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Table 1 Collective intelligence study design

From: Collective intelligence in medical decision-making: a systematic scoping review

Study author, year Description of experts Real or simulated cases Types of opinions aggregated Study design Relevance to collective intelligence
Gagliardi, 2007 [25] 20 general surgeons, 2 pathologists, 1 medical oncologist, 1 radiation oncologist Real Diagnosis, Treatment Qualitative observational study to explore the role of multidisciplinary cancer conferences in practice Describe collective output generated in multidisciplinary cancer conferences
Douzgou, 2016 [24] Physicians with patients with malformation syndromes Real Diagnosis Descriptive study of a consultation tool which generates collective insight Assess a collective intelligence tool
Sternberg, 2017 [36] International colleagues with urologic expertise Real Treatment Use Twitter as a potential collective intelligence tool Describe social media as a collective intelligence tool
Sims, 2014 [35] Clinicians affiliated with academic departments: 28 from pediatrics, 27 from neurology, 10 from internal medicine, 4 from psychiatric, 11 from pediatric neurology, 5 others Real Diagnosis, Treatment Descriptive study of a clinical consultation system which generates collective insight and qualitative evaluation of the tool Describe a collective intelligence tool
Nault, 2009 [33] 5 spinal deformity surgeons Real Treatment Feasibility study of a surgical decision-making tool as compared to a group of experienced surgeons Compare collective intelligence generated by experts with a technology tool
Alby, 2015 [22] 1 oncologist and others from hematology, anesthesiology, surgery, and nephrology Real Diagnosis Qualitative observational study of conversations about cancer cases between the chief oncologist and other physicians at a hospital Characterize collective intelligence generated in usual practice
Kattan, 2013 [28] 24 urologists and oncologists Real Prognosis Analysis of physician group accuracy as compared to a nomogram Compare collective intelligence generated by experts with a technology tool
Kunina-Habenicht, 2015 [29] 283 medical students, 20 expert physicians Real Diagnosis Descriptive study of the development of a computerized test to assess diagnostic accuracy; results were compared among medical students and expert physicians Compare computer-generated collective intelligence of experienced physicians to medical students
Lajoie, 2012 [30] 14 third-year medical students Simulated Diagnosis, Treatment Qualitative observational study of team discussions with or without a technology tool to aid collaboration Optimize metacognitive activities in collective intelligence with a technology tool
Kalf, 1996 [27] 21 geriatricians, 21 geriatric-psychiatrists, 21 internists Simulated Diagnosis Analysis of diagnoses generated by different specialties Compare collective intelligence among different specialists
Larson, 1996 [31] 24 first-year interns, 24 residents, 24 medical students Simulated Diagnosis Qualitative observational study of team diagnostic discussions when teams are exposed to different case information Characterize collective intelligence generated when groups have different amounts of information about a case
Christensen, 2000 [23] 24 first year interns, 24 residents, 24 medical students Simulated Diagnosis Qualitative observational study of team diagnostic discussions when given different amounts of shared and unshared information Characterize collective intelligence generated when groups have different amounts of information about a case
Larson, 1998 [32] 48 interns and 24 third-year medical students Simulated Diagnosis Qualitative observational study of team diagnostic discussions when teams are exposed to different case information and given instructions about sharing information Characterize collective intelligence generated when groups have different amounts of information about a case
Semigran, 2016 [34] 234 physicians, including fellows and residents Simulated Diagnosis Analysis of a collective intelligence tool as compared to the accuracy of symptom checker websites Compare a collective intelligence tool to online symptom checkers
Hautz, 2015 [26] 88 medical students Simulated Diagnosis Analysis of diagnostic accuracy when participants worked in pairs or individually Compare collective intelligence of pairs to individual aptitude