DeepScribe
Reducing physican workload through AI-powered note taking.
Project Highlight
The TL;DR. Read the full case study, here.
Final Design:
Original

My Design:
My Design
Case STudy
Reducing physican workload through AI-powered note taking.
My Role:
Product Designer
Key Colaborators:
Kairui Zeng, DeepScribe CTO
My Responsibilities:
UX Researcher and Synthesizer
UX Designer
UI Designer and Prototyper
Design Brief:
DeepScribe has created an innovative tool help fight burnout in medical professions through technology. The increased complexity of insurance systems has caused doctors to spend large amounts of time outside of work documenting their exams. This can result in 50-60 hour work weeks and skyrocking rates of physician burnout. The team at DeepScribe has developed an AI engine that can pull relevant concepts from a transcript of a doctor/paitent exam and file them correctly into a correct electronic health record (EHR), requring only a quick check and approval by the doctor.
While the product works well, DeepScribe’s user experience needed some help. Specifically, the team needed an interface for navigating a physican’s daily schedule of DeepScribe exams that created trust in the AI system working in the background.
Research and Scope Definition
I had a long coversation with DeepScribe’s CTO about the genesis of their automated medical notetaking idea, their implementation, successes and failures. He provided me with common user profiles, reviews, and an overview of a physician’s day-to-day interaction with DeepScribe.
User Journey Map
User Persona
To gain more insight into this experience and the motivations for it, I designed a survey to distribute to existing DeepScribe physicians that would investigate their feelings about the efficacy of the product.

- DeepScribe sometimes doesn’t catch data for complex paitents and can file it into the wrong places, requiring more effort to review and edit notes at the end of the day.
- Physicans often don’t know if a recording session has started or ended, creating confusion and anxiety.
- Physicans would like to know ahead of time if a particular exam will require a detailed note review so they can do so sooner rather than later.
- How might we increase physician confidence in DeepScribe’s recording of their notes during an exam?
- How might we let physicians know where possible problems with the recording are?
Feature Prioritization and MVP

Feature Concepts

Prioritization
Using the RICE method, I organized the various features by priority. Communicating with the development team to determine how difficult each feature would be to implement.
The RICE method prioritizes feature by assigning them as score based on this formula:
- RICE Score= (Reach x Impact x Confidence) / Effort
Where,
- Reach is the number of customers affected by the feature in a given time period. We’ll rate this on a 1-3 scale with 3 being the entire customer base and 1 being a small number of customers.
- Impact is the resulting increase in trust and confidence in DeepScribe caused by a physician using the feature. Measured on a 1-3 scale.
- Confidence is the level of surety associated with the impact rating. Measured as a percentage with 100% being high confidence and 50% or lower being a total moonshot.
- Effort is the amount of time and resources associated with shipping the feature. Measured in person-months.
Reach: 3
The feature would impact all users.
Impact: 2
The feature would be moderately useful for increasing trust in the system since it would be responding directly to users.
Confidence: 80%
I’m fairly sure it would have this effect based on the results of my initial survey.
Effort: 2 person-months
Developers indicated that this feature would be difficult to build and require a lot of engineering resources.
These values resulted in a RICE score of 2.4, putting it low on the list of priorities.
The method helps cut through subjectivity through quantification and identifies features with high value and low cost that should be worked on first.
MVP FEATURES
- Clear start/stop messaging for exam sessions.
- A survey to capture system failings and negetive expereinces.
- A summary of basic paitent information to be delivered soon after an exam’s conclusion.
- Live audio visualization of a conversation when recording.
- A marker to indicate when an exam was complext and the note is likely in need of review.
Prototyping and User Testing
I used their existing design as a basis and made improvements from there.
Before

After




Before
After





Reception was positive but there were several suggestions for improvement.

Final Designs





