Potential Applications of Machine Learning at Multidisciplinary Medical Team Meetings
This work addresses the problem of enhancing complex cooperative work in healthcare settings, but it is incremental as it focuses on reviewing and identifying opportunities rather than presenting new breakthroughs.
The paper examines the potential of machine learning to support multidisciplinary medical team meetings (MDTMs) by analyzing their characteristics over ten years and developing ML methods, identifying opportunities and challenges for implementation.
While machine learning (ML) systems have produced great advances in several domains, their use in support of complex cooperative work remains a research challenge. A particularly challenging setting, and one that may benefit from ML support is the work of multidisciplinary medical teams (MDTs). This paper focuses on the activities performed during the multidisciplinary medical team meeting (MDTM), reviewing their main characteristics in light of a longitudinal analysis of several MDTs in a large teaching hospital over a period of ten years and of our development of ML methods to support MDTMs, and identifying opportunities and possible pitfalls for the use of ML to support MDTMs.