Crowdsourcing Diagnosis for Patients With Undiagnosed Illnesses: An Evaluation of CrowdMed
http://www.jmir.org/2016/1/e12/
1Houston Veterans Affairs Center for Innovations in Quality, Effectiveness and Safety, Health Services Research and Development, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, United States
2Section of Health Services Research, Department of Medicine, Baylor College of Medicine, Houston, TX, United States
3Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, United States
Corresponding Author:
Hardeep Singh, MD, MPH
Houston Veterans Affairs Center for Innovations in Quality, Effectiveness and Safety
Michael E. DeBakey Veterans Affairs Medical Center
VA HSR&D Center of Innovation (152)
2002 Holcombe Boulevard
Houston, TX, 77030
United States
Phone: 1 713 794 8601
Fax:1 713 748 7359
Email: hardeeps [at] bcm.edu
ABSTRACT
Background: Despite visits to multiple physicians, many patients remain undiagnosed. A new online program, CrowdMed, aims to leverage the “wisdom of the crowd” by giving patients an opportunity to submit their cases and interact with case solvers to obtain diagnostic possibilities.
Objective: To describe CrowdMed and provide an independent assessment of its impact.
Methods: Patients submit their cases online to CrowdMed and case solvers sign up to help diagnose patients. Case solvers attempt to solve patients’ diagnostic dilemmas and often have an interactive online discussion with patients, including an exchange of additional diagnostic details. At the end, patients receive detailed reports containing diagnostic suggestions to discuss with their physicians and fill out surveys about their outcomes. We independently analyzed data collected from cases between May 2013 and April 2015 to determine patient and case solver characteristics and case outcomes.
Results: During the study period, 397 cases were completed. These patients previously visited a median of 5 physicians, incurred a median of US $10,000 in medical expenses, spent a median of 50 hours researching their illnesses online, and had symptoms for a median of 2.6 years. During this period, 357 active case solvers participated, of which 37.9% (132/348) were male and 58.3% (208/357) worked or studied in the medical industry. About half (50.9%, 202/397) of patients were likely to recommend CrowdMed to a friend, 59.6% (233/391) reported that the process gave insights that led them closer to the correct diagnoses, 57% (52/92) reported estimated decreases in medical expenses, and 38% (29/77) reported estimated improvement in school or work productivity.
Conclusions: Some patients with undiagnosed illnesses reported receiving helpful guidance from crowdsourcing their diagnoses during their difficult diagnostic journeys. However, further development and use of crowdsourcing methods to facilitate diagnosis requires long-term evaluation as well as validation to account for patients’ ultimate correct diagnoses.
J Med Internet Res 2016;18(1):e12
doi:10.2196/jmir.4887