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Crowdsourcing is a specific type of outsourcing where an organization uses contributions from mainly unidentified users to quickly create a large amount of knowledge.[1] Some of the most well known crowdsourced work includes sites like Wikipedia and Reddit. FEMA has used crowdsourcing to help with disaster management. This method can also be applied to Clinical informatics through the use of Big Data.


Crowdsourcing as it pertains to medicine can generally be divided into 3 main categories: health promotion, health research, and health maintenance. However, the potential exists for crowdsourcing to improve Clinical Decision Support (CDS) for work up, diagnosis, and treatment of medical conditions. Each day, massive amounts of medical data are created, which can be leveraged to help guide "best practice." Conceptually, users would be patients and/or physicians who, through being treated for/treating conditions, contribute to a collective knowledge base.


Typically, crowdsourced activities have utilized outside websites. However, other platforms for this exist, including mobile applications and dashboards. Additionally, it would be possible to set up a system that fully integrates into an Electronic Health Record (EHR).

Potential Medical Applications

Novel websites have already been created (see Examples) to outsource rare diagnoses or share successful treatments, although these haven't been adopted into mainstream medicine. Some research and pilot studies are ongoing, looking into incorporating crowdsourcing into the EHR. One potential application would be to run a CDS algorithm for a patient's Emergency Department work up. Through analyzing all other prior patients with the same age, gender, and chief complaint, the system could suggest similar studies for the current patient. This concept could also be used to suggest treatments for various conditions, matching similar patients and ICD-10 codes with the medications given, procedures done, etc.[2] Another difficulty in clinical medicine is prognosis. If patients could be matched to a similar cohort within the system, this may give clinicians another tool to help prognosticate. Not only can crowdsourcing help with clinical care, but it can also be used for large scale population research.[3]


Depending on the system, there may be technical challenges to implementing projects integrated with the EHR. Even if this is successful, studies are ongoing, and there is currently insufficient evidence to prove the effectiveness of this method. Additionally, there are ethical concerns about crowdsourcing medical problems to the general public, who are not medically trained.






Medical Examples





  1. Crowdsourcing. (2016, October 19). In Wikipedia, The Free Encyclopedia. Retrieved 09:08, October 19, 2016, from
  2. McCoy AB, Wright A, Rogith D, Fathiamini S, Ottenbacher AJ, Sittig DF. Development of a clinician reputation metric to identify appropriate problem-medication pairs in a crowdsourced knowledge base.

Further Reading

Howe J. The rise of crowdsourcing. Wired Mag. 2006;14:1–4.

Boston Globe Crowdsourcing the diagnosis

Dynamic Clinical Data Mining

Submitted by Andrew Muth, MD