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Clinical database. Structure, development strategies and expected clinical applications.

M Torchio1, F Molino, C Seidenari

  • 1Laboratory of Clinical Informatics, Department of Medicine, San Giovanni Battista Hospital, Torino, Italy.

Minerva Medica
|July 15, 2003
PubMed
Summary
This summary is machine-generated.

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This study introduces a structured clinical database for hospital patient management. It balances research complexity with practical medical needs, enhancing data organization and sharing.

Area of Science:

  • Medical Informatics
  • Health Data Management
  • Clinical Systems Engineering

Background:

  • Current clinical data management systems often present a complex trade-off between advanced research capabilities and practical hospital needs.
  • A patient-centered approach is crucial for effective clinical database design in healthcare settings.

Purpose of the Study:

  • To describe a novel computerized clinical database designed for structured patient data management in hospitals.
  • To present a prototype system balancing research demands with clinical practice requirements.
  • To address challenges in clinical database management, including knowledge acquisition and functional aspects.

Main Methods:

  • Defining requirements for a patient-centered computerized clinical database.
  • Developing a prototype that classifies and structures clinical data hierarchically.

Related Experiment Videos

  • Identifying and proposing solutions for system management, upgrading, knowledge acquisition, and clinical functionality.

    • Main Results:

    • A structured clinical database prototype was developed, classifying data hierarchically.
    • Key challenges in system management, knowledge refinement, and clinical application functionality were identified and addressed.
    • The database serves as a standard for knowledge organization and data sharing in clinical applications.

    Conclusions:

    • The developed clinical database offers a practical solution for managing hospital patient data.
    • The system facilitates better knowledge organization and data sharing, supporting clinical applications.
    • This approach provides a viable compromise between complex research solutions and real-world medical practice needs.