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

Alex C Cheng; Li Wen; Yanwei Li; Tatsuki Koyama; Lynne D Berry; Tuya Pal; Debra L Friedman; Travis J Osterman

doi:10.1093/jamiaopen/ooab090

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

JAMIA Open. 2021 Nov 6;4(4):ooab090. doi: 10.1093/jamiaopen/ooab090. eCollection 2021 Oct.

Authors

Alex C Cheng¹, Li Wen², Yanwei Li³, Tatsuki Koyama⁴, Lynne D Berry⁴, Tuya Pal⁵, Debra L Friedman⁶, Travis J Osterman¹

Affiliations

¹ Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
² Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
³ School of Engineering, Vanderbilt University, Nashville, Tennessee, USA.
⁴ Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
⁵ Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
⁶ Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

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.

Keywords: cancer survivors (D000073116); crowdsourcing (D063045); expert systems (D005103); risk factors (D012307).