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Healthcare Big Data in Hong Kong: Development and Implementation of Artificial Intelligence-Enhanced Predictive

Gary Tse1, Quinncy Lee2, Oscar Hou In Chou3

  • 1School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China; Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin 300211, China.

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Summary
This summary is machine-generated.

Electronic health records (EHRs) and artificial intelligence (AI) enable powerful big data studies for disease prediction. AI models derived from EHR data offer superior accuracy and clinical utility compared to traditional methods.

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Area of Science:

  • Epidemiology
  • Health Informatics
  • Artificial Intelligence

Background:

  • Routinely collected electronic health records (EHRs) offer rich data for epidemiological research.
  • Big data studies, particularly in Hong Kong, have surged since 2015, with increasing use of artificial intelligence (AI).

Purpose of the Study:

  • To highlight the advantages of big data and AI in developing generalizable and accurate predictive models using EHRs.
  • To illustrate the application of AI-driven models in identifying disease risks using multi-modal data.

Main Methods:

  • Systematic review of big data studies.
  • Development of predictive models using territory-wide electronic health records.
  • Application of artificial intelligence algorithms for enhanced model performance.

Main Results:

  • AI-driven models demonstrate superior performance (sensitivity, specificity, accuracy) over traditional models.
  • Routinely collected EHR data are sufficient for developing high-performance predictive models.
  • Web and mobile versions of risk models facilitate rapid clinical decision-making.

Conclusions:

  • Big data analytics and AI, leveraging EHRs, significantly enhance disease prediction and clinical decision support.
  • The integration of AI into clinical workflows via accessible platforms is crucial for real-time patient risk stratification.