Nursing home adoption of Alzheimer’s disease special care units and financial performance

Introduction

Nursing homes (NHs) have been operating in a rapidly changing environment. Challenges such as changes in the reimbursement system (1), regulatory reforms (e.g., staffing requirements) (2), increasing competition from alternative providers (3), and changes in the payer mix (i.e., a higher proportion of Medicaid residents) (4) have impacted NHs’ financial viability. For example, the increased competition from alternative providers includes the expansion of the assisted living industry and access to federal and state programs such as Medicaid home and community-based services (HCBS). The number of assisted living facilities (ALFs) has risen since the 1990s. As of 2018, ALFs provide services to nearly 1 million people in the United States (5). Previous research has shown that the growth of assisted living communities has impacted the NH market structure, reducing NH demand (6) and negatively impacting NHs’ financial performance (3). Medicaid community-based and home services spending rose from 43% in 2008 to 56% in 2018 (7). Access and coverage of home health and respite care services for people with dementia provide community-based alternatives to institutional long-term care, affecting NHs’ occupancy rates. A recent study reported lower occupancy levels as one of the main reasons for NHs’ closure between 2008 and 2018 (8).

In response to environmental uncertainty, NHs may adopt strategies to mitigate the adverse effects of competition and improve their financial performance (9). One such approach is care specialization through the adoption of Alzheimer’s disease special care units (AD SCUs) (10). Alzheimer’s disease (AD), a progressive, neurodegenerative brain disease leading to cognitive and functional decline, is the most common reason for NH placement (11). In 2024, 6.9 million Americans are living with Alzheimer’s dementia; prevalence will continue to rise as the population ages (12). Alzheimer’s disease and other dementias (ADRD) affect nearly 58% of long-stay residents in NHs (5). Together, these facts suggest the need for specialized care that serves people with dementia in long-term care settings. AD SCUs typically feature designated areas or a specific number of beds within the facility, and are characterized by tailored structural characteristics (physical environment, staffing training) and care interventions designed for cognitively impaired residents, whether or not they have a confirmed AD diagnosis (13,14). Organizations leverage their unique internal resources to develop market strategies to achieve competitive advantage (15). NHs may utilize their assets and capabilities to adopt an AD SCU as a unique service to distinguish themselves from the competition and improve financial performance. Castle found a positive relationship between the establishment of an AD SCU and private-pay occupancy. These improvements in payer mix and occupancy rate may enhance facilities’ financial performance (10). In addition to changes in payer mix and occupancy rate, implemented payment incentives may lead to AD SCUs adoption (16). For example, Medicaid established higher reimbursement rates for care provided in special care units and dual-eligible residents with a diagnosis of AD (16). Average annual Medicaid payments per Medicare beneficiary with ADRD ($6,771) were 22 times greater than average Medicaid payments for Medicare beneficiaries without ADRD ($305) in 2023 (12). However, prior studies have not yet ascertained the relationship between delivering specialized care with financial performance in the NH industry. Adopting specialized care through AD SCUs might lead to a competitive advantage in the market. Using the resource-based view of the firm (RBV) as a conceptual framework, this study examined the relationship between AD SCU adoption and financial performance among NHs. Our findings provide a better understanding of the attributes and performance differences across organizations in the NH industry. This can equip managers with valuable insights to identify the most effective strategies for enhancing performance within specific market segments.

Conceptual framework

According to the RBV, organizational performance differences are related to variance in the organization’s assets (17). Firms deploy their internal resources to adopt strategies to improve efficiency and effectiveness, and ultimately achieve competitive advantage (17). Resources consist of an attribute, process, or capability that can be considered an asset for the organization, making it different from its competitors (17). These resources are classified as physical capital resources, human capital resources, and organizational capital resources. Physical capital resources include equipment, facilities’ infrastructure, and location. Human capital resources involve staff training, knowledge, and experience. Organizational capital resources include firms’ structure, strategic planning, and systems. Among these resources, RBV highlights human capital resources as particularly unique and difficult to imitate (17).

Building on the core tenets of RBV, this study defined firm resources as the adoption of AD SCU, which are a combination of physical resources (NHs physical structure), human capital resources (specialized care trained staff), and organizational capital resources (care procedures and processes) that can contribute to competitive advantage (10).

RBV has been extensively used in the healthcare industry to explore associations between organizational strategies, environment combinations, and organizational performance (13,18-23). Previous research has found that NHs adopt strategic responses to achieve a competitive advantage (18). For example, a valuable and tangible asset such as the implementation of health information technology has been associated with the superior financial performance of high Medicaid NHs. Similarly, service differentiation has been considered an organizational competitive strategy to acquire a greater share of private pay residents by delivering value-added services (24). Weech-Maldonado et al. (24) reported that NHs with higher process quality (e.g., restorative ambulation and pressure sore prevention) experienced better financial performance.

According to RBV, assets and capabilities that are valuable, rare, and inimitable lead to a competitive advantage over rival firms (17). An organization achieves a competitive advantage by implementing a value-added, profitable approach that is not being adopted by its competitors (8). The adoption of AD SCUs can be viewed as a valuable, relatively uncommon, and difficult to substitute and imitate. First, AD SCUs provide an environment designed to maintain residents’ functional status and minimize behavioral problems (25). Second, AD SCUs have staff specifically trained to meet residents’ needs and prevent and address care resistance behaviors (26,27). Third, although AD SCUs are the most common type of specialized care (13), they are still relatively uncommon, with only 14% of NHs having a designated AD SCU (5). In addition to their lower prevalence compared to other specialized care units (5), adoption of AD SCUs differs by geographic distribution. For instance, the presence of AD SCUs is higher in the North Central and Middle Atlantic regions and metropolitan areas compared to other geographic regions in the U.S. and rural communities (28). Finally, delivering behavioral health services can be challenging due to inadequate infrastructure, care coordination, and staff training (29), making it difficult to imitate effectively.

The RBV theory provides a theoretical framework to explore how specialized care delivery contributes to NHs’ financial performance. Based on this perspective, we posit that the unique structural characteristics of AD SCUs may allow NHs to differentiate from other organizations by providing specialized care for people with dementia (26). This strategy focuses on seeking innovative financial opportunities through revenue-generating services. This study aims to assess whether AD SCU adoption is associated with better financial performance among NHs. Thus, we hypothesize that NHs that adopt an AD SCU will be associated with better financial performance compared to those facilities that do not.

Methods

Data and sample

Data for our study came from three sources. First, Skilled Nursing Facility (SNF) Cost Reports from the Centers for Medicare & Medicaid Services (CMS) sourced from the Healthcare Provider Cost Reporting Information System (HCRIS), offer detailed annual data on financial performance, utilization, and operational characteristics. Second, the Area Health Resources Files (AHRF), maintained by the Health Resources and Services Administration (HRSA), provide county-level data on demographics, socioeconomic conditions, and healthcare resources. Third, data from Brown University’s LTCFocus.org contribute facility-level details on staffing, resident demographics, and operational variables. These datasets were merged at the facility-year level.

The data consist of a longitudinal panel of NHs in the United States over a 14-year period (2006–2019). The sample included freestanding facilities with complete data on financial performance, AD SCU status, and covariates used to construct the propensity score weights. Additionally, hospital-based facilities were excluded from the analysis to focus on freestanding facilities and avoid confounding from operational differences associated with hospital affiliation. Treated facilities were those that introduced AD SCUs during the study period, while non-adopter facilities served as the control group. The timing of treatment is captured using an event time variable, where t=0 represents the year of AD SCU adoption, negative values represent pre-treatment periods, and positive values denote post-treatment years. The reference period for comparison is set at t=−1.

Variables

The dependent variable was the financial performance of NHs. NHs’ financial performance was operationalized as total margin and operating margin. Total margin (net income/total revenue) is an indicator of overall profitability, including all revenues (operating and non-operating revenues) and all expenses (operating and non-operating expenses) (24). The operating margin [(operating revenue − operating expenses)/operating revenue] focuses on the core activities of a business (patient-related activities) and eliminates the effect of non-core revenues such as investment income and philanthropic contributions (30). Total and operating margins have been used in the NH literature as indicators of organizational financial performance (3,31,32).

Control variables include facility-level characteristics such as total beds, ownership status (for profit/not-for-profit), chain affiliation, occupancy rate (percent of occupied beds), payer mix (Medicare, Medicaid, private pay), resident acuity index, and racial composition (percentages of White, Black/African American, Hispanic, and other race/ethnicity residents). Market-level controls at the county level include the Herfindahl index (sum of the squared market shares based on beds) to account for competition, Medicare Advantage penetration rates (percentage of Medicare beneficiaries enrolled in managed care plans) to reflect payer dynamics, per capita income to capture socioeconomic variation, and urban versus rural location to capture differences in demand and supply of long-term care. These variables control for structural and contextual factors that may influence financial performance.

Statistical analysis

A panel event study methodology was used to assess the causal effects of adopting AD SCUs on the financial performance of SNFs between 2006 and 2019. The financial outcomes examined include total margin and operating margin, which are modeled as functions of time relative to the adoption of AD SCUs. This approach builds on the difference-in-differences framework but incorporates dynamic treatment effects to estimate how financial performance evolves before and after the intervention. The event study design is particularly appropriate for this analysis because the adoption of AD SCUs varies across facilities and years, creating a staggered treatment setting that enables the estimation of both pre- and post-treatment effects. To reduce bias due to differences between facilities with and without AD SCUs, propensity score weighting was used to adjust for observable differences in facility characteristics. Propensity scores were estimated using a logistic regression model based on the following variables: total beds, payer mix (Medicare, Medicaid, private pay), urban versus rural location, ownership status (for-profit versus not-for-profit), and chain affiliation (chain affiliated versus independent). Facilities with similar characteristics were assigned comparable weights, ensuring better balance between treated and untreated groups.

Results are presented using event study plots, which display the estimated coefficients for event time dummies and their confidence intervals. These plots provide a visual representation of the timing and magnitude of treatment effects. A P≤0.05 was used as the statistical significance threshold.

Results

Table 1 provides descriptive statistics for the study sample, which includes 176,974 NH-year observations from 13,223 unique SNFs between 2006 and 2019. The average total margin across facilities is 0.84%, while the average operating margin is −1.96%. Approximately 17% of NHs operated AD SCUs. The average facility had 110.40 beds, with an occupancy rate of 82.69%. The sample was predominantly for-profit (76.9%) and urban-based (71.9%), with 60.1% of facilities affiliated with a chain. The payer mix reveals that Medicaid constitutes the largest share of payments (60.34%), followed by private pay (24.92%) and Medicare (14.77%). The racial composition showed that the majority of residents were White (79.41%), while Black/African American, Hispanic, and other race/ethnicity accounted for 9.56%, 2.61%, and 8.43%, respectively.

Table 2 presents the event study estimates for the impact of AD SCUs on total margin and operating margin, controlling for organizational and market-level factors. For total margin, the pre-treatment period showed no statistically significant deviations from the reference period (t=−1), supporting the parallel trends assumption. In the post-treatment period, total margin increased significantly beginning at t=2 (β=1.76, P<0.05) and remained positive and significant through t=4, demonstrating a lagged but sustained improvement in total margin following the adoption of AD SCUs. The results for operating margin indicated a stronger and more immediate effect. Beginning at t=1, operating margin improved by 2.1 percentage points (P<0.01) and peaked at t=2 with a significant increase of 3.2 percentage points (P<0.001). The positive effects remained statistically significant through t=4, again showing the long-term operational benefits of AD SCUs. Figures 1,2 visually depict the dynamic effects of AD SCUs on total margin and operating margin, respectively.

Figure 1 Event study estimates of the impact of Alzheimer’s units on total margin.

Figure 2 Event study estimates of the impact of Alzheimer’s units on operating margin.

Among the control variables, occupancy rate exhibited a positive association with both total margin (β=0.21, P<0.001) and operating margin (β=0.20, P<0.001). Not-for-profit facilities showed significantly lower financial margins compared to for-profit facilities, with total margin decreasing by 2.1 percentage points (P<0.001) and operating margin by 7.95 percentage points (P<0.001). Chain affiliation had mixed effects: independent facilities reported a small but significant positive impact on total margin (β=0.72, P<0.01) but a negative effect on operating margin (β=−0.58, P<0.05). Among payer mix variables, the share of Medicare beneficiaries was positively associated with both total and operating margins, while the Medicaid share showed mixed results. Finally, decreased market-level competition, as captured by the Herfindahl index, had a positive and significant relationship with total (β=1.26, P<0.001) and operating margins (β=2.30, P<0.001).

Discussion

Care specialization can be described as a combination of valuable and difficult to imitate assets to deliver differentiated services in NHs’ markets. NHs may focus on adopting AD SCUs as an innovative financial opportunity to create innovative revenue-generating services. However, to date, to our knowledge, no study has empirically evaluated the impact of AD SCUs adoption on NHs’ financial performance. Based on RBV and using a panel event study methodology, this research aimed to ascertain the effects of AD SCUs adoption by NHs on their financial performance. Specifically, this study sought to determine whether there were financial performance differences associated with the adoption of AD SCUs, as measured by total margin and operating margin.

Our hypothesis that the adoption of AD SCUs would be associated with higher financial performance was supported. In this sample of freestanding NHs, the adoption of AD SCUs was significantly associated with financial performance over time, although the timing and magnitude of these effects differ between total and operating margins. The results reveal distinct temporal patterns in the financial benefits of AD SCUs. For total margin, the positive effects emerge after a lag of one to two years following adoption, with statistically significant improvements observed in subsequent years. This suggests that the financial gains from AD SCUs may be driven by gradual increases in demand, higher reimbursement rates, or operational efficiencies that take time to materialize. In contrast, the operating margin demonstrates a more immediate and pronounced improvement, with significant increases beginning in the first year and peaking within two years of adoption. This indicates that operational adjustments, such as staffing and resource allocation, may yield quicker financial returns, particularly in the context of specialized dementia care, which often commands premium reimbursement rates.

The distinction between total and operating margins underscores the importance of understanding both overall profitability and operational efficiency. While total margin captures the facilities’ overall financial health, operating margin provides a focused view of the core operational impact. The stronger and earlier response observed in operating margin suggests that AD SCUs may enhance efficiency through better resource utilization and patient mix optimization. However, the delayed effects on total margin highlight the role of external factors, such as payer dynamics and community demand, in determining long-term financial outcomes. While there are no prior studies examining the effects of AD SCUs adoption on financial performance, the results for the association between care specialization and revenues align with prior research. Establishing AD SCUs has been associated with higher occupancy rates and private pay occupancy, which may influence organizational performance (10). Despite the potentially higher costs associated with an AD SCU, such as specialized training, increased staffing ratios, and facility modifications, NH administrators may still choose to implement AD SCUs for strategic reasons. This strategy offers a potential dual benefit: attracting private-pay residents and increasing overall occupancy rates. Furthermore, AD SCUs can lead to higher revenue streams due to the higher reimbursement rates associated with caring for residents with specialized needs (16).

Among the organizational control factors, our study found that for-profit NHs had better financial performance than not-for-profit NHs. For-profit NHs generally seek to provide services at a lower average cost to maximize profitability. Higher occupancy rates and the percentage of Medicare residents were positively associated with higher financial performance. Medicare reimbursement rates are higher than Medicaid rates; therefore, a larger Medicare census may compensate for Medicaid reimbursement deficits. Likewise, facilities with higher occupancy rates might maximize their use of resources to increase profitability.

With respect to market control variables, NHs operating in less competitive markets had higher total and operating margins. NHs in more competitive markets may experience higher operating costs due to less munificent environments and scarcity of nurse staffing resources.

This study provides several implications for policymakers, researchers, and NH administrators. Our findings may help NH administrators make informed financial decisions when considering delivering specialized care. For instance, administrators should anticipate a lag in total margin improvements and consider the upfront costs of establishing AD SCUs as an investment in long-term financial sustainability. Policymakers could explore incentives to support the adoption of AD SCUs, particularly in underserved regions, to enhance care delivery while promoting financial stability in the sector.

Our study has several limitations. First, this study used observational data, which introduces potential confounding, even with the use of propensity score weighting and control variables. Additionally, the analysis focuses on financial metrics, and future research could explore complementary outcomes, such as care quality and resident satisfaction, to provide a holistic view of the impacts of AD SCUs. Second, this study relied on secondary data from LTCFocus for the independent variable, which combines data from other sources such as OSCAR & CASPER datafiles. Given that these are self-reported data and collected mainly for reporting rather than research purposes, this might introduce bias in our results due to underreporting or overreporting. In addition, there is a lack of a standardized definition of AD SCU, and thus, adoption may be underestimated.

Our study presents several strengths. First, by using a resource-based view perspective, this study is one of the first papers to explore the relationships between the adoption of AD SCU and NH’s financial performance. Second, this study applied a panel event study methodology, using a national sample over a period of 14 years.

Conclusions

This study contributes to the extant literature on AD SCUs by exploring the relationship between the adoption of AD SCUs and NH financial performance. As NHs seek to deliver high-value care, the adoption of AD SCUs may be a potential strategy to improve financial performance. Our findings suggest that AD SCUs may enhance SNF financial performance, with more immediate and pronounced effects on operating margin and sustained improvements in total margin over time. These findings highlight the financial viability of investing in specialized dementia care and highlight the strategic importance of AD SCUs in meeting the dual goals of financial sustainability and high-quality care. Further research should explore how changing demographics, the racial/ethnic composition of NH markets, and the presence of alternative providers like ALFs influence NH financial performance that adopt AD SCUs.

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.

Peer Review File: Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-24-61/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-24-61/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 the Journal of Hospital Management and Health Policy from March 2025 to December 2027. R.A.J. reports receiving royalties from Amazon for a self-published book and support for conference travel from the University of Alabama at Birmingham School of Nursing. She reports that she served as the Chair of Data and Safety Monitoring Board (DSMB) for a National Institute on Aging (NIA)-funded study and received an honorarium, and she is the owner of Dementia Centric Solutions LLC. G.K.D. is supported in part by the National Institute of General Medical Sciences (grant/award number: U5GM104938) awarded to the University of Oklahoma Health Sciences. 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 the University of Alabama at Birmingham’s Institutional Review Board considered this study exempt from ethical review.

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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