
Product & Design
Advancing Health Equity through AI-Driven Design
Social workers manage dozens of patients with different needs, but AI recommendations were hard to interpret and act on. We redesigned Alvee’s platform to make those insights clear, transparent, and easy to use — so care teams can act faster and with confidence. Through Arionkoder’s Reshape Health Grant, we built an AI platform for health equity.
Social workers manage dozens of patients with different needs, but AI recommendations were hard to interpret and act on. We redesigned Alvee’s platform to make those insights clear, transparent, and easy to use — so care teams can act faster and with confidence. Through Arionkoder’s Reshape Health Grant, we built an AI platform for health equity.

Product & Design
Advancing Health Equity through AI-Driven Design
Social workers manage dozens of patients with different needs, but AI recommendations were hard to interpret and act on. We redesigned Alvee’s platform to make those insights clear, transparent, and easy to use — so care teams can act faster and with confidence. Through Arionkoder’s Reshape Health Grant, we built an AI platform for health equity.



ALIGN
Alvee is an AI-driven health-equity data management platform that helps practices and health systems identify and measure disparities, predict social needs, and promote equitable care.
Before designing, we immersed ourselves in their research and data. Together with Alvee’s founder, Nicole Cook, we reviewed academic studies, previous user interviews, and internal research on Social Determinants of Health (SDoH).
Our process began with a custom Design Sprint for AI-driven products. We focused on understanding the daily realities of our main users — Social Workers and Care Managers — across outpatient, inpatient, emergency, and chronic care settings.

ALIGN
Alvee is an AI-driven health-equity data management platform that helps practices and health systems identify and measure disparities, predict social needs, and promote equitable care.
Before designing, we immersed ourselves in their research and data. Together with Alvee’s founder, Nicole Cook, we reviewed academic studies, previous user interviews, and internal research on Social Determinants of Health (SDoH).
Our process began with a custom Design Sprint for AI-driven products. We focused on understanding the daily realities of our main users — Social Workers and Care Managers — across outpatient, inpatient, emergency, and chronic care settings.

ALIGN
Alvee is an AI-driven health-equity data management platform that helps practices and health systems identify and measure disparities, predict social needs, and promote equitable care.
Before designing, we immersed ourselves in their research and data. Together with Alvee’s founder, Nicole Cook, we reviewed academic studies, previous user interviews, and internal research on Social Determinants of Health (SDoH).
Our process began with a custom Design Sprint for AI-driven products. We focused on understanding the daily realities of our main users — Social Workers and Care Managers — across outpatient, inpatient, emergency, and chronic care settings.
PRIORITIZE
We mapped each role’s workflow and identified design priorities that would improve adoption:
Simplify the Social Worker and Care Manager journeys.
Build transparency into AI-powered recommendations.
Create a shared design language for AI-based actions and confidence states.
Collaborative sketching sessions brought together Arionkoder’s product designers, ML experts, and Alvee’s stakeholders, fostering transparency and shared ownership in decision-making.
PRIORITIZE
We mapped each role’s workflow and identified design priorities that would improve adoption:
Simplify the Social Worker and Care Manager journeys.
Build transparency into AI-powered recommendations.
Create a shared design language for AI-based actions and confidence states.
Collaborative sketching sessions brought together Arionkoder’s product designers, ML experts, and Alvee’s stakeholders, fostering transparency and shared ownership in decision-making.
PRIORITIZE
We mapped each role’s workflow and identified design priorities that would improve adoption:
Simplify the Social Worker and Care Manager journeys.
Build transparency into AI-powered recommendations.
Create a shared design language for AI-based actions and confidence states.
Collaborative sketching sessions brought together Arionkoder’s product designers, ML experts, and Alvee’s stakeholders, fostering transparency and shared ownership in decision-making.
Prove
We developed interaction patterns that made AI’s role clear, empowering users rather than overwhelming them.
Key design principles tested:
The shared arena: AI outputs appear as “Recommended First Actions” that users can accept or modify, ensuring human control.
Transparency before generation: Social Workers can review and adjust inputs before content is generated, reducing rework.
AI-supported summaries: LLMs generate concise highlights of long clinical or social data, saving time and improving focus.
Editable highlights: Users can refine what the AI highlights, creating a subtle feedback loop for future model tuning.
This phase produced a high-fidelity Figma prototype, ready for implementation with clear documentation of prioritized features.
Prove
We developed interaction patterns that made AI’s role clear, empowering users rather than overwhelming them.
Key design principles tested:
The shared arena: AI outputs appear as “Recommended First Actions” that users can accept or modify, ensuring human control.
Transparency before generation: Social Workers can review and adjust inputs before content is generated, reducing rework.
AI-supported summaries: LLMs generate concise highlights of long clinical or social data, saving time and improving focus.
Editable highlights: Users can refine what the AI highlights, creating a subtle feedback loop for future model tuning.
This phase produced a high-fidelity Figma prototype, ready for implementation with clear documentation of prioritized features.
Prove
We developed interaction patterns that made AI’s role clear, empowering users rather than overwhelming them.
Key design principles tested:
The shared arena: AI outputs appear as “Recommended First Actions” that users can accept or modify, ensuring human control.
Transparency before generation: Social Workers can review and adjust inputs before content is generated, reducing rework.
AI-supported summaries: LLMs generate concise highlights of long clinical or social data, saving time and improving focus.
Editable highlights: Users can refine what the AI highlights, creating a subtle feedback loop for future model tuning.
This phase produced a high-fidelity Figma prototype, ready for implementation with clear documentation of prioritized features.
Integrate
We worked closely with Alvee’s team to align design outputs with their Epic and Salesforce integration roadmap, ensuring the new workflows would fit seamlessly within existing systems.
AI-ready design patterns and UI components were delivered with full documentation to guide engineering implementation and maintain long-term design consistency.
Integrate
We worked closely with Alvee’s team to align design outputs with their Epic and Salesforce integration roadmap, ensuring the new workflows would fit seamlessly within existing systems.
AI-ready design patterns and UI components were delivered with full documentation to guide engineering implementation and maintain long-term design consistency.
Integrate
We worked closely with Alvee’s team to align design outputs with their Epic and Salesforce integration roadmap, ensuring the new workflows would fit seamlessly within existing systems.
AI-ready design patterns and UI components were delivered with full documentation to guide engineering implementation and maintain long-term design consistency.
Scale
The improved Alvee platform now enables care teams to:
Understand social determinants through AI-driven insights.
Track and predict patient social needs in real time.
Collaborate more efficiently across care networks.
By aligning with U.S. health initiatives on SDoH, Alvee is positioned to help health systems move toward a more holistic, equitable approach to patient care.
Scale
The improved Alvee platform now enables care teams to:
Understand social determinants through AI-driven insights.
Track and predict patient social needs in real time.
Collaborate more efficiently across care networks.
By aligning with U.S. health initiatives on SDoH, Alvee is positioned to help health systems move toward a more holistic, equitable approach to patient care.
Scale
The improved Alvee platform now enables care teams to:
Understand social determinants through AI-driven insights.
Track and predict patient social needs in real time.
Collaborate more efficiently across care networks.
By aligning with U.S. health initiatives on SDoH, Alvee is positioned to help health systems move toward a more holistic, equitable approach to patient care.
“Everybody was really invested in Alvee — in our company and our mission. The team truly cared about the outcomes, and it was evident. I’ve worked with other development groups before, and Arionkoder was far above some of the others I’ve worked with.”
Nicole Cook
Founder
@
Alvee
How we helped:
1.
Diagnosed Workflow Gaps
Conducted desk research, interviews, and mapping to understand Social Workers’ real-world challenges.
2.
Applied a Custom Design Sprint
Used AI-product-specific frameworks to translate insights into design opportunities.
3.
Introduced AI-Aware Design Patterns
Built interfaces that communicate model confidence and keep users in control.
4.
Collaborated Across Disciplines
Brought ML experts into design sessions to align interaction logic with model behavior.
5.
Delivered Implementation-Ready Assets
Provided high-fidelity Figma prototype, documentation, and prioritized feature list.















Team
Product Manager & Facilitator UX/UI Designer
Delivery
UX/UI Design High-Fidelity Figma Prototype Implementation Roadmap
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