The relationship between agency nursing staff and nursing home financial performance: a longitudinal study

Introduction

Background

Nursing staff, including registered nurses (RNs), licensed practical nurses (LPNs), and certified nursing assistants (CNAs) are the primary caregivers in United States (U.S.) nursing homes (1). The presence and expertise of nursing staff have been associated with better resident outcomes such as fewer pressure ulcers, reduced emergency department visits, and lower rates of coronavirus disease 2019 (COVID-19) related cases and mortality (2,3).

Nursing homes have long been plagued by chronic nursing staff shortages due to various reasons such as low levels of job satisfaction and onerous administrative burdens (1,4). Additionally, nursing home employment may lack prestige with significantly lower salaries as compared to other healthcare providers, such as hospitals (5). The COVID-19 pandemic only exacerbated the situation with an influx of infections and increased workloads that have accelerated staff dissatisfaction, turnover and attrition (6,7). Nearly one in three facilities have reported staffing shortages (8) with nursing homes estimated to lose 15% of their workforce permanently due to COVID 19 related burnout (9).

Healthcare organizations often respond to staffing challenges by employing contract or agency staff, who are temporary workers provided by third-party agencies to cover workforce shortages across multiple facilities (10). Agency nursing staff can shield nursing homes from sudden shortages due to high turnover or absenteeism (11). Research suggests that the nursing home industry has increasingly relied on agency nurses to meet urgent staffing needs (12). However, the use of agency nursing staff raises significant concerns regarding its potential effects on nursing home performance (13), particularly in relation to financial stability.

Agency nursing staff are significantly more expensive than regular full-time equivalent (FTE) staff—typically adding 50% or more to the hourly rate (11). Considering the intense human resource requirement—two-thirds of an average facility’s costs are related to direct resident care (14)—an increase in labor costs can have a disproportionate effect on nursing home financial metrics. The nursing home industry has continued to face the economic repercussions of the COVID-19 pandemic (15), struggling to balance operational requirements and financial exigencies (16). These challenges have contributed to the industry reporting a median negative operating margin of −0.3% (14). Moreover, the high rates of inflation the United States (U.S.) has experienced in recent years has escalated overall costs for nursing homes, including labor expenses. For instance, external staffing agencies are now charging 22–28% more for agency labor than they did before the pandemic (14). The loss of Public Health Emergency Funding may further challenge the financial viability of nursing homes (17).

Given these factors, the financial strain of employing agency nursing staff may further stretch nursing home budgets (18). Financially distressed nursing homes would have fewer resources available to invest in residents and may even face closures, affecting access to long-term care. Furthermore, work by Pradhan and colleagues (13) has found that agency nursing staff may negatively impact nursing home quality. Poor processes of care and minimally staffed nursing homes may result in increased resident care expenses, stiffer penalties and decreased Medicare reimbursements under value-based purchasing models (19,20). A recent study conducted among U.S. hospitals-found that the use of agency labor may be linked to poorer financial outcomes (21).

As the shortage of nursing staff in the U.S. healthcare system is a long-term phenomenon with no immediate solution (22), nursing homes will continue to rely on agency nursing staff to ameliorate immediate shortages. Solvency is an important concern for a significant proportion of nursing homes (15,17). Therefore, it is essential to empirically examine the impact of utilizing agency staff on nursing home financial performance.

Previous studies that have examined the issue of agency staffing and nursing home financial performance have either been state specific (23) or country specific (Japan) (24). Furthermore, these studies are relatively old and may not account for changing U.S. nursing home landscape, particularly in the post COVID-19 landscape. The dearth of recent research on the impact of agency nursing staff utilization on nursing home financial performance represents a significant knowledge gap. This study aimed to address this issue by exploring how the use of agency nursing staff affects financial outcomes in nursing homes. The findings of this study will enhance understanding of the economic impacts of adopting flexible staffing models in the nursing home industry.

Conceptual framework

We integrated tenets from the Resource Dependency Theory (RDT) and Transaction Cost Economics (TCE) to analyze the impact of agent staffing on nursing home financial performance (Figure 1). RDT posits that an organization’s survival relies on its capacity to obtain, maintain, and efficiently use available resources (25). TCE explores the transaction costs involved in economic exchanges and assesses how these costs shape organizational structures and behavioral pattens (26).

Figure 1 Conceptual model. Higher use of agency nursing staff is theorized to lower financial performance through mechanisms explained by Resource Dependency Theory and Transaction Cost Economics. Environmental and organizational factors are included as control variables to account for external and internal influences on financial performance.

RDT suggests that reliance on external staffing agencies would increase a nursing home’s dependency on external resources, which can lead to vulnerabilities. RDT explicitly characterizes dependence as the extent to which an organization is subject to external control (25). Nursing staff are critical to delivering high-quality resident care, and the reliance on agency nursing staff may amplify the dependence on external staffing agencies. This dependency may hinder the nursing home’s ability to limit costs and maintain operational efficiency.

TCE provides insight into the costs associated with the use of agency staffing. TCE asserts that in exchange relationships, one or both parties will commit specialized investments essential for supporting the inter-organizational transaction (26). TCE has been used in the literature to explain issues such as quality’s influence on consumer choice of nursing home (27) and hospital vertical integration (28).

Thus, both RDT and TCE address nursing homes’ dependence on external organizations, with RDT highlighting the risks of over-reliance on external resources and TCE emphasizing how dependence on a limited number of providers can prevent nursing homes from reducing transaction costs and achieving operational efficiency (29). For instance, reliance on a small number of external staffing agencies can leave nursing homes vulnerable to abrupt price increases and limit their ability to reduce transaction costs (agency fees).

Therefore, the combination of these factors—increased dependency (RDT) and higher transaction costs (TCE)—may negatively affect nursing homes financial performance by elevating both direct and indirect costs. Direct costs may increase as a result of higher fees and wages paid to agencies and temporary staff, while indirect costs may escalate due to potential declines in care quality associated with the use of agency labor (11). Worse quality of care can result in higher healthcare costs, regulatory penalties, and reduced reimbursement rates (13). Additionally, poor care quality can damage the facility’s reputation, leading to decreased occupancy rates and, consequently, reduced revenues (20).

Based on the preceding discussions, we hypothesize that:

Hypothesis: an increase in agency nurse staffing would be associated with poorer financial performance in nursing homes.

We present this article in accordance with the STROBE reporting checklist (available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-24-80/rc).

Methods

Data sources

Our study utilized the multiple publicly available secondary data sets complied by federal and regulatory agencies in the U.S. The PBJ Dataset, overseen by the Centers for Medicare & Medicaid Services (CMS), contains daily, auditable staffing data submitted quarterly by all CMS-certified nursing homes to comply with federal reporting requirements. Financial data were sourced from the Healthcare Cost Report Information System (HCRIS—Medicare cost reports), which includes annual submissions from nursing homes detailing their revenue, expenses, and payer composition. To measure facility quality, we used the CMS’s Care Compare: Five-Star Quality Rating System (Five-Star QRS), which evaluates facilities based on health inspections, staffing levels, and selected clinical quality indicators. We incorporated facility and market-level data from LTCFocus.org, a resource developed by the Brown University Center that aggregates multiple public data streams into a unified database focused on long-term care. County-level variables capturing healthcare market characteristics were obtained from the Area Health Resources Files (AHRF), maintained by the Health Resources and Services Administration.

The CMS Certification Number (CCN) was used to merge the datasets at the facility level, while AHRF data were merged using the Federal Information Processing Standards (FIPS) code. The initial sample included all Medicare and Medicaid certified U.S. nursing homes listed in these databases (n=75,681). Facilities were included if they had active certification status during the study period. Facilities were excluded if they had incomplete records, missing key variables, duplicate entries, or if they were no longer operational, resulting in a final analytic sample of 65,821 facility-year observations, representing an average of 13,164 unique facilities per year over the 2018–2022 study period (see Figure S1 for details on the data merge). Missingness was assessed across all analytic variables, and no variable exceeded the 5% missing data threshold; therefore, complete case analysis was performed (30).

Variables

Dependent variable

The dependent variable in this analysis was the operating margin, a widely recognized financial metric that reflects an organization’s operating profitability. Using HCRIS data, we calculated operating margin by first deriving adjusted operating costs, defined as total operating expenses less all capital-related costs (including expenses for capital building, equipment and interest). After calculating adjusted operating costs, the operating margin was determined as follows: Operating margin = (net patient revenue − adjusted operating costs)/net patient revenue.

Independent variable

The independent variables were the agency nursing staff-to-total nursing staff hour ratios(Agencynursingstaffhours/Totalnursingstaffhours)∗100 for RNs, LPNs, and CNAs calculated at the facility level (12,13). Only nursing staff engaged in direct resident care were included in the calculation.

Control variables

We controlled facility-level and community-level characteristics of a nursing home that may affect its financial performance (15,20). Facility-level control variables include the following: quality (quality domain of the Five-Star QRS), size (resident count), nursing staff intensity is represented by total RN, LPN, and CNA hours per resident day (PRD), chain affiliation (0= free-standing; 1= chain affiliated), ownership (0= not-for-profit; 1= for-profit, 3= government), and occupancy rate. We also controlled for payer-mix, which reflected the percentage of nursing home residents on Medicare, Medicaid residents, and private-pay. Acuity index/risk score reflected the resident’s level of care (31). Resident acuity was assessed based on the number of residents requiring varying levels of assistance with mobility, activities of daily living, and special treatments, as well as the proportion of residents who are bedfast, exhibit dementia, or require assistance with ambulation or transfers. Although facilities with higher resident acuity may require adjustments through case-mix payment systems, these residents often demand greater resource intensity. Resident race/ethnicity reflected the proportion of nursing home residents who identify as Black, Asian, Hispanic, American Indian, and other race.

We controlled for the following nursing home market-level characteristics at the county level: percentage of the population 65 years and older; uninsurance rate (percentage of individuals without insurance); poverty rate; household income; Medicare Advantage (MA) penetration (proportion of Medicare enrollees in a MA plan), and level of competition—as measured by Herfindahl-Hirschman Index (HHI). HHI was calculated by summing the squares of each nursing home’s market share based on nursing home inpatient days, with values ranging between 0 and 1,

HHI=∑i=1Nsi2[1]

Higher values indicate more concentrated markets (less competition), while lower values reflect higher competition.

Statistical analysis

The unit of analysis was the nursing home. We used descriptive statistics to summarize the dependent, independent, and control variables: mean and standard deviation for continuous variables, and frequency and percentage for categorical variables. Given the continuous nature of the operating margin and the panel structure of the data, a multivariable linear regression model with two-way (facility and state) fixed effects was used to examine the relationship between agency nursing staff utilization and nursing home financial performance (operating margin). This approach controlled for unobserved differences between facilities and captured the variability within facilities over time while accounting for the time-invariant unobservable relationships that may explain differences between facilities. We ran separate models for each type of agency nursing staff: RNs, LPNs, and CNAs. To account for the COVID-19 pandemic, we included a control variable that accounted for the years prior to the pandemic (2018 and 2019) and for the years during the pandemic (2020, 2021 and 2022).

We did not find any evidence of multicollinearity among the variables [i.e., variance inflation factor (VIF) ≤5, r<0.8]. Stata 16.1 was used for statistical analysis. Statistical significance was evaluated at a 0.05 or smaller alpha level.

Results

Table 1 shows the summary descriptive statistics for the period of 2018 through 2022. The dependent variable, operating margin, averaged 2.96 for the study period. Among the independent variables, the mean proportions of agency staffing for RNs, LPNs, and CNAs were 3.49%, 4.91%, and 4.50%, respectively. Facility-level characteristics indicated an average size of 84 residents, with most facilities being for-profit (74.47%) and an average occupancy rate of 76.34%. Medicaid accounts for the largest share of nursing home residents at 55.46%, followed by private pay (30.62%) and Medicare (13.92%). Racial/ethnic minorities represented approximately 20% of the resident population.

A separate analysis of the utilization of agency nursing staff showed a consistent increase across all three types of nursing staff from 2018–2022, with RNs and CNAs numbers climbing from 2% to 9%, and LPNs from 2% to 11%. However, this upward trajectory may have reached a plateau in 2022 as the pandemic receded.

Tables 2-4 present the regression results examining the relationship between agency nursing staff and financial performance. Table 2 reports the results for RN agency staff (Model 1), showing that a 1 percentage point increase in agency RNs is associated with a 0.32 percentage point decrease in operating margin (β=−0.32, P<0.001). Table 3 displays the results for LPN agency staff (Model 2), indicating a 0.34 percentage point decrease in operating margin for every 1 percentage point increase in agency LPNs (β=−0.34, P<0.001). Table 4 shows the results for CNA agency staff (Model 3), where a 1 percentage point increase in agency CNAs corresponds to a 0.37 percentage point decrease in operating margin (β=−0.37, P<0.001).

Several control variables were significant across all models. Size, occupancy rate, quality, the proportion of Medicare residents, and lower competition were positively associated with operating margin (P<0.001). Nursing staff hours (RNs, LPNs and CNAs) were all negatively associated (P<0.001) with operating margin. Environmental/market factors, such as the COVID-19, poverty, and MA penetration were negatively associated with operating margin.

Discussion

Key findings

Using tenets from RDT and TCE, the purpose of this study was to examine the association between agency nursing staff use and nursing home financial performance. As hypothesized, the results suggest a negative relationship between agency nursing staff utilization and nursing home profitability (operating margin). The results provide support for our conceptual model.

An important reason for the poorer financial outcomes associated with agency nursing staff may be their short tenures with the typical assignment lasting just thirteen weeks (32). Staff turnover and unfamiliarity with residents’ specific histories, preferences, and care routines may result in inefficiencies, increased errors, and inconsistent care delivery (33). These issues can lead to higher costs associated with correcting errors, addressing adverse outcomes, and managing additional staff training.

Moreover, the significantly higher wages demanded by staffing agencies may also adversely affect nursing home financial performance. For instance, some staffing agencies have reportedly charged pay rates up to 148% higher than those of FTE nurses (34). Labor constitutes one of the largest expenses for nursing homes (14), and the substantial cost of utilizing agency nursing staff may have exacerbated the financial burden. As nursing shortages persist and demand for healthcare services grows, wages for agency labor in long-term care facilities are expected to continue rising (35). Additional pressures, such as the new CMS nursing homes minimum staffing standards, are likely to drive greater demand for nursing staff, particularly RNs (36). The use of agency staffing is typically driven by necessity, but the resulting competition for adequate nursing staff may further inflate labor costs, thereby negatively impacting their operating margins.

The findings of this study contribute to the growing literature examining the utilization of agency labor in healthcare organizations, including its antecedents and consequence (11,21,37-39). For instance, Bowblis and colleagues (11) have shown that the use of agency nursing staff in U.S. nursing homes has increased substantially since 2017, with a sharp acceleration after 2020. In some cases, agency staff have been reported to cost up to 50% more than permanent nursing staff. These elevated costs may have significant financial implications for facilities, which could be reflected in the results of this study. Similarly, Pradhan and colleagues (21) have reported that increased agency labor in hospitals was associated with higher revenues and higher expenses, albeit at increased cost, illustrating the financial complexity of relying on agency labor. Our findings build on this emerging evidence by examining how agency labor relates specifically to financial outcomes in nursing homes. Our results contribute to the growing body of literature that has examined the impact of agency labor on the performance of healthcare organizations. Overall, our results suggest that greater reliance on agency labor may be linked to poorer financial performance in nursing homes.

In a recent study, Pradhan and colleagues (13) have identified a negative association between the use agency nursing staff and nursing home quality. Poor quality not only adversely impacts resident care but can also negatively impact financial performance (20). For instance, it may reduce the organization’s ability to attract financially desirable residents. Agency nursing staff may also disrupt the cohesiveness of the existing nursing team contributing to worse outcomes (33). Consistent with this, we found that nursing homes with higher quality ratings (Five-Star QRS) achieved better financial performance compared to lower-rated facilities. Achieving the optimal balance between staffing, quality, and cost-efficiency remains a significant challenge for administrators, particularly when relying on agency labor.

In summary, this study suggests that agency nursing staff in nursing homes may be associated with poorer financial performance, as measured by operating margin. Possible explanations include higher wages, shorter tenure, and inefficiencies linked to agency labor. The increased reliance of agency staff may also reflect persistent workforce challenges in the nursing home sector such as chronic staffing shortages and high turnover among permanent staff, long-term trends that have become particularly pronounced since the COVID-19 pandemic. Although flexible staffing models can offer certain advantages, administrators should carefully evaluate the inherent trade-offs, such as the decremental impact on nursing home outcomes, including financial performance.

Limitations and future directions

There are some potential limitations to this research. First, as inherent with the use of secondary data, the information was not originally collected to address the specific research question and may lack certain variables necessary to fully test our hypotheses. Additionally, reliance on secondary data introduces other limitations, including the presence of missing values and its retrospective design. Second, the regression coefficients on the relationships between agency nursing staff and financial performance are relatively small. However, they are still practically significant when considered within the context of the nursing home industry’s typically thin operating margins. The nursing home industry is still recovering from the lingering effects of the COVID-19 pandemic, with facilities struggling with increased costs, workforce shortages, and new regulatory demands (15,17). As such, even small reductions in profitability can have significant implications for a facility’s ability to sustain operations and deliver high quality resident care. Third, while examining nursing homes at the national level increases the generalizability of findings, it limits the ability to account for state­ level differences in regulations and other environmental factors. However, the inclusion of facility-level fixed effects in this study should account for these variations by controlling for time-invariant characteristics specific to each nursing home.

Our findings may also be reinforced by conducting qualitative studies or more focused research into different cost/expense categories to understand the specific mechanisms through which agency labor negatively affects financial performance. Finally, additional research is needed to examine the relationships and perceptions of nursing home administrators, nurse staff and residents about agency staffing.

Practice/policy implications

Considering the additional financial burden that agency labor represents, nursing homes should carefully consider their use. However, nursing homes may not be able to fully exclude agency labor given the acute shortage of nursing staff in the US healthcare system. For instance, there is a projected 10% shortage of RNs by 2026 and the shortages are only expected to worsen (40). Nursing staff may also prefer the contractual model due to factors such as flexibility, higher financial returns, and the perceived lack of respect and autonomy in full time employment (41).

Therefore, a more practical approach for nursing home administrators may be to optimize the use of agency labor, while minimizing the potential drawbacks, rather than the elimination of agency labor entirely. This may require a reconceptualization of human resource management systems and employee relations to create a positive work environment. Targeted interventions such as competency assessment and fostering collaborative opportunities that harness the potential of agency nursing staff while safeguarding nursing home performance are crucial. Nursing homes should also prioritize the retention and recruitment of permanent nursing staff. Strategies such as offering greater flexibility in scheduling and work locations, providing career advancement opportunities for CNAs and LPNs, and establishing partnership with staffing agencies could provide viable solutions (1,42).

Policymakers have an important role in encouraging stable staffing models by offering financial incentives, establishing more rigorous and consistent qualification and training standards for agency nursing staff, and providing sufficient funding to support long-term care workforce development (42). A strong collaborative approach between policymakers and administrators is required to build a sustainable, high-quality workforce that can adequately address resident needs while ensuring the long-term financial viability of the nursing home industry.

Conclusions

Nursing staff are critical to the overall performance of nursing homes. In response to persistent workforce shortages, many facilities have increasingly relied on agency labor to fill staffing gaps. However, this study highlights a significant drawback of this strategy, as it imposes additional financial pressure on already constrained operating margins. This additional burden not only threatens the financial viability of nursing homes but may also limit their ability to invest in key quality improvement in initiatives, such as staff training, technology upgrades, and resident-centered programs. Therefore, nursing homes should prioritize the recruitment and retention of permanent nursing staff by implementing targeted and effective strategies. Additional policy support may be necessary to ensure that nursing homes can meet their staffing requirements without an overt reliance on expensive agency labor. Collectively, these steps can strengthen nursing home workforce development—an essential prerequisite for delivering consistent, high quality resident care.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Journal of Hospital Management and Health Policy, for the series “Healthcare Finance: Drivers and Strategies to Improve Performance”. The article has undergone external peer review.

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-24-80/rc

Peer Review File: Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-24-80/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-24-80/coif). The series “Healthcare Finance: Drivers and Strategies to Improve Performance” was commissioned by the editorial office without any funding or sponsorship. R.W.M. served as the unpaid Guest Editor of the series and serves as an unpaid editorial board member of Journal of Hospital Management and Health Policy from March 2025 to December 2027. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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