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dmi:Design Value Award Winner - GE Healthcare
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dmi:Design Value Award Winner

 

 

 

Simplicity. Speed. Safety: Designing AI for Healthcare

GE Healthcare - Design Teams, Product Management, Customers/Users

 

Many facts point to the need to enhance the use of AI (artificial intelligence) in healthcare imaging software. For example, hospitals consistently rank “imaging time to diagnosis” as one of top factors for decreasing time and readmission rates for patients. More than 250,000 people die every year in the U.S. due to medical errors—almost one-third of all preventable deaths. If we could reduce the error rate in the 3.6 billion annual imaging exams by using AI, then we could potentially prevent up to 144 million errors. Training a radiologist (MD) costs over $250,000 and takes 12–15 years; there is a shortage of radiologists globally.

GE Healthcare’s software solutions increasingly utilize AI (artificial intelligence) and Analytics to increase the accuracy and efficiency of healthcare providers and improve outcomes for patients. AI inferencing models enable the products to help humans make better decisions, and these decision support systems use advanced visualizations to quickly convey findings to the doctors and healthcare providers who use the systems.

The challenge for the Digital Design UX team was to define and validate a set of consistent, yet simple, common design standards that could help improve speed for users, safety for patients, and usability and intuitiveness in the use of AI across healthcare imaging software. GE Healthcare has more than 52,000 employees, including thousands on our Edison platform spanning AI, Analytics, Cloud and more; and over 250,000 devices installed worldwide. A coordinated approach to user interaction is critical given the scope of products and users, as well as the complex and emerging field of Healthcare AI.
The process incorporated three aspects:

  • Extensive design iteration: For example, more than 150 icon concepts and dozens of image overlay patterns were conceived, refined, and tested over two years.
  • Evidence-based user validation: More than ten user discovery and evaluative studies were conducted with over 90 participants to understand current medical user habits, practices, and preferences for interacting with AI.
  • Engagement: The proposed approaches came from a cross-organization, community-driven contribution model that taps into over 100 designers, engineers, and product leaders for feedback and review.

A comprehensive set of common AI design standards and principles that emerged from a collaborative community design review and contribution model help unite the organization through design and reduce product inconsistencies. AI pattern guidance is helping to transform GE Healthcare into a digitalfirst, Design-focused company in three ways:

  • Using a community contribution and review model to create a UX design platform,
  • Aligning the focus areas with the needs of product program adoption, and
  • Taking a more consistent and analytical approach to user validation.

Improved user experience for AI integration in healthcare imaging software is measured based on greater efficiency for radiologists in their daily workflows. This can reduce reading costs per image and, consequently, widen access to imaging-based diagnostic care, especially in emerging markets that have an acute shortage of radiologists. Diagnostic imaging is costly and requires specialized, costly machines like MRI’s as well as specialized medical training for radiologists to read the results. GE Healthcare is increasingly looking at AI to assist with not only imaging workflow optimization to improve read times or optimize output, but also other process improvements for the scan itself, and accuracy, quality and safety checks. All of these enhancements will result in better patient outcomes


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