Beyond numbers: a holistic exploration of nursing home staffing patterns and financial performance in the Patient-Driven Payment Model landscape

Introduction

The financial performance of US nursing homes is intricately tied to reimbursements from Medicare and Medicaid, which are key sources of their revenue stream and influence their overall financial performance (1). Poor nursing home financial performance can affect the quality of care for residents and may have negative repercussions for the surrounding community (2). Nursing homes that struggle financially face a higher risk of closure or consolidation (3,4). Changes in state and federal reimbursement policies impact nursing homes industry’s earnings (2,5). The implementation of the Patient-Driven Payment Model (PDPM) on October 1, 2019, represents a significant change with potential implications for nursing home financial performance. To navigate this evolving landscape, nursing homes must comprehensively assess their internal capabilities, formulate improved strategies, and remain attuned to external environmental trends for swift adaptation. A critical internal capability for nursing homes is their staffing patterns/intensity, and studies have shown that changes in reimbursement methodology can impact staffing levels (6,7).

PDPM is a significant reimbursement methodology reform introduced by the Centers for Medicare and Medicaid Services (CMS), replaced the Resource Utilization Group IV (RUG-IV) under the Prospective Payment System (PPS). This change was motivated by a need to address the inherent flaws within RUG-IV that led to unnecessary variations in care and a surge in Medicare expenditures, increasing from $12 billion in 2001 to $29 billion in 2016 (8-11). Under the RUG system, nursing homes were incentivized to provide higher volumes of therapy services, which often led to upcoding, assigning residents to higher paying therapy categories regardless of actual needs (12,13). Mroz et al.’s study showed that the use of more therapy assistants in nursing homes may be linked to profit-driven motives. Higher percentages of therapy assistants were associated with an increased share of ultra-high and very-high rehabilitation RUGs and a high volume of Medicare Part A stays (14). A critical critique of RUG-IV was its emphasis on rewarding therapy services irrespective of residents’ needs, which not only fueled a high reimbursement category but also overlooked the diverse clinical needs of residents (15). This approach significantly contributed to the sharp increase in spending, largely driven by the escalation in therapy services (11). PDPM shifts the basis for reimbursement from the volume of services provided to the clinical characteristics and needs of residents (15). This change reduces the incentives to upcode by linking payment more closely to the complexity and acuity of residents conditions rather than the quantity of the therapy minutes provided (10,11).

PDPM introduces a more refined framework for payment that is broken down into specific components: a nursing component, which is further divided into six nursing case mix categories each associated with different payment levels based on the resident’s clinical characteristics, and therapy components, which include physical therapy (PT), physical therapy assistant (PTA), occupational therapy (OT), occupational therapy assistant (OTA), and speech-language therapy (SLT) (15). These components ensure payment is closely tied to the resident’s conditions and needs such as functional status, cognitive impairment, and comorbidities rather than the quantity of therapy provided (10,11,16). Additionally, this promotes a patient-centered approach to care but also introduces a variable payment rate that adjusts according to the resident’s clinical characteristics, fostering a more tailored reimbursement strategy (10,11,16).

The introduction of the PDPM has necessitated significant adjustments within nursing homes, particularly concerning staffing patterns and the intensity of services offered, to safeguard resident safety and promote well-being (17). Livingstone et al. [2019] underscores the anticipation of increased variability in staffing as a direct consequence of PDPM’s implementation, stressing the critical need for nursing homes to devise effective strategies to adapt to these changes (18). The literature has shown that after the implementation of PDPM, there was a decline in staffing for therapists and their assistants. These studies indicated that the changes were more pronounced among contract employees and were larger in facilities with a higher share of Medicare-eligible short-stay residents (10,19). Research has also underscored the compounded effect of PDPM and coronavirus disease 2019 (COVID-19), revealing that while PDPM initiated a decrease in therapy staffing, the COVID-19 pandemic may have intensified these reductions (20). Moreover, it was observed that facilities engaging in profit-maximizing practices faced more substantial cuts in their staffing levels (20). It is also worth noting the complexity of the post-PDPM environment. Some states enacted minimum staffing ratios for nursing homes, influencing workforce management (21). Additionally, competition from the broader healthcare sector offering higher wages draws qualified staff away from nursing homes, further challenging these facilities to maintain effective staffing under PDPM (22). In response to these challenges, some states issued waivers for specific staffing qualifications, like those for Certified Nursing Assistants (CNAs), to mitigate shortages, albeit at the potential cost of care quality, necessitating significant operational adaptations (21).

Responses to the policy change may vary among nursing homes, with nimble institutions potentially benefiting from financial incentives and achieving better financial performance. This study, grounded in contingency theory, explores whether staffing changes, particularly in nursing and therapy, contribute to improved financial outcomes. Notably, it uniquely examines staffing patterns as a moderator in the PDPM-financial performance relationship. The study’s robust methodology, encompassing facility fixed effects, organizational and market characteristics, COVID incidence rates, and relief funds, are a strength of the study. This study examines a 4-year timeframe, contributing valuable insights to the existing literature.

Conceptual framework

Contingency theory, often utilized by management scholars, examines how well an organization aligns with its external environment to ensure optimal operational fit environment (23,24). A notion of contingency theory is that organizations pursue various strategies in response to environmental changes, and that when the organization’s strategy aligns with external contingencies, better financial performance results (19). Contingency theory also suggests that organizations achieve optimal organizational performance not by maximizing resources but by aligning their internal structures, such as staffing patterns, to external contingencies. This theory is particularly relevant to nursing homes, given their operation within a highly regulated space where strategic decisions are influenced by external factors such as policy changes.

Figure 1 illustrates the conceptual framework for our study, focusing on how nursing homes may have adapted their staffing patterns over time in response to the PDPM. The study centers on the idea that the external environment plays a crucial role, acting as a powerful factor that influences organizational behavior, as highlighted in contingency-based research (25,26). Within this context, PDPM is viewed as a significant external factor, which is the policy change implemented by the CMS. “Staffing Intensity” represents the amount of time spent with the residents by either therapy or clinical staff. For an organization to achieve better financial performance, having the right level of staffing intensity that suits the contingency rather than the maximum level is important (23,27). Financial performance on the other hand is assessed by the operating margin reflecting the financial health of the organization.

Figure 1 Conceptual model—PDPM assessment of nursing home staffing patterns and financial performance. PDPM, Patient-Driven Payment Model.

Nursing homes employ various staffing patterns, including registered nurse (RN), licensed practical nurse (LPN), and certified nurse assistant (CNA) for nursing care, along with PTs, PTAs, OTs, OTAs, and SLTs for rehabilitation services. Each role contributes uniquely to resident care, from overseeing care plans and administering medication to aiding in daily activities and providing specialized therapy. Nursing home administrators and directors of nursing are the primary decision-makers concerning staffing patterns; however, these decisions are influenced by corporate management (in chain facilities) as well as state and federal regulations.

Having the right level of staffing intensity is important to help residents develop, regain, or maintain their ability to perform daily activities in their current environment. An effective organization according to the notion of contingency theory is one that can continue to meet the needs of customers while adjusting its organizational structure such as staffing intensity to align with the external environment changes. For nursing homes, deciding on staffing patterns is a strategic managerial decision that affects both the organization’s structure and its financial performance. To improve overall performance, it is crucial for these facilities to maintain staffing levels that are not only appropriate for their specific environment but also adequate to meet residents’ needs. Proper staffing can significantly improve the quality of care provided and reduce the chances of negative incidents involving residents (28-31). These improvements in care and safety can, in turn, have a positive indirect effect on the nursing home’s revenue (1).

Under the RUG system, nursing homes were motivated to offer a lot of therapy because this led to higher payments from the CMS, thus improving their financial outcomes (32,33). In contrast, PDPM’s financial incentive is centered on the resident’s needs, shorter SNF stays, and less therapy, with resident needs taking precedence over volume (11). Nursing homes that can find the right balance within their staffing patterns may be able to minimize staffing costs and benefit from PDPM financial incentives, which may lead to better financial performance. Staffing hours, which are critical in delivering care in nursing homes, play a significant role in this context (1). Changes in staffing patterns can influence costs and, consequently, the financial health of these nursing homes (34). Although the shift to PDPM requires greater involvement from clinical staff, both clinical and therapy staff need to collaborate closely, focusing on resident needs and outcomes. Therapy services are still provided under PDPM, but the compensation is now based on the individual care needs of each resident rather than the quantity of services delivered.

The contingency theory posits that the success of organizations is influenced by external factors that they cannot directly control (25,26,35). This theory is particularly relevant when considering the impact of the external environment on nursing homes, especially in light of changes brought about by PDPM. PDPM alters reimbursement practices, basing them on the care complexity of individual residents. This means that nursing homes are incentivized to cater to residents with more significant needs, which necessitates more specialized nursing staff (36). On the other hand, since reimbursements under the PDPM are no longer tied to the volume of therapy provided—as was the case with the RUGs, where nursing homes prioritized therapy services to enhance profitability by leveraging the cost-benefit of employing therapists over nurses—nursing homes might be inclined to reduce therapist staffing as a cost-saving measure (10). Given these shifts, we expect that nursing homes that decrease their therapist staffing might see an improvement in their financial performance due to reduced overheads. Similarly, those that increase their nursing staff to better serve residents with higher care needs are also expected to see an increase in financial performance, aligning with the PDPM’s reimbursement incentives for providing more complex care. We therefore propose the following hypotheses:

• Hypothesis 1a: NHs with lower therapy staffing intensity post-PDPM will experience better financial performance.
• Hypothesis 1b: NHs with higher nursing staffing intensity post-PDPM will experience better financial performance.

Methods

Data sources

The study period was 2018 (pre-PDPM) and 2020–2022 (post-PDPM). The year 2019 was excluded from the study sample because PDPM went into effect in October of that year. This study utilized data from eight different sources: CMS Medicare cost reports, Brown University’s Long Term Care Focus (LTCFocus), CMS Payroll-Based Journal (PBJ), CMS Care Compare, Area Health Resource File (AHRF), Provider Relief Fund distribution from the U.S. Department of Health and Human Services (HHS), Nursing home COVID-19 public file from Centers for Disease Control and Prevention (CDC), and COVID-19 Data Tracker from CDC. The Medicare cost report contains payer mix and nursing home financial information. LTCFocus data provides nursing home organizational information such as occupancy rate, acuity index, and racial/ethnic mix. The PBJ was used to measure staffing changes and it contains daily facility-level data on resident census and staffing hours. CMS Care Compare contains facility information such as ownership type, size, star ratings, and expected staffing level. The Area Health Resource File (AHRF) contains market and demographic data at the county level. Provider Relief Fund HHS comprises of financial incentives given to nursing home as a result of the COVID-19 pandemic to alleviate some of the financial hardship they faced. Nursing home COVID-19 public file includes new COVID-19 cases reported by nursing homes to the Centers for Disease Control. Finally, the COVID-19 Data Tracker from CDC reports new COVID-19 cases at the county level. The University of Alabama at Birmingham Institutional Review Board deemed the study a non-human subject.

Study samples

The study sample consisted of all Medicare—and Medicaid-certified U.S. SNFs from 2018 and 2020–2022. The study sample included 62,469 nursing home-year observations. After excluding hospital-based facilities (n=2,427) as their operating environment is different compared to free-standing SNFs, merging with other datasets, and cleaning the data, the final sample that was used for the analysis was 42,698 nursing home-year observations. This represented an average of 10,674 nursing homes per year.

Measures

The dependent variable for Hypothesis 1a and 1b, financial performance was operationalized using the nursing home operating margin (37). The operating margin of a business focuses on the core component of the business and excludes non-operating income and expense from the equation, to determine the true profitability percentage resulting from the revenue and cost associated with residents. Operating margin is calculated as operating revenue less operating expenses divided by operating revenue.

The independent variable, PDPM, was operationalized by using 2018 as the period before PDPM was implemented, and 2020–2022 as the period after the implementation of PDPM. The moderator variables for Hypothesis 1a and 1b, consisted of staffing intensity which is the number of hours clinical and therapy staff spend with residents, and operationalized as the reported therapy and clinical average staffing hours per resident day (HRD) for RNs, LPNs, CNAs, PTs, PTs assistant, OTs, and OTs assistant. To test for moderation, interaction terms of PDPM and the staffing intensity variables were included in the model. Since speech language therapists make up a very small percentage of the therapy personnel in nursing homes, we excluded them from the analysis.

The study included organizational [occupancy, payer mix, acuity index, resident racial/ethnic mix, registered nurse (RN) skill mix] and market characteristics [Herfindahl-Hirschman index (HHI), Medicare advantage (MA) penetration rate, per capita income, percent of individuals 65+] as control variables. The control variables have been widely used in the literature by various studies (1,2,10,19,38,39). The study also controlled for other COVID-19 factor variables. These control variables are used to account for the impact of the pandemic on nursing homes (CARES act relief fund which accounts for the financial assistant nursing home received from the government during the pandemic, and COVID-19 cases which is a measure of new COVID cases at the facility and community level) (40-42).

Occupancy rate represents the number of occupied beds divided by the total number of beds within the nursing home. Payer mix is the percentage of residents whose main support is either one of Medicaid, Medicare, or Private Pay. Given that PDPM may have a larger effect on facilities with a high proportion of Medicare residents, we tested whether high Medicare moderates the effect of PDPM. First, we created a high Medicare dummy variable to represent whether a facility was in the top 25^th percentile of Medicare residents (1= top 25^th percentile; 0= bottom 75^th percentile). Then we included an interaction term of the high Medicare and PDPM variables. The acuity index is a measure of how much care a resident need daily. This metric is based on the number of residents who require varying levels of support such as mobility, activities of daily living (ADL), and specific therapies, as well as the proportion of residents who are bedridden, or have dementia, and need aid with ambulation or transfers. Resident racial/ethnic mix is the percentage of nursing home residents who are White, Black, Hispanic, and other race/ethnicity. RN skill mix is the proportion of RNs to total nursing staff.

The HHI is a continuous variable that ranges from 0 to 1. HHI was calculated as the sum of the squared market shares of nursing homes in a county. When the index is closer to 1, it signifies a less competitive market. MA/managed care market penetration is measured as the percentage of all Medicare beneficiaries in the county enrolled in an MA plan. Per capita income is measured by an individual’s average wealth in the county. The percent of individuals who are 65 and older is the proportion of all residents 65 and older in the county. The COVID-19 control factor variables are only available in the post-PDPM period. The COVID-19 relief funding represents the funds nursing homes received from the government because of the financial hardship they faced during the pandemic. COVID-19 cases were the number of cases at both the facility level and the county level in a given year.

Statistical analysis

We performed bivariate analysis consisting of pre- and post-PDPM comparisons for all included variables. Analyses were conducted using independent samples t-tests and chi-square tests. We modeled the data using a fixed-effect regression, with staffing intensity and PDPM interaction terms to test for moderation. Facility fixed effects accounted for time-invariant, unobservable factors that may influence financial performance, and year fixed effects controlled for time trends. Robust clusters were applied to address correlation within groups at the facility level (43). Stata v17 was used to conduct the analysis of the study and a P value <0.05 was deemed statistically significant.

We also conducted a sensitivity analysis to examine whether the staffing intensity variables had a different moderating effect in facilities with high Medicare. We did this by running regression analyses on two sub-samples: (I) facilities in the top 25^th percentile of Medicare; and (II) facilities in the bottom 75^th percentile of Medicare. Given that the results were largely consistent with those from the overall sample, we report here the results for the overall sample.

Results

Table 1 presents the bivariate statistics for the study sample. Operating margins in nursing homes decreased from 2.14% pre-PDPM to −3.55% post-PDPM. Nursing staffing HRD for RN and LPN increased from 0.39 RN HRD, 0.79 LPN HRD pre-PDPM to 0.40 RN HRD, 0.80 LPN HRD post-PDPM, but decreased for CNA from 2.12 CNA HRD pre-PDPM to 1.95 CNA HRD post-PDPM. Therapy staffing HRD for PT and PT assistant (PTA) decreased from 0.07 PT HRD and 0.10 PTA HRD pre-PDPM to 0.06 PT HRD and 0.09 PTA HRD post-PDPM. In addition, therapy staffing HRD for OT HRD 0.07 and OT assistant (OTA) HRD 0.09 decreased to OT HRD 0.06 and OTA 0.07. Occupancy rate dropped from 79.42% pre-PDPM to 75.37% post-PDPM.

Proportion of Medicaid increased from 56.62% pre-PDPM to 57.00% post-PDPM. Those residents covered by other funding sources decreased from 30.54% pre-PDPM to 29.09% post-PDPM. The nursing home population’s average case-mix (Acuity index) was 12.16 pre-PDPM and 12.12 post-PDPM. Nursing home residents that were Whites increased from 78.41% pre-PDPM to 79.56% post-PDPM, Hispanics also increased from 2.84% pre-PDPM to 3.26% post-PDPM while Black 9.75%, and other race 8.99% pre-PDPM decreased to Black 7.96%, and other race 8.25% post-PDPM. RN skill mix increased from 11.45 pre-PDPM to 12.42 post-PDPM. HHI remained stable at 0.2 over time, indicating highly competitive markets. The proportion of Medicare beneficiaries in MA increased from 33.02% pre-PDPM to 41.93% post-PDPM. Per capita income increased from $49,080 per year pre-PDPM to $63,265 post-PDPM. The percent of individuals who are 65 and older increased from 17.35% pre-PDPM to 18.01% post-PDPM. There were on average about 1,046 total resident confirmed COVID-19 cases per 1,000 residents in the nursing facility, and 10,055 reported cases in the community.

Table 2 presents the effects of the interactions of nursing and therapy staffing intensity with PDPM on financial performance. Hypothesis 1a, which hypothesized that nursing homes with lower therapy staffing intensity post-PDPM will experience better financial performance was not supported. Therapy staffing intensity interaction with PDPM was not statistically significant for PT, PTA, OT, and OTA.

On the other hand, Hypothesis 1b which hypothesized that nursing homes with higher nursing staffing intensity post-PDPM would experience better financial performance, yielded results in the opposite direction of our hypothesis. An increase of one hour RN per resident day, LPN per resident day and CNA per resident day post-PDPM is associated with a decrease of operating margin of −0.04%, −0.03%, and −0.01%, respectively (P<0.001).

The interaction of PDPM and high Medicare was significant, indicating the effects of PDPM varied based on the facility’s degree of post-acute specialization. High Medicare facilities experienced a higher increase in operating margin by 2.1% compared to a decline in lower Medicare facilities by −0.90% (Table 3).

Regarding the control variables post-PDPM, a one percent increase in occupancy rate was associated with a 0.2% increase in operating margin, while a one percent increase in Medicaid payer mix was associated with a −0.03 decrease in operating margin (P<0.001). Facilities with higher RN skill mix reported a lower operating margin while those nursing homes located in counties with greater COVID cases reported a higher operating margin.

Discussion

This longitudinal study examined nursing homes’ responses to PDPM policy change from 2018 to 2022. We classified pre-PDPM as the year 2018 and post-PDPM from 2020 to 2022. The study findings add to the body of knowledge more specifically, gaining an understanding whether nursing and therapy staffing changes are associated with better financial performance post-PDPM. The findings from our study showed that the interaction effects of PDPM and nursing staffing were negatively associated with financial performance, which was contrary to our hypothesis. An increase in nursing staffing intensity for RNs, LPNs, and CNAs was associated with lower financial performance post-PDPM. Given the environmental factor shifts during the pandemic in the cost of care for residents in nursing homes, particularly given the increase in labor costs (44,45), PDPM may not have been sufficient to offset these expenses.

Our study found no statistically significant relationship between therapy staffing intensity and the PDPM in terms of financial performance. This suggests that merely adjusting therapy staffing levels may not lead to improved financial performance. It is crucial to acknowledge the complexity and multifaceted nature of PDPM, as well as the numerous external factors and evolving operational strategies within facilities that influence financial performance. During the COVID-19 pandemic, many administrators addressed critical staffing shortages by cross-training staff. For instance, therapy staff were often reassigned to work as CNAs under the CNA waiver policy (46,47).

The findings from our study indicate that nursing homes were differentially affected by the PDPM based on their Medicare payer mix. Nursing homes with a high percentage of Medicare residents (top 25^th percentile) experienced a 2.1% increase in operating margin, while those with a lower percentage of Medicare residents (bottom 75^th percentile) saw a nearly 1% decline in operating margin. This disparity in financial performance can be attributed to several factors: the ability of nursing homes to adapt to the new payment model, the complexity of care they provide, the prevailing environmental conditions, and their existing resources and infrastructure.

Facilities with a higher proportion of Medicare residents typically have advantages in these areas, contributing to their improved financial outcomes. This is largely due to the distinct differences in profitability and staffing needs between post-acute and long-term care. Post-acute care, primarily covered by Medicare, requires intensive, short-term medical and rehabilitative services, necessitating specialized staff and leading to higher profitability due to Medicare’s higher reimbursement rates. Conversely, long-term care focuses on daily living assistance and ongoing medical care for residents over extended periods. It is less intensive and less profitable, as it is mainly covered by Medicaid, which offers lower reimbursement rates compared to Medicare.

The study revealed that higher occupancy rate results in better operating margins. This can be attributed to the fact that higher occupancy rates generate more revenue from room and board fees, which constitute a substantial portion of nursing home income. Nursing homes have fixed costs such as staffing, rent and overhead, and higher occupancy rates distribute these costs over a greater number of residents, lowering the cost per resident and increasing profit margins.

Our study also found that nursing homes that had a higher proportion of RNs had lower operating margins, this may be likely due to the higher labor costs associated with their employment (44). The challenge for nursing homes is to identify the staffing mix that achieves the best balance between cost and quality. There may be a point beyond which additional RN staffing does not equate to proportional financial gains. Recognizing this balance is crucial for optimizing both financial performance and care quality.

Some managerial and policy implications stem from this study. First, administrators need to reassess their strategy given the constantly changing environment, to ensure they are aligning their staffing patterns to the policy change, but more importantly that they are able to achieve better financial performance by improving their revenue stream. Aligning staffing with resident acuity may be extremely important as PDPM payment rates are based on resident characteristics such as their clinical conditions, comorbidities, and functional status.

Secondly, as PDPM is a new payment system, it may be subject to further changes similar to the RUG methodology. Nursing homes should advocate for policy changes that support the goals of PDPM, such as adequate reimbursement rates, accurate risk adjustment, and support for quality improvement initiatives. This may require engagement with policymakers and other stakeholders to influence policy decisions. Thirdly, nursing homes may need to invest in technological solutions, such as electronic health records and clinical decision support systems, to ensure that documentation and coding are accurate and timely. This may necessitate additional resources and training to implement and maintain such technologies.

Finally, there is a pressing need for nursing homes to improve the accuracy of their cost reports and maintain financial transparency and accountability (48). This aims to prevent deceptive practices such as under-reporting disallowances, concealing profits through related party transactions, transferring assets to Real Estate Investment Trusts (REITs) or affiliates, and inflating losses to minimize taxes, ensuring funds are dedicated to resident care (49).

Future studies should also focus on examining changes in hospital-based SNFs and changes in market structure that may result from payment reforms. Additionally, it will be crucial to pay attention to how nursing homes are billing for services. This will help determine whether nursing homes are altering their behavior over time or continue to employ the same tactics as before PDPM, but more importantly how it affects their survivability. The impact of PDPM on nursing home quality of care is another area that needs more attention. Future research should focus on nursing homes that have a higher proportion of Medicare residents as the impact of PDPM on such facilities may differ.

There were several limitations in our study. The impact of the COVID-19 pandemic on our study data was a limitation of our study. PDPM went into effect on October 1, 2019, and shortly after, COVID-19 struck, having an immediate impact on nursing homes; it is possible our analysis does not accurately depict the genuine effects of PDPM on financial performance and staffing. This is due to the inability to fully account for the true complexity of the pandemic in nursing homes. Although we tried to control for some COVID-19-related variables, there might have been other variables that may not have been included in our controls, such as illness, working conditions, family commitments where schools and daycares were closed, staffing shortages, elective surgery pause shutting off the pipeline of traditional rehab patients, COVID-19 testing frequency, social distancing measures, personal protective equipment (PPE) use, and vaccination status. Additionally, even though we included state variation in our analysis, our model may not have fully accounted for any policy changes within the states during the study period. Since our analysis relied on CMS Cost Reports, there could have been timing differences in the reporting, given that the data is based on a fiscal year. Increasing research has focused on nursing homes concealing profits through related party transactions. This study did not investigate the effects of these transactions on financial performance. However, there is a need for further research in this area. Our study relied on secondary data for our analysis, such as CMS Cost Reports, which are self-reported to CMS by facilities and are not audited routinely.

Interestingly, during the study period, there was a trend of buying and selling nursing homes as investors perceived nursing homes as a lucrative asset (50). We did not account for ownership changes due to the buying and selling, as further research is needed. Lastly, the trend towards using contract nurses, exacerbated by COVID-19, may have presented challenges for nursing homes in balancing costs alongside delivering high-quality resident care, which is crucial under PDPM. Administrators reported using cross-training strategies to address critical staffing shortages during the pandemic (46,47). For example, therapy staff were utilized as CNAs under the CNA waiver. This practice may have complicated the analysis of staffing intensity since the study period included the pandemic.

Conclusions

Overall, our study underscores the complex interplay between staffing patterns, PDPM, and financial performance in nursing homes. Contrary to our expectations, our study revealed that increased nursing staffing intensity post-PDPM was associated with lower financial performance, and there was no significant result for therapy staffing intensity. Interestingly, our study revealed that higher Medicare payer mix (top 25^th percentile) were associated with better financial performance. The differential impact of PDPM based on Medicare payer mix highlights the importance of strategic adaptability and resource management in nursing homes. Facilities with more Medicare residents may have leveraged their advantages to optimize financial performance under the new reimbursement structure, while those with fewer Medicare residents faced greater challenges.

Acknowledgments

Funding: 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.

Peer Review File: Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-24-5/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-24-5/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 2023 to February 2025. R.W.M. reports grant support through University of Alabama at Birmingham for search activities. 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. The study utilized secondary datasets and no IRB was required to conduct this study. The University of Alabama at Birmingham Institutional Review Board deemed the study a non-human subject.

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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