Team structure and quality improvement in collaborative environments

Teams comprising diverse individuals have been shown to increase the collective creativity in jointly solving problems. However, in contexts where the purpose of collaboration is knowledge diffusion in complex environments, it is not clear whether team diversity will help or hinder effective learning. For example, in organized quality improvement collaboratives (QICs), healthcare institutions exchange information on clinical practices and outcomes with the aim of improving health outcomes at their own institutions. However, what works in one hospital may not work in others with different local contexts, due to non-linear interactions among various treatments and practices. While there is limited evidence that some QICs have resulted in improved care, it is not yet clear what factors contribute to the effectiveness of these team collaborations. In this study, we use an agent-based model to study how different strategies of team formation, including team diversity and size, affect quality improvement in simulated collaborative environments. We show that, in this context, teams comprising similar individuals outperform those with more diverse teams, and that this advantage increases with the complexity of the landscape and level of noise in assessing fitness. Furthermore, we show that larger teams of relatively homogeneous agents perform better than smaller teams, and that effective learning through team collaborations is dependent on the level of knowledge of team members' performance levels. Thus, our results suggest that groups of similar hospitals should collaborate as a single team and openly share detailed information regarding their clinical practices and outcomes. To facilitate this, we propose a virtual collaboration framework that would allow hospitals to efficiently identify potentially better practices in use at other institutions similar to theirs, without any institutions having to sacrifice the privacy of their own data. Our results may also have implications for other types of data-driven diffusive learning, such as in personalized medicine.