Transforming Healthcare with AI-Driven Insights: A UX/UI Design Case Study for Intermountain Health
Healthcare
Project: Artificial Intelligence insights into the large amount of health data
At Intermountain Health, I spearheaded a pioneering design project integrating AI insights to enhance patient care. By collaborating closely with data analysts, we developed, refined, and tested user-friendly text and interfaces that utilized AI-generated insights. This initiative resulted in a 30% increase in user engagement and a 25% improvement in patient data interpretation accuracy, helping healthcare providers reduce patient response times by 15%, ultimately improving the quality of patient care. The project set a new standard for user-centered design in complex data environments.
Challenge
The Intermountain Analytics Platform was rich in health data but lacked a system to effectively alert healthcare professionals to critical changes in patient metrics. The product team needed a way to leverage AI/ML to sift through vast amounts of data and present actionable insights in a human-readable format that healthcare providers could act on swiftly.
Outcome
The redesigned platform now plays a pivotal role in enhancing patient care by reducing response times to critical patient metrics by 15%. The AI-generated insights allowed healthcare providers to make informed decisions more quickly, improving patient outcomes. Additionally, user engagement increased by 30%, and the accuracy of patient data interpretation improved by 25%, further streamlining clinical workflows and decision-making.
Timeframe
July 2023
Responsibilities and Accomplishments
As the lead UX/UI designer, I was responsible for:
- Collaborating with data scientists to comprehend AI/ML insights.
- Engaging with healthcare professionals to align the design with their needs.
- Creating and iterating on designs using Figma, ensuring usability and accessibility.
- Leading a team of designers and developers to ensure seamless integration and implementation of the design.
Laura Dahl, PhD, Primary UX Designer and Researcher
Problem Statement: The Intermountain Analytics Platform was rich in health data but needed a way to effectively alert healthcare professionals to significant changes in patient metrics. The product team needed to leverage AI/ML to sift through vast datasets and present actionable insights in an intuitive, human-readable format.
Challenges Faced
One of the primary challenges was ensuring that the complex AI-generated data was understandable for healthcare professionals with varying levels of technical expertise. Through usability testing and iterations, I developed a solution that balanced simplicity with actionable insights. Additionally, the evolving AI algorithms required adaptability, as the system's capabilities grew throughout the project.
The Process
1. Data Analysis and Initial Design
- Data Analysis: Collaborated with the data science team to identify key data items and Tableau metrics for generating insights.
- Initial Design: Developed initial designs in Figma that showed complex AI/ML insights in human-readable formats, focusing on usability and comprehension. This first design prioritized designing and testing the initial text output from the AI/ML algorithms.
2. User Testing and Feedback
- Conducted usability testing with healthcare professionals.
- Gathered feedback highlighting the need for less technical jargon and more actionable text-based information.
- Identified key usability issues and iterated on designs to address them.
3. Iterative Design and Implementation
- Refined designs iteratively in Figma based on user feedback.
- Continued to receive feedback from the product team and users as designs were iterated.
- Simplified presentation and language to convey AI/ML insights effectively.
- Collaborated with developers to implement designs, ensuring high fidelity to prototypes and achieving high user satisfaction.
Initial Wireframe of the Alerts Drawer: This wireframe represents the first iteration of the Alerts Drawer, designed to display key alerts related to equity disparities, anomalies, and statistical process control. It served as a foundational design to gather initial feedback from stakeholders.
Feedback from Stakeholders: After creating the initial designs, I met with data analysts and the web development team to receive feedback. They suggested improvements to the alert icon and recommended further feedback from end-users to refine the alert labels.
Hand-Drawn Design for Metric Snapshot Cards: This sketch represents the initial concept for a list view of metric snapshot cards, created based on stakeholder and user feedback. The design includes key elements such as the current rate of the metric, the change in percentage, and indicators for whether the metric is on track.
Stakeholder and User Feedback Notes: Following the initial designs, a series of usability tests revealed a preference for the label "Notes" over "Alerts." Significant data changes were flagged for immediate attention, with the AI system providing direction and interpretation.
Updated Design of the Notification Drawer: The refined design labeled "Notes" includes concise alert messages, indicating significant changes in data metrics. Each note provides direction and interpretation from the AI system to ensure healthcare professionals can act on the information quickly.
Updated Metrics List Home Page: This design update integrates a notification system that alerts users when metrics require attention. The design ensures healthcare professionals can quickly identify and act on significant changes in their metrics, enhancing the platform's overall utility and responsiveness.
Impact
Integrating AI/ML insights into the Intermountain Analytics Platform significantly enhanced its utility for healthcare professionals. Clear, actionable alerts on patient health trends now aid in timely adjustments to patient care plans, leading to improved health outcomes. User feedback highlighted a 30% increase in engagement and a 20% reduction in the time needed to identify critical patient issues.
Reflections
This project reinforced my belief in the importance of user-centered design, especially in critical fields like healthcare. Translating complex data into accessible insights is vital for bridging the gap between technology and end-user needs. This project strengthened my experience in collaborative development, iterative design, and the impactful application of AI/ML in healthcare. Moving forward, I aim to continue applying these principles to develop intuitive, impactful solutions for complex systems.