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Hospitals and academia must collaborate to share health data for advancing precision medicine. Incentivizing data sharing and exploring solutions like federated learning can overcome barriers to translational research.

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Area of Science:

  • Health Informatics
  • Translational Medicine
  • Biotechnology

Background:

  • Increasing generation of health big data from professional systems and wearable devices.
  • Potential of big data for optimizing patient treatments through precision medicine.
  • Challenges in data sharing between hospitals, academia, and industry, hindering translational medicine.

Purpose of the Study:

  • To analyze current literature on medical data sharing.
  • To discuss key aspects of data sharing in the medical domain.
  • To explore potential solutions and future developments for effective data sharing.

Main Methods:

  • Literature analysis of existing research on medical data sharing.
  • Discussion of five critical aspects: publisher requirements, data ownership, support for sharing, initiatives, and federated data.
  • Exploration of future trends like medical crowdsourcing and data generalists.

Main Results:

  • Hospitals and academia often hesitate to share data due to publication incentives and investment in data collection.
  • Delayed data availability after study completion poses a significant bottleneck.
  • Federated data approaches, incentives, and evolving initiatives are crucial for overcoming data sharing reluctance.

Conclusions:

  • Collaborative efforts and incentivized data sharing are essential to bridge the gap in translational medicine.
  • Addressing concerns about data ownership and publisher requirements can foster greater data sharing.
  • Exploring innovative solutions like federated learning and medical crowdsourcing is vital for future advancements in precision medicine.