Electronic Information Systems Use in Residential Care Facilities

Abstract

The use of electronic information systems (EISs) including electronic health records continues to increase in all sectors of the health care industry. Research shows that EISs may be useful for improving care delivery and decreasing medical errors. The purpose of this project is twofold: First, we describe the prevalence of EIS use among residential care facilities (RCFs), and second, we explore utilization differences by ownership status and chain affiliation. We anticipate that RCFs that are non-profit and non-chain will use more EIS than other categories of RCFs. Data for this project come from the 2010 National Survey of Residential Care Facilities. The sample consists of 2,300 facilities. Overall use of EIS was greatest among RCFs that are non-profit and chain-affiliated. Conversely, the use was lowest among for-profit RCFs that were also non-chain affiliated. This may suggest that these facilities lack the necessary resources or motivation to invest in information systems.

Keywords

information systems residential care facilities electronic health records

Introduction

The use of electronic information systems (EISs) including electronic health records (EHRs) continues to increase in all sectors of the health care industry. Research shows that EISs are crucial for improving care delivery and decreasing medical errors (Krüger, Strand, Geitung, Eide, & Grimsmo, 2011). However, much of this research focuses primarily on hospitals and physicians and, to a lesser extent, nursing homes, home health care, and hospice providers (Iglehart, 2005; Issel, Ford, & Menachemi, 2012). Hence, there is a clear gap in the literature describing EIS use among residential care providers in general. The purpose of this project is twofold: First, we describe the prevalence of EIS use among residential care facilities (RCFs), and second, we explore utilization differences by ownership status and chain affiliation. Prior research has shown that ownership and chain affiliation have a differential effect on the structures and processes of health care providers (Anderson, Weeks, Hobbs, & Webb, 2003; Kruzich, 2005; Weech-Maldonado et al., 2012).

EISs Prevalence and Utilization

It is well established that hospitals and physician offices are technologically driven. For example, recent literature found that approximately 73% of hospitals in the United States use some type of EIS (Abraham, McCullough, Parente, & Gaynor, 2011). Likewise, one study reported that nationally, approximately 68% of family practice physicians have implemented an EHR (Xierali et al., 2013). In addition to EHRs, physicians most commonly use technology for billing, as well as computerized access to laboratory results (Audet et al., 2004). Some of the reasons behind the steady increase in use by hospitals and physician office practices are the positive association between technology, such as computerized physician order entry, and improvements in quality of care (McCullough, Casey, Moscovice, & Prasad, 2010), cost savings, the need for greater efficiency, and requirements by third-party payers to submit bills electronically. Acknowledging the importance of EHRs, in 2009, President Barack Obama signed the Health Information Technology for Economic and Clinical Health Act into law (Health Information Technology for Economic and Clinical Health Act, 2009). This act uses a “carrot and stick” approach to encouraging the use of EHRs by providing financial incentives for the use of these technologies from 2011 to 2015. After 2015, facilities not using EHRs may be penalized. Although long-term care providers are not covered under these incentives, this act signifies the direction that EHRs are headed.

Research across various health care settings has suggested many positive outcomes from EISs implementation. Abernethy, Wheeler, and Bull (2011) found that using a health information technology-based infrastructure helped to generate better understanding of residents and their outcomes, and supported quality assessment and improvement. The Munyisia, Yu, and Hailey (2011) study noted that nursing home staff reported that EIS use led to records that were more accurate and legible, with complete information. EISs also improved communication between providers, consultants, hospital, nursing staff, and patients (Brandeis, Hogan, Murphy, & Murray, 2007; Lindner, Ben Davoren, Vollmer, Williams, & Seth Landefeld, 2007).

Despite the benefits touted by some users and researchers, other studies report contrary findings. DesRoches and Rosenbaum (2010), for example, found that there is a lack of clinical and statistical significance between EHRs and hospital quality and efficiency. Keyhani et al. (2008) found a lack of association between this technology and patient care (such as management of chronic conditions). Munyisia, Yu, and Hailey (2012) found that implementing EIS did not consistently reduce the amount of time nurses spent on documentation. Furthermore, there are variations in the organizational characteristics of users versus non-users. The Jha et al. (2009) study reported that larger hospitals, teaching hospitals, and those located in urban areas are more likely than smaller, non-academic, and rural hospitals to use electronic records. These findings suggest there are still barriers that need to be addressed as the industry moves toward more wired providers.

Nursing homes, like other health care providers, are now moving toward greater use of information systems despite the various challenges. For example, Resnick, Manard, Stone, and Alwan (2009) and Davis, Brannon, and Whitman (2009) found that the majority of U.S. nursing homes have already implemented and utilize electronic record-keeping tools. Although some of the EISs used by nursing homes are in response to federal reporting requirements, that is, the Minimum-Data Set system, the Certification and Survey Provider Enhanced Reporting system, and the Quality Improvement Evaluation System, 80% of nursing homes use EISs for other activities such as admissions, transfers, and discharge (Resnick et al., 2009).

To date, data on EISs’ presence and use by RCFs have been lacking. This project seeks to fill this gap. Furthermore, little is known about the association between ownership status, chain affiliation, and EIS use within RCFs.

Ownership Status and Chain Affiliation

Ownership status is a frequently studied dynamic in the health care industry. There are several theoretical and empirical studies in the literature addressing the motivating principles guiding profit and non-profit facilities’ behavior (Schlesinger & Gray, 2006; Vitaliano, 2003). According to the seminal theories of Weisbrod (1988), (Bays, 1983), and Hansmann (1980), non-profits may focus on aspects or qualities of a service that for-profit firms may fail to adequately address. For instance, a review by Hillmer, Wodchis, Gill, Anderson, and Rochon (2005) of 12 years of research on differences in quality of care by profit status shows a strong trend toward non-profit nursing homes offering better quality of care than for-profit nursing homes. Conversely, however, for-profit facilities tend to exhibit greater efficiency and are more cost-effective. Studies by Fizel and Nunnikhoven (1993) and Knox, Blankmeyer, and Stutzman (2006) both found that for-profit nursing homes were more efficient than non-profits. Luksetich, Edwards, and Carroll (2000) reported that for-profit nursing homes spend less per resident day than non-profit nursing homes. When it comes to EISs use, Resnick and Alwan (2010) reported that non-profit home health and hospice providers used more electronic medical records (EMRs) than did for-profit providers.

Researchers have found that the benefits of membership in a multi-facility chain may allow organizations better access to additional resources, knowledge, skills, capital procurement, shared labor costs, and various care technologies (Castle, 2001; Fizel & Nunnikhoven, 1993; Zinn, Mor, Castle, Intrator, & Brannon, 1999). In addition, chain affiliation may reduce the cost of capital, facilitate the transfer of knowledge between member facilities, and allow for greater economy of scale if the applications are centrally located and managed. Despite the potential benefits of chain membership, some researchers have noted that non-chain facilities may be better able to meet market demands (Anderson et al., 2003). For example, Resnick et al. (2009) reported that non-chain nursing homes benefit from greater decision-making independence than did chain-affiliated facilities. This type of decision making permits them the freedom to choose which projects to implement. Therefore, non-chain facilities may be free to implement whatever services or activities deemed useful.

To summarize, the literature suggests that there are key differences in facilities by ownership status and chain affiliation. It may be that, among other benefits, access to financial resources may increase the likelihood of one category of provider experiencing a differential outcome as compared with other providers (Amirkhanyan, Kim, & Lambright, 2008). Thus, we hypothesize as follows:

Hypothesis 1: Non-profit RCFs will be more likely to use EIS focused on clinical care, pharmaceuticals, and EIS for residents and guests than will for-profit RCFs.

Hypothesis 2: For-profit RCFs will be more likely to use EIS for electronic communication than will non-profit RCFs.

Hypothesis 3: Non-chain affiliated RCFs will be more likely to use EIS for clinical care, pharmaceuticals, and EIS for residents and guests than will chain-affiliated RCFs.

Hypothesis 4: Chain-affiliated RCFs will be more likely to use EIS focused on electronic communication than will non-chain affiliated RCFs.

Hypothesis 5: RCFs that are both non-profit and non-chain affiliated will be more likely to use EIS for clinical care, pharmaceuticals, and residents and guests than will any other chain affiliation by ownership category of RCFs.

Hypothesis 6: RCFs that are both for-profit and chain-affiliated will be more likely to use EIS for electronic communication than will RCFs of any other chain affiliation by ownership category.

Method

Data for this project come from the 2010 National Survey of Residential Care Facilities (NSRCF) in the United States.

The RCFs . . .

. . . included are residential care facilities; assisted living residences; board and care homes; congregate care enriched housing programs; homes for the aged; personal care homes; and shared housing establishments that are licensed, registered, listed, certified, or otherwise regulated by a state. Eligibility also requires that these communities offer help with personal care or health-related services, provide two meals a day, around-the-clock supervision, and serve an adult population. Residences licensed to serve exclusively persons with mental illness, mental retardation, or developmental disabilities are ineligible. (Centers for Disease Control and Prevention, n.d.)

The NSRCF is a national probability sample survey of U.S. residential care services providers (http://www.cdc.gov/nchs/data/nsrcf/2010NSRCF_SurveyMethodologyandDocumentation.pdf). The study used a stratified two-stage probability sampling design with facility selection occurring in the first stage. The nature of the data collection allows for generalizability to the population of RCFs (N = 39,635). The final sample within the data set consists of 2,302 facilities. However, two facilities failed to answer one or more of the survey questions specific to information systems use. Therefore, the final sample for this project was 2,300 (weighted n = 31,089). The data file contains information on facility characteristics; type of staff employed; and policies on admission, retention, and discharge.

Variables

The respondents were asked, “What is the type of ownership of this facility?” The responses were collapsed into two groups: Non-profit and for-profit RCFs. Non-profit RCFs, coded 0, were owned either privately or by state, county, or local government. The publicly available data did not allow for separating government-owned RCFs from privately owned non-profit RCFs. Privately owned for-profit RCFs were coded 1.

The chain affiliation question asked, “Is this facility owned by a chain, group, or multi-facility system?” The choices were simply yes (coded 1) or no (coded 0).

The next series of questions on EISs were each coded dichotomously with positive responses indicated by the score of 1. The survey included a list of 31 EISs-related items, which we grouped into four categories: EIS for Clinical Care, Pharmaceutical EIS, Electronic Communication, and EIS for Resident and Guests.

EIS for clinical care

Respondents were asked, “Other than for accounting or billing purposes, does this facility use Electronic Health Records? This is a computerized version of the resident’s health and personal information used in the management of the resident’s health care.”

This was followed by a general question about the facility’s computer capabilities: “Other than for accounting or billing purposes, does this facility have a computerized system for its Resident Service Records to keep track of the services provided to each resident?” The next series of questions focused on specific tasks available in the computerized system. Respondents were asked whether the facility’s computerized system had the capabilities for 12 different tasks.

Pharmaceutical EIS

Included in the list of computerized capabilities available to the facility were five different types of EIS focused on pharmaceuticals. RCFs were asked whether their EIS offered medication administration, maintaining lists of residents’ medications, maintaining active medication allergy list, orders for prescriptions, warning of drug interactions, or contraindications.

Electronic communication

The survey then delved into electronic communication by asking whether the facilities’ computerized system supports electronic health information exchange with 10 different providers or entities. Respondents were also asked whether the facility “. . . staff used any system for Electronic Point of Care Documentation? This includes PDAs (Personal Digital Assistants), Notebook PCs, or other portable.”

EIS for residents and guests

Finally, there were two questions specific to Internet access for residents and guests: “Does this facility offer Internet access in resident rooms and apartments?” and “Does this facility have public Internet access elsewhere in the facility?”

Analysis

Of the 3,605 facilities chosen for the original sample, 1,303 were excluded because they were either ineligible, provided services not consistent with the definition of “residential,” their eligibility could not be determined, or the facility contact person refused to participate (see Moss et al., 2011, for a full description of non-respondents). In addition, according to the information available with the data, some of the missing data were imputed or gathered from other sources (e.g., facility websites; National Center for Health Statistics, 2011). To limit the bias from the differential response rate, a non-response adjustment factor was included in the calculation of the facility-level weights.

All analyses were conducted with the complex survey procedures—the SVY command—available in STATA Version 12. This procedure takes into account the strata, cluster, and weight variables that make up the sampling approach of the NSRCF. Because this study is descriptive in nature, the data were analyzed using chi-square tests of the individual EISs by ownership status and chain affiliation. The SVY procedure was used to generate the proportions and analyze the data. Furthermore, the finite population correction was used per National Center for Health Statistics instructions for analyzing the NSRCF data. Following these procedures, the weighted proportions are generalizable to the population of RCFs.

This project was approved by the Institutional Review Board of the University of Alabama (IRB EX-13-CM-009).

Results

Table 1 presents the sample descriptives. Approximately 17% of all RCFs report using an EHR. The results also show that among the measures of EISs for clinical care and pharmaceutical EIS, systems designed to capture resident demographics (37%) and medical provider information (37%) are the most common followed by service plans (34%) and resident medication lists (34%). Among the least common EISs used are those for screening reminders (8%) and public health report (8%). Of the measures of EISs used for electronic communication, communicating with other providers (27%), followed by pharmacies (14%), and last, physician offices (13%) are the most common. The least common forms of electronic communication are systems designed to communicate with public health departments (5%). Finally, the majority of RCFs provided residents (51%) and guests with Internet access (60%).

Table 1.

Weighted Percent of EIS Access by Profit Status and Chain Affiliation.

	Total % [CI]	For-profit % [CI]	Non-profit % [CI]	Chain % [CI]	Non-chain % [CI]
	(N = 2,302)	(n = 1,776)	(n = 526)	(N = 974)	(N = 1,328)
EIS for clinical care
EHRs^a,b	17.43 [15.78, 19.21]	15.56 [13.76, 17.56]	26.14 [22.35, 30.32]	22.71 [20.08, 25.58]	14.24 [12.16, 16.59]
Resident service records^b	16.18 [14.44, 18.22]	16.30 [14.35, 18.46]	15.52 [12.24, 19.49]	23.29 [20.32, 26.56]	12.29 [10.19, 14.76]
Resident demographics^a,b	37.42 [35.34, 39.56]	33.24 [30.89, 35.68]	56.99 [52.22, 61.64]	48.27 [44.86, 51.69]	30.87 [28.12, 33.77]
Medical provider information^a,b	36.76 [34.62, 38.96]	32.85 [30.44, 35.35]	55.06 [50.31, 59.71]	45.03 [41.63, 48.48]	31.76 [28.95, 34.71]
Functional assessments^b	28.20 [26.22, 30.27]	27.55 [25.32, 29.89]	31.25 [27.14, 35.66]	37.42 [34.23, 40.71]	22.63 [20.10, 25.37]
Service plans^b	34.41 [32.29, 36.59]	33.78 [31.38, 36.25]	37.36 [33.00, 41.94]	44.16 [40.84, 47.53]	28.51 [25.73, 31.47]
Clinical notes^a	19.67 [17.83, 21.64]	18.24 [16.21, 20.46]	26.33 [22.33, 30.77]	20.19 [17.54, 23.12]	19.35 [16.92, 22.04]
Patient problems list	18.26 [16.47, 20.20]	17.96 [15.93, 20.18]	19.65 [16.24, 23.58]	20.31 [17.63, 23.28]	17.02 [14.70, 19.62]
Orders for tests^a	11.81 [10.41, 13.37]	10.52 [8.99, 12.27]	17.86 [14.68, 21.55]	13.64 [11.44, 16.19]	10.70 [8.96, 12.75]
Viewing lab/imaging results^a,b	9.72 [8.48, 11.13]	7.85 [6.55, 9.39]	18.48 [15.21, 22.26]	13.15 [10.93, 15.73]	7.65 [6.25, 9.35]
Screening reminders	8.3 [7.07, 9.72]	8.10 [6.72, 9.74]	9.22 [6.93, 12.17]	9.75 [7.84, 12.06]	7.42 [5.90, 9.30]
Discharge/transfer summaries^a	19.32 [17.56, 21.20]	17.36 [15.44, 19.46]	28.49 [24.47, 32.88]	21.25 [18.62, 24.13]	18.15 [15.87, 20.69]
Public health reporting	8.08 [6.91, 9.43]	7.78 [6.46, 9.33]	9.51 [7.24, 12.41]	8.20 [6.53, 10.24]	8.02 [6.48, 9.87]
Pharmaceutical EIS
Medication administration^a	29.15 [27.06, 31.34]	27.85 [25.50, 30.34]	35.23 [30.98, 39.72]	30.27 [27.25, 33.47]	28.47 [25.68, 31.45]
Resident medication list^a	34.48 [32.30, 36.74]	31.82 [29.36, 34.38]	46.97 [42.36, 51.62]	36.57 [33.36, 39.91]	33.22 [30.30, 36.28]
Active medication allergy list^a,b	31.57 [29.48, 33.73]	28.78 [26.44, 31.24]	44.59 [40.09, 49.19]	36.44 [33.22, 39.78]	28.62 [25.90, 31.51]
Prescription orders^a	19.72 [17.93, 21.65]	18.00 [16.01, 20.17]	27.79 [23.90, 32.06]	21.82 [19.13, 24.78]	18.46 [16.13, 21.04]
Warning of drug interactions^a	17.91 [16.20, 19.77]	16.33 [14.43, 18.43]	25.33 [21.62, 29.43]	19.53 [16.96, 22.39]	16.93 [14.70, 19.43]
Electronic communication
Physicians	13.11 [11.06, 15.47]	13.61 [11.15, 16.51]	11.42 [8.45, 15.28]	10.75 [8.26, 13.87]	15.03 [11.98, 18.68]
Nursing home	8.38 [6.80, 10.29]	7.95 [6.11, 10.29]	9.82 [7.25, 13.17]	9.42 [7.10, 12.40]	7.54 [5.54, 10.17]
Hospital	9.60 [7.88, 11.66]	9.97 [7.90, 12.51]	8.38 [6.00, 11.59]	9.04 [6.77, 11.97]	10.06 [7.66, 13.11]
Pharmacy^b	14.47 [12.45, 16.75]	14.55 [12.18, 17.29]	14.19 [10.85, 18.36]	17.23 [14.29, 20.63]	12.22 [9.62, 15.41]
Laboratory/tests	7.80 [6.32, 9.58]	7.26 [5.56, 9.42]	9.59 [7.04, 12.94]	8.19 [6.08, 10.95]	7.48 [5.58, 9.95]
Other health or LTC provider	6.59 [5.19, 8.34]	6.99 [5.30, 9.16]	5.27 [3.43, 8.01]	5.96 [4.22, 8.36]	7.11 [5.13, 9.77]
Resident’s personal health record	6.01 [4.66, 7.72]	6.24 [4.63, 8.35]	5.24 [3.32, 8.19]	5.09 [3.46, 7.43]	6.76 [4.82, 9.40]
Public health	5.09 [3.99, 6.48]	4.84 [3.59, 6.51]	5.93 [3.99, 8.75]	4.29 [3.02, 6.07]	5.75 [4.12, 7.95]
Corporate office^b	9.89 [8.61, 11.34]	9.58 [8.17, 11.20]	10.95 [8.17, 14.35]	18.19 [15.61, 21.09]	3.14 [2.20, 4.46]
Communication with any provider^b	27.46 [24.92, 30.15]	27.32 [24.31, 30.55]	27.92 [23.56, 32.74]	33.50 [29.87, 37.33]	22.54 [19.15, 26.35]
Portable device used by staff^b	6.04 [4.96, 7.34]	6.00 [4.77, 7.51]	6.24 [4.39, 8.77]	6.95 [5.24, 9.17]	5.49 [4.17, 7.18]
EIS for residents and guests
Internet access in rooms^a,b	51.44 [49.04, 53.83]	50.04 [47.30, 52.78]	57.97 [53.23, 62.56]	58.12 [54.55, 61.61]	47.40 [44.21, 50.60]
Public Internet access^a	60.40 [58.02, 62.74]	59.03 [56.31, 61.70]	66.81 [62.16, 71.15]	58.16 [54.62, 61.63]	61.76 [58.58, 64.84]

Note. EIS = electronic information system; CI = confidence interval; EHR = electronic health record; LTC= long-term care.

Profit status statistically significant at p < .05 or better.

Chain affiliation statistically significant at p < .05 or better.

The results by profit status and chain affiliation (also shown in Table 1) reveal that non-profit RCFs and chain-affiliated RCFs outpaced for-profit and non-chain RCFs when it came to EISs for clinical care and pharmaceuticals. Approximately 26% of non-profit RCFs and 16% of for-profit report using an EHR. Twenty-three percent of chain RCFs report using an EHR as compared with 14% of non-chain RCFs.

Table 2 presents the findings of the chi-square analysis.

Table 2.

Weighted Differences by Ownership Status and Chain Affiliation.

	For-profit [99% CI]		Non-profit [99% CI]		p value
	Chain	Non-chain	Chain	Non-chain	p value
EIS for clinical care
EHRs	.21 [.17, .25]	.12 [.09, .16]	.30 [.23, .39]	.23 [.17, .31]	.000
Resident service records	.24 [.20, .29]	.12 [.09, .16]	.20 [.13, .29]	.13 [.08, .20]	.000
Resident demographics	.45 [.40, .50]	.27 [.23, .31]	.65 [.55, .73]	.52 [.44, .60]	.000
Medical provider information	.41 [.36, .47]	.28 [.24, .32]	.61 [.52, .70]	.51 [.43, .59]	.000
Functional assessments	.39 [.34, .44]	.21 [.17, .25]	.31 [.24, .40]	.31 [.24, .39]	.000
Service plans	.45 [.40, .50]	.27 [.23, .32]	.40 [.31, .49]	.36 [.28, .44]	.000
Clinical notes	.19 [.15, .23]	.18 [.15, .22]	.26 [.19, .35]	.26 [.19, .34]	.007
Patient problems list	.21 [.17, .26]	.16 [.13, .20]	.18 [.12, .22]	.21 [.25, .29]	.063
Orders for tests	.13 [.10, .17]	.09 [.07, .12]	.18 [.12, .26]	.18 [.12, .25]	.000
Viewing lab/imaging results	.12 [.09, .16]	.06 [.04, .08]	.20 [.14, .28]	.18 [.12, .24]	.000
Screening reminders	.10 [.07, .14]	.07 [.05, .10]	.09 [.05, .15]	.10 [.06, .15]	.139
Discharge/transfer summaries	.20 [.16, .24]	.16 [.13, .20]	.27 [.20, .36]	.30 [.23, .38]	.000
Public health reporting	.08 [.06, .11]	.08 [.05, .11]	.09 [.05, .16]	.10 [.06, .15]	.663
Pharmaceutical EIS
Medication administration	.29 [.25, .34]	.27 [.23, .32]	.35 [.27, .28]	.35 [.28, .44]	.028
Resident medication list	.34 [.29, .39]	.31 [.26, .35]	.48 [.39, .57]	.46 [.38, .54]	.000
Active medication allergy list	.34 [.29, .39]	.26 [.22, .30]	.46 [.38, .55]	.43 [.36, .52]	.000
Prescription orders	.20 [.16, .25]	.17 [.13, .20]	.28 [.21, .37]	.28 [.21, .35]	.000
Warning of drug interactions	.19 [.15, .23]	.15 [.12, .19]	.23 [.17, .31]	.27 [.20, .34]	.000
Electronic communication
Physicians	.10 [.07, .16]	.16 [.12, .22]	.12 [.07, .20]	.11 [.06, .19]	.063
Nursing home	.08 [.05, .13]	.08 [.05, .12]	.13 [.07, .21]	.07 [.04, .13]	.288
Hospital	.09 [.05, .14]	.11 [.07, .16]	.10 [.06, .18]	.07 [.04, .12]	.354
Pharmacy	.17 [.13, .23]	.12 [.08, .18]	.17 [.10, .26]	.12 [.07, .20]	.115
Laboratory/tests	.08 [.05, .12]	.07 [.04, .11]	.10 [.05, .18]	.09 [.06, .16]	.574
Other health or LTC provider	.06 [.03, .10]	.08 [.05, .13]	.07 [.03, .14]	.04 [.02, .09]	.313
Resident’s personal health record	.05 [.02, .09]	.08 [.05, .12]	.07 [.03, .14]	.04 [.02, .10]	.219
Public health	.04 [.02, .07]	.06 [.03, .10]	.06 [.03, .13]	.06 [.03, .11]	.405
Corporate office	.18 [.14, .22]	.03 [.02, .05]	.20 [.13, .30]	.04 [.02, .08]	.000
Other provider	.34 [.29, .40]	.21 [.16, .28]	.30 [.22, .40]	.26 [.19, .35]	.000
Portable device used by staff	.07 [.05, .11]	.05 [.03, .08]	.05 [.02, .09]	.07 [.04, .13]	.166
EIS for residents and guests
Internet access in rooms	.58 [.53, .63]	.45 [.40, .50]	.58 [.49, .67]	.58 [.50, .66]	.000
Public Internet access	.57 [.51, .62]	.60 [.56, .65]	.64 [.55, .73]	.68 [.60, .76]	.017

Note. CI = Confidence Interval; EIS = electronic information system; EHR = electronic health record.

Approximately 30% of RCFs that were both non-profit and chain-affiliated used EHRs as compared with 23% of RCFs that were non-profit and non-chain, 21% of RCFs that were for-profit and chain-affiliated, and 12% of RCFs that were both for-profit and non-chain. Furthermore, when it comes to EIS for resident demographics, medical provider information, and viewing lab or imaging results, non-profit RCFs that were also chain-affiliated were more likely to report having implemented such a system than were any other category of RCF. However, for resident service records, functional assessments, and service plans, use among RCFs that were for-profit and chain-affiliated outpaced other categories of RCFs.

In summary, the significant results from Table 2 show that RCFs that are both non-profit and chain-affiliated were the most likely category of RCFs to use EIS for clinical care and pharmaceuticals than were the other ownership by chain categories.

Discussion

The use of EISs is steadily increasing across all categories of health care organizations. Although hospitals and physician practices clearly outpace all other provider types, the long-term care industry is attempting to move in this direction, albeit slowly. Although some of the increase in usage is fueled by the need for providers to submit required data electronically, long-term care providers may also be discovering that EISs offer many benefits. The purpose of this study was to explore EIS use within a population of RCFs within the United States. We hypothesized that the results would show differences in EIS use by ownership status and chain affiliation. The results of the chi-square analyses reveal that there is a differential pattern in use by both of these structural characteristics. We found support for Hypotheses 1 and 4; thus, we reject the null for both. Although we also reject the null for Hypothesis 3, we did not find support for non-chain affiliated RCFs using more EIS than chain-affiliated RCFs. Similarly, we also found that Hypotheses 5 and 6 were not consistently supported for all types of EIS. There were no significant differences among the EIS for communication by ownership status (Hypothesis 2).

The analyses consistently indicate that RCFs that are both for-profit and not chain-affiliated were the least likely to report EIS usage. Conversely, facilities that were both non-profit and chain-affiliated were the most likely to use a given EIS. This may suggest that among all users of information systems, for-profit RCFs that are independent may lack the necessary resources to invest in information systems or possibly that functioning under a profit maximization model may result in decisions that improve financial performance at the cost of other types of investment (Vitaliano, 2003). Other potential reasons for the differences in use by ownership may be facility age, with non-profit RCFs being younger and better able to incorporate these systems into the facility design, or size differences, with larger facilities using more information systems (Resnick & Alwan, 2010; Resnick et al., 2009). Perhaps with time, as Grabowski and Hirth (2003) proffered for the nursing home industry, as non-profit RCFs increase usage of these systems, for-profit RCFs will follow suit.

In summary, these findings may suggest that ownership status should be tied to chain affiliation to better analyze the effect of organizational structure on the outcome of interest. Resnick and Alwan (2010), for example, suggest that the numerous relationships among facility characteristics, such ownership status and chain affiliation, may influence how EHRs are adopted into that facility. It is important to understand the use of EHRs by chain membership in the context of facility ownership, and vice versa. Thus, the interaction of chain and profit may mitigate the individual findings often associated with either construct.

Limitations

This study has several limitations. Among the relevant limitations, the data documentation notes that, to preserve the anonymity of responding facilities, certain responses were collapsed into dichotomous groups (e.g., profit status). Thus, we cannot explore differences among governmental facilities. In addition, as noted earlier, some of the missing data were imputed or gathered from other sources and the survey organizers did not provide further information as to the accuracy of these other sources (National Center for Health Statistics, 2011). A third limitation of this project is that the survey only asked whether RCFs used EIS. Nothing is known about the extent of use within each facility, the type of staff that uses these systems, or the length of time these systems have been in use. Nonetheless, it is useful to first understand the prevalence of information systems within this portion of the industry. Finally, the publicly available data do not include information on the distribution of facilities by type. In other words, it is not known how many assisted living facilities, congregate care homes, homes for the aged, and so on responded to the survey. Given that there are no federal requirements for RCFs to implement EHRs or submit data electronically and RCFs are primarily designed to provide only minimal to no clinical care, RCFs may legitimately be less likely to implement EMRs. Yet, the presence of EHRs within this industry may suggest that some managers value technology as a component of care delivery. In addition, the findings of this study may be relevant to policymakers and others concerned with understanding trends in the use of EIS within the long-term care industry.

Conclusion

This research adds to the extant literature examining the use of EISs in health care settings. In addition to linking the use of EIS to organizational characteristics, the findings may illustrate another important phenomenon occurring in the long-term care industry. The low use of EISs by for-profit facilities that are not members of a chain may be a cause for concern. It may be that these facilities lack access to appropriate resources to invest in these systems. Future research should focus more on explanatory capabilities of EIS use in the context of its structural characteristics. In addition, further research is needed to better understand why this finding was consistent across all types of systems. Although the overall use of EHRs is low, some facilities are using clinical components associated with EHR. Yet, it may be that more should be done on a policy or vendor level to encourage use and limit the challenges.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Jullet A. Davis received her master’s and doctorate in Health Policy and Administration from the Pennsylvania State University. Her dual concentrations as a doctoral student were in gerontology and organizational theory. Her research focuses on nursing homes and the aging population. She is primarily interested in the strategies used by nursing home managers to achieve financial and long-term viability. She is currently exploring new methods by which to categorize innovativeness in the nursing home industry.

Karolina Zakoscielna is a fourth-year clinical psychology doctoral candidate at the University of Alabama, specializing in geropsychology. She is in the data collection stage of her dissertation, which examines pain and affect variability and mindfulness in community-dwelling older adults with chronic illness. Her other research interests include quality of life and quality of care in long-term care settings. Originally from Poland, she has spent most of her life in New York where she received a BA from Stony Brook and an MA from Columbia University.

Lindsey Jacobs is a clinical geropsychology fellow at the Salem VA Medical Center. She received her doctoral training in clinical geropsychology from the University of Alabama and obtained a master’s degree in health care outcomes research at the University of Alabama at Birmingham. Her research interests include systems-wide interventions to improve quality of care and quality of life in long-term care settings as well as pain in older adults.

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