Sensemaking Through Making: Developing Clinical Domain Knowledge by Crafting Synthetic Datasets and Prototyping System Architectures

2025-07-01

AI Summary

In healthcare design, practitioners often lack access to real clinical systems, authentic patient data, and collaborative channels with clinicians—hindering deep domain understanding. Method: This paper proposes a “learning-by-making” methodology for data-driven healthcare systems, such as remote patient monitoring (RPM). Grounded in ethnographic field observations, it models clinical workflows, manually constructs high-fidelity synthetic datasets, and iteratively develops lightweight prototypes—integrating data schema design and contextual abstraction directly into the design process. Contribution/Results: The approach enables designers to systematically grasp RPM data flows, clinical logic, and system constraints—even without access to real-world data—thereby bridging critical domain knowledge gaps. Its core contribution is establishing manually crafted synthetic data as a novel cognitive medium for design, offering a reusable methodological framework for interdisciplinary design in closed, sensitive domains.

Technology Category

• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Designers have ample opportunities to impact the healthcare domain. However, hospitals are often closed ecosystems that pose challenges in engaging clinical stakeholders, developing domain knowledge, and accessing relevant systems and data. In this paper, we introduce a making-oriented approach to help designers understand the intricacies of their target healthcare context. Using Remote Patient Monitoring (RPM) as a case study, we explore how manually crafting synthetic datasets based on real-world observations enables designers to learn about complex data-driven healthcare systems. Our process involves observing and modeling the real-world RPM context, crafting synthetic datasets, and iteratively prototyping a simplified RPM system that balances contextual richness and intentional abstraction. Through this iterative process of sensemaking through making, designers can still develop context familiarity when direct access to the actual healthcare system is limited. Our approach emphasizes the value of hands-on interaction with data structures to support designers in understanding opaque healthcare systems.

Problem

• Helping designers understand healthcare complexities without direct access
• Using synthetic datasets to learn about data-driven healthcare systems
• Developing clinical domain knowledge through iterative prototyping

Innovation

• Crafting synthetic datasets for healthcare learning
• Prototyping simplified RPM system iteratively
• Hands-on interaction with opaque data structures