Evidence map›Paper›PMID 26769236›Full record

ArticleJournal of medical Internet research2016

Crowdsourcing Diagnosis for Patients With Undiagnosed Illnesses: An Evaluation of CrowdMed.

Ashley N D Meyer, Christopher A Longhurst, Hardeep Singh

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Mapping of Crowdsourcing in Health: Systematic Review.Journal of medical Internet research · 2018
    Pooled it
  3. Rising to the Challenge of Rare Diagnoses.Journal of general internal medicine · 2025
    Article
  4. Experimental evidence for structured information-sharing networks reducing medical errors.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  5. Article
  6. [Digital diagnostic support in rheumatology].Zeitschrift fur Rheumatologie · 2021
    Review
  7. Article
  8. Article
  9. Article
  10. Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes.Genetics in medicine : official journal of the American College of Medical Genetics · 2020
    Article
  11. Examining Peer-to-Peer and Patient-Provider Interactions on a Social Media Community Facilitating Ask the Doctor Services.Proceedings of the ... International AAAI Conference on Weblogs and Social Media. International AAAI Conference on Weblogs and Social Media · 2020
    Article
  12. Sharing Is Caring-Data Sharing Initiatives in Healthcare.International journal of environmental research and public health · 2020
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ashley N D MeyerHouston 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.ORCID http://orcid.org/0000-0001-7993-8584

Funding

Decision Making and Clinical Work of Test Result Follow-up in Health IT SettingsR01HS022087 · AHRQ · BAYLOR COLLEGE OF MEDICINE · PI SINGH, HARDEEP · 2013 to 2016
$2.0M
Improving Direct Notification of Abnormal Test Results via Patient PortalsR21HS023602 · AHRQ · BAYLOR COLLEGE OF MEDICINE · PI SINGH, HARDEEP · 2014 to 2015
$300k
AHRQ HHS R01 HS022087AHRQ HHS R01HS022087AHRQ HHS R21 HS023602AHRQ HHS R21HS023602
6 · The paper itself

Abstract

backgroundDespite 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.

objectiveTo describe CrowdMed and provide an independent assessment of its impact.

methodsPatients 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.

resultsDuring 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.

conclusionsSome 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.

Indexed as

CrowdsourcingDiagnosis, DifferentialInternetFemaleHumansMalePhysiciansSurveys and Questionnairescrowdsourcingdiagnosisdiagnostic errorspatient safetyWorld Wide Web

Identifiers

PMID26769236
PMCPMC4731679

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.