Self-reported treatment effectiveness for Crohn's disease using a novel crowdsourcing web-based platform

Tal Engel*, Eran Dotan, Yossi Synett, Ron Held, Shelly Soffer, Shomron Ben-Horin, Uri Kopylov

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background and Aims: Internet and social media platforms have become an unprecedented source for sharing self-experience, potentially allowing the collection and integration of health data with patient experience. StuffThatWorks (STW) is an online open platform that applies machine learning and the power of crowdsourcing, where patients with chronic medical conditions can self-report and compare their individual outcomes using a structured online questionnaire. We aimed to conduct a cross-sectional, international, crowdsourcing, artificial-intelligence (AI) web-based study of patients with Crohn's disease (CD) self-reporting their outcomes. Methods: A proprietary STW Bayesian inference model was built to measure improvement in CD severity (on scale of 1–5) for each treatment and ranked treatments using effectiveness. The effectiveness of first-line biological treatments was analyzed by multiple comparisons and by calculating odds ratios and 95% confidence intervals for each treatment pair. Results: We included 7593 self-reported CD patients for the analysis. Most of the participants were female (75.8%) and from English-speaking countries (95.7%). Overall, anti-TNF drugs were the most reported tried treatment (52.8%). Infliximab (IFX) was ranked as the most effective treatment by the STW effectiveness model followed by bowel surgery (second), adalimumab (ADA, third), ustekinumab (UST, 4rd), and vedolizumab (VDZ, fifth). In paired comparison analyses, IFX was most effective, ADA had similar effectiveness compared to UST and all three were more effective than VDZ. Conclusion: We present the first online crowdsourcing AI platform-based study of self-reported treatment effectiveness in CD. Net-based crowdsourcing patient-reported outcome platforms can potentially help both clinicians and patients select the best treatment for their condition.

Original languageEnglish
Pages (from-to)621-632
Number of pages12
JournalUnited European Gastroenterology Journal
Volume11
Issue number7
DOIs
StatePublished - Sep 2023

Keywords

  • anti TNF drugs
  • artificial intelligence
  • crohn's disease
  • social media
  • stuffthatworks

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