Vol. 48, No. 3, 338–348
Copyright 2008 by The Gerontological Society of America
Nursing Home Care Quality: Insights From a
Bayesian Network Approach
Justin Goodson, MS,1Wooseung Jang, PhD,2and Marilyn Rantz, RN, PhD, FAAN3
Purpose: The purpose of this research is twofold.
The first purpose is to utilize a new methodology
(Bayesian networks) for aggregating various quality
indicators to measure the overall quality of care in
nursing homes. The second is to provide new insight
into the relationships that exist among various mea-
sures of quality and how such measures affect the
overall quality of nursing home care as measured by
the Observable Indicators of Nursing Home Care
Quality Instrument. In contrast to many methods used
for the same purpose, our method yields both quali-
tative and quantitative insight into nursing home care
Design and Methods: We construct several
Bayesian networks to study the influences among
factors associated with the quality of nursing home
care; we compare and measure their accuracy
against other predictive models.
the best Bayesian network to perform better than
other commonly used methods. We also identify key
factors, including number of certified nurse assistant
hours, prevalence of bedfast residents, and preva-
lence of daily physical restraints, that significantly
affect the quality of nursing home care. Furthermore,
the results of our analysis identify their probabilistic
Implications: The findings of this re-
search indicate that nursing home care quality is most
accurately represented through a mix of structural,
process, and outcome measures of quality. We
also observe that the factors affecting the quality of
nursing home care collectively determine the overall
Results: We find
quality. Hence, focusing on only key factors without
addressing other related factors may not substantially
improve the quality of nursing home care.
Key Words: Bayesian networks, Nurse staffing,
Nursing home quality, Occupancy rate, Quality
Determining what factors significantly influence
the quality of care in nursing homes has presented
a difficult challenge and is a growing concern.
Despite calls for reform (Institute of Medicine,
1986), recent reports conclude that the quality of
care provided in some facilities still leaves much to
be desired (Institute of Medicine, 1996, 2001). As the
United States will encounter an unprecedented
number of Americans who require skilled nursing
care in the upcoming decades, the need for a more
thorough understanding of nursing home care qual-
ity has never been more prominent.
In this study we examine what factors influence
the quality of nursing home care through a multivar-
iate framework known as Bayesian networks. The
intent of our research is twofold. We seek to utilize
a new methodology for aggregating various quality
indicators to measure the overall quality of care in
nursing homes. We also seek to provide new insight
into the relationships that exist among various
measures of quality and how such measures affect
the overall quality of nursing home care as measured
by the Observable Indicators of Nursing Home Care
Quality Instrument (Rantz & Zwygart-Stauffacher,
2006; Rantz, Mehr, et al., 2006). Two aspects of the
study provide such insight. First, the Bayesian
network framework allows us to examine previous
research to determine which combinations of vari-
ables most significantly influence the quality of care.
Second, an inspection of the structure and parame-
ters that characterize a Bayesian network both
qualify and quantify aspects of nursing home care
quality. The insights gained from this analysis not
only provide a unique perspective on the makeup of
We acknowledge the contributions of other University of Missouri–
Columbia Minimum Data Set and Nursing Home Quality Research
Team members. Research activities were partially supported by the
National Institute of Nursing Research under Grant 1R01NR/AG05287-
01A2. The opinions expressed herein are those of the authors and do not
represent the institute.
Address correspondenceto Wooseung
Industrial and Manufacturing Systems Engineering, University of
Missouri–Columbia, E3437 Lafferre Hall, Columbia, MO 65211.
1Department of Management Sciences, University of Iowa, Iowa City.
2Department of Industrial and Manufacturing Systems Engineering,
University of Missouri–Columbia.
3Sinclair School of Nursing, University of Missouri–Columbia.
Jang, Department of
338 The Gerontologist
nursing home care quality, but they also afford
practical applications for quality improvement.
In recent decades, researchers have conducted
numerous studies to gain a better understanding of
the quality of nursing home care. In this section we
do not intend to provide an exhaustive review of
such studies. Rather, we offer a survey of studies,
many of which we think are representative of
important advances in this field. The survey is
divided into two sections. The first addresses studies
focusing on specific factors that influence nursing
home care quality; the second is devoted to studies
that aggregate information to measure the overall
quality of care in nursing homes.
Factors Influencing the Quality of
Nursing Home Care
Factors influencing the quality of nursing home
care delivery can be separated into three categories:
structure, process, and outcome. Common structural
measures of quality include facility size, occupancy,
ownership type, staffing, and percentage of Medicaid
and Medicare residents. Most studies concur that
larger facilities exhibit lower levels of quality than do
smaller facilities (Harrington, O’Meara, Kitchener,
Simon, & Schnelle, 2003; O’Neill, Harrington,
Kitchener, & Saliba, 2003). Research examining the
relationship between the quality of nursing home
care and occupancy rate has produced mixed results.
Some studies show that higher occupancy rates are
associated with a higher use of restraints, more
pressure sores, a greater use of psychoactive drugs,
and lower total nurse and registered nurse staffing
hours (Castle, 2001; Harrington & Swan, 2003). In
contrast, research conducted by Zinn, Aaronson,
and Rosko (1993) indicates that higher mortality
rates are associated with lower occupancy rates.
Investigations into the relationship between the
proprietary status of nursing homes and the quality
of care overwhelmingly favor nonprofit institutions
over for-profit institutions (Aaronson, Zinn, &
Rosko, 1994; Harrington, Woolhandler, Mullan,
Carrillo, & Himmelstein, 2001; O’Neill et al.).
Although one study reports that staffing levels
account for only a small portion of the total varia-
tion in quality of care (Harrington, Zimmerman,
Karon, Robinson, & Beutel, 2000), the general con-
sensus of research is that more staffing of all types
improves nursing quality (Schnelle et al., 2004).
According to the authors of some studies, higher
percentages of Medicaid residents tend to negatively
influence nursing quality and staff levels (Grabowski,
Helgheim, Randall, and Wardell (2005) posited
that, in their study, the percentage of Medicaid
residents did not contribute adversely to outcome
quality. Research conducted by Harrington and
colleagues (2001, 2003) seems to indicate that the
percentage of Medicare residents has a positive affect
on the quality of care in nursing homes. Although
research to date has yielded various results on the
relationship of structural measures to overall nursing
home quality, such measures are clearly an influen-
tial aspect of the quality of nursing home care.
Process measures of nursing home quality describe
the process of care provided. Outcome quality
measures, in contrast, measure the results of nursing
care processes. An often utilized set of clinical
quality indicators (which encompass both process
and outcome measures) was developed by research-
ers at the Center for Health Systems Research and
Analysis (CHSRA) at the University of Wisconsin–
Madison. These quality indicators (QIs) measure the
proportion of nursing home residents with the QI
condition. The latter portion of Table 1 lists the
24 CHSRA QIs (QI variable names followed by the
letter N were revised in 1997). Despite their wide-
spread use, clinical QIs are not without criticism.
Critics point to flaws with the Minimum Data Set as
well as with the QIs themselves (General Accounting
Office, 2002; Office of the Inspector General, 2001).
Although a number of studies indicate that clinical
QIs require further validation and testing in order
for researchers to determine how accurately they
predict the quality of care in nursing homes, critics
admit that facilities with better quality can be dif-
ferentiated among those with average or poorer
quality on the basis of such indicators (Harrington
et al., 2003).
When a facility does not comply with federally
imposed standards, the facility may receive a defi-
ciency. As deficiencies may address standards related
to processes or outcomes (or both) in a nursing
home, they have often been utilized as process and
outcome measures, depending on their type and
severity. In addition, the number and type of defi-
ciencies have often been utilized as a measure of
overall quality in nursing homes. Consequently, rela-
tionships between deficiencies and many of the
aforementioned structural measures of quality have
been rigorously researched.
Models for Assessing the Quality of
Nursing Home Care
A popular model that has been applied to the
assessment of nursing home care quality was de-
veloped by Donabedian (1988). Donabedian pro-
posed three aspects of quality assessment: structure,
process, and outcome, known as the SPO frame-
work. The SPO framework served as the foundation
for two notable models. Atchley (1991) modified the
framework to include a time dimension. Unruh and
Wan (2004), noting the lack of causality among
structure, process, and outcomes in models utilizing
Vol. 48, No. 3, 2008 339
homes, other measures may offer further insight and
may yield more accurate results. Despite the need for
some improvements in future research, the positive
implications of assessing the QOC in nursing homes
through a Bayesian network—as illustrated in this
analysis—outweigh any limitations or difficulties
encountered in this research.
Aaronson, W. E., Zinn, J. S., & Rosko, M. D. (1994). Do for-profit and not-
for-profit nursing facilities behave differently? The Gerontologist, 34,
Atchley, S. (1991). A time-ordered, systems approach to quality assurance in
longterm care. Journal of Applied Gerontology, 10, 19–34.
Bøttcher, S. G. (2001). Learning Bayesian networks with mixed variables.
Artificial Intelligence and Statistics (pp. 149–156). San Francisco:
Bøttcher, S. G., & Dethlefsen, C. (2004). Deal: Learning Bayesian networks
with mixed variables. R package version 1.2-22 (Version 1.2-22)
Castle, N. G. (2001). Deficiency citations for physical restraint use in nursing
homes. Journal of Gerontology: Social Sciences, 55B, S33–S40.
Chesteen, S., Helgheim, B., Randall, T., & Wardell, D. (2005). Comparing
quality of care in non-profit and for-profit nursing homes: A process
perspective. Journal of Operations Management, 23, 229–242.
Donabedian, A. (1988). The quality of care: How can it be assessed? Journal
of the American Medical Association, 260, 1743–1748.
Geiger, D., & Heckerman, D. (1994). Learning Gaussian networks. In
Uncertainty in artificial intelligence: Proceedings of the Tenth
Conference. San Mateo, CA: Kaufmann.
General Accounting Office. (2002). Nursing homes: Federal efforts to
monitor resident assessment data should complement state activities
(Publication No. GAO-02-279). Washington, DC: Author.
Grabowski, D. C. (2001). Does an increase in the Medicaid reimbursement
rate improve nursing home quality? Journal of Gerontology: Social
Sciences, 56B, S84–S93.
Harrington, C., O’Meara, J., Kitchener, M., Simon, L. P., & Schnelle, J. F.
(2003). Designing a report card for nursing facilities: What information is
needed and why. The Gerontologist, 43(Spec. Issue II), 47–57.
Harrington, C., & Swan, J. H. (2003). Nursing home staffing, turnover, and
case mix. Medical Care Research and Review, 60, 366–392.
Harrington, C., Woolhandler, S., Mullan, J., Carrillo, M. S., & Himmelstein,
D. U. (2001). Does investor ownership of nursing homes compromise the
quality of care? American Journal of Public Health, 91, 1452–1455.
Harrington, C., Zimmerman, D., Karon, S. L., Robinson, J., & Beutel, P.
(2000). Nursing home staffing and its relationship to deficiencies. Journal
of Gerontology: Social Sciences, 55B, S278–S287.
Hogg, R., Craig, A., & McKean, J. (2004). Introduction to mathematical
statistics. Englewood Cliffs, NJ: Prentice-Hall.
Institute of Medicine. (1986). Improving the quality of care in nursing
homes (No. Report IOM-85-10). Washington, DC: Author.
Institute of Medicine. (1996). Nursing staff in hospitals and nursing homes.
Washington, DC: Author.
Institute of Medicine. (2001). Improving the quality of long-term care.
Washington, DC: Author.
Jensen, F. V. (1996). An introduction to Bayesian networks. London: UCL
Karon, S. L., Sainfort, F., & Zimmerman, D. R. (1999). Stability of nursing
home quality indicators over time. Medical Care Research and Review,
Lee, S., & Abbott, P. (2003). Bayesian networks for knowledge discovery in
large datasets: Basics for nurse researchers. Journal of Biomedical
Informatics, 36, 389–399.
Marshall, A., McClean, S., Shapcott, C., Hastie, I., & Millard, P. (2001).
Developing a Bayesian belief network for the management of geriatric
hospital care. Health Care Management Science, 4, 25–30.
O’Neill, C., Harrington, C., Kitchener, M., & Saliba, D. (2003). Quality of
care in nursing homes—An analysis of relationships among profit,
quality, and ownership. Medical Care, 41, 1318–1330.
Office of the Inspector General. (2001). Nursing home resident assessment:
Quality of care. Washington, DC: Department f Health and Human
Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Mateo,
Rantz, M. J., Hicks, L., Grando, V., Petroski, G. F., Madsen, R. W., Mehr,
D. R., et al. (2004). Nursing home quality, cost, staffing, and staff mix.
The Gerontologist, 44, 24–38.
Rantz, M., Mehr, D., Petroski, G., Madsen, R., Popejoy, L., Hicks, L., et al.
(2000). Initial field testing of an instrument to measure ‘‘Observable
indicators of nursing home care quality.’’ Journal of Nursing Care
Quality, 14, 1–12.
Rantz, M., Petroski, G. F., Madsen, R. W., Mehr, D. R., Popejoy, L., Hicks,
L., et al. (2000). Setting thresholds for quality indicators derived from
MDS data for nursing home quality improvement reports: An update.
The Joint Commission Journal on Quality Improvement, 26, 101–110.
Rantz, M., & Zwygart-Stauffacher, M. (2006). A new reliable tool for nurse
administrators, nursing staff, regulators, consumers, and researchers for
measuring quality of care in nursing homes. Nursing Administration
Quarterly, 30, 178–181.
Rantz, M., Zwygart-Stauffacher, M., Mehr, D., Petroski, G., Owen, S.,
Madsen, R., et al. (2006). Field testing, refinement, and psychometric
evaluation of a new measure of nursing home care quality. Journal of
Nursing Measurement, 14, 129–148.
Rantz, M., Zwygart-Stauffacher, M., Popejoy, L., Grando, V., Mehr, D.,
Hicks, L., et al. (1999). Nursing home care quality: A multidimensional
theoretical model integrating the views of consumers and providers.
Journal of Nursing Care Quality, 14, 16–37.
Schnelle, J. F., Simmons, S. F., Harrington, C., Cadogan, M., Garcia, E., &
Bates-Jensen, B. M. (2004). Relationship of nursing home staffing to
quality of care. Quality, 39, 225–250.
Spiegelhalter, D. J., Dawid, A. P., Lauritzen, S. L., & Cowell, R. G. (1993).
Bayesian analysis in expert systems. Statistical Science, 8, 219–283.
Tan, P., Steinbach, N., & Kumar, V. (2005). Introduction to Data mining.
Reading, MA: Addison-Wesley.
Unruh, L., & Wan, T. T. H. (2004). A systems framework for evaluating
nursing care quality in nursing homes. Journal of Medical Systems, 28,
Zinn, J. S., Aaronson, W. E., & Rosko, M. D. (1993). Variations in the
outcomes of care provided in Pennsylvania nursing homes: Facility and
environmental correlates. Medical Care, 31, 475–487.
Received May 16, 2007
Accepted October 30, 2007
Decision Editor: William J. McAuley, PhD
348 The Gerontologist