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

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.