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AI-Augmented Co-Design in Healthcare: Log-Based Markers of Teamwork Behaviors and Collective Intelligence Outcomes

Year: 2025

Type: article

Abstract: Co-design in healthcare settings requires teams to utilize each other's knowledge effectively, but practical guidance and simple methods for observing collaboration are often lacking. We tested whether a lightweight AI assistant that guides the process-and automatically logs who speaks, when, and how work progresses-can make teamwork easier to manage and easier to track. Six four-person teams completed the same five-phase session. The assistant nudged timing, turn-taking, and artifact hand-offs; all interactions were recorded in a shared workspace. We assessed usability and acceptance, expert-rated product quality (technical performance), perceived team performance, and self-rated technical contribution, and we summarized basic log signals of participation and pacing (e.g., turn-taking balance, average turn duration). Analyses were descriptive. All teams finished the protocol with complete logs. Outcomes were favorable (expert ratings averaged 4.18/5; perceived performance 6.14/7; self-rated contribution 4.08/5). Teams with more balanced participation and clearer pacing tended to report better performance, whereas simply having more turns did not. A process-guiding AI assistant can quantify teamwork behaviors as markers of collective intelligence and support reflection in everyday clinical co-design; future work will examine the generalizability of these findings across different sites.

Source: Behavioral Sciences

DOI: 10.3390/bs15121704

Authors: Yue Jiang, Jing Chen, Zhaoqi Li, Long Liu, P. John Clarkson

Institutions: Tongji University, University of Cambridge, Shanghai University of Medicine and Health Sciences

Language: English

Topic: Team Dynamics and Performance

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,654