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.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Hoy, D., Bain, C., William, G., March, L., Brooks, P., Blyth, F., et al.: A systematic review of the global prevalence of low back pain. Arthritis Rheum. 64, 2028–2037 (2012)
Chou, R., Deyo, R.A., Jarvik, J.G.: Appropriate use of lumbar imaging for evaluation of low back pain. Radiol. Clin. North Am. 50, 569–585 (2012)
Deyo, R.A., Dworkin, S.F., Amtmann, D., et al.: Report of the NIH task force on research standards for chronic low back pain. Int. J. Ther. Massage Bodyw. 8(3), 16–33 (2015)
Hersh, W.R., Weiner, M.G., Embi, P.J., et al.: Caveats for the use of operational electronic health record data in comparative effectiveness research. Med. Care 5, S30–S37 (2014)
Tannen, R.L., Weiner, M.G., Xie, D.: Use of primary care electronic medical record database in drug efficacy research on cardiovascular outcomes: comparison of database and randomised controlled trial findings. Br. Med. J. 338, b81 (2009)
Tatonett, N.P., Ye, P.P., Daneshjou, R., Altman, R.B.: Data-driven prediction of drug effects and interactions. Sci. Trans. Med. 4(125), 1–26 (2013)
Harpaz, R., DuMouchel, W., Shah, N.H., et al.: Novel data mining methodologies for adverse drug event discovery and analysis. Clin. Pharmacol. Ther. 91(6), 1010–1021 (2012)
Bhandari, R.P., Feinstein, A.B., Huestis, S.E., et al.: Pediatric-collaborative health outcomes information registry (Peds-CHOIR): a learning health system to guide pediatric pain research and treatment. Pain 157(9), 2033–2044 (2016)
Bove, R., Secor, E., Healy, B., et al.: Evaluation of an online platform for multiple sclerosis research: patient description, validation of severity scale, and exploration of BMI effects on disease course. PLoS ONE 8(3), e59707 (2013)
Nakamura, C., Bromberg, M., Bhargava, S., et al.: Mining online social network data for biomedical research: a comparison of clinicians’ and patients’ perceptions about amyotrophic lateral sclerosis treatments. J. Med. Internet Res. 14(3), e90 (2012)
Peleg, M.: Appendices (2017). http://mis.haifa.ac.il/~morpeleg/PatientOutcomesAppend.html
Morone, N.E., Greco, C.M., Moore, C.G., et al.: A mind-body program for older adults with chronic low back pain: a randomized clinical trial. JAMA Int. Med. 3, 329–337 (2016)
Heuch, I., Hagen, K., Heuch, I., et al.: The impact of body mass index on the prevalence of low back pain: the HUNT study. Spine (Phila Pa 1976) 35(7), 764–768 (2010)
Chou, R., Huffman, L.H.: Nonpharmacologic therapies for acute and chronic low back pain: a review of the evidence for an American Pain Soc. Ann. Int. Med. 147, 492–504 (2007)
Chou, R., Atlas, S.J., Stanos, S.P., Rosenquist, R.W.: Nonsurgical interventional therapies for low back pain: a review of the evidence for an American Pain Soc. Spine 34, 1078–1093 (2009)
Chou, R., Huffman, L.H.: Medications for acute and chronic low back pain: a review of the evidence for an American Pain Soc. Ann. Intern. Med. 147, 505–514 (2007)
Institute for Clinical Systems Improvement. Adult Acute and Subacute Low Back Pain, November 2012
Biondi-Zoccai, G., Romagnol, E., Agostoni, P., et al.: Are propensity scores really superior to standard multivariable analysis? Contemp. Clin. Trials. 32(5), 731–740 (2011)
Wang, Y.C., Burke, M., Kraut, R.E.: Gender, topic, and audience response: an analysis of user-generated content on facebook. In: SIGCHI Conference on Human Factors in Computing Systems, pp. 31–34 (2013)
Acknowledgement
We thank Ofer Ben-Shachar for supplying the HealthOutcome data and thank him and Tobias Konitzer for the valuable discussions.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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: ten Teije, A., Popow, C., Holmes, J., Sacchi, L. (eds) Artificial Intelligence in Medicine. AIME 2017. Lecture Notes in Computer Science(), vol 10259. Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-59758-4_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-59757-7
Online ISBN: 978-3-319-59758-4
eBook Packages: Computer ScienceComputer Science (R0)