Design Ethics
Healthcare AI Interaction Patterns
An AI interaction pattern library for symptom intake, wearable context, and urgency routing — governing the patient-to-clinician handoff in enterprise healthcare workflows.
Year :
Design Ethicists
Industry :
Healthcare
Client :
Claude Design, Perplexity
Project Duration :
6 weeks
The problem between the patient and the clinician
When a patient feels unwell and reaches for their phone, two things happen at the same time.
They open an app or send a message. Somewhere else, a clinician's inbox receives one more entry that may or may not need urgent attention.
Neither person is doing anything wrong. The problem is the handoff.
Patients describe symptoms the way they experience them in fragments, in everyday language, shaped by anxiety, uncertainty, or understatement. They say things like "I feel weird," "my chest feels tight," "I've been really tired," or "something just feels off."
Clinicians receive those messages without the context that would help them quickly understand what matters, what is missing, and what needs action now.
AI seems like an obvious bridge. But many AI health tools fail in two ways:
They ask too many clinical questions too early.
Or they give generic advice that does not help the clinician prioritize.
This project started with a different question:
What if AI did not try to diagnose the patient, but instead helped create a better handoff between the patient and clinician?
The two people this system is for
The handoff has two sides. Both need to be designed for. Before defining any pattern, the system needed to be grounded in the people whose daily work it would change — or fail.
What shaped the project
This project did not begin with primary research. It began with design experience, pattern observation, and secondary research themes that had already shaped how I think about AI in clinical workflows.
Why I designed a pattern library
The first instinct with this problem is to design an app. A patient-facing chatbot. A symptom checker. A triage flow. But that framing misses the deeper problem.
So I designed a pattern library instead.
Visual Idea:
Pattern Library
Golden Rules
The design principles
Before designing any individual pattern, five behavioral rules had to be locked. In healthcare AI, tone is not enough. A friendly message can still be unsafe if it implies certainty the system does not have.
The 9 patterns
The final library includes 9 patterns across 3 groups.
More Projects
Design Ethics
Healthcare AI Interaction Patterns
An AI interaction pattern library for symptom intake, wearable context, and urgency routing — governing the patient-to-clinician handoff in enterprise healthcare workflows.
Year :
Design Ethicists
Industry :
Healthcare
Client :
Claude Design, Perplexity
Project Duration :
6 weeks
The problem between the patient and the clinician
When a patient feels unwell and reaches for their phone, two things happen at the same time.
They open an app or send a message. Somewhere else, a clinician's inbox receives one more entry that may or may not need urgent attention.
Neither person is doing anything wrong. The problem is the handoff.
Patients describe symptoms the way they experience them in fragments, in everyday language, shaped by anxiety, uncertainty, or understatement. They say things like "I feel weird," "my chest feels tight," "I've been really tired," or "something just feels off."
Clinicians receive those messages without the context that would help them quickly understand what matters, what is missing, and what needs action now.
AI seems like an obvious bridge. But many AI health tools fail in two ways:
They ask too many clinical questions too early.
Or they give generic advice that does not help the clinician prioritize.
This project started with a different question:
What if AI did not try to diagnose the patient, but instead helped create a better handoff between the patient and clinician?
The two people this system is for
The handoff has two sides. Both need to be designed for. Before defining any pattern, the system needed to be grounded in the people whose daily work it would change — or fail.
What shaped the project
This project did not begin with primary research. It began with design experience, pattern observation, and secondary research themes that had already shaped how I think about AI in clinical workflows.
Why I designed a pattern library
The first instinct with this problem is to design an app. A patient-facing chatbot. A symptom checker. A triage flow. But that framing misses the deeper problem.
So I designed a pattern library instead.
Visual Idea:
Pattern Library
Golden Rules
The design principles
Before designing any individual pattern, five behavioral rules had to be locked. In healthcare AI, tone is not enough. A friendly message can still be unsafe if it implies certainty the system does not have.
The 9 patterns
The final library includes 9 patterns across 3 groups.
More Projects
Design Ethics
Healthcare AI Interaction Patterns
An AI interaction pattern library for symptom intake, wearable context, and urgency routing — governing the patient-to-clinician handoff in enterprise healthcare workflows.
Year :
Design Ethicists
Industry :
Healthcare
Client :
Claude Design, Perplexity
Project Duration :
6 weeks
The problem between the patient and the clinician
When a patient feels unwell and reaches for their phone, two things happen at the same time.
They open an app or send a message. Somewhere else, a clinician's inbox receives one more entry that may or may not need urgent attention.
Neither person is doing anything wrong. The problem is the handoff.
Patients describe symptoms the way they experience them in fragments, in everyday language, shaped by anxiety, uncertainty, or understatement. They say things like "I feel weird," "my chest feels tight," "I've been really tired," or "something just feels off."
Clinicians receive those messages without the context that would help them quickly understand what matters, what is missing, and what needs action now.
AI seems like an obvious bridge. But many AI health tools fail in two ways:
They ask too many clinical questions too early.
Or they give generic advice that does not help the clinician prioritize.
This project started with a different question:
What if AI did not try to diagnose the patient, but instead helped create a better handoff between the patient and clinician?
The two people this system is for
The handoff has two sides. Both need to be designed for. Before defining any pattern, the system needed to be grounded in the people whose daily work it would change — or fail.
What shaped the project
This project did not begin with primary research. It began with design experience, pattern observation, and secondary research themes that had already shaped how I think about AI in clinical workflows.
Why I designed a pattern library
The first instinct with this problem is to design an app. A patient-facing chatbot. A symptom checker. A triage flow. But that framing misses the deeper problem.
So I designed a pattern library instead.
Visual Idea:
Pattern Library
Golden Rules
The design principles
Before designing any individual pattern, five behavioral rules had to be locked. In healthcare AI, tone is not enough. A friendly message can still be unsafe if it implies certainty the system does not have.
The 9 patterns
The final library includes 9 patterns across 3 groups.