
Luna. AI Clinical Assistant
LUNA is an AI-powered clinical assistant designed to support healthcare professionals in creating cohesive patient profiles and delivering evidence-based treatment recommendations. This tool aims to streamline the patient evaluation and treatment planning process, ultimately improving the efficiency and effectiveness of clinical care. A core focus of the design was to optimize the AI's utility and trustworthiness.

Fine-Tuning LUNA AI for Human-Centered Clinical Collaboration
This case study explores how we fine-tuned LUNA to act less like an algorithm—and more like a thoughtful assistant. Through prompt design, intent recognition, and tone calibration, we taught LUNA to listen, adapt, and defer to human judgment. The result: better adoption, higher trust, and a more human-centered AI experience in clinical care.

LUNA Clinical Insight Engine
LUNA AI is a clinical insight assistant that helps mental health practitioners surface, summarize, and act on meaningful patient patterns. It uses session transcripts, assessments, and notes to dynamically evaluate how well different areas of a patient’s emotional and cognitive profile are understood. For each domain, LUNA classifies the data as present, partial, or missing—then delivers tailored insights with charts, source quotes, and clinical reasoning. When data is limited, it guides the clinician with smart prompts to gather what’s needed. Every output is transparent, traceable, and designed to reduce time-to-insight while enhancing care quality.

Silver Chain. Enhancing In-Home Services through User Research
This case study delves into the transformative journey of Silver Chain's In-Home Care Services, where user navigation and information architecture underwent a remarkable revamp. By harnessing the power of Cluster Analysis and leveraging intelligent information architecture, Silver Chain elevated their care experiences to new heights. Through a deep understanding of user segments, Silver Chain implemented strategic improvements that catered to the unique needs and preferences of their diverse user base.
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