ATOMS Project Technical Report - Multiattribute Utility Theory
Summarizing a Methodology and an Evolving Instrument for AT Outcomes
Bobbi Blaser Johnson, Eli Gratz, Kathy Longenecker Rust & Roger O.Smith
Updated: March 28, 2007
Table of Contents
There are many measurement and research methodologies that are not typically used for assistive technology (AT) outcomes that we need to understand for their potential contribution to an outcomes system. Examples include goal-attainment scaling (GAS), dynamic norming, subjective elicitation of data, multiattribute utility techniques (MAUT or MAU) and Bayes.Many of these are based on data elicitation principles from the decision sciences. Decision analysis data collection and models such as MAU and Bayes are heavily used in engineering, economics, mathematics, military strategy and medical practice. These may provide new strategies for measuring key components of AT outcomes. This report summarizes an investigation and review of the use of MAU models in decision-making and discusses their relevance to AT outcomes and instrumentation.
Frequently, decisions regarding rehabilitation services, including AT, must be made with atypical and complex sets of variables. Individual circumstances of people who have disabilities can require unique decision processes. Diagnostic populations may be small and function can be very idiosyncratic. Thus, data collection and questioning strategies may need to be flexible, customized to the individual, and created on the spot with the client. This is especially true for people who use AT. In addition to their diverse circumstances, these individuals may use more than one device for more than one task in more than one environment.
Researchers and AT practitioners lack reliable and valid tools for outcomes data collection and decision-making and consequently they must adapt existing tools to fit their measurement needs. Earlier ATOMS project work identified that, while dozens of AT measurement instruments exist, few have been devised with outcomes in mind. Most have been created as part of the process to identify and select devices to match a need to an individual AT consumer (Rust & Smith, 2004).Additionally, health and rehabilitation functional performance and related outcomes measures rarely include AT as a co-variate. Many treat AT as an impairment that lowers performance scores, and even fewer instruments isolate the impact of AT in the outcomes score (Rust & Smith, 2005). They argue that the failure to understand the role of technology in the outcomes of people who have a spectrum of types and intensities of disabilities neglects a significant opportunity to scientifically better understand the interaction between technology and human disablement. They present a need for a next-generation outcomes measurement system that utilizes measurement theory not typical to the field of AT outcomes research to measure the impact of assistive technology devices (ATDs).
This effort reviewed the literature on MAU theory to identify the scope in which it is used in order to recommend potential applications relevant to AT outcomes. A team of engineering and health students started this project in 2004 and completed the work in 2006. They searched engineering, business, and health databases using key words primarily containing the multiattribute utility theory, (MAU) and derivatives. The collected articles were coded into three categories 1) general literature, 2) engineering, and 3) health. Appendix A displays a bibliography of these articles.
This report describes the fundamental measurement characteristics and background of MAU models and how they have been used in health. It also discusses the implications drawn from the MAU literature and describes the methodology used in decision analysis. This validates MAU models to strengthen the reliability and validity of qualitative client-centered methods such as GAS and instruments like the Canadian Occupational Performance Measure (COPM). Lastly, the report discusses the implications of using MAU in AT measurement and data collection.
MAU is a method to effectively integrate subjective and objective data onto a common scale or index (Garre, 1992) that can be used for decision-making. The general literature describing MAU reveals that it is a method for decision-making and not traditionally an evaluation tool. This technique uses gathered data with a specific and sensitive weighting system to assess a given decision regarding various attributes (variables or outcomes), in order to find the optimal decision given a specific set of criteria (Barron and Barrett 1996; Herrmann and Code 1996). Five key steps central for all MAU procedures were described by von Winterfeldt and Edwards (1986):
- Define alternative and value-relevant attributes;
- Evaluate each alternative separately on each attribute;
- Assign relative weights to the attributes;
- Aggregate the weights of attributes…to obtain an overall evaluation of alternatives;
- Perform sensitivity analyses and make recommendations.
More simply, Chatburn & Primiano (2001) summarize the MAU method as a model that:
- Incorporates input from the various stakeholders in the decision;
- Identifies the factors that are important in the decision and the alternative decision options;
- Weights the factors;
- Ranks the alternative decision on how well they serve the factors; and finally,
- Provides an overall score that identifies the best options.
Garre (1992) provides an easy-to-follow method that expands the five steps proposed by many MAU enthusiasts to 10 steps. The following outlines Garre’s process. Using 10 steps, Garre reported on a case study of a MAUT method used by health care managers to make a decision on whether to keep a 32-hour work week at 40-hour pay or eliminate the program (Table 1).
|General Steps||Case Example|
1. Determine the appropriate viewpoint for the decision. Form a committee of stakeholders who are vested in the outcomes of the decision (option; this may be a unitary decision).
1. A committee was formed, including the chief executive officer, vice president of finance, vice president of nursing services, vice president of human resources and vice president of marketing.
2. Identify decision alternative. What options are being compared? What are the variables?
2. The variables in the decision were whether to retain, modify, or delete a program in which nurses who worked 32 hours a week on evening or night shifts were paid for 40 hours.
3. Identify attributes for evaluation. What are the attributes that characterized the variables affecting the decision?
3. Three attributes characterized the variables affecting the decision: cost, attrition, and morale.
4. Identify factors for evaluating the attributes (option; used if variables can be broken down further).
4. The factors for evaluating these attributes include a) cost: the dollar amount to replace nurses who decide to leave based on the final decision, b) attrition: number of nurses who decide to stay, and c) morale: nursing staffs’ attitude change from severe decrease to no change.
|5. Establish a Utility Scale. Committee members rate each factor on a scale (i.e. 0-10). Each member assigns a relative contribution value for each factor.||5. The committee members rated each factor to assign a relative contribution value for each factor; with 0 as “worst” and 10 as “best.”|
|6. Transform (or aggregate) each factor value to a utility scale.||6. The scores for each attribute were averaged.|
|7. Determine the relative weights of each attribute or factor.||7. The committee then assessed the relative importance of cost (42%), attrition (33%), and morale (25%). This committee obviously valued the dollar in this case. If nursing staff members had been on this committee, it is likely that morale and attrition would have rated higher.|
|8. Calculate total utility for each of the decision alternatives.||8. The total utility was calculated by multiplying the utility score of each factor times the ratio weights for each attribute.|
|9. Determine which alternative has the greatest total utility score. Make a decision.||9. The decision to retain the “32 for 40” program had the highest utility score.|
|10. Perform sensitivity analysis to determine the strength of the analysis.||10. The committee performed a sensitivity analysis to evaluate the strength of the decision. The committee asked whether a change in weights or in differential scaling would alter the decision. For example if total ratio weights were changed to .40, .36 and .24 for cost, attrition, and morale, would the decision remain the same? It was determined that these changes did not alter the ranking of the decision.|
Multiattribute theory was formulated and refined in the late 1960s and early 1970s (Gustafson & Holloway 1975; Kenney 1970,) during which time a few groundbreaking articles were published. The literature revealed that MAU analysis has been employed in a wide variety of fields.
Management sciences: As a means of structuring decision-making (Bier & Connel, 1994; Carroll & Johnson, 1990; Christenson, 1993; Doyle, 1995; Dyer, Fishburn, Steuer, Walleins & Zionts, 1992; Hanson, Kidwell & Ray, 1991; Huber, 1974; Keeney & Raiffa, 1976; Pandey & Kengpol, 1995; Poole & DeSanctis, 1990; Samuelson, 1993).
Assessing programs: These methods have been proposed for evaluating program alternatives (Dicker & Dicker, 1991; Edwards & Newman, 1982), and are often applied in the fields of public health (Alemi, Stephens, Llorens & Orris, 1995; Camasso & Dick, 1993; Kaplan, Atkins & Wilson, 1988; Salazar & de Moor, 1995), in social services (Hidalgo-Hardeman, 1993; Kemp & Willetts, 1995; Lewis, Johnson & Mangen, 1998), related to consumer choice (Kahn & Baron, 1995; Kahn & Meyer, 1991), in environmental studies (Brown, 1991; McDaniels, 1996; Tzeng, Teng & Hu, 1991), transportation studies (Levine, 1996), in education (Levin, 1983; Lewis, 1989; Lewis & Kallsen, 1995), and in the criminal justice system (Edwards, 1980).
In the field of disability studies and rehabilitation, such a technique has been recently proposed for use in making decisions about program goals and alternatives (Lewis, Johnson, Erickson & Bruininks, 1994; Lewis et al., 1998; Lewis & Johnson, 2000).
In health related fields an early MAU study was completed in 1969, published by Gustafson and Holloway (1975). This study used the example of burn victims to test a model created to examine severity of illness, and eventually expand to the more general case of health status. By specifically determining severity of burns, cost-benefit analysis can find the effectiveness of treatment, both financially and in terms of patient health preference. A group of specialists assigned the weights used in the model based on their expert opinion.
Lewis, Johnson, and Scholl (2003) used MAU analysis as a methodology for evaluating the goals and services of a state vocational rehabilitation (VR) agency that was undergoing a comprehensive strategic planning process and had adopted the MAU model to support aspects of its planning. In the course of the planning exercise, the agency was interested in: (a) Identifying and reaffirming the agency goals and services; (b) obtaining feedback and establishing consensus with stakeholders (i.e., consumers with disabilities, VR agency staff, others) on what were the most important measurable attributes of these goals; (c) establishing benchmark estimates for each of these attributes for use in program evaluation; and (d) in using MAU evaluation results in program improvement planning and future evaluation comparison.
One of the issues addressed by the decision sciences is how to robustly measure subjective variables such as preferences, expert estimates and intuitive variables, yet combine the information with empirical data. These seem consistent with assessment needs in occupational therapy (OT) where variables of interest for people with disabilities include subjective and soft information, (e.g. pain reduction, quality of life, or aesthetic preferences); along with hard data such as range of motion, learning rates, or functional performance. There are several specific techniques used by decision analysts regarding health-related outcomes, including MAU models. MAU is traditionally used for decision modeling, but is also a mechanism of innovative data collection and application. In OT, we use aspects of MAU theory often, but implicitly.
MAU also addresses an issue that has been viewed in rehabilitation measurement as extremely important: acquiring an interval level scale for measurement. This is viewed as important because to add scores, intervals must be equal. Otherwise, 2+3 may not equal 5. Thus, equal intervals are essential for comparing scores between situations or individuals. Merbitz, Morris & Grip (1989) in an article on “Ordinal Scales and Foundations of Misinference,” depicted the problems with ordinal level data for use in rehabilitation.
We also know that one of the parameters for using inferential parametric statistical analyses is interval or better level data. Fortunately, some methods and strategies are available to remedy ordinal data once collected. Some have suggested that ordinal data may be irrelevant. The phase and important measurement validity concept was suggested by Hamilton & Granger in 1989. They said “the proof is in the pudding”. They stated that if predictive validity was evident, then maybe the need for a perfect interval scale was moot. This makes some sense. Furthermore, many parametric inferential statistical methods (e.g. the student t-test) are robust. Maybe interval level data are not as important as statistical purity has classically required.
Some scholars have attempted to describe how occupational therapists participate in clinical decision-making through strategies of clinical reasoning. More recently, attention to client-centered practice has begun to suggest strategies that might be used to improve decision-making in practice. Plus, some suggestions related to measurement have prompted thinking in the area of new decision and measurement methods. Examples are the COPM and GAS. However, as a profession we have not yet seriously considered the theory and mechanisms behind decision analysis and how they might substantiate OT decisions in practice. For example, might the selection of the best AT device or determining the best treatment intervention for a client benefit from some decision science?
In general though, decision analysis techniques have not been formally adopted in health care. As an example, twenty years ago, Alemi (1986) wrote an article arguing for training health care administrators in decision analysis. Despite widespread acceptance of the idea at the time, as demonstrated by published commentaries, decision analysis has not caught on with health care administrators as much as it has in other industries.
Three valid reasons for performing evaluations include making decisions regarding monitoring, fine-tuning, and programmatic choice (Edwards & Newman, 1982). There are two common characteristics that make MAU applicable to all of these reasons. First, they require comparison of one thing to another. Second, nearly all decisions have multiple objectives; consequently evaluations should assess as many as are important. The literature now justifies the approximations and/or assumptions in the applicability of MAU as an evaluation method for arriving at a decision (Edwards, et al.).
Assessment instruments that individualize questions for clients have been attractive to OT practice for several decades (Kiresuk, Smith, & Cardillov, 1994; Ottenbacher, & Cusick, 1990). Qualitative approaches such as GAS and COPM reflect this interest. These assessment methods may be cutting-edge but without good arguments to substantiate their use, they are susceptible to question by others. Qualitative data is difficult to compare across individuals and settings. The flexibility of these instruments loses the confidence of traditional test and measurement theory based on expected distributions of data and static question sets. Nevertheless, OT practitioners have become very interested in these approaches as matching the individual client perspective to intervention has high-perceived validity.
What these practioners may not be aware of is that GAS and the COPM have substantial quantitative theoretical support as well. These assessments borrow certain characteristics typically found in the Multiattribute Utility Theory (Edwards & Newman, 1982). MAU models can be devised so they represent interval scales, which increase the robustness of the data collected. Thus, the qualitative data gathered in the COPM or by GAS can be measured quantitatively. Additionally, by using "sensitivity analysis" an internal check on reliability can be performed. Applying key aspects of the MAU model and its supporting decision analysis theory and techniques gives the practitioners better tools to articulate and justify their application.
Using Chatburn & Primiano’s (2001) summary of MAUT, clinicians or researchers can assess how the COPM and GAS can qualify as an approximation in the applicability of MAUT. To explain this we can refer to Chatburn & Primiano’s summary of the MAUT method, the Carswell, McColl, Baptiste, Law, Polatajko, & Pollock (2004) description of the COPM process and the Ottenbacher & Cusick, (1993) synopsis of the GAS.
Chatburn & Primiano’s (2001)
Carswell et. el. (2004), Law et al, (1998)
Ottenbacher & Cusick, (1993)
|Determine the specific decision to be made.||Incorporate input from the various stakeholders in the decision.||The client identifies issues in areas of self-care, productivity, and leisure.||The therapist, client, and family (or other team members) decide on the expected level of outcomes for a particular goal.|
|Identify the variables in the decision.||Identify the factors that are important in the decision and the alternative decision options.||The client rates their perceptions of the importance of each activity on a scale from 1 to 10.||Outcomes that are both more or less favorable than the expected outcomes also are determined for each goal. Each level of performance is associated with a numeric value ranging from +2 to –2 with 0 associated with the expected level of outcomes.|
|Weigh the importance of each variable.||Weight the factors.||The clients choose the top five problems they wish to focus on during therapy.||Relative weights are assigned to each of three or more goals identified for the client. For example, if four goals are identified, they are each assigned a number between 1 and 4|
|Rank the items.||Rank the alternative decision on how well they serve the factors||For each of these five problems clients rate their performance & satisfaction with performance on a scale from 1-10.||The weights are ranked; +4 for the most important goal and +1 for the least important.|
|Assess the scores to make a decision.||Provide an overall score that identifies the best options.||The scores are summed and averaged over the number of problems, to produce scores out of 10.||After completion of treatment the progress toward achieving the outcomes are derived for each goal. This is determined using the +2 to –2 scale (level at which the goal was achieved or failed to be achieved). This information is used to calculate a goal-attainment score.|
Several MAU-like instruments were developed and piloted during the ATOMS Project for the purpose of AT outcomes measures. An iterative process as three instruments were piloted has led to what we now call the Isolating the Impact of Interventions (I3) Instrument.
The original instruments were The Relative Advantage of Assistive Technology and Services (RAATS), the Student Performance Profile (SPP), and the ATOMS-Division of Vocational Rehabilitation Consumer Survey (A/D-CS). All three instruments were based on the Integrated Multi-Intervention Paradigm for Assessment and Application of Concurrent Treatments (IMPACT2) Model, (Smith, 2002). The model describes the theoretical relationship of key intervention approaches used to optimize function of people with disabilities and delineates variables we must measure to understand outcomes of AT interventions as they are practiced in the natural environment.
Although these instruments are not applied to decision-making, MAU concepts are imbedded in their data-collection strategies. All three provide subjective measures of the amount of contribution of each type of intervention to the AT users’ outcomes. MAU modeling provides a method to estimate the amount of contribution each approach has on a given outcome. In brief, the MAU process generates a set of scores on the various attributes within the six rehabilitation approaches. These scores are then normalized into percentages that can be used to weight the various interventions.
In response to the lack of an AT instrument suitable to their research needs, the ATOMS Project staff and some of their partners developed RAATS to measure the impact of AT relative to other interventions that are working concurrently or in conjunction with AT. It uses the IMPACT 2 Model as its theoretical foundation. The items on the RAATS use a seven-point scale to probe the user about the overall impact of the six interventions. The original RAATS questions were designed for qualitative research and initially used during an unpublished, open-ended interview process. Lenker (manuscript in development) used the instrument in a study of 62 individuals representing three disability groups (physical, vision, and learning) and the impact of computer-based assistive technology. The Assistive Technology Infusion Project (ATIP) of the Ohio Department of Education collaborated with the ATOMS Project team in development of the Student Performance Profile (SPP), used to measure the impact of their project (Fennema-Jansen, Smith, Edyburn, 2005). One challenge identified to measuring AT in the schools is that students received concurrent interventions specific to classrooms. Therefore, they revised and updated 10 questions based on the original RAATS work as a portion of their instrument.
Outcomes data had been reported on 1,760 students at the time of the cutoff date for inclusion in the first study by Fennema-Jansen, Smith, Edyburn & Binion (2005). We now have a database with more than 2,000 records and are creating dynamic Web-based displays. Students with a variety of disabilities from across the state of Ohio are represented in this group.
The percent of the total for all interventions that was attributed to assistive technology devices was multiplied by the ability rating on the relevant IEP goals. The results of the analysis of variance indicated a significant difference between the amount that AT devices contributed to progress on the goals prior to the assistive technology intervention and after using the technology for eight months. The authors concluded that the results of this study provide confirmation that the method used to determine the relative contribution of a given intervention has potential, and should be researched more extensively.
Johnson extended the method of investigation that began with the RAATS in her study of AT (or RT – rehabilitation technology, as used in vocational rehabilitation) use of clients served by the State of Wisconsin Division of Vocational Rehabilitation Services (2006). As part of a multifaceted exploration of RT across stakeholders in the system, she developed and implemented the ATOMS-Division of Vocational Rehabilitation Consumer Survey (A/D-CS), which included questions about experiences with RT services and devices, including mechanisms to make comparisons with other interventions. During survey development the initial six intervention questions grew to 19. Reasons were specific to the vocational rehabilitation model.
Among the findings, Johnson reports that A/D-CS is an effective instrument in isolating the contribution of concurrent interventions and to measure the impact of AT on employment goals. It gathered data on interventions the participants received regardless of whether the VR counselor or another rehabilitation professional provided the services. It therefore is useful in determining service outcomes using a wider lens. Data collected in this study included intervention approaches provided by the vocational, medical, education and independent living models. During the write-up of the VR studies, “I3” was adopted as the nomenclature to represent this line of outcomes inquiry.
This report reviewed the use of MAU models in decision-making, which has been identified as a vital component of performance evaluation. Because most decisions have multiple objectives, decisions should assess as many objectives as are deemed important in the specific circumstance being analyzed.
Occupational therapists have been attracted to assessment instruments that individualize questions for clients for several decades. A recent trend toward client-centered practice suggests strategies that might be used to improve decision-making in practice, thus putting emphasis on decision and measurement tools.
When the methodologies of the MAU method, COPM, and GAS are compared side-by-side, similar characteristics are shown that can be used to improve decision-making in practice. GAS and the COPM have substantial quantitative theoretical support. Review of MAU literature suggests that MAU, also, may offer significant relevance to AT outcomes systems.
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