AI SaaS to boost cancer research & mitigate Scientists' data fatigue
Overview
Pomi.ai is an innovative AI SaaS platform to enhance personalized cancer treatment research. By leveraging an advanced information visualization tool, it aims to mitigate data fatigue among scientists and boost research efficiency. The core transformation revolves around redefining the process of determining potential treatment pathways, making it more engaging and user-friendly.
Team
Senior UX/UXR Designer, Stakeholders, Developers, Researchers.
Goals
To redesign the existing linear brain mapping tool into an interactive data mapping platform encouraging engagement, collaboration, and efficiency in cancer research. The aim was to simplify complex analysis and offer synthesized, patient-centric information, effectively combating data fatigue among medical professionals.
Role & Process
Senior UX/UXR Designer Responsible for understanding technological and medical nuances, I translated them into an intuitive interface. The challenge was to make this sophisticated platform accessible and engaging. My role encompassed interviewing stakeholders, developing personas, and identifying critical issues in medical research. I proposed a new vision, drawing upon comprehensive knowledge of information visualization metaphors, and conducted extensive research.
Design Process
Research: Online research, competitor analysis, academic literature review, stakeholder interviews, and persona development.
Challenges & Vision: Identification of underlying data fatigue issue, conceptualizing a new interactive data mapping tool, and adaptation of visual algorithm.
Design Approach: Designing based on information visualization and Gestalt principles, collaboration with stakeholders, and alignment with vision and needs.
Key Features and Design Decisions: Focus on the physician persona, catering to both research tendencies (bottom-up and top-down), guidelines for feature distribution, and the app's primary paradigm.
Prototyping & Final Design: Designing an innovative navigation system inspired by Google Slides, close collaboration with the development team, and finalizing UI based on existing design systems.
Research
An in-depth analysis of existing whiteboards, brainstorming tools, and competitor applications yielded insights into a paradigm shift based on coherent and intuitive information visualization principles applied to model the probability of the proposed solution instead of mapping the entire connection landscape. We discovered a guiding principle: the time spent creating the data map mirrors actual research.
User Flow investigation and current mapping tools analysis paved the way toward a seamless user experience by always considering the goal of people using these maps. The task of mapping entire connection landscapes is overwhelming, and many developers of such tools are unaware of the need to incorporate the users' cognitive process so that any visual means taken will lead to fulfilling the function of intuitive discovery.
Here, through fruitful dialogue with stakeholders and based on my profound understanding of information visualization cognitive aspects, we offered a new way to communicate complexity and source insights.
Wireframing & Prototyping
Redesigning the Existing Whiteboard:
They are transforming it into an interactive tool based on information visualization guided by a user-centric approach.
Designs Creation:
Developing an intuitive method for navigating, visualizing, and modeling the process of determining potential treatment pathways and incorporating elements that gamify the user experience.
See inspiration
Result
The introduction of the advanced information visualization solution has fundamentally transformed Pomi.ai into a more intuitive platform for personalized cancer care research. This paradigm shift redefined the platform, facilitating navigation, visualization, and modeling of potential treatment pathways in the context of customized care. The result is a seamless integration of the iterative process of refining and detailing treatment paths, gamifying the user experience, and encouraging continued engagement.
This transformation's immediate and substantial impact includes improved user satisfaction and streamlined creation and sharing of data maps in the beta phase. The innovative, precise, and adaptable new info bubbles have simplified research procedures, captivating stakeholders and easing data fatigue among identified personas.
The promising potential for future developments in personalized cancer treatment research continues to be explored, with the next steps focusing on continuous improvement, stakeholder feedback, and adaptation to emerging needs in the field of biomedical research.
In conclusion, the innovations introduced have significantly enhanced the functionality and user experience of the Pomi.ai platform, holding a promising future for the evolution of personalized cancer treatment research.
Lessons Learned
Reflecting on our process, recognizing the importance of accommodating various research approaches, such as bottom-up and top-down, emerged later than ideal. We devoted substantial effort to balancing a variety of visualization methodologies to suit different paradigms. When the central design paradigm crystallized - the concept that creating, revisiting, and refining the data map is the embodiment of the research process - it became evident that our application needed to be versatile and fully support all research tendencies. This inclusivity is crucial since these are inherent tendencies that any tool designed to enhance and streamline the research process must cater to.
I liked the challenge of designing a SAAS that addresses ML complexities, and I take great pride in integrating interactive information visualization techniques into the process.
We evaluated two contrasting approaches for the info blobs: one offering extensive flexibility and providing a broader overview, and the other being more narrow, in line with current ML constraints. These diverse modes will blend more harmoniously with time and further iterations.
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