h 23 (2010) 30–35 www.elsevier.com/locate/apnr Applied Nursing Researc Utilizing conjoint analysis to explicate health care decision making by emergency department nurses: a feasibility study Kathleen Fisher, PhD, CRNPa,⁎, Fredrick Orkin, MD, MBA, Mscb, Christine Frazer, MSN, CNSb aCollege of Nursing and Health Professions, Drexel University, Philadelphia, PA 19102, USA bPenn State University, Hersey Medical Center, Hersey, PA 17033, USA Received 13 August 2007; revised 10 March 2008; accepted 22 March 2008 Abstract This descriptive study tests the feasibility of using clinical simulation to understand proxy decision ⁎ Corresponding E-mail address: k 0897-1897/$ – see fro doi:10.1016/j.apnr.200 making by emergency department nurses for individuals with intellectual disability (ID). Results from a conjoint analysis used to identify decision-making patterns indicated that nurses relied on future health status, functional status, and family input while making important health care decisions for their clients. This information enhances our understanding of the complex array of services and supports that nurses are expected to provide. As individuals with ID age and experience increased morbidity, the role of the nurse and caregivers as critical health care decision makers is increasing. © 2010 Elsevier Inc. All rights reserved. 1. Introduction Recently, intellectual disability (ID) has emerged as the preferred term to describe individuals who have significant limitations in intellectual functioning and adaptive beha- vior, the disability historically referred to as mental retardation (Schalock, Luckasson, & Shogren, 2007). According to the Diagnostic and Statistical Manual of Mental Disorders, three criteria must be met to establish this diagnosis, including impaired intellectual functioning level (IQ) 70 or less; onset before the age of 18; and significant limitations in two or more adaptive skill areas, including communication, self-care, home living, social and interpersonal skills, use of community resources, self- direction, functional academic skills, work, leisure, health, and safety (American Psychiatric Association, 1994). Adaptive skills are needed to actively engage in commu- nity living, and persons with ID by definition have difficulty interacting with their environment. They are a vulnerable population that places additional demands on the health care system by virtue of their specific needs (Fisher, Frazer, Hasson, & Orkin, 2007). author. Tel.: +1 2157621208; fax: +1 2157621259. [email protected] (K. Fisher). nt matter © 2010 Elsevier Inc. All rights reserved. 8.03.004 2. Background After the deinstitutionalization movement of the 1970s, many persons with ID moved into community residential agencies, such as group homes, where others routinely make health care decisions for them for access to health care and assistance with daily living. In a previous study of community agency directors, proxy decision making was found to ...

1. h 23 (2010) 30–35
www.elsevier.com/locate/apnr
Applied Nursing Researc
Utilizing conjoint analysis to explicate health care decision
making by
emergency department nurses: a feasibility study
Kathleen Fisher, PhD, CRNPa,⁎, Fredrick Orkin, MD, MBA,
Mscb,
Christine Frazer, MSN, CNSb
aCollege of Nursing and Health Professions, Drexel University,
Philadelphia, PA 19102, USA
bPenn State University, Hersey Medical Center, Hersey, PA
17033, USA
Received 13 August 2007; revised 10 March 2008; accepted 22
March 2008
Abstract This descriptive study tests the feasibility of using
clinical simulation to understand proxy decision
⁎ Corresponding
E-mail address: k
0897-1897/$ – see fro
doi:10.1016/j.apnr.200
making by emergency department nurses for individuals with
intellectual disability (ID). Results from
a conjoint analysis used to identify decision-making patterns
indicated that nurses relied on future
health status, functional status, and family input while making
important health care decisions for their

2. clients. This information enhances our understanding of the
complex array of services and supports that
nurses are expected to provide. As individuals with ID age and
experience increased morbidity, the role
of the nurse and caregivers as critical health care decision
makers is increasing.
© 2010 Elsevier Inc. All rights reserved.
1. Introduction
Recently, intellectual disability (ID) has emerged as the
preferred term to describe individuals who have significant
limitations in intellectual functioning and adaptive beha-
vior, the disability historically referred to as mental
retardation (Schalock, Luckasson, & Shogren, 2007).
According to the Diagnostic and Statistical Manual of
Mental Disorders, three criteria must be met to establish
this diagnosis, including impaired intellectual functioning
level (IQ) 70 or less; onset before the age of 18; and
significant limitations in two or more adaptive skill areas,
including communication, self-care, home living, social
and interpersonal skills, use of community resources, self-
direction, functional academic skills, work, leisure, health,
and safety (American Psychiatric Association, 1994).
Adaptive skills are needed to actively engage in commu-
nity living, and persons with ID by definition have
difficulty interacting with their environment. They are a
vulnerable population that places additional demands on
the health care system by virtue of their specific needs
(Fisher, Frazer, Hasson, & Orkin, 2007).
author. Tel.: +1 2157621208; fax: +1 2157621259.
[email protected] (K. Fisher).
nt matter © 2010 Elsevier Inc. All rights reserved.
8.03.004

3. 2. Background
After the deinstitutionalization movement of the 1970s,
many persons with ID moved into community residential
agencies, such as group homes, where others routinely make
health care decisions for them for access to health care and
assistance with daily living. In a previous study of community
agency directors, proxy decision making was found to affect
the provision of appropriate health care services for indivi-
duals with ID and, in some situations, resulted in a delay or
even denial of health care. Disparities were particularly
evident when health care providers recommended less care for
individuals with ID when they perceived a lesser quality of life
as compared with that of individuals without ID (Fisher,
Haagen, & Orkin, 2005). The decision-making processes used
by proxies for persons with ID have not been well studied but
likely include assessment and synthesis of medical informa-
tion, personal beliefs and values, level of family involvement,
opinions of significant others including caregivers who know
the individual well, cognitive and functional status of the
individual, perceptions about the individual's quality of life,
and institutional priorities and financial constraints. The
emphasis on individual variables probably differs from case
to case, such that decisions and the distribution of services
become unpredictable and disparate (Fisher et al., 2007).
mailto:[email protected]
http://dx.doi.org/10.1016/j.apnr.2008.03.004
31K. Fisher et al. / Applied Nursing Research 23 (2010) 30–35
An estimated 7.5 million people in the United States have
an ID, representing approximately 3% of the U.S. population
(President's Commission on Mental Retardation, 2002).
Health care issues associated with aging, chronic illness, and
end of life are new concerns for this vulnerable population.

4. In the past, those with ID did not live long enough to have
ongoing or chronic health problems (Fisher & Kettl, 2005).
This study's focus on proxy health care decision making as it
relates to health promotion and access to care and services is
a critical, costly, and rising issue within the ID population.
Individuals with ID require assistance or supervision with
activities of daily living and health care decision making.
Many individuals with ID are aging, experiencing chronic
illness, outliving family caregivers, and can expect a return
to community residential support services.
3. Study purpose
The purpose of this pilot study was to test the feasibility of
conjoint analysis in studying the proxy decision-making
process among emergency department (ED) nurses and in
ascertaining their experiences with and perceptions of caring
for individuals with ID. The ED is a critical study site
because decisions made there may result in hospital
admission or discharge back into the community. Nurses
typically provide care for individuals with ID in the
community and acute care settings such as the ED, are
involved in health care decision making, and have an
influential role in determining health care outcomes.
Conjoint analysis is an innovative multivariate statistical
method that identifies, during an actual decision, the relative
“importance” of the factors in a decision and the ways
individual decision makers combine the factors in making
their decisions (Phillips, Johnson, & Maddala, 2002;
Phillips, Maddala, & Johnson, 2002). A clinical simulation
using conjoint analysis was developed with the assistance of
five nurses experienced in working with individuals with ID
and two ED nurse managers. The presenting clinical problem
described an individual with ID and a dental abscess.
4. Theoretical framework: Decision making
Decision making, also termed problem solving, informa-

5. tion processing, and judgment, has been studied extensively
during the past 30 years (Watson, 1994). Theories of
decision making exist in other disciplines and within
scientific and social science paradigms. Typically, these
decision-making models may not always apply to the real
world of decision making, particularly when attempting to
identify the optimal decision. The best option is not always
the one chosen (Noone, 2002).
Decision theory, which evolved from the field of cognitive
psychology, offers a model to examine the processes, out-
comes, and factors involved in decision making (Harbison,
2001). Most often, these theories view decision making as a
linear sequential process (Thompson, 1999). Utility theory,
one such theory, describes a management approach to
decision making under conditions of risk, although it has
not been widely used in nursing studies (Taylor, 2000). This
theory addresses one aspect of decision making for
individuals with ID and is explicated by conjoint analysis.
To understand a decision-making process, one might
merely ask an individual to explain how he or she made a
particular decision. However laudably simple that approach,
many individuals may be unable to verbalize precisely how
the decision was made, may overestimate and underestimate
the roles of given factors in the decision, or may offer a more
socially acceptable response. Also, such a simple approach
ignores the complexity inherent in any decision-making
process that involves the simultaneous evaluation and
combination of multiple factors, as in proxy health care
decision making for individuals with ID. These difficulties
may be avoided by studying decision making in the
controlled setting of a simulation in which the investigator
presents the decision maker with factors believed relevant to
a given decision. In such a simulation, a formal experimental
design dictates the groupings of factors presented simulta-

6. neously, such that it becomes possible to ascertain in an
unbiased manner the relative importance of individual
factors in decision making.
Decision making has been studied extensively in nursing
practice (Noone, 2002; Harbison, 2001). These studies offer
a foundation for understanding how nurses make decisions
with patients, but the context of a nurse–patient relationship
is different from a proxy relationship in which the person
receiving the care may have limited decision-making
capacity. Few studies have addressed proxy decision
making, and there is little knowledge to guide decisions,
particularly for a stigmatized population such as individuals
with ID (Fisher et al., 2005; Fisher et al., 2007).
5. Study sample
After receiving the institutional review board's approval,
we undertook this study in spring 2004. We assembled a
convenience sample of 23 emergency department nurses
from two academic medical centers, located more than
100 miles apart. Each nurse gave informed consent before
participating.
6. Study design and instruments
Conjoint analysis is a measurement technique that uses
simulation coupled with a rigorous experimental design to
mathematically model decision processes at the level of the
individual decision maker (Green & Wind, 1975; Ryan &
Farrar, 2000). This multivariate statistical method is an
especially suitable analytic tool for studying proxy decision
Table 1
Hypothetical factors and factor levels for individual with ID
having a minor

7. surgical procedure
Factors Factor levels
Mental competence Unable to make decisions
Legally incompetent
Functional status Ambulatory
Needing assistance
Bedfast
Likely future health status Unchanged
Improvement
Deterioration
Family input Absent
Approve
Disapprove
Extra cost to agency None
$1,000
$3,000
Person's age (years) 7
30
62
32 K. Fisher et al. / Applied Nursing Research 23 (2010) 30–35
making because it explicates and describes decision making
and predicts outcomes of decisions made by proxies
(Phillips, Maddala, et al., 2002).
Conjoint analysis involves several steps, the first of which
is explicit specification of the decision to be modeled. Here,
the decision is approval of a minor surgical procedure (dental
extraction with anesthesia for a dental abscess) by the

8. designated health care decision maker for a person with ID.
The second step is selecting the factors believed relevant to
the decision. Using a literature review and interviews with
health care personnel who are faced with such decisions, we
identified a set of candidate factors for study: mental
competence, functional status, likely future health status,
person's age, family input, and extra cost to agency (beyond
whatever health care insurance may be available). The third
step is assigning to each factor two or more plausible,
meaningful, and actionable factor levels to each of the factors
(Table 1). The factor levels and the factors are selected such
that decisions about each would be unlikely to be associated
with decisions about others (see Table 2).
Having thus developed the substrate for the simulation,
the fourth step is designing the scenarios in which each factor
is presented at the one-factor level (“full-profile” design). It
is not feasible to present all possible scenarios (i.e., 2 × 3 ×
3× 3 × 3 × 3 = 486) to the decision maker due to resultant
Table 2
Experimental design (fractional factorial design) for the first 6
scenarios among 2
Scenario Mental competence Functional status Likely future h
1 Unable to make decisions Bedfast Deterioration
2 Incompetent Ambulatory Deterioration
3 Unable to make decisions Ambulatory Deterioration
4 Incompetent Ambulatory Improvement
5 Unable to make decisions Bedfast Improvement
6 Unable to make decisions Bedfast Improvement
respondent fatigue that, in turn, would lead to decision
makers withdrawing from the study or oversimplifying their
decision making (e.g., decisions based solely on the “most
important” factor). To reduce the potential scenarios to a
manageable number, we used an experimental design

9. (fractional factorial design) that dictated the presentation of
22 scenarios, a subset of all possible combinations of factor
levels (Table 2); this highly favorable design requires that the
factors and factor levels be statistically independent (i.e., that
the underlying decision making relating to a given factor at a
given factor level is not influenced by that of other factor–
factor level combinations). Because of this design choice, the
analysis is limited to the role of each factor at each factor
level in decisions (“main effects”) and specifically cannot
explore potential influences (“interactions”) of factors at
given factor levels on one another. The description of each of
the 22 hypothetical scenarios, as dictated by the experimental
design, was presented on an index card (Table 2). The fifth
step is eliciting the decision makers' preferences in relation
to the decision under study. The decision maker is asked to
rank order (most likely to least likely) or score (e.g., 1 to 100)
their likelihood of, in this case, approving the minor surgery
in each of the hypothetic scenarios. To reduce the intellectual
burden, we opted for rank ordering. In studies requiring more
than a half dozen factors and/or more factor levels than used
here, elicitation involves a large number of two-way
comparisons (e.g., Scenario A vs. Scenario B, Scenario A
vs. Scenario D); such a “discrete-choice” design seemed
excessively complicated for this application. With the data
collected, the final step is data analysis that is tailored to the
experimental design. Because the factors and factor levels
were chosen such that they are independent of each other in
the simulated decision, the analysis is analogous to an
analysis of variance with no interaction terms. For example,
a simpler decision involving two factors, each at three levels,
can be represented mathematically as follows: U(x) = B0 +
B1(X11) + B2(X12) + B3(X13) + B4(X21) + B5(X22) +
B6(X23) +
Error, where U(x) is the overall perceive value (“utility”) of a
set of scenarios (xi through xk) composed of the two factors
(X1 and X2), B1 through B6 are the coefficients of factor

10. level (1 through 3) for each factor (1, 2), and B0 is the utility
when both factors are present at their first level. The
contribution of a factor at a given factor level (e.g., B1[X11])
to the overall utility is called the “partworth” and can be
computed in a dummy variable multiple regression analysis
2 hypothetical scenarios in conjoint analysis simulation
ealth status Family input Extra cost to agency, $ Person's age
Disapprove 1,000 30
Disapprove 1,000 7
Approve None 30
Disapprove 3,000 30
Disapprove None 62
Approve 3,000 7
Fig. 1. Mean utility values for each factor at each factor level.
33K. Fisher et al. / Applied Nursing Research 23 (2010) 30–35
because the equation is composed of zeroes (in the absence
of a given factor at a given factor level) and ones (presence),
according to the factorial design. The overall perceive value
(utility), U(x), of a given scenario for a given decision maker
is its rank order. Thus, the regression analysis estimates each
decision maker's set of utility values for each factor at each
factor level. In turn, the importance of a given factor for a
given decision maker is computed as that factor's proportion
of the total utility (partworth).
7. Application to decision making for individuals
with ID
The conjoint analysis simulation required ED nurses to
place themselves in the role of decision maker for an
individual with ID, using a clinical scenario developed with

11. experienced ID nurses and the two ED clinical nurse man-
agers, based on experiences with individuals with ID who
frequented the ED for care. After completing a brief survey
that inquired about their age, gender, education, and years of
working experience, each nurse was asked to complete the
simulation task, requiring the rank ordering of 22 cards.
An example of one such card appears on the top row
of Table 2. Each nurse was read the following statements
by the nurse researcher and then handed the 22 cards for
rank ordering:
Like others, persons with ID have health care needs but may
not be capable of making decisions. Legal guardians,
including agencies overseeing residential homes, often make
health care decisions for the individual with ID. We are
studying the relative importance of different factors that may
influence these decisions. Assume that you are the designated
health-care decision maker for a person with ID who has a
dental abscess requiring a dental extraction and anesthesia
(i.e., a “minor” surgical procedure). The characteristics of
each of 22 such individuals are presented on these cards.
Please rank-order the cards so that the card describing the
individual for whom you would most likely approve the care
is first, the individual for whom you would least likely
approve the care is last, and the other cards are ranked in
between according to your likelihood of approving the care.
8. Data analysis
Conjoint analysis transformed each nurse's set of
rankings into individual-factor utilities, from which we
computed the total utility of each care decision and the
percentage contribution of each factor to the care decisions
made by each nurse. To estimate the consistency with which
each nurse applied the utilities in their ranking decisions, we
correlated their actual rankings of a small subset of scenarios
not used to compute utilities with rankings predicted on the

12. basis of the derived utilities.
The importance of a given factor in decision making was
computed as the proportion of total utility in a given decision
scenario accounted for by the factor. Cluster analysis enabled
the identification of nurses whose decision-making patterns
were similar based on their factor utilities. Using con-
tingency tables with nonparametric tests (chi-square and
Fisher's exact tests), we tried to explain the decision-making
patterns associated with the nurses' characteristics. All
Fig. 2. Importance (percentage contribution) of each factor to
the decision whether to approve a minor surgical procedure for
a hypothetical person with ID by a
decision-making pattern. Whereas Group 1 and Group 2 are
composed of 10 and 8 nurses, respectively, the other groups
each consisted of 1 nurse. Error bars
denote 95% confidence intervals.
34 K. Fisher et al. / Applied Nursing Research 23 (2010) 30–35
statistical procedures were conducted with SPSS (Version
12, SPSS, Inc., Chicago, IL).
9. Results
Most of the nurses were women (95.7%), with an average
of 7 years of ED experience. Most were educated as diploma
nurses (43.5%), with others possessing bachelor of science in
nursing (30.4%), associate (21.7%), or master's (4.3%)
degrees. Their mean age was 40 years, but ages ranged from
23 to 59 years. Each nurse took 20–25 minutes to complete
the rank ordering task; 2 nurses found the ranking task too
complicated. The 21 nurses who completed the task were
generally highly consistent in their rankings, with all but one
exhibiting a Pearson's r of ≥.928 for the correlation between

13. their predicted and observed rankings. Using the proportion
of total utility as a surrogate for the importance of each factor
in the decision, the mean importance values for each factor
for the group of 21 nurses were likely future health status,
39%; family input, 19%; person's age, 13%; extra cost to
agency, 12%; functional status, 10%; and mental compe-
tence, 6%. Underlying and accounting for these overall
group importance values were the participants' utilities for
the individual-factor levels comprising the decisions (Fig. 1):
The decision to approve care was more likely if family
approval, improved future health status, and, to a lesser
extent, young age, no extra cost, and ambulatory functional
status were present. On the other hand, approval decisions
were least likely if there was deterioration in future health
and family disapproval and, to a lesser extent, if the patient
was bedfast and old and cost was high. The participants were
indifferent to mental competence, assistance needs,
unchanged future health status, absence of family input,
modest cost, and age between youth and being old.
Cluster analysis enabled identification of subgroups within
the group of 21 nurses, which exhibited discrete decision-
making patterns (Fig. 2). The largest subgroup (10 nurses)
relied largely on future health status (58% of total utility in
decision making), with lesser attention to family input, extra
cost to agency, and person's age. Another subgroup (8 nurses)
relied moderately on future health status (25%) and family
input (31%), with lesser attention to functional status, extra
cost to agency, and person's age. There were three other
decision-making patterns, each exhibited by one nurse:
Whereas one nurse relied heavily on mental competence
(43%) and person's age (52%), another emphasized mental
competence (43%) and functional status (29%), and the third
used extra cost to agency (66%) supplemented by person's
age (18%). Nurse's work site, age, education, and years of
experience did not discriminate among these decision-
making patterns in this small pilot study sample.

14. 10. Discussion
Conjoint analysis is feasible and useful for studying
complex health care decision making by proxies for those
with ID. Although this preference measurement technique
has been used almost 40 years in psychology and marketing
research (Green & Wind, 1975), it has been applied in a wide
array of health care applications only more recently
(Eberhart, Morin, Wulf, & Geldner, 2002; Orkin & Green-
how, 1978; Phillips, Johnson, et al., 2002; Phillips, Maddala,
et al., 2002; Ryan & Farrar, 2000). Rather than providing a
prescriptive or normative perspective on decision making,
the methodology reveals how decisions are actually being
made, in this situation by using a “real-world” clinical
simulation, that is, an individual with ID and a dental
abscess. In making health care decisions for individuals with
35K. Fisher et al. / Applied Nursing Research 23 (2010) 30–35
ID, nurses placed greatest weight on future health status,
particularly the likelihood of improvement. This was more
important than family input, age, extra cost, or current
functional status for the nurses as a group. Proxy decision
making is a complex issue that was not addressed uniformly
by all nurse respondents. There were subgroups with discrete
decision-making patterns that emphasized certain factors in
their decision. Of concern was the one nurse who placed
almost all weight on extra cost to agency and the age of the
individual in decision making and determining care for the
individual. Fortunately, this decision-making pattern was
expressed by only one individual. The appropriateness of
most proxy decisions was aligned with individual's needs
and rights. Although conjoint analysis appears to be useful, it
is not known if the nurses responded as they might to an
actual ED patient or if there would be a difference in their

15. decision-making responses if they actually knew the
individual versus completing a simulation exercise.
11. Limitations
Real-world decision making may depart from what was
found in this study because simulation provides only an
approximation of reality, and conjoint analysis relies on an
additive utility model of decision making that arguably may
not capture the complexity of a particular decision. More-
over, given the multiple challenges in studying decision
making noted earlier and the absence of a gold-standard
methodology that might provide comparison results, it is not
possible to assess convergent, criterion, or discriminate
validity, even though the results reported herein appear to
have face and content validities. However, conjoint analysis
has become a mainstay approach in psychological and
marketing research because its results have been proven to be
robust, and more complicated models (e.g., alternatives to
additive linear model) have generally not been found better
or more informative. Although adequate for a feasibility
study, the sample size was insufficient to undertake a
meaningful explanation of the observed decision-making
patterns. Generalizability of our findings may be limited to
the two EDs studied. Study findings, however, suggest that
the simulation task was feasible and meaningful to this group
of nurses, supporting the use of conjoint analysis in future
research in proxy decision making.
12. Conclusions and implications
There is a gap in nursing knowledge related to proxy
decision making. This study demonstrates use of an
innovative method (conjoint analysis) to measure individual
variables in the decision-making process and describes how
study participants are currently making these decisions. We
concluded that the nurses used subsets of information in their
decision making and that almost all of the nurses (95%)

16. made their decisions on the basis of factors relating to the
individual with ID rather than on external issues (i.e., extra
cost to agency). Future health status was ranked most
important among studied factors by nurses in making health
care decisions for individuals with ID. Nurses in their role as
health educators and advocates for their clients need to know
what information proxy decision makers value. With this
knowledge, nurses can better serve their clients in institu-
tional and community settings, which should improve the
process and impact the recipient of the services. Further, we
conclude that the simulation task was feasible and mean-
ingful to this group of nurses, supporting the use of conjoint
analysis in future research.
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http://www.acf.dhhs.gov/programs/pcmr/mission.htmUtilizing
conjoint analysis to explicate health care decision making by
emergency department nu.....IntroductionBackgroundStudy
purposeTheoretical framework: Decision makingStudy
sampleStudy design and instrumentsApplication to decision
making for individuals �with IDData
analysisResultsDiscussionLimitationsConclusions and
implicationsReferences