Journal of Aging and Health
25(4) 535 –554
© The Author(s) 2013
Reprints and permissions:
Utilization of Technology
by Long-Term Care
Between For-Profit and
Darla J. Hamann, PhD1 and
Karabi C. Bezboruah, PhD1
Objective: We examine ownership differences in the use of technology in
long-term care facilities. Method: We analyze two nationally representative
surveys of administrators collected by the Centers for Disease Control
(CDC): the 2004 National Nursing Home Survey and the 2010 National
Survey of Residential Care Facilities. Results: We find that nonprofit
nursing homes are more likely to use some computerized administrative
functions and digital laboratory reports, and report use rates similar to for-
profit organizations in other areas of health IT. Nonprofit residential care
facilities are more likely to use electronic medical records and information
exchange systems than their for-profit counterparts. In addition, nonprofit
residential care facilities are more likely than for-profit facilities to digitize
more types of information and use larger health information exchange
networks. Discussion: The reasons for which nonprofit long-term care
organizations report higher levels of some types of technology utilization
are explored, and future research is recommended.
health information technology, nonprofit, ownership, nursing homes, innovation
1University of Texas at Arlington, TX, USA
Darla J. Hamann, PhD, University of Texas at Arlington, 701 S. Nedderman Dr., Box 19588,
Arlington, TX 76019, USA.
480238 JAH25410.1177/0898264313480238Journal of Aging and HealthHamann and Bezboruah
536 Journal of Aging and Health 25(4)
During this era of health care reform, it is important to consider how public
or nonprofit options should exist for the provision of health care, and whether
federal programs to assist older adults, such as Medicare, are more effec-
tively delivered through for-profit or nonprofit institutions. In order for pol-
icy makers to make informed choices about such issues, understanding the
ownership differences in the processes and outcomes of long-term care is
essential. One important consideration is how organizational ownership
affects health IT utilization, because health IT impacts health outcomes.
Various forms of health information technology (IT) have been shown to
relate positively to operational outcomes (Cherry, Carter, Owen, & Lockhart,
2008; Parente & VanHorn, 2006); patient outcomes (Alexander & Wakefield,
2009); patient satisfaction (Kazley, Diana, Ford, & Menachemi, 2012); resi-
dent subjective reactions (Pillemer et al., 2012) and health care quality
(Chaudhry et al., 2006). Health IT may even be able to reduce disparities in
health care outcomes across patients with differing socioeconomic status
(Gibbons & Casale, 2010; Robert Wood Johnson Foundation et al., 2007),
which is a significant problem in long-term care (Konetzka & Werner, 2009).1
Few studies have compared health IT utilization in for-profit and non-
profit long-term care facilities. Davis, Brannon, & Whitman (2009) consid-
ered the use of administrative (such as billing and scheduling) and clinical
(such as electronic medical records) information systems by nonprofit and
for-profit nursing homes. They found that clinical information systems
were similar across sectors, but administrative systems were more preva-
lent in the nonprofit sector. Since health information systems were not the
focus of their study, their theoretical and empirical analysis was not detailed.
Birkovitz, Sengupta, and Jamison (2010) found that nonprofit home health
and hospice workers were more likely to use electronic medical records
than their for-profit counterparts, but they did not test the statistical signifi-
cance of the result or conduct multivariate analysis. The purpose of our
article is to analyze in more depth ownership differences in the use of tech-
nology in nursing homes. In addition, we consider ownership differences in
health IT adoption in the residential care industry, which has not been pre-
viously studied. Research on the impact of ownership on health IT in the
residential care industry is important because this industry is now nearly
twice the size of the nursing home industry (Whittington, 2011). Also,
studying the relationship between ownership and health IT utilization in
two industries, rather than one industry as is typically done, provides infor-
mation regarding the robustness of results to different long-term care
Hamann and Bezboruah
We contribute to the long-term care health IT literature in three ways.
First, we discuss why differences in health IT utilization may differ by own-
ership sector. Second, we use nationally representative databases from two
long-term care industries to test our hypotheses. Third, in addition to bivari-
ate hypothesis testing, we use multivariate testing to ascertain whether other
structural factors of the long-term care facilities could be responsible for
ownership differences. We conclude by comparing the results of these anal-
yses with the results of previous research and suggest avenues for future
Organizational ownership can influence the decision to adopt health IT.
Often, ownership determines organizational goals and objectives in nursing
homes (Luksetich, Edwards, & Carroll, 2000). For-profit managers usually
value profit for owners, while nonprofit managers value quality of care
(Amirkhanyan, Kim, & Lambright, 2008; Ben-Ner & Ren, 2010). In addi-
tion, nonprofit managers are more concerned with equity than their for-profit
counterparts (Sass, Liao-Troth, & Wonder, 2011; Leete, 2000), and their
innovations are primarily service oriented (Castle, 2001), reflecting their
values-based decision-making objectives. This can result in health IT invest-
ments that further the nonprofit’s mission, and benefit multiple stakeholders.
For-profit managers are more likely to invest in a comparatively narrow
range of health IT initiatives—those that improve profitability—due to their
accountability to owners. Indeed, research has shown that the introduction of
health IT is associated with quantity outcomes in for-profit hospitals and
quality outcomes in nonprofit hospitals (Parente & VanHorn, 2006).
Because administrative expenses are viewed more negatively by nonprofit
stakeholders than nursing expenses, nonprofit organizations invest a greater
proportion of their revenues in nursing care (Luksetich et al., 2000), and
maintain lower administrative costs (Mukamel, Spector, & Bajorska, 2005).
To the extent that health IT reduces the percentage of administrative costs
relative to care costs, nonprofit organizations have an incentive to invest in
them. Nonprofit organizations may, therefore, be relatively more likely to
implement administrative systems, which improve the efficiency of adminis-
trative tasks, than computerized nursing notes, where the efficiency gains
would occur in direct care provision portion of their budget. For-profit orga-
nizations have incentives to invest not only in health IT that minimizes
administrative expenses, but also health IT that minimizes care expenses.
538 Journal of Aging and Health 25(4)
Since health IT programs have high initial costs, which are frequently cited
by managers as a barrier to their adoption (Simon et al., 2007), the adoption of
health IT may depend on revenue streams. The decisions of nonprofit manag-
ers regarding how to utilize revenues that exceed expenses are constrained—
these revenues cannot be distributed to stakeholders (Harel & Tzafrir, 2001;
Mirvis & Hackett, 1983). These revenues must be reinvested in the organiza-
tion (Hansmann, 1980), and the purchase of technology is one form of rein-
vestment. Conversely, for-profit health care organizations distribute their
revenues exceeding costs to owners or shareholders, who endure the costs of
an initial health IT investment as a reduction in current returns.
Information and Uncertainty
The health IT benefits for long-term care organizations depend on several
factors ranging from employee relations during the implementation process
to the customization of the software (Bezboruah, Hamann, & Smith, in press;
Lipsky, Avgar, & Lamare, 2009). Managers often lack information about the
benefits of health IT, and sometimes, even about its costs (Bezboruah et al.,
in press). Acquiring information about the efficacy of specific forms of health
IT in specific long-term care environments can be costly, as it takes a lot of
managerial time. With current profits at stake, for-profit managers may be
less likely to invest in technologies with uncertain or unclear benefits.
In contrast, nonprofit organization theory posits that nonprofits are more
innovative than their for-profit counterparts because they are more likely to
adopt new measures to address gaps in service that for-profit firms are unwill-
ing to fill (Hansmann, 1980; Schlesinger & Gray, 2006; Weisbrod, 1988).
They are more likely to serve populations where profit is not a likely out-
come, and more likely to make investments for social rather than financial
reasons. For example, they are relatively more motivated to invest in health
IT to improve patient care for the benefit of their residents rather than cost-
benefit analysis or competitive pressures (Parente & VanHorn, 2006). They
may be better able to justify the risk of the large up-front expenditures in
health IT to their stakeholders citing these social considerations.
In summary, we posit that nonprofit long-term care facilities usually adopt
health IT earlier than for-profit facilities. This is because nonprofit managers
are more likely to invest in health IT when only care related, but not financial,
benefits are likely to accrue. Both nonprofit and for-profit organizations are
concerned about cost containment, although the containment of nursing costs
may be more important to for-profit organizations. Health IT investment,
Hamann and Bezboruah
though, has high up-front costs and uncertain financial benefits, and for-
profit organizations are accountable to owners who have personal financial
resources at risk. In contrast, nonprofit organizations by law must reinvest
surplus funds in the organization, and health IT is such an investment.
Data and Method
Our empirical analysis is based on two nationally representative datasets,
both collected by the Centers for Disease Control (CDC). We make no claim
that the data sets are similar, except that they both survey top administrators
in long-term care facilities and both ask questions about technology utiliza-
tion. We intend to examine our research question, which is the extent to
which health IT utilization differs by ownership type in long-term care, in
two different contexts to improve the generalizability of our results.
Nursing homes are a highly regulated industry, subject to state regulations,
and federal regulations if they seek federal funding. The largest purchaser of
their services is the Federal Government, through the Medicare, and espe-
cially the Medicaid, programs. Residential care facilities have emerged as an
alternative to nursing homes for some people. In general, residential care
facilities serve healthier populations than nursing homes and have relatively
more strict admission requirements (Zimmerman et al., 2003). While nursing
homes are highly regulated by the federal government, assisted living facili-
ties are regulated at the state level. The majority of their residents pay for
their own services (Mor, Miller, & Clark, 2010). Assisted living regulations
and reporting requirements are therefore variable, but usually not nearly as
onerous (Mor et al., 2010). Because of these minimal and varied reporting
requirements, there is a dearth of available information about assisted living
facilities. The CDC’s residential care survey is first nationally representative
data set on this industry.
Regulations constrain the actions of managers in for-profit and nonprofit
organizations (Luksetich et al., 2000). As a result, managers in more regu-
lated for-profit and nonprofit organizations behave more similarly than for-
profit and nonprofit organizations in less regulated industries (Brown &
Slivinski, 2006). They have a similar objective—meeting regulatory require-
ments. Some regulations require the use of technology. For example, the
Centers for Medicare and Medicaid services requires electronic provision of
the Minimum Data Set (MDS), which is why nearly all nursing homes have
adopted this type of health IT. We therefore expect the differences between
for-profit and nonprofit technology use to be more significant in residential
care facilities than in nursing homes, and we expect that Medicare and
Medicaid funding will be related to some types of health IT use.
540 Journal of Aging and Health 25(4)
Nursing Home Data
The first data source is from a nationally representative survey of nursing
homes from August 2004 to January 2005 (National Center for Health
Statistics, 2008). We use the facility level data, which was gathered via face-
to-face interviews with nursing home administrators or other executives. A
stratified, multiphase sampling technique was utilized to ensure that the sam-
ple of 1,174 participating nursing homes were representative of all nursing
homes in the United States. The response rate was 81%.
Residential Care Data
The second data source is a nationally representative sample of residential
care facilities (also called assisted living facilities). The residential care sur-
vey was conducted in person by trained interviewers with top executives in
2,302 facilities in 2010. Its response rate was 81%. Similar to the nursing
home data set, the sampling procedure consisted of stratification on a number
of organizational variables followed by simple random sampling within clus-
ters (see Moss et al., 2011, for more details). Since nonprofit and public sec-
tor nursing homes employ similar employees and have similar goals (Corder,
2001; Hamann & Foster, 2012), we categorize them under “nonprofit nursing
homes” in this article.
The nursing home data measures 12 types of health IT, including both admin-
istrative and clinical care systems, each treated separately in analysis. The
residential care data is narrower in focus, concentrating on electronic medical
records, but also includes electronic information exchange. Computerized
administrative systems (like billing or human resources) were excluded from
the residential care data.
The CDC used more items to measure health IT in the residential care
survey than in the nursing home survey. We created four variables from these
items. Our first variable, electronic medical records, is an indicator variable
coded as 1 if the administrator responded affirmatively to the following ques-
tion: “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.” Our second variable, computerized service records, includes
administrators who responded affirmatively to the previous question as well
as the following question: “Other than for accounting or billing purposes,
Hamann and Bezboruah
does this facility have a computerized system for its Resident Service Records
to keep track of the services provided to each resident?” This second variable
is a more complete measure of electronic medical records, including facilities
who responded affirmatively to the first or second question.
After asking these broad questions, the residential care survey asked
administrators whether 16 types of data were computerized. We created a
variable that counted the number of computerized functions as a measure of
the extent of computerization. Next, the residential care survey asked whether
the facility shares information electronically with eight types of care partners
(e.g., hospitals, pharmacies) or the corporate office. Our last dependent vari-
able, extent of information exchange, counts the number of entities with
which the facility electronically shares information.
We conducted two types of analysis in Stata using the survey function to
weight the data to be representative of the population. First, we conducted
Pearson χ2 tests to detect differences in health IT utilization by nonprofit and
for-profit long-term care facilities. Second, we conducted regression analysis
to measure the extent to which ownership and health IT were associated after
controlling for organizational variables that may confound the relationship.
With the nursing home data, we used logit regressions. With the residential
care data, the indicator variables were analyzed using logit, while the vari-
ables that count the types of data digitized and number of health information
exchange partners were analyzed using ordered logit.2
In both datasets, we controlled for multifacility affiliation (chain ownership),
size of home, and type of residents in multivariate analysis. Long-term care
facilities that are part of a multifacility conglomerate (chain) and larger nurs-
ing homes are more likely to implement health IT due to economies of scale,
which reduces the per-resident cost of the technology (Castle, 2001; Davis,
Brannon, & Whitman, 2009). Since for-profit and nonprofit organizations
may serve different populations, and the costs and benefits of health IT may
vary by type of resident, we also controlled for whether the long-term care
facility contained units that cared for hard-to-serve populations. In addition,
we controlled for the use of volunteers, since the facility may be less likely to
implement technology that necessitates volunteer training (Corder, 2001).
While legal ownership status has implications for managerial decision
making, so too does government funding (Coursey & Bozeman, 1990).
542 Journal of Aging and Health 25(4)
Therefore, we controlled for the percentage of the organization’s revenue
received from the federal Medicaid programs. We controlled for the percent-
age of revenue received from Medicare in nursing homes, though not in resi-
dential care facilities due to very low levels of such payments in this
Pearson χ2 Test Results in Nursing Homes
Table 1 lists the 12 types of technology utilization in nursing homes mea-
sured by the survey. It shows the mean percentage of adoption of each type of
health IT in nonprofit and for-profit organizations, and includes the Pearson
χ2 test results. We expected to find that nonprofit facilities utilized health IT,
especially administrative systems, more than their for-profit counterparts. Of
the five types of administrative systems, we found that staffing and schedul-
ing and human resource information systems supported our hypotheses.
Billing and financial information systems and the MDS were almost univer-
sally used by both nonprofits and for-profits. Differences in admission, dis-
charge, or transfer information systems were not found. Among clinical care
systems, we did not find support for our hypotheses. While nonprofit organi-
zations were significantly more likely to use information systems for labora-
tory procedures, they were less likely to use them for dietary information or
daily personal care logs by certified nursing assistants (CNAs). Differences
between nonprofit and for-profit utilization of information systems were not
significant for electronic medical records, medication information, medica-
tion orders, or physician orders.
Pearson χ2 Test Results in Residential Care Facilities
Although the Residential Care Facility data measures only clinical health IT,
the test results displayed in Table 2 all support our hypothesis. In these facili-
ties, nonprofit organizations were more likely to use electronic medical
records, and to digitize more types of medical data, than their for-profit coun-
terparts. Nonprofit organizations also were more likely to use health informa-
tion exchange networks, and they shared information electronically with
more care partners than their for-profit counterparts.
Regression Results in Nursing Homes
In our multivariate analysis of nursing homes, displayed in Table 3, we found
a statistically and practically significant difference between for-profits and
Hamann and Bezboruah
Table 1. χ2 Tests for Health IT Use in Nonprofit and For-Profit Nursing Homes.
For-Profit NonprofitPearson χ2
Corrected F* p-value
Minimum data set
Daily personal care by
0.96 0.960.36 0.33 .57
0.95 0.960.600.54 .46
Note. *The F-test corrects the Pearson χ2 test for the structure of the survey, using the Rao
and Scott method recommended in the Stata 12 survey manual for testing the statistical
significance of subpopulation estimates.
The sample size is 1,174. 61% of sample homes have for-profit ownership.
their nonprofit counterparts in the utilization of digitized laboratory reports
(p < .10), billing/financial systems (p < .05), and human resource information
systems (p < .01). For-profit nursing homes were roughly one-fourth less
likely to use digitized laboratory reports, and nearly half as likely to use
human resource information systems. We did not find evidence that nonprofit
and for-profit nursing homes differed in the use of the nine other forms of
health IT. Increased reliance on Medicare payments was associated with
MDS use (as expected), but there was little variance in the dependent variable
so the result was not strong. The MDS software programs sold by many sup-
pliers have optional programs that can be used by nursing homes, so it is not
surprising that Medicare and Medicaid use predicts the use of several other
forms of health IT as well.
544 Journal of Aging and Health 25(4)
With the nursing-home data, a few other interesting patterns emerged.
First, multi-facility ownership was positively related to health IT adoption,
and this relationship was statistically significant in the areas of electronic
dietary information, admission, transfer or discharge systems, human
resource information systems, and electronic medical records. This is consis-
tent with previous research (Castle, 2001; Davis et al., 2009). Being part of a
chain allows nursing homes access to knowledge, skills, and resources of the
centralized system, which could ease the health IT implementation process
and create economies of scale. With effect sizes ranging from an increased
probability of health IT use in chain facilities compared to free-standing
facilities of approximately 30% to 60%, these relationships were practically
Second, large nursing homes were more likely than small nursing homes
to use health IT. This could be due to more resources available for imple-
menting health IT. Further, large organizations are more likely to conform to
regulatory, normative, and technical pressures. Such conformity could result
in an enhanced implementation of changes within the organization. Third,
facilities with volunteers were nearly universally more likely to adopt
Table 2. χ2 Tests for Health IT Use in Nonprofit and For-Profit Residential Care
For Profit Nonprofit Pearson χ2
Corrected F* p-value
Number of care
0.160.26 26.0 26.2 .00
0.29 0.3810.49 10.73.00
0.14 0.21 9.49 10.29.00
0.32 0.43 14.662.56.02
Note. *The F-test corrects the Pearson χ2 test for the structure of the survey using the Rao
and Scott method recommended in the Stata 12 survey manual for testing the statistical
significance of subpopulation estimates.
The sample size is 2,303. 74% of sample homes have for-profit ownership.
Hamann and Bezboruah
Table 3. Logit Regressions for the Impact of Ownership on Health IT Use in
Admin Physician Med order Lab EMRMed admin
at nursing home
Federal funding (ref: Medicare < 10% & Medicaid < 20%)
over 20% [0.703]
Medicaid funding 1.997**
Nursing home size (ref: less than 50 beds)
Number of beds1.175
greater than 50[0.274]
Number of beds2.047***
greater than 100[0.532]
Number of beds 5.515***
greater than 200[3.198]
Prob (F) 0.000
MDSDietaryNA notesBillingSchedule HRM
at nursing home
546 Journal of Aging and Health 25(4)
MDS Dietary NA notesBillingScheduleHRM
Federal funding (ref: Medicare less than 10% & Medicaid less than 20% of residents)
Medicare funding 4.057* 1.41
over 20% [3.379] [0.314]
Medicaid funding9.525** 0.856
Medicaid funding 4.687** 0.736
Medicaid funding4.715** 0.647
Medicaid funding4.651** 0.662
over 80%[3.013] [0.200]
Nursing home size (ref: less than 50 beds)
Number of beds0.777 1.183
greater than 50 [0.313][0.239]
Number of beds 0.5861.730***
greater than 100[0.254][0.368]
Number of beds 0.7162.842***
greater than 200 [0.723][0.983]
Prob (F)0.000 0.000
Notes. Survey weights used. Odds ratios reported instead of coefficients. Standard errors robust to
clustered nature of the data in brackets. *p < .10, **p < .05, ***p < .01 in two-tailed statistical tests. Regres-
sions also controlled for the existence of the following specialty units in the nursing home (not reported to
conserve space): dementia, behavioral, rehabilitation, subacute, and pulmonary.
Table 3. (continued)
information systems, a finding that is in direct conflict with Corder’s (2001)
survey of managers in human service agencies. This suggests that in nursing
homes, either volunteers did not often use health IT systems or volunteer
training was not a significant barrier to their utilization.
Residential Care Facilities
Table 4 reports the results for our analyses of residential care facilities. For-
profit long-term care facilities were about 40% less likely to use electronic
medical records than nonprofit long-term care facilities, although when all
computerized forms of health IT are considered, this discrepancy was only
about 20%. Nonprofit facilities were more likely to digitize more types of
Hamann and Bezboruah
Table 4. Regressions for Impact of Ownership on Health IT Adoption in
Residential Care Facilities.
Logit Ordered logit
Volunteers in the
Federal funding (ref: Medicaid less than 20% of residents)
Medicaid (20-50% of
Medicaid (50-75% of
Medicaid (over 75% of
Residential care facility size (ref: less than 11 residents)
Notes. Survey weights used. Odds ratios reported instead of coefficients. Standard errors
are robust to the clustered nature of the data. *p < .10, **p < .05, ***p < .01 in two-tailed
tests. Regressions also controlled for the age of the facility, whether the home was built as a
residential care facility, and the following types of resident care at the facility (not reported to
conserve space): respite, daycare, mental illness/developmental disabilities, dementia (indica-
tor variables for admittance and specialty wing), skilled nursing, daily monitoring, end of life,
and chemical dependency.
information, and exchange information electronically with a greater number
of care partners, than for-profit facilities. The percentage of residents whose
care was funded by Medicaid was not associated with health IT or health
information exchange utilization. This may be because less than 10% of
548 Journal of Aging and Health 25(4)
residents use Medicaid in the residential care industry, and Medicaid gener-
ally only pays a portion of those residents’ bills (Mor et al., 2010).
In this article, we have tested the hypothesis that nonprofit long-term care
organizations are more likely to use health IT than their for-profit counter-
parts. We have also suggested that these differences are greater in adminis-
trative rather than clinical systems, and under light rather than heavy
regulation. Our results in the residential care industry supported our hypoth-
eses, and our hypotheses were partially supported in the nursing home
industry. While nursing homes are the most highly regulated long-term care
industry, residential care facilities are lightly regulated. These findings are
therefore consistent with the contention that because government regulation
matters little in the residential care industry, residential care managers have
relatively more discretion to make decisions that are congruent with their
objectives than their nursing home counterparts, and objectives are influ-
enced by ownership.
There are several other reasons that may explain the fact that ownership
differences in residential care facilities were stronger than the ownership dif-
ferences in nursing homes. First, the nursing home facility data were col-
lected in 2004, while the residential care facility data were collected in 2010.
Several aspects of health IT may have changed during this time interval,
which could have impacted how nonprofit and for-profit managers view
health IT. Especially if for-profits are laggards in technology investment, it is
possible that by 2010, the nonprofit nursing homes would have used health IT
to a greater extent than their for-profit counterparts. We are unable to test the
possibility that the differences between our nursing home and residential care
facility results were not due to industry or regulatory environment, but rather
were due to the timing of survey collection.
Second, the residential care facility survey more carefully measures health
IT utilization, resulting in less measurement error. Less measurement error
reduces the size of standard errors, increasing the precision of estimates and
the likelihood of finding existing small effects. It is important to note, how-
ever, that nonprofit nursing homes were either equally likely or more likely
to use health IT than for-profit nursing homes, so our results are not entirely
Finally, while theory and relationships found in other disciplines and other
industries can inform theory about long-term care markets, it is not prudent
to assume that these theories and relationships generalize to health care set-
tings. Our study contains two examples of this—findings that for-profit
Hamann and Bezboruah
organizations and organizations employing fewer volunteers invest relatively
more in information technology (Corder, 2001; Rocheleau, 2006; Rocheleau
& Wu, 2002) do not generalize to the long-term care setting. Having been
found using cross-industry data, they could be industry- or occupation-related
effects masquerading as ownership effects.
As is true with most single industry studies, we cannot claim that our results
generalize to other health care organizational types, but the representative-
ness of our data suggests that our results generalize to long-term care facili-
ties in the United States. Our results are also consistent with data from other
health care organizations. In their study of health IT implementation and
patient satisfaction, Kazley et al. (2012) report that 18.75% of nonprofit hos-
pitals had implemented a form of health IT compared to only 8.29% of for-
profit hospitals. Similarly, Parente and VanHorn (2006) report that 70% of
nonprofit and 66% of for-profit hospitals had implemented patient care IT
systems and that the nonprofit hospitals adopted the technology earlier than
the for-profit hospitals. While hypothesis tests were not completed, their raw
data mimic our findings.
Second, for privacy reasons, most variables included in the study were
dichotomized rather than used in continuous form. This increases measure-
ment error, thus increasing standard errors and reducing the likelihood of
finding statistically significant results. In addition, our dichotomous depen-
dent variable (the use or nonuse of the technological system) in nursing
homes does not allow for comparisons of the sophistication of the technologi-
cal system, although the count of digitized data forms and health information
exchange partners does provide a measure of the intensity of health IT and
information exchange utilization in residential care facilities. Third, we
assume that public and nonprofit decision making regarding health IT is simi-
lar, an assumption that should be tested using data that allows for finer own-
ership distinctions to be made. Fourth, we understand that nursing homes
embedded within hospitals may use more technology than nursing homes in
stand-alone facilities. We were unable to control for this problem due to data
limitations, but encourage future research to do so.
We would also like to note the importance of controlling for government
regulations, which we did at the federal level by controlling for Medicare and
Medicaid expenditures. However, since assisted living facilities are regulated at
the state level, we were unable to control for regulation in these data. We encour-
age future research to use more precisely measured data, and control statistically
for differences in regulatory environments and location within a hospital.
550 Journal of Aging and Health 25(4)
Implications and Future Directions
An increasing number of elderly persons are expected to live in nursing
homes or residential care units as the population ages (Pillemer et al., 2012).
It is imperative for nursing home administrators to effectively track of their
residents’ health and well-being, and manage their facilities efficiently, as the
industry grows. Effective measures for monitoring health and regular activity
schedules, such as those offered by health IT systems, could be a benefit for
expanding facilities, but are not always related to the goals of for-profit or
nonprofit managers. Possibly for-profit managers were more cognizant of,
and sensitive to, information suggesting that health IT systems are not neces-
sarily cost effective, or they may be delaying implementation of health IT
systems due to a lack of information. Bezboruah et al. (in press) found that
little information was utilized in the decisions to implement health IT sys-
tems. Liu and Castle (2008) suggest that health IT may be underutilized in
long-term care. Consequently, we infer that the lack of information about the
cost effectiveness of health IT could be behind for-profit managers’ hesitation
to adopt health IT, and encourage future researchers to explore this in more
detail. This has implications for the health IT providers. Innovations related
to cost-control are critical, and providing accessible information could assist
managers in making health IT decisions.
This research also has implications for policy makers. It suggests that non-
profit long-term care facilities use more technology than their for-profit
counterparts. This further substantiates the contention that nonprofit and for-
profit long-term care organizations use their resources differently (as argued
by Luksetich et al., 2000). Research applying similar but more detailed health
IT variables, with better control for the regulatory environment and location,
could provide new insights into the health IT adoption behavior of nonprofit
and for-profit entities. If these data could be linked to health care quality or
cost outcomes, they could significantly assist policy-making pertaining to
health IT in the long-term care facilities. Incentives for health IT investment
that work in one sector may not be optimal in another sector.
Using nationally representative samples of nursing homes and residential care
facilities, our study finds that nonprofit long-term care facilities are either
equally likely or more likely to invest in technology than their for-profit coun-
terparts. Most types of health IT utilization were similar in nonprofit and for-
profit nursing homes, but in both χ2 and regression tests, nonprofit organizations
were significantly more likely to have adopted digitized laboratory reports and
Hamann and Bezboruah
human resource systems. Findings that for-profit nursing homes had adopted
digital nursing and dietary notes that were found in χ2 tests were no longer
significant when organizational variables were controlled. While we do not
know whether nonprofit residential care facilities invest more than their for-
profit counterparts do in administrative health IT systems, we have evidence
that they are more likely to use clinical systems. We also found that they uti-
lize clinical health IT and health information exchanges to a greater extent
than their for-profit counterparts.
We would like to thank two anonymous reviewers for providing helpful suggestions
and comments that improved our article. We would also like to thank Jason Smith for
assistance with locating journal articles, and the University of Texas at Arlington for
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
The authors received no financial support for the research, authorship, and/or publica-
tion of this article.
1. It is important to note that not all studies have found positive relationships
between health IT and organizational outcomes (Pillemer et al., 2012), so fur-
ther research on this topic is needed. Research that considers the antecedents
of health IT can assist with understanding the mechanisms by which health IT
impacts outcomes in long-term care.
The regression analysis has the benefit of holding constant other structural vari-
ables that could cause a spurious correlation between ownership and health IT,
but the accuracy of regression results are dependent on model specification. In
our data, pertinent control variables, such as location within a hospital, were
not included in the surveys. For functional form, we used logit for all dichoto-
mous dependent variables. While logit is a standard statistical choice for regres-
sion with dichotomous dependent variables, the choice of the best functional
specification for count data are disputed (Sturman, 1999). We therefore ran a
zero-inflated Poisson regression, negative binomial regression, and ordered logit
model. The ordered logit had the best fit to our data, and is remarkably good
at minimizing Type 1 error (Sturman, 1999). Also, the CDC dichotomized the
control variables used in both data sets, which increased measurement error,
552 Journal of Aging and Health 25(4)
increasing the likelihood of Type 2 error. The Pearson χ2 tests, which have the
disadvantage of lacking control for spurious relationships, also do not depend
on the accurate specification of a multivariate model. The Pearson χ2 tests also
have less measurement error. We argue that the use two separate estimation tech-
niques, one bivariate and the other multivariate, complement each other, and
increase the robustness of our results.
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