Is crowdsourcing patient-reported outcomes the future of evidence-based medicine?: A case study of back pain

Mor Peleg; Tiffany I. Leung; Manisha Desai; Michel Dumontier

doi:10.1007/978-3-319-59758-4_27

Is crowdsourcing patient-reported outcomes the future of evidence-based medicine? A case study of back pain

Mor Peleg, Tiffany I. Leung, Manisha Desai, Michel Dumontier

Department of Information Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings
Editors	Annette [surname]ten Teije, Christian Popow, Lucia Sacchi, John H. Holmes
Publisher	Springer Verlag
Pages	245-255
Number of pages	11
ISBN (Print)	9783319597577
DOIs	https://doi.org/10.1007/978-3-319-59758-4_27
State	Published - 2017
Event	16th Conference on Artificial Intelligence in Medicine, AIME 2017 - Vienna, Austria Duration: 21 Jun 2017 → 24 Jun 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10259 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th Conference on Artificial Intelligence in Medicine, AIME 2017
Country/Territory	Austria
City	Vienna
Period	21/06/17 → 24/06/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-59758-4_27

Cite this

Peleg, M., Leung, T. I., Desai, M., & Dumontier, M. (2017). Is crowdsourcing patient-reported outcomes the future of evidence-based medicine? A case study of back pain. In A. [surname]ten Teije, C. Popow, L. Sacchi, & J. H. Holmes (Eds.), Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings (pp. 245-255). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10259 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-59758-4_27

Peleg, Mor ; Leung, Tiffany I. ; Desai, Manisha et al. / Is crowdsourcing patient-reported outcomes the future of evidence-based medicine? A case study of back pain. Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings. editor / Annette [surname]ten Teije ; Christian Popow ; Lucia Sacchi ; John H. Holmes. Springer Verlag, 2017. pp. 245-255 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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{\textquoteright} 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{\textquoteright} ratings also point to new evidence that postural modification treatment is effective and that surgery is harmful to a large proportion of patients.",

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Peleg, M, Leung, TI, Desai, M & Dumontier, M 2017, Is crowdsourcing patient-reported outcomes the future of evidence-based medicine? A case study of back pain. in A [surname]ten Teije, C Popow, L Sacchi & JH Holmes (eds), Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10259 LNAI, Springer Verlag, pp. 245-255, 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, 21/06/17. https://doi.org/10.1007/978-3-319-59758-4_27

Is crowdsourcing patient-reported outcomes the future of evidence-based medicine? A case study of back pain. / Peleg, Mor; Leung, Tiffany I.; Desai, Manisha et al.
Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings. ed. / Annette [surname]ten Teije; Christian Popow; Lucia Sacchi; John H. Holmes. Springer Verlag, 2017. p. 245-255 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10259 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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.

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Peleg M, Leung TI, Desai M, Dumontier M. Is crowdsourcing patient-reported outcomes the future of evidence-based medicine? A case study of back pain. In [surname]ten Teije A, Popow C, Sacchi L, Holmes JH, editors, Artificial Intelligence in Medicine - 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Proceedings. Springer Verlag. 2017. p. 245-255. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-59758-4_27