Design Thinking and cognitive bias mitigation in Medical equipment production

Azade Selahvarzi
3 min readJun 26, 2023

SUMMARY: This research focuses on examining existing behavioral biases in the medical equipment industry by incorporating Design Thinking methodology in the production process. After conducting field studies and categorizing eleven decision biases, these biases were screened by expert opinions using the best-worst ranking method and scoring. Eventually, six biases remained from the categorization, and tools influencing the control of these biases were introduced. Considering that decision biases occur in various situations and for different individuals, controlling these criteria will have a significant impact on decision-making improvements. The effectiveness of the Design Thinking approach in enhancing the industry and meeting customer needs, along with its effects on limiting decision biases, has been recognized as an effective method.

The development of medical products is a complex and lengthy process that requires collaboration among various groups with different knowledge backgrounds and skills, such as designers, medical professionals, engineers, occupational therapists, social researchers, and more. This interdisciplinary collaboration adds value and requires successful new solution offerings. However, implementing such collaboration is challenging due to factors like ineffective communication, differences in priorities, and work styles In this regard, it is observable that biomedical engineering, despite being a combination of engineering and medicine, is referred to as two separate disciplines in practice.

When people rely on heuristics in decision-making under uncertain conditions, it often results in distorted outcomes that represent a misrepresentation of the actual goal. Cognitive biases have been defined as “systematic errors in judgment and decision-making.” The reasons behind these biases lie in motivational factors, cognitive limitations, and the alignment of humans with the natural environment. Cognitive biases are often referred to as biased decision-making or judgment. An ethical lens to biases considers them as predictable deviations from logic (Arnott, 2006). Therefore, these challenges are inevitable in decision-making for all decision-makers. By employing the methods and process of design thinking, some biases and prejudices can be reduced (Liedtka, 2015).

Numerous factors play a significant role in increasing production, and in this study, we have examined the behavioral biases that may occur in the production process at three macro levels (industry, university, and government). By expanding these factors to the medical equipment industry, we have investigated the extent of the impact of each criterion. As mentioned earlier, the outcome of this research is the presentation of a multi-level model of factors influencing the increase in the production of medical equipment.

In this study, after collecting relevant behavioral biases through field research, these criteria were distributed to 8 experts using two questionnaires. The best and worst factors were then identified and their weights were determined by the experts. The final result includes the order and magnitude of the impact of these biases, which are ranked as follows:

Rank Criterion Categorization

  1. Availability bias Industry 1.1828 2
  2. Planning fallacy bias Industry 1.5005
  3. Confirmation bias Industry/University/Government 1.2345
  4. Self-centered bias Industry 1.1828
  5. Personal judgment bias Government 1.0291
  6. Reflection bias Industry 0.3177

A review of the literature related to design thinking suggests that a suitable management approach for improvement is warranted. In a related domain, a review of decision-making literature centered around cognitive biases indicates that design thinking approaches have the potential to enhance innovation outcomes by mitigating a set of known perceptual biases. People often impose their worldviews on others, limit the options under consideration, and disregard data that contradicts their beliefs. They have excessive confidence in their predictions and prematurely terminate the search process, compromising the quality of production and hypothesis testing. A design thinking approach may help decision-makers address many of these shortcomings.