Follow-up Interactive Long-Term Expert Ranking (FILTER): a crowdsourcing platform to adjudicate risk for survivorship care

Abstract

Abstract Objectives To develop an online crowdsourcing platform where oncologists and other survivorship experts can adjudicate risk for complications in follow-up. Materials and Methods This platform, called Follow-up Interactive Long-Term Expert Ranking (FILTER), prompts participants to adjudicate risk between each of a series of pairs of synthetic cases. The Elo ranking algorithm is used to assign relative risk to each synthetic case. Results The FILTER application is currently live and implemented as a web application deployed on the cloud. Discussion While guidelines for following cancer survivors exist, refinement of survivorship care based on risk for complications after active treatment could improve both allocation of resources and individual outcomes in long-term follow-up. Conclusion FILTER provides a means for a large number of experts to adjudicate risk for survivorship complications with a low barrier of entry.