Journal of the Association for Information Systems


Medical crowdsourcing offers hope to patients who suffer from complex health conditions that are difficult to diagnose. Such crowdsourcing platforms empower patients to harness the “wisdom of the crowd” by providing access to a vast pool of diverse medical knowledge. Greater participation in crowdsourcing increases the likelihood of encountering a correct solution. However, more participation also leads to increased “noise,” which makes identifying the most likely solution from a broader pool of recommendations (i.e., diagnostic suggestions) difficult. The challenge for medical crowdsourcing platforms is to increase participation of both patients and solution providers, while simultaneously increasing the efficacy and accuracy of solutions. The primary objectives of this study are: (1) to investigate means to enhance the solution pool by increasing participation of solution providers referred to as “medical detectives” or “detectives,” and (2) to explore ways of selecting the most likely diagnosis from a set of alternative possibilities recommended by medical detectives. Our results suggest that our strategy of using multiple methods for evaluating recommendations by detectives leads to better predictions. Furthermore, cases with higher perceived quality and more negative emotional tones (e.g., sadness, fear, and anger) attract more detectives. Our findings have strong implications for research and practice.





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