AI in Healthcare | Experience Designer

USECASES & CONCEPTS: HEALTHCARE PROFESSIONALS SPECIFIC

NOTE

For this proof of concept, and due to time constraints, the flows below cater exclusively to one of the three targeted users: Healthcare Professionals.

The user and information flow described above is based on the following assumptions:

Healthcare professionals are assumed to have a foundational understanding of AI and its applications in healthcare. They are expected to have access to high-quality medical datasets and be interested in comparing AI models to find the most accurate and reliable one for their use case. Collaboration and learning more about AI applications are also assumed to be critical aspects of their workflow.

The platform assumes that patients are comfortable with AI being used in their healthcare, can comprehend simplified health reports and diagnostic results, will actively manage their consent settings for sharing personal health data, and are eager to communicate with their healthcare providers through the platform.

Exploration & Iterations

Armed with a clear understanding of the core functionalities, value proposition, and the information structure, I began iterating concepts, detailing, and defining the use cases for the platform's features and functionalities.

Onboarding/User Aunthentication Flow

Information Flow

CLIENT USER FLOW BRIEF

REFINED THOUGHT CHARTING

Information Architecture

After receiving a brief user flow from the client outlining their core value proposition, I used it as a reference point. I then expanded on the Information Architecture based on my research findings, keeping the core value propositions in mind. The Information Architecture for both user groups, which mainly focuses on Healthcare professionals and Patients/Caregivers, has been structured to address their specific needs and use cases.

Through a comprehensive evaluation of these competitors, I have identified key areas where the AI platform I am tasked with designing can establish its distinctiveness. This includes providing more accessible and user-friendly solutions, focusing on specific healthcare applications, and addressing data privacy concerns more effectively. This analysis also underscores the significance of proactively keeping pace with technological advancements and staying abreast of regulatory changes within the healthcare AI sector. The Opportunities and Gaps identified by conducting the above benchmarking are summarized below: 
Opportunities:

There is a significant opportunity to create a platform that is more accessible to non-technical healthcare professionals, bridging the gap between advanced AI capabilities and user-friendly interfaces. Streamlining implementation and reducing costs can attract a broader range of healthcare providers, especially smaller or underfunded institutions. Focusing on transparency, explainability, and ethical AI use in healthcare can differentiate your platform in a competitive market.

Gaps:

Existing platforms often cater to highly technical users, leaving a gap in the market for more intuitive, accessible platforms tailored for healthcare professionals without deep technical expertise. There is a lack of platforms that offer a seamless integration of AI with existing healthcare systems while maintaining affordability and ease of use. Addressing data privacy and security concerns more transparently could be a key differentiator, given the sensitivity of healthcare data.

Research and Discovery

Understanding the Landscape

In order to establish a strong foundation for the project, I have conducted thorough research on existing AI platforms and their applications in healthcare. I have identified major players in the market, such as IBM Watson Health and Google's DeepMind, and analyzed their strengths and weaknesses. Furthermore, I have also delved into the NVIDIA AI playground to gain an understanding of the platform's functionalities and various use cases that would aid in the conceptualization of the MedAI platform. The research conducted emphasized the necessity for a platform that not only provides advanced AI tools but also prioritizes user education and trust-building.

User Research and Defining targeted users

As I explored the AI healthcare landscape, it's crucial to understand the primary users and those impacted by it. This involves recognizing the central users in the healthcare industry, as well as the many others who play important and supportive roles. I have grouped the identified users into broader categories in order to facilitate the creation of role-specific use cases when designing the platform.

Healthcare professionals: Physicians, nurses, therapists and other healthcare providers.
Patients with a vested interest in understanding and utilizing AI for their health, informal caregivers and other individuals.
Administrators like hospital managers, insurance company representatives, government officials, etc.
Researchers seeking to study health phenomenon.

The convergence of healthcare and artificial intelligence (AI) promises to revolutionize patient care, medical research, and overall industry efficiency. However, navigating the complex landscape of AI tools and applications can be daunting for healthcare professionals, researchers, and even patients.

This UX case study explores the conceptualization and design of an AI exploration platform specifically tailored to the healthcare sector. The aim was to create a safe, accessible, and intuitive space where individuals across the healthcare spectrum could discover, experiment with, and leverage AI to drive innovation and improve patient outcomes.

Project Brief

The objective of this project was to create a platform that draws inspiration from existing AI platforms in the market while addressing the unique needs of the healthcare sector. The platform needed to:

Facilitate the discovery and experimentation of AI tools for healthcare.
Ensure accessibility and safety for all users, including medical professionals and patients.
Promote collaboration among healthcare providers.
Enhance understanding and trust in AI technologies

Impact

The successful execution of this project not only met the client’s expectations but significantly exceeded them. The proof of concept (POC) was well-received by the client, who appreciated the innovative approach and the attention to user needs demonstrated by our team. As a result, one of our team members was invited to join the client’s project team on a year-long contract to further develop and enhance the POC. This achievement not only solidified trust between Tata Elxsi and GE Healthcare but also paved the way for deeper collaboration in the healthcare domain. The project served as a catalyst for future engagements, strengthening our position as a valued partner in delivering AI-driven healthcare solutions.

MY ROLE

UX DESIGNER; RESEARCHER

TEAM

UX DESIGN TEAM

TIMELINE

2 WEEKS

Competitor Benchmarking

I researched the current market to understand the role of existing AI platforms and their applications in healthcare. I identified major players in this domain, focusing on the health sector, such as IBM Watson Health and Google DeepMind. To understand the usability and layout of such platforms, I studied the NVIDIA AI Playground Platform. I also conducted competitor benchmarking of the key players identified and performed a SWOT analysis of their features.

User Personas

To ensure the platform aligns seamlessly with the needs of healthcare professionals, I developed detailed user personas representing various roles within the healthcare ecosystem. These personas include clinicians, researchers, administrators, and patients, each with unique goals, challenges, and expectations. By identifying key user characteristics—such as daily workflows, pain points, and technology proficiency—I was able to tailor features like the AI Playground, Model Sandbox, and Dataset Library to better serve user requirements. This user-centered approach ensures that the platform remains accessible, intuitive, and valuable across different user segments, ultimately enhancing adoption and satisfaction.

Sketching out the Possibilities

After understanding the users' needs, challenges, and goals, I began the ideation phase, where creativity meets strategy. This stage was crucial in laying the groundwork for a platform that not only meets functional requirements but also resonates with users on an emotional level. The initial process involved brainstorming sessions aimed at exploring the full potential of AI within the healthcare sector. The focus was on identifying key features that would address the users’ pain points while enhancing their overall experience. I initially started with mind mapping to dissect issues and find innovative solutions. It helped me simplify complex information, generate ideas, and better understand and organize existing information.

Mind mapping helps to visually organize and capture ideas and concepts systematically.

I employed mind mapping techniques to visualize the intricate relationships between various platform functionalities and user needs. This process began with brainstorming key features, user actions, and potential pain points, then branching into interconnected pathways that highlighted logical workflows and dependencies. By mapping out these connections, I gained a clearer understanding of how elements like dataset management, model exploration, and collaborative tools intersect. This visual framework allowed us to refine our information architecture, ensuring a cohesive and seamless user experience. The mind map served as a dynamic guide, helping me to maintain clarity throughout the design phase.

EARLY MIND-MAPPING

CORE VALUE PROPOSITION