Is Crowdsourcing Patient-Reported Outcomes the Future of Evidence-Based Medicine? A Case Study of Back Pain

M. Peleg*, T.I. Leung, M. Desai, M. Dumontier

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

34 Downloads (Pure)

Abstract

Evidence is lacking for patient-reported effectiveness of treatments for most medical conditions and specifically for lower back pain. In this paper, we examined a consumer-based social network that collects patients' treatment ratings as a potential source of evidence. Acknowledging the potential biases of this data set, we used propensity score matching and generalized linear regression to account for confounding variables. To evaluate validity, we compared results obtained by analyzing the patient reported data to results of evidence-based studies. Overall, there was agreement on the relationship between back pain and being obese. In addition, there was agreement about which treatments were effective or had no benefit. The patients' ratings also point to new evidence that postural modification treatment is effective and that surgery is harmful to a large proportion of patients.
Original languageEnglish
Title of host publicationARTIFICIAL INTELLIGENCE IN MEDICINE, AIME 2017
PublisherSpringer International Publishing AG
Pages245-255
Number of pages11
Volume10259
ISBN (Print)9783319597577
DOIs
Publication statusPublished - 2017
Event16th European Conference on Artificial Intelligence in Medicine (AIME) - Vienna, Austria
Duration: 21 Jun 201724 Jun 2017
http://aime17.aimedicine.info/home.html

Publication series

SeriesLecture Notes in Artificial Intelligence
Volume10259
ISSN0302-9743

Conference

Conference16th European Conference on Artificial Intelligence in Medicine (AIME)
Country/TerritoryAustria
CityVienna
Period21/06/1724/06/17
Internet address

Keywords

  • PHYSICIANS
  • COLLEGE

Cite this