Big data in digital healthcare: lessons learnt and recommendations for general practice

Area of Science:

• Healthcare Informatics
• Big Data Analytics
• Digital Health

Background:

• Big Data is crucial for future technological advancements, offering new insights from extensive data.
• Healthcare applications of Big Data show great potential but are hindered by fragmentation, high costs, and data ownership issues.
• Balancing Big Data's benefits for patient outcomes with risks like physician burnout from poor implementation is key.

Purpose of the Study:

• To explore the role of Big Data in digital healthcare, using oncology as a case study.
• To analyze global approaches (US, UK, etc.) to Big Data implementation in patient care, focusing on centralization and regulation.
• To propose recommendations for Big Data guidelines and regulations in healthcare.

Main Methods:

• Comparative analysis of Big Data implementation strategies in oncology across different nations.
• Review of existing Big Data challenges in healthcare, including fragmentation, cost, and ownership.
• Examination of potential pitfalls such as lack of diversity and security risks of machine learning algorithms.

Main Results:

• Oncology provides an example of early Big Data adoption, highlighting diverse national approaches to centralization and regulation.
• Fragmentation, cost, and data ownership remain significant impediments to widespread Big Data adoption in healthcare.
• Lack of diversity in research and security/transparency risks associated with machine learning algorithms are critical concerns.

Conclusions:

• A unique global patient ID is proposed to integrate diverse healthcare data, enhancing Big Data utilization.
• Developing clear guidelines and regulations is essential for responsible Big Data implementation in healthcare.
• Addressing data diversity, security, and transparency is crucial for realizing Big Data's full potential in medicine.