Conjoint analysis
Conjoint analysis, which was originally developed by mathematical psychologists for use in market research, offers the potential for measuring consumers’ relative preferences for different healthcare attributes.189It has already been used by economists to measure consumers’
preferences and to establish priorities for decisions regarding transport development and the environment.190 This technique is appealing because it has a sound theoretical basis in expected utility theory. It involves identifying factors of importance to the client group, from which a range of scenarios based on combinations of these factors can be developed. In discrete choice modelling, the purest form of conjoint analysis, respondents make discrete choices between several pairs of hypothetical scenarios, from which the relative values of different service features can be determined by statistical analysis.
Conjoint analysis offers advantages over standard approaches to measuring satisfaction that use simple scales. First, it takes account of opportunity costs (the fact that choosing one alternative implies a sacrifice in terms of opportunities forgone) by
33 asking respondents to make trade-offs. Secondly,
it can chart the relative intensity of people’s preferences for different healthcare features,191 which is relevant in a decision-making context.15 If the options available to respondents include a monetary sum, values can be attached to the reported preferences and then used in cost-effectiveness analyses. When relative values are compared with the resource costs of service improvements, rational decisions can be made with regard to expenditures on improvements.
Expectations-based measures
Of particular importance in healthcare evaluations is the size of the difference between consumers’
expectations and their perceptions of the delivered service quality. This may be presented as the dis-confirmation paradigm. Service users’ expectations
may be either confirmed (if consumers perceive services are delivered as expected), or negatively/
positively disconfirmed (if consumers perceive services are delivered worse/better than expected).
There are good reasons for arguing that
evaluations of healthcare delivery should be based on this paradigm and that there is therefore a need to collect information about consumers’
expectations as well as their perceptions of their care experiences. This can be done by using separate scales for expectations and perceptions, or by combining them into one scale,192for example, as shown in Box 1.
The development and testing of alternative approaches that measure perceived differences between expectations and experiences are required.
BOX 1 Combined scale
Item statement Scale
0% 100% 200%
Information supplied Much worse As expected Much better
by doctor was: than expected than expected
35
Introduction
This chapter reviews the available evidence on the determinants of satisfaction. The structural frame-work for the review distinguished two groups of determinants: those relating to the characteristics of respondents and those relating to health service delivery features.
Three main types of individual factors were identified: expectations, health status, and socio-economic and demographic characteristics of respondents. The articles investigating the effect of health service factors on satisfaction were first grouped according to setting: in hospital, hospital outpatient, and general (or family) practice or primary care. This was because the important care delivery factors contributing to satisfaction may vary by type of care. Cutting across this categori-sation, however, two other issues were frequently addressed in the literature: evaluations of the patient–physician relationship, and of the different systems for organising and delivering care. Studies providing evidence on these two questions were therefore analysed separately.
Most studies investigated several potential influ-encing factors. Sociodemographic factors were included by many investigators as confounding variables in order to uncover the independent effects of service delivery features on reported satisfaction. This chapter proceeds by examining the characteristics of the corpus of determinants studies, and then by presenting and discussing the evidence about individual and health service factors.
Overview: characteristics of included studies
The literature search identified 139 articles, cover-ing 127 data sets, which provided evidence about the determinants of satisfaction. Reported empiri-cal evidence was gathered in a variety of settings, using a range of different methods. The majority of the articles were in specialist journals: 49% med-ical, 27% social science (including psychology), 16% health services (including management and quality), and 4% nursing. Summaries of these
articles are presented in appendix 13, in alpha-betical order of first author.
Over half (56%) of the studies identified were conducted in the USA, and a further 27% were UK based (Table 11). The majority of the work (56%) was published in the last decade (Table 12).
It covered the full range of settings (Table 13) and was largely (70%) empirical in orientation (Table 14). The number of respondents varied greatly (Table 15), although almost all studies (93%) were observational in design (Table 16).
Satisfaction was measured by the authors of 126 (91%) articles using survey instruments.
Among these, 58 (46%) used pre-existing instruments; the rest used instruments that were compiled in a variety of ways, for the study in question, by the investigators themselves.
Satisfaction was measured by multi-dimensional approaches in 79 (63%) studies, by global
measures in nine (7%), and by both in 26 (30%).
A total of 148 different means of measuring satisfaction were used in these 126 quantitative studies. Indirect approaches were most common (65%), followed by direct methods (24%), probing intention to return/recommend (8%), and reports of experiences (3%). No studies established preference ratings. The remaining articles reported qualitative studies (n = 10) or meta-analyses (n = 3). Respondents were approached by a variety of methods (Table 17).