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Time-oriented functional profile: practical applications in a stroke data-base model.

J S Feigenson, J F Fries, S Kunitz

    Archives of Physical Medicine and Rehabilitation
    |November 1, 1979
    PubMed
    Summary
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    This study demonstrates the successful transfer of stroke data between computerized systems. This integration facilitates improved stroke care analysis and management through enhanced data utilization.

    Area of Science:

    • Neurology
    • Medical Informatics
    • Health Services Research

    Background:

    • Large-scale stroke data management is crucial for advancing patient care and research.
    • Existing systems like ARAMIS offer proven utility for time-oriented health data analysis.
    • The integration of diverse data sources presents opportunities for comprehensive stroke informatics.

    Purpose of the Study:

    • To demonstrate the feasibility of transferring data from the BUSiness Oriented Stroke data-base (BUSTOP) to the ARAMIS system.
    • To highlight the potential benefits of integrated, time-oriented stroke databases for clinical and research applications.
    • To validate the use of existing computer models for large-scale stroke data analysis.

    Main Methods:

    • Data transfer protocols were established between the BUSiness Oriented Stroke data-base (BUSTOP) and the ARAMIS system.

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  • Existing computer models were utilized for data collection and statistical analysis.
  • The project focused on demonstrating the technical feasibility of system integration.

    • Main Results:

    • The transfer of data from the BUSiness Oriented Stroke data-base (BUSTOP) to the ARAMIS system was successfully demonstrated.
    • The feasibility of utilizing existing computer models for large-scale stroke data management was confirmed.
    • The study established a foundation for leveraging integrated databases in stroke research.

    Conclusions:

    • Transferring large stroke databases to time-oriented systems like ARAMIS is feasible.
    • Integrated stroke data systems offer significant advantages for prognostic indicator determination, therapeutic intervention suggestions, and cost-effectiveness analysis.
    • This project validates the use of current computational approaches for robust stroke data management and auditing.