Crowdsourcing medical expertise in near real time

Abstract

Given the pace of discovery in medicine, accessing the literature to make informed decisions at the point of care has become increasingly difficult. Although the Internet creates unprecedented access to information, gaps in the medical literature and inefficient searches often leave healthcare providers' questions unanswered. Advances in social computation and human computer interactions offer a potential solution to this problem. We developed and piloted the mobile application DocCHIRP, which uses a system of point‐to‐multipoint push notifications designed to help providers problem solve by crowdsourcing from their peers. Over the 244‐day pilot period, 85 registered users logged 1544 page views and sent 45 consult questions. The median initial first response from the crowd occurred within 19 minutes. Review of the transcripts revealed several dominant themes, including complex medical decision making and inquiries related to prescription medication use. Feedback from the post‐trial survey identified potential hurdles related to medical crowdsourcing, including a reluctance to expose personal knowledge gaps and the potential risk for “distracted doctoring.” Users also suggested program modifications that could support future adoption, including changes to the mobile interface and mechanisms that could expand the crowd of participating healthcare providers. Journal of Hospital Medicine 2014;9:451–456. © 2014 Society of Hospital Medicine

Crowdsourcing Vector Surveillance: Using Community Knowledge and Experiences to Predict Densities and Distribution of Outdoor-Biting Mosquitoes in Rural Tanzania

“…Ranard et al ., recently provided a systematic review of how knowledge of the masses has been and could be harnessed to advance medicine [ 18 ]. Other examples include using mobile phones for community based health reporting, in what is now called, participatory epidemiology [ 54 ], crowd sourcing malaria parasite quantification [ 55 ] and interestingly, crowd sourcing medical expertise in near-real time [ 56 ]. While our approach relied on analogue rather than digital data collection methods, so that we could capture information from all community members in rural areas without smartphones and computing capabilities, we have demonstrated that similar crowd sourcing approaches could be considered and improved to support mapping of densities of disease-transmitting mosquitoes.…”

A scoping review provided a framework for new ways of doing research through mobilizing collective intelligence

“…Regarding challenges in implementation, two articles discussed difficulties in recruitment and participant retention [56,57]. Two articles described challenges in communicating with participants, including lack of a platform for exchanging ideas among participants, dominant voices in the discussion, unclear communication from organizers causing mistrust and a feeling of being exploited, and unclear idea expression from participants, which slowed the idea selection [44,58].…”

“…Seven articles highlighted the challenges in sustaining the integration of CI in traditional business models, including resources and changes in the organization's culture when integrating new ideas from participants [44,54]; increased workload for organizers to prepare tasks for participants, screen and select the best solutions [56,61]; and the need for policies on data sharing and how participants could access data contributed by other participants [56,62].…”

Knowledge Management and Problem Solving in Real Time: The Role of Swarm Intelligence

“…Given new technologies that have enabled crowdsourcing and crowdsourced R&D usages in medical data collection (Adams, 2011;Armstrong, Harskamp, Cheeney, & Schupp, 2012;Prainsack & Wolinsky, 2010), interpretation (Foncubierta-Rodriguez & Müller, 2012;Yu, Willis, Sun, & Wang, 2013), problem solving (Sims, Bigham, Kautz, & Halterman, 2014), and medical disaster management (Zook, Graham, Shelton, & Gorman, 2012), human swarm theory is considered an important addition to the probabilistic innovation literature as it predicts an increasingly important analysis and decision-making role within the crowd itself and, therefore, synthesis of horizontal and vertical (see Figure 1 later in the paper) crowd engagement with real-time problem solving. If the solving of serious societal problems (such as Ebola, Zika, antibiotic resistance, cancer, diabetes, climate change, and chronic aging ailments) in hours or days instead of decades is to ultimately be realized, then the seminal knowledge aggregation problem (Hayek, 1945;Von Hippel, 1994) may require urgent attention, and it is argued probabilistic innovation offers insights toward this end.…”