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Cost supervision mining from EMR based on artificial intelligence technology.

Site Xu1,2,1, Tiantian Zhang1,1, Tao Sheng3

  • 1School of Public Health, Fudan University, Shanghai, China.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|January 9, 2023
PubMed
Summary
This summary is machine-generated.

This study developed an AI-powered model to assess inpatient medical costs, improving insurance fund efficiency. The machine learning approach accurately identifies cost rationality, guiding medical behavior and optimizing fund use.

Keywords:
EMRcost supervisioninformation systemmachine learningmedical insurance fund

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Medical Economics

Background:

  • Effective monitoring of medical insurance funds is crucial in the big data era.
  • Developing robust inpatient cost supervision methods is essential for financial sustainability.

Purpose of the Study:

  • To establish a foundation for applying AI in medical insurance cost control.
  • To provide practical tools and methods for cost control managers.

Main Methods:

  • Utilized electronic medical record (EMR) data (2016-2018) from East China.
  • Employed machine learning modeling and information system construction for cost supervision.
  • Focused on patient demographics and cost data for analysis.

Main Results:

  • Achieved up to 80% accuracy in rationality judgment for main diseases.
  • Maintained a false positive rate below 10%.
  • Identified rehabilitation, length of stay, and complications as key cost factors.

Conclusions:

  • The combined machine learning and information system model enables practical cost rationality judgment.
  • The method identifies key cost influencers and guides medical behavior.
  • Improved efficiency in medical insurance fund utilization was demonstrated.