durations or higher contract wages substantially reduced savings potential. Turnover rate and contract labor utilization were examined to assess how increased replacement demand, combined with reliance on high-cost contract staffing, amplifies cost variability. At the baseline of 24% turnover and 75.5% contract coverage, costs remained high. Reducing turnover to 16% and lowering contract utilization to 25% resulted in annual savings of more than $20 million. In contrast, increases in either factor, especially when combined, led to steep cost escalations. Lastly, core nurse hourly wage and contract labor hourly wage were analyzed as the wage differential between the two is a primary driver of wage-delta backfill costs. The baseline differential of $56.24 per hour for core nurses and $112.60 per hour for contract nurses fell near the midpoint of the tested range. Narrowing this gap by raising core wages toward $69 per hour while lowering contract wages toward $84 per hour yielded savings of more than $11.5 million, whereas widening the gap resulted in higher costs.

Finally, Figure 2 illustrates the operationalization of the RETAIN Framework through an executive dashboard that consolidates all preceding outputs into a single, interactive platform. The dashboard integrates cost categories, subcategories, total turnover cost, and backfill strategy details, presenting them in a format that can be updated in real time. It features five key value drivers that are modifiable variables: core staff hourly rate, contract labor hourly rate, turnover rate, time-to-fill, and percentage of contract labor utilization, which can be adjusted instantly to recalculate results. The display includes current-state metrics such as annual turnover cost, per-nurse turnover cost, headcount totals, hourly wage rates, clinical hours, and a detailed cost breakdown for contract labor, internal travel teams, and overtime. A built-in “what-if” analysis function allows the user to model alternative scenarios by changing variable inputs, making the dashboard a practical, real-time application of the RETAIN Framework for ongoing monitoring and strategic decision-making.

Figure 2.

RETAIN Framework Modeling Executive Dashboard

Discussion

This study applied the RETAIN Framework to produce updated, event-level estimates of nurse turnover costs and to show how specific operational decisions shape those costs within a large academic health system. By linking financial data to discrete turnover events, the analysis provides a transparent, replicable approach for quantifying the economic impact of turnover and identifying the organizational drivers that influence it. We found that a 24% turnover rate among medical–surgical registered nurses, who comprise just 23% of the organization’s total nursing workforce, resulted in nearly $28 million in annual costs. Notably, contract labor was used to cover more than three-quarters of the backfill hours and accounted for the majority of labor-related expenses. This aligns with previous research that shows contract labor represents a significant portion of hospital staffing costs (Brinster et al., 2023). Specifically, contract labor costs nearly twice as much per-nurse as internal travel teams and more than three times as much as overtime. These findings highlight that turnover costs are not fixed or inevitable; they are heavily influenced by the strategic and financial decisions that determine how vacancies are filled.

The financial burden of turnover is multifactorial, driven by the complex interplay of operational drivers that interact to amplify costs. Sensitivity analyses revealed that achieving meaningful savings requires a multi-layered, strategic approach. The greatest modeled savings, which exceeded $20 million annually, were realized when multiple value drivers adjusted simultaneously, such as reducing turnover rates and decreasing reliance on contract labor. Shortening time-to-fill in conjunction with lowering contract rates or narrowing the wage gap between core and contract nurses also produced multimillion-dollar savings. These findings highlight the importance of designing workforce investments as integrated, system-level strategies rather than isolated interventions. Notable interventions that emerged during the pandemic, such as artificial intelligence and virtual nursing implementation, demonstrate the substantial and measurable impact that coordinated, system-wide approaches can have on strengthening the nursing workforce (Huun & Spalding, 2024; Yakusheva et al., 2025).

Policy Implications

Strengthening leaders’ ability to quantify and interpret turnover costs enables CNOs, finance, and HR teams to plan recruitment and retention strategies proactively and monitor their impact over time. This capability is reflected in how the RETAIN Framework moves beyond measurement to support actionable organizational change. In 2024, the results as seen in the executive dashboard contributed to the evidence base that informed a major system-wide workforce investment: the allocation of approximately $150 million to increase base pay by about $5 per hour or roughly $9,000 annually per full-time employee (Dyrda, 2025). For nurses, this investment not only enhances financial stability but also has the potential to improve overall well-being and reduce the appeal of higher-paying roles such as travel nursing, reinforcing long-term retention within the organization (Dyrda, 2025; Razmpour et al., 2025b). For the health system, the strategy improved market competitiveness, significantly reduced reliance on costly agency staff, from 1,370 contract nurses in March 2023 to fewer than 300, and brought turnover rates back to pre-pandemic levels (Dyrda, 2025). At the community level, these changes could contribute to more consistent staffing and greater continuity of care (Davis et al., 2021). This case illustrates how financial evidence can directly influence organizational policy and underscores targeted salary adjustments as an intentional retention strategy with measurable system-wide benefits (Dyrda, 2025).

At the system-level, these findings highlight the need to frame workforce advocacy as a collaborative effort among nursing, finance, and human resources leadership. Chief Nursing Executives (CNEs) and Chief Nursing Officers (CNOs) provide the strategic and clinical leadership needed to define workforce priorities, creating the foundation for an integrated approach to decision-making. Together, the CNE/CNO, Chief Financial Officer, and the Chief Human Resources Officer form the core partnership that translates workforce needs into actionable plans, aligning clinical priorities with budgetary requirements, compensation structures, and staffing processes. This coordination ensures that decisions regarding salaries, staffing models, and resource allocation are grounded in both operational realities and financial feasibility. Actively engaging these stakeholders and articulating workforce initiatives in the shared language of financial performance, demonstrating both the “I” (investment) and the “R” (return) in ROI, can reposition retention and staffing strategies from being viewed as operational expenses to being recognized as strategic investments.

Our findings emphasize the need for more coordinated efforts to advance understanding of the financial and economic dimensions of nursing. While private consultancies conduct such analyses, their methodologies typically lack external validation. Existing entities such as the Health Resources and Services Administration’s National Center for Health Workforce Analysis, State Nursing Workforce Centers, and the National Council of State Boards of Nursing are well-positioned to expand their mandates to include systematic tracking of key financial metrics such as per-nurse turnover costs, vacancy durations, and contract labor utilization. Incorporating these measures would enable meaningful benchmarking across organizations and regions. Such an expansion would strengthen research, policy, and advocacy by providing credible, actionable evidence for workforce investment decisions at the system, state, and national levels.

Future Implications

Future research should broaden the current model to include additional nursing populations, such as those in specialty units, ambulatory care settings, and advanced practice roles, to capture the full spectrum of turnover dynamics. Conducting multi-site analyses across diverse healthcare systems would provide deeper insight into the complexity of turnover cost drivers and improve the generalizability of findings. These studies could help to identify how different market conditions, staffing models, and organizational strategies influence both the costs and potential returns of workforce investment.

Additionally, one critical gap in current research is the largely unmeasured revenue contribution of nursing. Developing methods to quantify these impacts would enable health systems to fully recognize nursing as not just a cost center but a key driver of organizational growth and financial performance. Future research should aim to quantify how workforce stability contributes to increased patient throughput, improved patient retention, and the expansion of service lines. Additional financial gains may come from avoided revenue losses tied to CMS reimbursement penalties, reduced hospital-acquired condition penalties, and the capacity to support higher procedural volume or bed occupancy rates. Capturing these revenue streams would offer a more comprehensive view of nursing’s contribution to top-line performance and reinforce the business case for sustained strategic investment in the nursing workforce.

Study Strengths and Limitations

This study advances the measurement of nurse turnover costs by integrating a robust theoretical framework with a practical design to inform operational decision-making by nursing leaders. Building on prior work (Jones, 2005) that established the economic importance of nurse turnover, this study employs a bottom-up approach to capture detailed, unit-level cost components linked to specific operational drivers. A major strength of our study is its grounding in a large, multi-site sample and a standardized analytic approach, which adds to internal consistency across all units, enhancing the reliability of findings. The framework’s modular, adaptable design enables health system leaders to precisely quantify turnover costs and model the financial impact of targeted retention strategies.

A further strength of this work is its continuity with the theoretical foundation established in Part I, where Covell’s Nursing Intellectual Capital Theory is embedded within the five-phase RETAIN cycle of assessment, planning, investment, execution, and evaluation (Covell, 2008). Covell distinguishes between nursing human capital, reflecting clinical expertise and institutional knowledge, and nursing structural capital, reflecting the systems and processes that support that expertise (Covell, 2008). This conceptual structure aligns with senior leaders’ strategic decision-making, as investments that strengthen human and structural capital are expected to enhance workforce stability and organizational performance. In this application, RETAIN supported the assessment phase by quantifying workforce trends, illuminating cost drivers, and identifying operational levers that could improve stability. These insights informed the planning phase and guided leadership’s compensation investments, aimed at strengthening nursing human capital and reducing costly external labor. Implementation corresponded to the execution phase, and early outcomes indicate the start of the evaluation phase. This combination of practical applicability and conceptual rigor positions the RETAIN framework as both a research tool and a decision-support resource for workforce planning.

While the framework helps standardize nurse turnover cost measurement, the limitations discussed stem from the empirical implementation and data context rather than the conceptual model itself. First, the analysis adopts conservative staffing assumptions, treating all hours from contract nurses, internal travel teams, and overtime as backfill that would otherwise have been covered by core staff if available. In practice, some premium hours reflect planned surges, elective procedures, or strategic expansions. This approach provides a clear baseline for estimating premium costs, which future studies with more granular data can refine to reflect mixed staffing strategies. Second, the data are from a single, large academic health system. While the organization’s size and diversity of this organization provide a robust sample, findings may not generalize to systems with different structures, sizes, or market conditions. Finally, the analysis focuses exclusively on voluntary turnover among medical–surgical and non-resident units, the group most affected by turnover and travel nurse utilization during the study period. This narrow scope improves comparability and modeling precision, though future research will extend the framework to other units, experience levels, and separation types to capture the full spectrum of turnover dynamics.

Conclusion

The nursing workforce is essential to every healthcare system and represents one of a hospital’s greatest assets. While not all turnover is avoidable, the factors that can be influenced present a challenge that requires strategic action and deliberate investment. This study quantified the annual cost of voluntary turnover among direct-care medical–surgical nurses, identified contract labor as the most expensive backfill strategy, and demonstrated that targeted operational changes can yield multimillion-dollar savings. The findings provide actionable evidence to strengthen the business case for investing in nursing, supported by a standardized, replicable framework for measuring costs and identifying high-impact interventions.

To secure the necessary resources and support, nursing must move beyond adapting to existing structures and instead lead efforts to reshape them, in doing so, redefining the future of healthcare delivery. Finance is the conduit that connects vision to action. By translating workforce needs into the language of budgets, margins, and returns, nursing leaders can position investment in their teams as essential to operational stability and organizational success. Financial awareness in nursing delivers measurable value, not only for individual leaders, but for the teams, systems, and communities they serve.