When Performance is not Enough -- A Multidisciplinary View on Clinical Decision Support
This work highlights the need for a multidisciplinary approach to make clinical decision support systems usable and sustainable, targeting computer scientists in healthcare.
The paper addresses the gap between developing high-performance machine learning models and their practical implementation in healthcare, using a pilot project in nephrology to illustrate multidisciplinary challenges.
Scientific publications about machine learning in healthcare are often about implementing novel methods and boosting the performance - at least from a computer science perspective. However, beyond such often short-lived improvements, much more needs to be taken into consideration if we want to arrive at a sustainable progress in healthcare. What does it take to actually implement such a system, make it usable for the domain expert, and possibly bring it into practical usage? Targeted at Computer Scientists, this work presents a multidisciplinary view on machine learning in medical decision support systems and covers information technology, medical, as well as ethical aspects. Along with an implemented risk prediction system in nephrology, challenges and lessons learned in a pilot project are presented.