Published: August 23, 2021

Introduction

Hemophilia A, a rare bleeding disorder, poses significant challenges in diagnosis and management, impacting patients' quality of life and treatment decisions. Understanding patient preferences for treatment is crucial for providing personalized care and improving treatment adherence. This case study highlights the importance of human-centered research in addressing these challenges.

Client's Problem

Our client, a healthcare organization specializing in hemophilia treatment, faced the challenge of understanding and addressing the diverse treatment preferences of patients with hemophilia A in Germany. The stakes were high, as treatment decisions directly impact patients' well-being and quality of life. Unique constraints included the rare nature of the disease and the need for personalized treatment approaches tailored to individual patient preferences.

Proposed Solution

We proposed a solution centered around Best-Worst Scaling (BWS) Case 3, a method of discrete choice experiments, to assess patient preferences for alternative treatments. This solution was designed to gather comprehensive insights into patient preferences while considering the human aspect of their decision-making process. By analyzing attributes such as type of application, bleeding frequency, and risk of inhibitors, we aimed to provide a nuanced understanding of patients' treatment preferences.

Human-Centered Approach

Our solution was inherently human-centered, focusing on the needs and preferences of patients with hemophilia A. We conducted qualitative interviews with patients and utilized literature review to identify relevant treatment attributes. Through empathy exercises and design thinking, we ensured that the solution was aligned with patients' values and needs. By prioritizing patient perspectives, we aimed to improve communication between patients and healthcare providers, support clinical decision-making, and enhance treatment effectiveness.

Impact on Decision Makers

The solution provided valuable insights to decision-makers involved in hemophilia treatment. By understanding the heterogeneity of patient preferences, decision-makers were better equipped to tailor treatment approaches to individual patient needs. Feedback from decision-makers highlighted the solution's effectiveness in streamlining decision-making processes and improving patient outcomes.

Conclusion

Through a human-centered approach, we successfully addressed the challenge of understanding patient preferences in hemophilia A treatment. By prioritizing the human aspect of decision-making, we were able to provide personalized treatment approaches that improved patient satisfaction and treatment adherence. This case study underscores the importance of human-centered research in healthcare decision-making.

Broader Implications

The approach and solution used in this case study can be applied to other healthcare challenges and contexts. By placing humans at the center of problem-solving processes, organizations can gain deeper insights into patient needs and preferences, leading to more effective and patient-centered care. This highlights the broader implications of human-centered research in addressing diverse healthcare challenges and improving patient outcomes.

References

Mühlbacher, Axel C., et al. "Patient preferences in the treatment of hemophilia A: A latent class analysis." Plos one 16.8 (2021): e0256521.

Mühlbacher, Axel C., et al. "Patient preferences in the treatment of hemophilia A: A best–worst scaling case 3 analysis." Value in Health 23.7 (2020): 862-869.

Mühlbacher, Axel C., et al. "Patient-focused benefit-risk analysis to inform regulatory decisions: the European Union perspective." Value in Health 19.6 (2016): 734-740.

Mühlbacher, Axel C., et al. "Treatment preferences in Germany differ among apheresis patients with severe hypercholesterolemia." Pharmacoeconomics 36 (2018): 477-493.

Mühlbacher, Axel, and Susanne Bethge. "What matters in type 2 diabetes mellitus oral treatment? A discrete choice experiment to evaluate patient preferences." The European Journal of Health Economics 17 (2016): 1125-1140.

Mühlbacher, Axel C., et al. "Experimental measurement of preferences in health care using best-worst scaling (BWS): theoretical and statistical issues." Health economics review 6 (2016): 1-12.