Longitudinal study links health information technology implementation to improved financial performance in nursing homes

Introduction

Background

Nursing homes house approximately 1.4 million vulnerable Americans who may suffer from a wide array of debilitating chronic conditions that require assistance with activities of daily living (ADL), such as dressing, bathing, and toileting (1). With the aging of the baby boomer generation, the absolute number of persons needing nursing home care is further expected to rise.

While total expenditures for nursing home care are striking in magnitude, the median nursing home operating margin was reported to be only −0.19% in 2021 (2). The precarious financial position of many nursing homes in the United States (U.S.) raises concerns over resident quality of care. Indeed, the variations in the quality of care within the nursing home industry may be exacerbated if lower margin facilities adopt processes to reduce costs at the expense of quality or forgo capital investments that may facilitate higher quality care, such as health information technology (HIT).

Rationale and knowledge gap

There is a broad academic and policy consensus that nursing homes would benefit from the implementation of HIT. It can facilitate access and sharing of resident information, reduce medical errors through applications such as alerts and reminders, and improve practice efficiency resulting in greater revenue capture and reduced costs (3-5). Nursing home researchers have reported that implementing HIT can yield substantial quality of care benefits such as reduced medication errors, fewer urinary tract infections, improved adverse incidence reporting, and increased immunization rates (6-8).

Despite its purported impact on performance, nursing homes lag in HIT implementation due to financial challenges and policy choices (9), including exclusion from the meaningful use (MU) incentive program created by the Health Information Technology for Economic and Clinical Health (HITECH) Act (10). The MU program incentivized healthcare providers to adopt and effectively utilize HIT systems to improve patient care through financial rewards and penalties. Experts and regulatory agencies have explicitly acknowledged the need to increase HIT penetration in nursing homes (11).

Understanding the financial implications of HIT implementation is crucial for nursing homes, as these facilities operate on narrow operating margins and face significant financial barriers. One of the commonly cited barriers to HIT implementation in nursing homes is the substantial capital investment required (12). Nursing homes may face uncertainty about justifying their HIT investments as they operate within narrow profit margins, navigating multiple regulations in a highly competitive and financially constrained environment (13). Empirical studies that have examined the impact of HIT implementation on financial performance have been primarily limited to hospitals, and not nursing homes (14,15). Compelling evidence on a return on investment (ROI) from HIT implementation may be required for reluctant nursing homes to consider investing in these expensive systems (16,17). Therefore, the primary goal of this study was to examine the financial implications of HIT implementation in the nursing home industry.

Understanding the financial implications of HIT implementation is crucial for nursing homes, as these facilities operate on narrow operating margins and face significant financial challenges. Improved financial performance can lead to greater sustainability and the ability to provide higher quality care. Additionally, HIT systems require substantial capital investment, and demonstrating their financial benefits can inform resource allocation and policy decisions for nursing home administrators. Policymakers may not be directly concerned with the financial solvency of nursing homes; however, financial solvency heavily influences the ability of nursing homes to deliver a minimally adequate level of care, which is crucial for the quality of life of residents with chronic physical and mental conditions. The study will provide empirical evidence to policymakers to make informed decisions on supporting HIT adoption in nursing homes, especially given their exclusion from previous incentive programs like the HITECH Act. This study provides a foundational understanding of the financial impact of HIT implementation in nursing homes, serving as a baseline for future research, and offering insights that can potentially guide policy and investment decisions.

The manuscript is structured as follows: we first provide the conceptual framework utilized in this study, followed by the methods section discussing the data sources, measures, and analytical techniques used in this study. The results section presents the findings of our analysis. Finally, the discussion section interprets the results, discusses the limitations of this study, and provides directions for future research.

Conceptual framework

We utilize the resource-based view (RBV) to examine the relationship between HIT implementation and financial performance. RBV posits that organizational resources can lead to firm’s sustained competitive advantage if they are valuable, rare, inimitable, and organization-wide supported (VRIO) (18,19). A firm’s information technology (IT)-based resources may be generic in nature per se; however, they can still result in sustained competitive advantage via tangible organizational resources and intangible IT-related resources (20).

The HIT system (IT infrastructure) adapted to the discrete needs of that facility and HIT-trained staff (human IT resources) can be regarded as tangible organizational resources in nursing homes. Intangible IT-related resources include workflow efficiencies, improved communication and coordination, and quicker access to information. After the introductory phase, the integration of the HIT processes in the workflow and HIT-trained staff, along with management practices, may elevate HIT from a widely available ‘off-the-shelf’ system to a valuable, inimitable, rare, and organization-wide supported resource serving as a source of sustained competitive advantage (21) translating into improved financial performance. The RBV framework has been previously used to explain the performance of hospitals after HIT adoption (19,22).

Superior financial performance may be derived from increased revenues and/or reduced costs. An increase in revenue can result from higher market power: the facility may be able to attract remunerative residents (Medicare/private pay), with HIT serving as a potential ‘signaling device’ indicating higher quality. The increased bed utilization would also lead to improved revenues. Revenues may also increase due to comprehensive capturing of charges and coding accuracy.

Therefore, we hypothesize that:

RBV suggests that efficient resource utilization reduces costs. HIT can enhance workflow efficiency, reduce paperwork, and minimize errors, leading to lower operating costs.

• H1: HIT implementation in nursing homes would be associated with lower operating costs.

Based on the RBV, resources that improve service quality and efficiency can attract more residents and better payer sources, leading to higher revenues. HIT can signal higher quality care, thereby attracting more Medicare or private-pay residents.

• H2: HIT implementation in nursing homes would be associated with higher operating revenues.

Combining the above two mechanisms, HIT can enhance an organization’s performance by streamlining operations, improving accuracy in billing, and reducing errors, leading to higher operating margins.

• H3: HIT implementation in nursing homes would be associated with higher operating margin.

Methods

Data sources

Five datasets for the years 2009–2013 were gathered, verified, and linked: (I) Health Information and Management Systems Survey (HIMSS); (II) Long-Term Care Facts of Care in the U.S. (LTCFocus.org); (III) Area Health Resource Files (AHRF); (IV) Online Survey Certification and Reporting (OSCAR)/Certification and Survey Provider Enhanced Reporting (CASPER); and (V) Medicare Cost Reports (MCR).

HIMSS conducts an annual survey to determine the software and hardware inventory of more than 53,000 healthcare providers across the U.S., including hospitals, nursing homes, ambulatory care centers, and home health agencies (23). The number of organizations targeted annually is not disclosed; however, once an organization is targeted, it will always be targeted in successive years. For nursing homes, the administrator is the target respondent. The HIMSS survey includes questions regarding the implementation of 100 different HIT applications, but the number of applications addressed depends on the relevancy to the type of healthcare organization questioned. For nursing homes, the applications most relevant and addressed in this study included: (I) Clinical Data Repository (CDR); (II) Clinical Decision Support System (CDSS); (III) Computerized Provider Order Entry (CPOE); (IV) Order Entry (OE); and (V) Physician Documentation (PD) (24).

LTCFocus.org included data on health and functional status of residents, the facility characteristics, relevant care policies, and market characteristics in which these nursing homes operate. The AHRF provided county-level market and demographic information. OSCAR/CASPER included nursing home provider information, staffing data and aggregated health information on residents, and survey deficiencies. MCR was used to calculate the nursing home financial metrics included in this study.

Sample

Our final analytical dataset was derived by merging the five datasets at the facility-year levels. The federal provider number [Centers for Medicare & Medicaid Services (CMS) certification number] and year provided a unique identifier for merging nursing home-level datasets (HIMSS, LTCFocus.org, OSCAR/CASPER, and MCR). Additionally, we used the Federal Information Processing System (FIPS) code to merge the AHRF data. Although the HIMSS surveys are sent to the same nursing homes every year, the response rate is not consistent, resulting in an unbalanced panel of 2,404 facility-year observations representing an average 480 unique nursing homes per year [2009–2013].

Measures

Dependent variables

The outcome of interest was financial performance, measured by three different variables: operating cost per resident day (PRD), and operating revenue PRD, and operating margin.

Operating cost represents the costs incurred by a firm in providing services to its customers. Operating cost PRD was calculated by dividing each nursing home’s annual operating cost by its total annual resident days.

Operatingcostperresidentday=OperatingexpenseTotalannualresidentdays[1]

Operating revenue represents the income derived from a firm’s core business operations. Operating revenue PRD was constructed by dividing each nursing home’s annual operating revenue by its total annual resident days.

Operatingrevenueperresidentday=OperatingrevenueTotalannualresidentdays[2]

Operating margin focuses on the core operation of a firm and excludes the influence of non-operating income or costs, and overhead expense. The operating margin was constructed by dividing each nursing home’s annual operating revenue minus annual operating expenses divided by annual operating revenue.

Operatingmargin=Operatingrevenue−operatingexpensesOperatingrevenue[3]

Operating costs and operating revenue were adjusted for inflation using the medical care consumer price index for 2013 (25).

Independent variables

The independent variable of interest was HIT implementation and was measured using a novel HIT Implementation Score (HIS) based on a nursing homes’ response on the following five applications: (I) CDR; (II) CDSS; (III) CPOE; (IV) OE; and (V) PD. A CDR is a real-time transaction processing database of patient clinical information for use by practitioners. CDSS is software designed to assist physicians and other health professionals in making clinical decisions. OE is an electronic form provided to streamline nursing facility operations by replacing faxes and paper forms. CPOE is a more sophisticated type of electronic OE for the treatment of residents. PD is usually communicated over a computer network to the medical staff and to departments such as pharmacy or radiology, as well as to hospitals where nursing home residents are transferred.

For each question on the implementation of the HIT applications, survey responses were: “live and operational”, “not automated”, “contracted/not yet installed”, “not yet contracted”, or “to be replaced”. A nursing home was considered to have implemented the HIT application if its response was “live and operational”, which was then coded as 20; all other responses were considered to be non-implementation, and were coded as 0. To create the overall HIS, the individual application implementation scores were summed, resulting in the HIS ranging from 0 to 100, in increments of 20. To address potential reverse causality or endogeneity, we lagged HIS by 1 year. The practice of substituting an explanatory variable with its lagged value to address endogeneity is prevalent across a wide variety of disciplines in economics and finance (13,26-28) (Table 1).

Control variables

A series of organizational and market characteristics were included in the analyses to isolate the effect of HIT implementation on financial performance (13,29-31) (Table 1). Nursing home organizational characteristics include the following: size (number of beds), occupancy rate, ownership (not-for-profit: 0, for-profit: 1), chain affiliation (independent: 0, chain affiliation: 1), hospital-based, payer mix (percentage Medicare residents, percentage Medicaid residents). Medicaid and Medicare are health coverage programs in the U.S. Medicare is a federal program for individuals 65 years and older, covering post-acute nursing home care, whereas Medicaid is a joint federal and state program that provides health coverage for low-income individuals, including long-term care services in nursing homes. It is important to include the proportion of these residents as Medicaid reimbursements significantly lag Medicare, potentially impact nursing home financial performance (32).

The acuity index is a measure of the care needed by residents in a nursing home. It is calculated based on the number of residents requiring assistance with ADL, such as bed mobility, transferring from bed to chair, as well as those needing special treatment like physical therapy or tube feeding. The acuity index ranges from 0 to 24, where the larger number indicates a resident requiring more specialized care (33).

Characteristics of the nursing home market include the following: market competition as measured by the Herfindahl-Hirschman index (HHI), per capita income, and location (1= urban, 0= rural). HHI is measured as the sum of the squared market shares (based on inpatient days) for nursing homes in a county. HHI is a continuous variable that ranges from 0 to 1 with lower values associated with higher competition—an HHI score of zero would represent perfect competition, while values closer to 1 represent a more monopolistic market (29) (Table 1).

Statistical analysis

Descriptive statistics [mean and standard deviation (SD) for continuous measures, and number and percentages for dichotomous measures] were calculated to examine survey responses and profile nursing home respondents by financial performance measures, HIT implementation, and organizational and market characteristics (Table 2).

Multicollinearity was tested using a correlation matrix. Values greater than five SDs from the mean of the financial performance measures were removed to ensure skewness and kurtosis were between 0 and 3.

To assess the effect of HIT implementation on the three financial performance measures of a nursing home for a given calendar year, three separate panel data multivariable linear regression models were estimated. Facility- and year-level fixed effects were applied to the regression models to account for time-invariant facility- and year-level characteristics.

YFi(t)=αi+γt+β1.HISi(t−1)+β2.Qualityi(t−1)+β3.Controli(t)+μi(t)[4]

F, financial performance (dependent variable);

HIS, independent variable of interest lagged by 1 year;

Quality, nursing home deficiencies (control variable lagged by 1 year);

Control, other control variables (size, occupancy rate, for-profit ownership, chain affiliation, hospital-based facility, percent Medicare, percent Medicaid, acuity index, skill mix, market competition, per capita income, and location);

i, individual facility;

t, year [2010, 2011, 2012, 2013];

µ, error term;

α_i, facility-level fixed effects;

γ_t, year-level fixed effects.

All analyses were performed using STATA 15^®. The alpha was set at 0.05. The study protocol was reviewed by the University of Arkansas for Medical Sciences (UAMS) Institutional Research Board, which classified this study as non-human subjects.

Results

Descriptive statistics

Table 2 presents the descriptive statistics of all the variables used, including HIS, and individual HIT applications.

For the dependent variables, mean operating margin was 7.98% (SD =9.74%), mean operating cost PRD was $256.33 (SD =$67.12), and mean operating revenue PRD was $273.54 ($66.75).

For the independent variable, the mean HIS of the study sample was 58.29 (SD =29.63). CDR was present in 86.80% of the sample; CDSS was present in 57.16%; CPOE was present in 88.11%, OE was present in 32.43%; and PD was present in 28.39%.

With respect to organizational-level control variables, nursing homes in the sample had an average of 127.88 beds (SD =65.36 beds), with an average occupancy rate of 87.86% (SD =10.73%). In total, 62.03% nursing homes were for-profit, 67.57% were chain affiliated, and 3.25% were hospital-based. The sample mean for percent Medicare was 17.13% (SD =12.67%), percent of Medicaid residents was 57.35% (SD =19.82%), acuity index was 11.80 (SD =1.39), skill mix was 0.36 (SD =0.19), with mean total deficiencies of 9.34 (SD =6.31).

For market-level control variables, mean market competition (HHI) was 0.19 (SD =0.23), per capita income was $40,118 (SD =$11,300), and 90.05% nursing homes were in an urban location. The organizational and market level descriptives are similar to the U.S. nursing homes averages, which provides support to the representativeness of our study (13).

Regression analysis

Table 3 provides the results of regression analyses to assess the effect of HIT implementation on nursing home financial performance.

As hypothesized, HIT implementation was associated with a decrease in operating cost by $7 PRD (P≤0.05). We also found that size (β=−0.31, P≤0.05), and higher occupancy rate (β=−0.66, P≤0.01) were linked with lower operating costs PRD. However, nursing homes with fewer total deficiencies (lagged) (β=0.20, P<0.10), a higher percentage of Medicaid residents (β=0.40, P≤0.01), a higher skill mix (β=23.98, P≤0.01), and those that were located in urban areas (β=46.77, P≤0.05) had higher operating costs PRD.

Hypothesis 2 was not supported as HIT implementation was not associated with operating revenue PRD (β=0.03, P≥0.10). Two organizational characteristics were found to be associated with operating revenue PRD: occupancy rate was linked with lower operating revenues (β=−0.51, P≤0.01) while conversely, a higher percentage of Medicaid residents was associated with higher operating revenues (β=0.34, P≤0.01).

Finally, hypothesis 3 was supported. Specifically, a 1-point increase in the HIS was associated with a 2 percentage-point increase in the operating margin (β=0.02, P≤0.05). For-profit ownership was associated with higher operating margins (β=1.70, P<0.10). No other organizational or market characteristics included in the model were found to have a significant association with the operating margin.

Discussion

Utilizing tenets from RBV, we anticipated that HIT implementation in nursing homes would be associated with better financial performance as reflected in higher revenues and improved margins. In addition, we argued that production efficiencies owing to HIT implementation would be reflected in lower operating costs. Our results suggest a nuanced impact of HIT implementation on nursing home financial performance with the positive association limited to operating cost and margin.

Studies suggest that HIT systems may reduce operating costs via enhanced scalability, maintainability, portability, and accessibility to data (34). One of the reasons why these mechanisms may be more pronounced in nursing homes is because their HIT systems are predominantly cloud-based (35), with the resident data storage, clinical management software, and patient being provided by the vendor over the internet rather than on-site which reduces operating costs (36). As the installation and maintenance of cloud-based HIT is performed by the vendor, nursing homes are relieved of those upfront costs and do not require on-site trained IT staff (34). These “averted” costs may also account for cost savings. Additionally, studies on hospitals have demonstrated that HIT implementation can reduce 30-day readmission rates (37). Applying these findings to nursing homes, HIT implementation may lead to cost savings from the Skilled Nursing Facility Value-Based Purchasing (SNF VBP) program by reducing 30-day readmission rates.

Superior operating margin may be derived from increased operating revenue and/or reduced operating costs. Our results suggested that nursing home HIT implementation was associated with lower operating costs. A cost-benefit analysis done on primary care facilities has suggested mechanisms that may reduce operating costs including decreased radiology utilization, lower billing errors, and accurate charge capture (38). Nursing homes that are severely under-resourced may experience cost savings from a reduction in drug-related medical errors and drug expenditures, and, therefore, improved profitability (39). In the case of hospitals, integration of patient scheduling systems, linking appointments directly to progress notes, automating coding and claims management, reducing billing errors, accurate coding, and centralized chart management have been found to improve efficiency and performance in clinical care (15) and reduce costs (40). Some of these mechanisms may be applicable to nursing homes as well. Additional workflow process innovations, such as automation of time-consuming paper-based and labor-intensive tasks that may allow for reduced resource requirements and more efficient use of nursing staff, could play a role as well. Such automation has been shown to reduce direct costs involved in medical transcription and chart pulls, and improved reimbursement coding (38). However, further empirical research is needed to confirm the exact mechanisms and the magnitude of each mechanism. For instance, MCR (Worksheet G2) can be used to calculate savings in drug expenditures due to e-prescriptions and avoided duplicate entries. Such studies have already been done on other healthcare settings (15,40).

Several factors could explain why we did not find a positive association between HIT implementation and nursing home operating revenues. Firstly, it is possible that the timeframe of our study was insufficient to capture the full impact of HIT implementation on operating revenues, as the benefits may take time to materialize. Additionally, variations in the level of HIT implementation across different nursing homes, coupled with differences in operational strategies and market dynamics, might have contributed to the lack of statistical significance. Furthermore, the complexity of the healthcare landscape, including changes in reimbursement policies and shifting patient demographics, could have obscured the direct relationship between HIT implementation and operating revenues. Lastly, unmeasured variables or confounding factors not accounted for in our analysis may have influenced the results.

One of the major challenges that has limited HIT implementation in nursing homes is the precarious economic environment they operate in. Our findings, suggesting a positive correlation between HIT implementation and profitability, provide nursing home administrators with a persuasive incentive to prioritize HIT-related investments. Given their purported impact on nursing home quality (40-43), a business case for HIT implementation would also advance the larger policy goal of delivering high quality care nursing home residents deserve (44).

Policymakers may not be concerned directly with the financial solvency of nursing homes. However, nursing homes’ ability to deliver a minimally adequate level of care is important for positive quality of life for people who are aging or have physical and mental conditions. Nursing homes were excluded from HITECH Act subsidies which has hampered HIT implementation within this sector. Even if subsidies were to be extended to nursing homes, would they have the financial wherewithal required to make the continued investments necessary to maximize their utility? We believe that our findings may be helpful here: a business case for HIT implementation suggests a possible scenario where subsidies motivate the initial implementation whereas the financial benefits ensure that the facilities treat them as an asset, and not a mere regulatory burden, and continue to make the requisite investments.

Limitations

The study presents a few limitations. Firstly, our data are from 2009 to 2013. The evolving technological landscape and operational practices could influence the current implementation and financial impact of HIT systems in nursing homes. Future research should focus on collecting more recent and granular nationwide primary data on HIT implementation to reassess the financial implications in the current context. However, our study provides the most comprehensive analysis available for the impact of HIT implementation on nursing home financial performance. Also, HIMSS data on nursing home HIT implementation has not been released beyond 2013. Therefore, we believe that the study findings are still important and relevant. Secondly, no weights were assigned to the individual HIT applications to indicate one application’s value over another. Calculating a more tailored weight for each HIT application would require making value judgments on their relative importance to nursing home performance, which was beyond the scope of this study. Thirdly, although the sampling methodology of HIMSS is undisclosed, the sample is comparable to U.S. nursing home averages on organizational and market characteristics (13). Finally, it is possible that nursing homes that are financially stronger are more likely to implement HIT applications. While we cannot completely eliminate reverse causality, the use of longitudinal data along with empirical techniques such as facility-level fixed effects and lagged independent variables lend greater credibility and strength to our findings.

Conclusions

Over the last decade, there has been an increasing secular trend in HIT implementation across the U.S. healthcare system. However, due to both policy exigencies and market factors, nursing homes lag other healthcare providers on HIT implementation. Given the imperative to improve nursing home quality, it is essential to increase HIT penetration in the nursing home industry. We believe that our findings, suggesting a sustainable economic argument for HIT implementation, offer a potential rationale for targeted policy efforts, including extending subsidies, to ensure that U.S. nursing homes are no longer the laggards in HIT implementation. For nursing home administrators, the potential financial benefits attached to HIT implementation may be particularly attractive as they struggle with acute workforce shortages and new government mandates such as infection control regulations instituted in response to the coronavirus disease 2019 (COVID-19) pandemic and looming staffing regulations. Given the unrealized potential of HIT in nursing homes, continued policy attention is necessary to address potential barriers to HIT implementation in nursing homes.

Acknowledgments

Funding: None.

Footnote

Provenance and Peer Review: This article was commissioned by the Guest Editors (Robert Weech-Maldonado and Nancy Borkowski) for the series “Healthcare Finance: Drivers and Strategies to Improve Performance” published in Journal of Hospital Management and Health Policy. The article has undergone external peer review.

Data Sharing Statement: Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-24-51/dss

Peer Review File: Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-24-51/prf

Conflicts of Interest: All authors except M.M. have completed the ICMJE uniform disclosure form (available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-24-51/coif). The series “Healthcare Finance: Drivers and Strategies to Improve Performance” was commissioned by the editorial office without any funding or sponsorship. M.M. deceased as of 2023. 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 protocol was reviewed by the University of Arkansas for Medical Sciences Institutional Research Board, which classified this study as non-human subjects.

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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