Empowering Doctors with Customizable AI-Transcribed Medical Notes
CONTEXT
Who is FosterHealth AI?
MY ROLE
FosterHealth AI is an AI healthtech startup developing tools to support clinical documentation and workflow efficiency. The product helps doctors generate structured medical notes more efficiently while maintaining accuracy and control.
MY ROLE
UX / Product Designer
MY ROLE
I owned the UX design of a personalization feature for AI-generated clinical notes, enabling doctors to tailor outputs to their preferences. In a rapid MVP build, I collaborated closely with founder and engineering, supported internal QA and testing, and ensured the solution held up against real-world clinical workflows and operational constraints.
The Problem
Doctors feel disconnected from AI-generated notes and often spend extra time re-editing them to match their personal tone, style, and documentation preferences.
The Solution
Designed a customizable feature that enables doctors to create self-defined templates based on their past medical notes, allowing Foster to learn and generate AI-powered drafts that match their tone, structure, and writing style—while giving them the flexibility to edit the templates as needed.
Leading the Design Process
Problem Discovery & Research
To understand the challenges doctors face with AI-generated medical documentation, we conducted interviews, workflow observations, and industry analysis to uncover both user needs and gaps in the current market.
🩺 Doctor Interviews & Workflow Insights
Lack of Personalization
"I use specific medical terms and shorthand that aren’t showing up correctly in the AI version."
"The structure is off. I always have to rearrange things to match how I usually present the case."
Increased Editing Time
"It’s faster for me to just write the note myself."
"I thought automation would help—but now it feels like double documentation."
Legal Requirements
"If my note format is off, it might get flagged or denied by insurance."
"Every detail matters when you’re documenting for insurance. One mistake and it delays everything."
We also conducted a landscape analysis of existing AI medical documentation tools to evaluate how current solutions meet—or fail to meet—these complex needs.
🧠 Industry & Competitor Research
AI tools in healthcare are rising, aimed at reducing administrative burden and preventing physician burnout.
Current solutions prioritize automation and speed, but neglect customization and doctor-specific workflows.
Many tools are not flexible enough to adapt to varied clinical contexts or legal documentation standards.
Key Insight
The market is saturated with generic AI note solutions that focus on speed but fail to support the nuanced, deeply personal ways doctors document. This leaves doctors editing AI-generated content instead of trusting and using it—defeating the purpose of automation.
Ideation & Concepting
Through visualizing the current user flow of the note transcribing process, we identified areas for improvement in efficiency and highlighted key challenges faced by stakeholders. Specifically, we pinpointed two primary pain points: the excessive editing time required by doctors to finalize documentation, and the lack of accuracy in the initial transcriptions.
Existing Documentation Experience
Opportunity
There is a clear need for an AI-powered documentation tool that allows doctors to personalize templates using their own past notes, adapting to their voice, structure, and clinical context—while remaining compliant with insurance standards.
New Documentation Experience with the personalized template
Prototyping & Usability Testing
After identifying key pain points in the current documentation process - particularly around editing time and transcription accuracy, we moved into the ideation and prototyping phase. We developed a series of low- and mid-fidelity wireframes to explore potential solutions aimed at streamlining the editing workflow and enhancing clarity in the user interface.
Our wireframes focused on simplifying the editing experience for doctors by introducing features such as inline editing, customizable templates, and visual indicators for low-confidence transcription segments. These concepts were brought into usability testing sessions with real users, including physicians and administrative staff.
During this exploration, I also conducted meetings with engineers to better understand technical constraints and possibilities. These cross-functional discussions helped ensure our solutions were not only user-centered but also technically feasible.
Final Design
After internal testing and gathering feedback, I went through several rounds of iteration and eventually completed two key flows:
1. Creating a template in the Settings – integrated where the current default template is located to align with the existing structure.
2. Adding a dropdown menu on the Transcribing page – this allows users to select a template either before or after transcribing and gives them the option to add a personalized template directly from there.
I also designed two different user flows for first-time and returning users. For first-time users, I created popup instructions to help them understand where the key modules are located and guide them through the initial experience more intuitively.
Achievement
Reduced Repetitive Editing for Doctors
Inline editing and customizable templates cut repetitive tasks, reducing average editing time by 30%.
Accelerated template creation
Reduced setup time and simplified customization by optimizing the note upload process.
Guided First-Time Experience
Guided pop-ups and a clearer UI helped first-time users navigate key modules, reducing training time.