Experimentelle Präferenzmessung im Gesundheitswesen mit Hilfe von Best-Worst Scaling (BWS)

Mühlbacher, Axel; Kaczynski, Anika; Zweifel, Peter

doi:10.1007/s40275-014-0012-7

Cited by 7 publications

(5 citation statements)

References 47 publications

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“…Accordingly, we calculated scores based on the number of times an attribute was selected as the best and the worst across all questions included in the survey [29,30]. The number derived for the least important factor was subtracted from the count for the most important factor (total(best)-total(worst)) [31]. The obtained number was divided by the number of times the factor appeared in the survey, creating a scale (-1 to +1) in which a high score indicates that the factor is more important to the respondent [29,30].…”

Section: Discussionmentioning

confidence: 99%

Best-worst scaling to assess the most important barriers and facilitators for the use of health technology assessment in Austria

Feig

Cheung

Hiligsmann

et al. 2017

Expert Review of Pharmacoeconomics & Outcomes Research

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Section: Discussionmentioning

confidence: 99%

Best-worst scaling to assess the most important barriers and facilitators for the use of health technology assessment in Austria

Feig

Cheung

Hiligsmann

et al. 2017

Expert Review of Pharmacoeconomics & Outcomes Research

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“…As patients are the ones who ultimately experience the positive and negative outcomes of treatment, decisions related to health care intervention options should be patient centered and reflect patient values. This is where HPR comes in, providing information about patient values relevant to decision-making [ 16 , 17 ]. This study will inform decision makers about the factors impacting patient and public acceptance.…”

Section: Discussionmentioning

confidence: 99%

“…Discrete choice experiments (DCEs) are a way to identify trade-offs by analyzing preferences [15]. The acceptance of new therapies is a key success factor and is determined by the fulfillment of needs [16][17][18]. When acceptance information is considered in decision-making, the utility of care decisions can be maximized in the short term [19,20], can strengthen patient orientation and adherence in the intermediate term, and can improve clinical effects in the long term.…”

Section: Objectivesmentioning

confidence: 99%

Patient and Public Acceptance of Digital Technologies in Health Care: Protocol for a Discrete Choice Experiment

Fischer

Mühlbacher

2023

JMIR Res Protoc

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Background Strokes pose a particular challenge to the health care system. Although stroke-related mortality has declined in recent decades, the absolute number of new strokes (incidence), stroke deaths, and survivors of stroke has increased. With the increasing need of neurorehabilitation and the decreasing number of professionals, innovations are needed to ensure adequate care. Digital technologies are increasingly used to meet patients’ unfilled needs during their patient journey. Patients must adhere to unfamiliar digital technologies to engage in health interventions. Therefore, the acceptance of the benefits and burdens of digital technologies in health interventions is a key factor in implementing these innovations. Objective This study aims to describe the development of a discrete choice experiment (DCE) to weigh criteria that impact patient and public acceptance. Secondary study objectives are a benefit-burden assessment (estimation of the maximum acceptable burden of technical features and therapy-related characteristics for the patient or individual, eg, no human contact), overall comparison (assessment of the relative importance of attributes for comparing digital technologies), and adherence (identification of key attributes that influence patient adherence). The exploratory objectives include heterogeneity assessment and subgroup analysis. The methodological aims are to investigate the use of DCE. Methods To obtain information on the criteria impacting acceptance, a DCE will be conducted including 7 attributes based on formative qualitative research. Patients with stroke (experimental group) and the general population (control group) are surveyed. The final instrument includes 6 best-best choice tasks in partial design. The experimental design is a fractional-factorial efficient Bayesian design (D-error). A conditional logit regression model and mixed logistic regression models will be used for analysis. To consider the heterogeneity of subgroups, a latent class analysis and an analysis of heteroscedasticity will be performed. Results The literature review, qualitative preliminary study, survey development, and pretesting were completed. Data collection and analysis will be completed in the last quarter of 2023. Conclusions Our results will inform decision makers about patients’ and publics’ acceptance of digital technologies used in innovative interventions. The patient preference information will improve decisions regarding the development, adoption, and pricing of innovative interventions. The behavioral changes in the choice of digital intervention alternatives are observable and can therefore be statistically analyzed. They can be translated into preferences, which define the value. This study will investigate the influences on the acceptance of digital interventions and thus support decisions and future research. International Registered Report Identifier (IRRID) DERR1-10.2196/46056

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“…Another limitation is that although the analyses made of the AHP and data allow statements to be made on the significance and ranking of the attributes, this cannot be performed in terms of the boundary rate of substitution, because this cannot be ascertained relative to the level of the other attributes (Mühlbacher and Kaczynski, ; Mühlbacher et al, ).…”

Section: Discussionmentioning

confidence: 99%

“…These include in particular discrete choice experiments (DCE) and the specific form of best–worst scaling (BWS). This method enables a comparison of the preference structures of experts and patients and also has the potential to improve clinical decision‐making as well as the quality of patient care (Mühlbacher and Kaczynski, ; Mühlbacher et al, ; Sloane et al, ).…”

Section: Introductionmentioning

confidence: 99%

The Expert Perspective in Treatment of Functional Gastrointestinal Conditions: A Multi‐Criteria Decision Analysis Using AHP and BWS

Mühlbacher

Kaczynski

2016

Multi Criteria Decision Anal

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Objectives The aim of this study was to identify and weight the expert's relevant decision criteria with regard to drug treatment of functional dyspepsia and motility disorders. Attributes such as onset of action, reduction of symptoms and side effects were to be examined in order to test their relevance to healthcare decision makers. Moreover, the objective was to verify the extent to which the analytic hierarchy process (AHP) and best-worst scaling (BWS) are suitable for use in context of an expert panel.Method In order to identify patient-relevant therapy characteristics, a literature search and qualitative interviews were conducted. On this basis, a questionnaire was developed using multi-criteria decision analysis. By means of the AHP and BWS, the study elicited the experts' priorities as regards the various aspects of treatments and gastrointestinal symptoms. The data collection from experts in gastroenterology was performed in real time within a group discussion using an itemresponse system. Results As a result of the interviews, seven characteristics and 10 symptoms were established. A total of N = 20 experts took part in the survey. The following criteria were shown to be the most relevant in the AHP: 'reduction of abdominal cramps' (w: 0.302), 'reduction of epigastric pain' (w: 0.250) and 'time to onset of action' (w: 0.117). In the BWS, it was shown that the gastrointestinal symptoms 'vomiting' (sqrt(b/w): 0.118), 'abdominal cramps' (sqrt(b/w): 0.157) and 'epigastric pain' (sqrt(b/w): 0.356) were the most significant. Discussion The methods represent a suitable and scientifically transparent approach for the elicitation of experts' priorities. Using AHP, it was possible to ascertain the most important aspects of drug treatment and to correlate positive and negative characteristics. The BWS showed which gastrointestinal symptoms had the least and most pronounced negative impact on patient well-being from expert perspective. The item-response system served as a valuable instrument to collect data based on a group discussion.

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Experimentelle Präferenzmessung im Gesundheitswesen mit Hilfe von Best-Worst Scaling (BWS)

Cited by 7 publications

References 47 publications

Best-worst scaling to assess the most important barriers and facilitators for the use of health technology assessment in Austria

Best-worst scaling to assess the most important barriers and facilitators for the use of health technology assessment in Austria

Patient and Public Acceptance of Digital Technologies in Health Care: Protocol for a Discrete Choice Experiment

The Expert Perspective in Treatment of Functional Gastrointestinal Conditions: A Multi‐Criteria Decision Analysis Using AHP and BWS

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