¹Ludwig-Maximilians-Universität München, München, Germany
²Institute for Ethics, History and Theory of Medicine, Ludwig-Maximilians-Universität München, München, Germany
³Ludwig-Maximilians-Universität München; Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), München, Germany

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Introduction: Incorporating patient preferences (PP) is vital for shared decision-making, but detailed assessment can be time-consuming and inconsistent. Structured methods can help ensure that prefernces receive the same attention as other key factors by integrating their assessment into clinical practice. While a plethora of preference assessment methods exists within decision theory, these approaches often need adaptation to the medical context. Computer-supported Preference assessments have been shown to facilitate expressing values in a clinical context [1]. In this study, we describe the adaptation and evaluation of several preference assessment methods applied to a hypothetical treatment decision scenario.

Methods: Following a scoping review on how patient preferences have been integrated into medical decision algorithms and models [2], we selected five methods for investigation: Best-worst scaling, direct weighting, Potentially All Pairwise Rankings of all possible Alternatives (PAPRIKA) [3], time trade-off and standard gamble. The selection of the methods reflects the diversity of approaches and covers a spectrum of complexity and cognitive demand.

We conducted a non-representative cross-sectional questionnaire study in 123 volunteers using an online survey platform over one month. Most participants were female (65%), with age evenly distributed from 18 to 75 years.

After introducing participants to a hypothetical disease scenario, we presented them with the five methods to assess their preferences. Participants rated each method by expressing their agreement with six statements on a 5-point Likert scale. In addition, they rated three statements on their acceptance and satisfaction with shared decision-making, the overall concept of algorithm-assisted PP assessment, and three statements regarding the questionnaire’s clarity.

Results: Participants showed a strong overall preference for the PAPRIKA method. Simpler methods (direct weighting and best-worst scaling) were perceived as overly restrictive in capturing detailed individual preferences and balancing treatment aspects. Although the more abstract methods (standard gamble and time trade-off) received better ratings than the simpler approaches, they were still rated lower than PAPRIKA. Most participants strongly agreed with the statement, “It is important to me to be able to express my preferences in detail when choosing a therapy” (mean = 4.7, sd = 0.5), and agreed with “The methods presented helped me become aware of my preferences” (mean = 3.8, sd = 1.1). Moreover, 82% (n = 101) of participants answered “yes” to the question, “Can you imagine that your preferences for selecting a therapy could be assessed using one of the methods shown earlier?”

Discussion: It is important to note that the shared decision-making tools examined in this study are not universally applicable across all patients and healthcare contexts. Specifically, these methods are not designed for emergency situations and require the availability of at least two treatment options. Nonetheless, in cases of chronic diseases with variable prognoses [4], they can be highly beneficial. The primary limitations of our study were the sample composition and the hypothetical nature of the scenario.

Conclusion: Our study indicates that participants are generally open to structured tools for assessing PP and value the ability to express detailed preferences. In particular, PAPRIKA was strongly favoured, likely due to its incorporation of specific and concrete information relevant to the health context.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.

References

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[4] Heesen C, Solari A. Editorial: Shared decision-making in neurology. Front Neurol. 2023;14:1222433. DOI: 10.3389/fneur.2023.1222433