Seeing the unseen - Helping physicians to leverage AI in detecting Prognosis
app.whiteboard.microsoft.com
Cobb, Jessica
Cobb, Jessica
Co-pilot
Notes
Prognosis Report
The patient is a 58-year-old male with a history of type 2 diabetes and hypertension. He presented with chest pain and was diagnosed with a non-ST elevation myocardial infarction (NSTEMI). A stent was placed successfully. Given his stable vitals post-procedure and absence of complications, the prognosis is favorable. He is expected to recover well with adherence to medication, diet, and cardiac
EHR Consistency Check
Co-Pilot Control
Risk Factor Scan
Additional checkpoints
Comorbidities
Accounted for diabetes and hypertension in recovery outlook?
Long-Term Risks
Considered heart failure or recurrent MI?
Rehab Impact
Rehab compliance flagged as a recovery risk?
BMI Factor
Have you considered how the patient’s weight (BMI 32) might influence recovery?
Adherence History
Is “expected to recover well” supported by this patient’s past adherence in the EHR?
Create Note
Ambient 1
Prog 2
Prog 3
My Note
ED Provider Name - 4/22/2025 11:25AM
ROS
History of Present Illness
Review of Systems
Objective
Physical exam
Response to interventions
Overview
Most physicians provide standardized prognoses to all patients, despite each having a unique medical history and lifestyle. This can lead to serious consequences, including the loss of patients. The goal is to design a system that can predict critical issues based on a patient’s history before the situation becomes dire.
Timeline
4 months
(Jan-Apr 2025)
Role
UX Researcher
UX/UI Designer
Platform
Web application
Status
Developed
Contribution
UX Research
UX Design
Vibe coding
Team
It was a research-informed design project, and I worked on it independently with guidance from my mentors as part of my capstone.
Some context on prognosis
How it’s done?
Day 1: Patient presents symptoms and Doctor makes a diagnosis (based on tests, history)
Then comes prognosis:
Doctor discusses what to expect going forward, how the condition may progress, chances of recovery, possible complications and outcomes
Some Background based on true story
From the story below, you’ll understand the unpredictability and how hard it is for physicians to predict outcomes
Two middle-aged men are both wincing in chest pain
Doctors reviewed identical issues and performed the same life-saving procedure on both patients
Despite the same care, their journeys end very differently
But why is it unpredictable?
Based on my interviews with 2 physicians, I found that several factors contribute to this unpredictability.
Limited information about the patient
Patient’s health uniqueness
Complex symptoms post surgical
I found a way to solve this problem
Based on secondary research, AI has been doing wonders in healthcare industry
AI can aid prognosis by identifying risk factors
Contextual understanding
Limited knowledge
Can work 24/7 accurately
Relies solely on data
Both have their own strengths and limitations
So, how might we enable seamless collaboration between Physicians and AI?
but there was a problem!!
While I started the research I realized that there’s already a research on AI assisted decision making systems in healthcare and it didn’t work because
Expectation
Physician
AI
Physician + AI
Accuracy
Reality
Physician
AI
Physician + AI
Accuracy
Over - reliance and
Less cognitive engagement
So I pivoted my problem statement to
How might we make AI and Physicians work together to improve prognosis without over-reliance or dismissal?
So the first thing I did is to understand why over-reliance is happening to understand the overlying problem
When conversational agents(AI) become more human-like
When users have limited expertise in a subject area
When AI provides quick answers, leading users to choose efficiency over critical evaluation
Priming beliefs about AI motives as empathetic can lead to trust but in reality it’s not the actual behavior
So changed my solution to be
seamless
integration
reduce
over-reliance
during
interaction
My solution
Solution exploration
Solution 1.1 - To address the over-reliance
Concept 1.1
Initial Approach
I introduced reasoning checkpoints - questions the AI asks during interactions to prompt physicians to verify decisions.
What I Learned
I realized the same type of questions might bore some users or challenge others, depending on their background.
Pivot
I shifted to context-aware reasoning checkpoints that adapt based on the physician’s experience and clinical context.
Outcome
This ensures conversations stayed relevant and neither overwhelming nor too simplistic.
Reasoning checkpoints to the physicians
Limited knowledge in the subject
Expertise in the subject
Eg: Before accepting this suggestion consider:
Check differential diagnosis
Check atypical symptoms
Eg: Does this match with your assessment?
Does it align with the latest research?
Solution 1.2 - Positioning of the app and seamless integration
Concept 1.2.1
Initial Approach
I initially planned to integrate the system with scribing, using voice recognition during physician–patient interactions.
What I scribing
Scribing refers to real-time documentation of clinical encounters either by a human or voice-enabled software.
Why I pivoted?
Scribing aims to minimize screen time for physicians during interaction, so adding extra steps could defeat its purpose. Also, many doctors prefer using their phone's microphone for convenience, being on a phone during patient interaction may not be ideal.
Key insight I received from my primary research
Unlike diagnosis, prognosis isn’t about real-time interaction. It focuses on post-diagnosis planning and communication, making it ideal for asynchronous AI support.
Concept 1.2.2
I then pivoted to chatbot integration, allowing physicians to use it for generating prognoses. However, I encountered significant friction the chatbot wasn’t seamlessly integrated into the systems physicians already use. This approach also risked doubling their workload.
I developed a chatbot using the Perplexity API and prompted it to act as an assistant
Concept 1.2.3
To improve workflow integration, I designed and developed a co-pilot system where AI suggestions appear as physicians write their notes.
How it works
As the physician takes notes, the system analyzes their input in real time. A personalized pop-up appears if any important detail is missing, based on both what’s being written and the patient’s medical history.
The co-pilot nudges physicians to reflect and consider alternative prognoses, factoring in variables like age, blood pressure, gender, and more.
User Testing Insights
I conducted usability testing with my peers to gather feedback on the user experience
The sudden pop-up could disrupt the physician’s train of thought.
It may also serve as a distraction during note-taking.
Final design
In my final design, I replicated an EHR interface and integrated prognosis features to ensure a seamless experience. Based on earlier feedback, I combined two concepts: constant context-aware checkpoints (marked as 1 in the image) to guide without overwhelming, and a risk factor scan at submission for final review.
app.whiteboard.microsoft.com
Cobb, Jessica
Cobb, Jessica
Co-pilot
Notes
Prognosis Report
The patient is a 58-year-old male with a history of type 2 diabetes and hypertension. He presented with chest pain and was diagnosed with a non-ST elevation myocardial infarction (NSTEMI). A stent was placed successfully. Given his stable vitals post-procedure and absence of complications, the prognosis is favorable. He is expected to recover well with adherence to medication, diet, and cardiac
EHR Consistency Check
Co-Pilot Control
Risk Factor Scan
Additional checkpoints
Comorbidities
Accounted for diabetes and hypertension in recovery outlook?
Long-Term Risks
Considered heart failure or recurrent MI?
Rehab Impact
Rehab compliance flagged as a recovery risk?
BMI Factor
Have you considered how the patient’s weight (BMI 32) might influence recovery?
Adherence History
Is “expected to recover well” supported by this patient’s past adherence in the EHR?
Create Note
Ambient 1
Prog 2
Prog 3
My Note
ED Provider Name - 4/22/2025 11:25AM
ROS
History of Present Illness
Review of Systems
Objective
Physical exam
Response to interventions
Set how active your co-pilot should be from Light Touch → Moderate Help → Full Insight
Standardize notes for clinical documentation - concise and structured
Context aware reasoning checkpoints
Junior physicians need guidance whereas senior physicians benefit from being challenged to validate their knowledge
Risk Factor Scan - for additional check
Acts as an intelligent assistant after prognosis writing, offering an additional layer of checks by analyzing the patient’s medical history
Trade offs
I’m not trying to build trust in AI among physicians who are resistant to it. My focus is specifically on those who are already open to using AI in their workflow.
I acknowledge that for physicians who resist AI, this solution might not be the right fit, they may simply ignore it altogether. Additionally, the AI model will take time to adapt to each physician’s preferences and needs.
Impact
My co-pilot system enhances clinical decision-making by providing context-aware suggestions as physicians draft patient notes.
My co-pilot system enhances clinical decision-making by providing context-aware suggestions as physicians draft patient notes.
While minimizing disruption to physicians’ workflow, it fosters a co-learning environment where both the AI and the physician improve over time. This can potentially lower prognostic errors and improve patient outcomes.
Reflection
Designing for AI–Human Collaboration
As a designer, I’ve come to realize how important our role is in bridging the gap between AI and humans. AI can feel like a maze, and it’s our job to guide users through it to show them what’s possible in a clear, meaningful way.
Growth Through Feedback
Throughout this process, I’ve learned to be more open to feedback and less defensive. This project helped me grow as a designer who is curious and collaborative.
Returning to My Engineering Roots
By the end of this project, I realized I can’t move away from my background as a software engineer. I began developing the solution to ensure it was technically feasible, especially since designers are often dismissed with comments like “this isn’t possible” and AI is not magic. I wanted to avoid that situation.
Other works
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