Statistical Analyses in Nursing

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. athleen.mary.fisher@drexel.edu (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 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).

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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. 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- 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- 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 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

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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 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 (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 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 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 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 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.

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

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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 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%)

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.

References

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders. (4th ed.) Washington, DC: American Psychiatric Press, Inc.

Eberhart, L., Morin, A., Wulf, H., & Geldner, G. (2002). Patient preferences for immediate postoperative recovery. British Journal of Anesthesia, 89, 760−761.

Fisher, K., Frazer, C., Hasson, K., & Orkin, F. (2007). A qualitative study of emergency department nurses' perceptions and experience in caring for individuals with intellectual and developmental disabilities. Interna- tional Journal of Nursing in Intellectual and Developmental Disabilities, 3(1), 1.

Fisher, K., Haagen, B., & Orkin, F. (2005). Acquiring medical services for individuals with mental retardation in community based housing facilities. Applied Nursing Research, 18(3), 155−159.

Fisher, K., & Kettl, P. (2005). Aging with mental retardation: Increasing population of older adults with MR require health intervention and prevention strategies. Geriatrics, 60(4), 26−30.

Green, P. E., & Wind, Y. (1975). New way to measure consumers' judgments. Harvard Business Review, 53, 107−117.

Harbison, J. (2001). Clinical decision making in nursing: Theoretical perspectives and their relevance to practice. Journal of Advanced Nursing, 35(5), 126−133.

Noone, J. (2002). Concept analysis of decision making. Nursing Forum, 37 (3), 21−32.

Orkin, F. K., & Greenhow, D. E. (1978). A study of decision making: How faculty define competence. Anesthesiology, 48, 267−271.

Phillips, K. A., Johnson, F. R., & Maddala, T. (2002). Measuring what people value: A ED nurses decision making 15 comparison of “attitude” and “preference” surveys. Health Services Research, 37, 1659−1679.

Phillips, K. A., Maddala, T., & Johnson, F. R. (2002). Measuring preference for health care interventions using conjoint analysis: An application to HIV testing. Health Services Research, 37, 1681−1705.

President's Commission on Mental Retardation. (2002). Retrieved December 2002, from www.acf.dhhs.gov/programs/pcmr/mission.htm.

Ryan, M., & Farrar, S. (2000). Using conjoint analysis to elicit preferences for health care. British Medical Journal, 320, 1530−1533.

Schalock, R. L., Luckasson, R. A., & Shogren, K. A. (2007). The renaming of mental retardation: Understanding the change to the term intellectual disability. Journal of Intellectual and Developmental Disabilities, 45(2), 116−124.

Taylor, C. (2000). Clinical problem solving in nursing: Insights from the literature. Journal of Advanced Nursing, 31(4), 842−849.

Thompson, C. (1999). A conceptual treadmill: The need for “middle ground” in clinical decision making theory. Journal of Advanced Nursing, 30(5), 1222−1229.

Watson, S. (1994). An exploratory study into a methodology for the examination of decision making by nurses in the clinical area. Journal of Advanced Nursing, 20, 351−360.