EHR Implementation and Financial Performance: The Case of Under-Resourced (High Medicaid) Nursing Homes


 Background: The nursing home industry operates in a two-tiered system—high Medicaid nursing homes (Medicaid census > 85%) cater disproportionately to marginalized populations, and exhibit both poorer quality of care and financial performance. Despite EHRs’ purported positive impact on nursing home performance, high Medicaid nursing homes may be precluded from implementing EHR due to their precarious financial position. Compelling evidence on a return on investment (ROI) from HIT adoption may be required for reluctant nursing homes to invest in HIT systems. Utilizing the Resource-Based View (RBV), the primary purpose of this study was to understand whether EHR implementation in high Medicaid nursing homes is associated with improved financial performance. Methods: EHR implementation data in high Medicaid nursing homes was collected via mail surveys sent to the Directors of Nursing (DON). We surveyed 1050 high Medicaid nursing homes and received 391 responses. The survey data was merged with the following secondary data sources: Brown University’s LTCFocus, Area Health Resource File (AHRF), and the CMS Medicare Cost Reports. Multivariable regression model was used to understand the effect of EHR implementation on financial performance (total margin), adjusting for both organizational and market-level variables and potential non-response bias using propensity score inverse probability weighting. Results: Approximately 76% of nursing homes in our study sample had either fully or partially implemented EHR. In our multivariable regression model, for every unit increase in total(average) EHR implementation, there was a 3.12% increase in the total margin (p<0.05). Conclusions: High Medicaid nursing homes with higher EHR implementation experienced superior financial performance. Improved financial performance may result from increased revenues and/or reduced costs via the ability to attract more remunerative residents, greater charge capture, improved practice efficiency, documentation management, medication safety, and adverse event reporting.Implications for Policy or Practice: From a policy standpoint, our findings suggesting a sustainable economic argument for EHR implementation offer a strong rationale for targeted policy efforts, including extending subsidies, to ensure that US nursing homes are no longer the laggards in EHR implementation. For nursing home administrators, our findings suggest a potential business case—there may be long-term financial returns for the initial financial burden of EHR implementation.


Introduction
Nursing homes occupy an important position in the US healthcare system, providing services to a population that often has limited physical and cognitive abilities. The nursing home industry operates in a highly competitive and resource-constrained environment and faces several challenges, including nancial viability and regulatory demands [1]. Mor and colleagues (2010) have argued that nursing homes operate in a two-tiered system with high Medicaid facilities (Medicaid census > 85%) reporting lower quality of care as indicated by higher levels of health-related de ciencies and understa ng. These facilities typically report poorer nancial performance, and therefore, face the perfect storm: poor quality of care coupled with limited resource availability. Unaddressed nancial di culties can not only lead to a further decline in quality but ultimately may lead to insolvency and closures, affecting the availability of institutional long-term care in rural and other underserved areas [2].
There is a broad academic and policy consensus that nursing homes would bene t from the implementation of Electronic Health Records (EHR). EHR can facilitate access and sharing of resident information improving resident safety and care coordination, reducing medical errors through applications such as alerts, reminders and clinical decision assists, and improving practice e ciency [3][4][5][6]. Nursing home researchers have reported that EHR can effective at reducing medication errors and urinary tract infections, improving adverse incidence reporting, and increasing immunization rates and resident satisfaction [7][8][9].
Prior literature has associated higher quality in nursing homes with improved nancial performance; facilities that performed better on quality as measured by Donabedian's structure-process-outcome (SPO) model had a higher operating margin [10]. Therefore, EHR implementation in high Medicaid nursing homes has the potential to concomitantly address the two challenges administrators face: quality and nancial performance.
Despite its purported impact on performance, market vagaries and policy choices have ensured that nursing homes lag in EHR implementation [11]. Nursing homes were excluded from the nancial incentives created by the Health Information Technology for Economic and Clinical Health (HITECH) Act [12]. Experts and regulatory agencies have explicitly acknowledged the need to increase EHR penetration in nursing homes [13,14]. Financial concerns are one of the main barriers impeding widespread EHR implementation in nursing homes [15,16]. Installing EHR systems may involve investing in hardware, data migration, training, Information Technology (IT) support, and recurring maintenance costs [17].  [19].
Therefore, we face a conundrum: nursing homes that would potentially bene t the most from EHR implementation may be the ones most reluctant to invest both as a matter of nancial ability and choice. How do we encourage EHR implementation in high Medicaid nursing homes? Short of direct policy interventions, a possible avenue available is to establish a business case by demonstrating its positive impact on nancial performance.
Although several studies have assessed the impact of EHR on hospital nancial performance, [20][21][22] the empirical evidence in nursing homes is extremely limited. Hitt and Tambe (2016) have reported that operating costs were 2.3 percent higher for nursing homes that implemented EHR [23]. However, this study was limited to one state and the data may be out of date.
Our study contributes to the literature in three speci c ways. First, by utilizing primary data collection, we offer a recent, comprehensive, and national-level examination of the nancial impact of EHR implementation on nursing homes. Second, by focusing on high Medicaid nursing homes that serve a higher proportion of racial/ethnic minorities and residents of lower socioeconomic status, our ndings may have a positive impact on the health and well-being of some of the most vulnerable Americans.
Finally, our conceptual model and the employment of a rigorous analytical design, including controlling for organizational and market factors that may in uence nursing home nancial performance, lend greater credibility and strength to our ndings.

Conceptual Framework
We utilize the resource-based view (RBV) of the rm to examine the relationship between EHR implementation and nancial performance. RBV posits that organizational resources can lead to a rm's sustained competitive advantage if they are valuable, rare, inimitable, and organization-wide supported (VRIO) [24,25]. A rm'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 [26]. The EHR system (IT infrastructure) adapted to the discrete needs of that facility and EHR-trained staff (human IT resources) can be regarded as tangible organizational resources in nursing homes. Intangible IT-related resources include work ow e ciencies, improved communication and coordination, and quicker access to information. After the introductory phase, the integration of the EHR processes in the work ow and EHR-trained staff may elevate EHR 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 [27] translating into improved nancial performance.
Superior nancial 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 EHR 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. Reduced costs can result from gains in work ow e ciency, reduced expenditures on paper and transcription costs, and an overall reduction in waste [28-31]. In summary, EHR implementation can result in increased revenues and lower costs, translating into improved nancial performance ( Figure 1).
Therefore, we hypothesize that: H 1 : High Medicaid nursing homes with higher EHR implementation will experience better nancial performance Methods Data: The study was conducted by merging survey and secondary data sources for the year 2017-2018. EHR implementation data was collected via mail surveys sent to Directors of Nursing (DON) in high Medicaid nursing homes in the US. To ensure a higher response rate, we followed a modi ed approach to Dillman's Total Design Method: three rounds of surveys with post-card reminders and follow-up phone calls from November 2017 through March 2018 [32]. All mailings included a link to the online survey. An incentive payment of $25 was provided to survey respondents.
We constructed the analytical sample as follows: 85% or higher Medicaid census, and consistent with the prior approaches, we excluded nursing homes with 10% or higher private pay and/or 8% or higher Medicare (Mor et al. 2004). Our nal sample size was 1050; we received 391 survey completes for a response rate of 37%. We believe that our survey response rate was analogous to studies of this kind.
The survey data was merged with the following secondary data sources: Brown University's Long-Term Care Focus (LTCFocus), Area Health Resource File (AHRF), and the Centers for Medicare and Medicaid Services' (CMS) Medicare cost reports. LTCFocus provided nursing home organizational, demographic, and market data. AHRF was utilized to obtain demographic and market data at the county level.
Medicare cost reports provided data on nursing home nancial performance. The primary and secondary data were merged using nursing home provider ID and year. Variables: Dependent variable: Total (pro t) margin is a measure of overall nancial performance and is de ned as net income divided by total revenue. Net income is calculated as the difference between total revenue and total expenses of the nursing home.
order entry 4. results viewing 5. clinical tools. Administrative functions included processes and reporting such as scheduling systems and clinical task assignments. Documentation included health information and data such as resident demographics and medical history. Order entry had order management information including medication order entry. Results viewing had data on routing, managing, and presenting test results to clinical personnel for review. Clinical tool had decision support system and telemonitoring/telehealth data. The items included in each functionality and their summary statistics are provided in Appendix A. Each item had four response options (0= not available, 1= paper only, 2= paper and electronic, 3= fully electronic). The composite EHR implementation score was the average of administrative (6 items), documentation (9 items), order entry (2 items), results viewing (4 items), and clinical tools (2 items) (Appendix A).

Control variables:
Control variables were identi ed based upon the factors that may affect the nancial performance of a nursing home [1,10] and include the following: organizational-level (size, occupancy rate, chain a liation, ownership, payer mix, use of nurse practitioners/physician assistants, nurse sta ng, acuity Index, and proportion of racial/ethnic minorities), and market/county-level factors (competition/Her ndahl-Hirschman Index (HHI), Medicare Advantage (MA) market penetration, per capita income, unemployment rate, location, poverty level, education level, and percent of population 65 and older).
In terms of organizational factors, size captured the total number of beds within the nursing home. Occupancy rate was the percentage of occupied nursing home beds. Chain a liation re ected whether the nursing home was part of a chain (0= freestanding; 1= chain a liated). Ownership identi ed whether a nursing home was for-pro t (0=for-pro t), not-for-pro t (1= not-for-pro t) or government-owned (3= government-run). Payer mix was the proportion of the residents covered by Medicaid, Medicare, or private pay. Whether nurse practitioners/physician assistants were present at the nursing facility was coded as Yes (=1) and No (=0). Nurse sta ng re ects four measures: Registered Nurse (RN) sta ng mix, RN hours per resident day (PRD), licensed practical nurse (LPN) hours PRD certi ed nursing assistant (CNA) hours PRD. The acuity index is an average measure of the resident's level of care needed. This measure was based on the number of residents needing assistance with various activities including mobility and activities of daily living (ADL). Proportion of racial/ethnic minorities was the proportion of nursing home residents who were Black, Hispanic, and other race/ethnicity.
In terms of market/county-level factors, market competition is measured as the sum of the squared of the market shares (based on beds) 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 close to zero would represent perfect competition. MA market penetration was calculated as the proportion of all Medicare bene ciaries in the county who were enrolled in a MA plan. Per capita income is a measure of the average income of individuals in a county. Unemployment rate was the percentage of individuals in the county who were unemployed. The location variable was coded as urban and rural where the urban was the reference category. Poverty level is percentage of persons in the county in poverty. Education level is the percentage of persons in the county aged 25 or above with a high school diploma or more. Percentage of population age 65 and older is calculated at the county level with total county population as the denominator.
Data analysis: To adjust for potential non-response bias, we included propensity score weights in the regression analysis [33]. The propensity score weights were calculated as the inverse of the propensity scores for nursing homes that participated in the survey. To estimate the propensity score, we used a logistic regression model where we regressed respondence status (respondent=1, non-respondent=0) on the following variables: size, ownership status, chain a liation, payer mix, acuity index, occupancy rate, race/ethnicity, RN sta ng mix, RN hours per resident day, LPN hours per resident day, CNA hours per resident day, Medicare Advantage market penetration, per capita income, poverty, unemployment, education, competition (HHI), location, and percent of individuals over 65. Then we calculated the inverse of the propensity score, the propensity score weight to include in the models.
Multivariable linear regression was used to model the relationship between EHR implementation and nancial performance, adjusting for both organizational and market level variables. Two models were run: rst, the association of total (average) EHR functionalities with total margin; and second, the association of individual EHR functionalities with total margin. Stata 16 was utilized for data management and analysis, and statistical tests were evaluated at the 0.05 level of signi cance. The study was approved by the Institutional Review Board of the University of Alabama at Birmingham (IRB-140828005) and informed consent was taken from all the survey participants. [34] Results Figure 2 shows the status of Electronic Health Record (EHR) implementation in our sample. Approximately 44% of nursing homes in the study sample had a fully implemented and operational EHR, 32% of nursing homes had a partially implemented and operational EHR, 11% of nursing homes were planning to implement EHR in the future, while 7% nursing homes currently did not have a plan to implement EHR. Therefore, approximately 76% of nursing homes in our study sample had implemented EHR-either fully or partially. Figure 3 shows EHR implementation among nursing homes in our sample in terms of the ve functionalities. While 46% nursing homes had fully implemented EHR for the more mundane administrative tasks, only 13% had implemented EHR for clinical tools (13%).   Table 2 presents the results of the regression analysis for EHR functionalities on total margin. For Model 1, with total (average) EHR functionalities as the independent variable, every unit increase in total (average) EHR implementation was associated with a 3.1% increase in total margin (p<0.05). In Model 2, there was no signi cant relationship between individual EHR functionality implementation and total margin (results not shown). With respect to the control variables, total margin was 0.13% points higher for a unit increase in occupancy rate (p<0.001).

Discussion
The primary purpose of this study was to understand whether EHR implementation in high Medicaid nursing homes is associated with improved nancial performance. We focused on high Medicaid nursing homes because they are classi ed as the "lower tier" among the nursing homes: experience poor quality of care, are under-resourced, and disproportionately serve the poor and the minorities. [35]. Our study provides encouraging new evidence that these nursing homes "at-risk" for closure may be able to improve their scal situation through EHR implementation.  [21]. Nursing homes could potentially justify greater reimbursement adjustment rates by improving coding e ciency, fully utilizing reimbursement methods, and appropriately recording comorbid illnesses (Britton, 2015). Documentation management, medication safety, adverse event reporting, reduced time to access resident data, automated coding, and claims management are some of the elements that may enhance e ciency and lower costs in nursing homes. [9]. Savings in operating expenses also could stem from a reduction in redundant tests, costs related to paper records, chart pulls, and transcription [30,31,41]. Finally, we also speculate that nursing homes may be able to achieve a more remunerative patient pro le by taking advantage of EHR implementation to signal superior quality enhancing their market power.
This study should provide cautious optimism to administrators of high Medicaid nursing homes wrestling with the decision to invest in an EHR system while navigating a nancially precarious climate.
Our ndings suggesting a positive correlation between EHR implementation and pro tability provides nursing home administrators with a persuasive incentive to prioritize EHR-related investments. It's important to note that our data shows a signi cant gap between EHR implementation for relatively simple administrative tasks and more sophisticated clinical tasks that necessitate a higher level of employee training. Therefore, it is critical to emphasize that to realize the full potential of EHR systems, healthcare organizations must continue investing in IT infrastructure and human resources.
Policymakers and regulators may not be concerned directly with nursing homes' nancial performance but their future viability and the ability to deliver a minimally adequate level of care are certainly important considerations. Nursing homes were excluded from HITECH Act subsidies which has hampered EHR implementation within this sector [11]. However, policymakers may have a genuine concern: even if subsidies were extended to nursing homes, would facilities, especially those with high Medicaid census, have the nancial wherewithal required to make the continued investments necessary to maximize their utility? We believe that our ndings may be helpful here: A business case for EHR implementation suggests a possible scenario where subsidies motivate the initial implementation whereas the nancial bene ts ensure that the facilities treat them as an asset and not a mere regulatory burden and continue to make the requisite investments. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. 44.3% nursing homes (blue) had a fully implemented and operational EHR. 31.5% (green) nursing homes had a partially implemented and operational EHR. 6.5% (red) nursing homes had selected a vendor and signed vendor contract but did not have an EHR in operation. 10.9% (yellow) of nursing homes were planning to implement EHR in the future. 6.8% (gray) nursing homes currently did not have a plan to implement EHR.

Supplementary Files
This is a list of supplementary les associated with this preprint. Click to download. AppendixA.docx