Crowdsourcing knowledge discovery and innovations in medicine

Area of Science:

• Digital Health
• Medical Informatics
• Translational Research

Background:

• Clinicians often face treatment decisions lacking robust research evidence.
• Existing research evidence may not adequately address complex clinical scenarios.
• Silos between clinicians, researchers, and data scientists hinder knowledge discovery.

Purpose of the Study:

• To explore how interdisciplinary collaboration can address gaps in clinical evidence.
• To investigate the potential of digital medicine for knowledge discovery.
• To propose solutions for overcoming communication barriers between diverse expert groups.

Main Methods:

• Utilizing "health hackathons" and "data marathons" as collaborative frameworks.
• Leveraging readily available digital health data.
• Fostering interaction between clinicians, researchers, and data scientists.

Main Results:

• Demonstrated a model for democratizing and improving the knowledge discovery process.
• Enabled the formulation of system-level innovations through complementary expertise.
• Facilitated the solving of real-world clinical problems.

Conclusions:

• Cross-disciplinary collaboration, particularly through structured events, is key to advancing medical knowledge.
• Digital data and diverse expertise can overcome limitations in traditional research evidence.
• Innovations in healthcare require integrated efforts from clinical and data science domains.