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[Improving data warehouse environments for efficient analysis of long time-series data].
Hiromi Kataoka1, Yutaka Hatakeyama, Yoshiyasu Okuhara
1Kochi Medical School, Center of Medical Information Science 783-8505, Japan. kataokah@kochi-u.ac.jp
Rinsho Byori. the Japanese Journal of Clinical Pathology
|September 15, 2012
Summary
This study introduces a new system infrastructure to manage shifting data in hospital information systems. It enables seamless analysis of long time-series data for improved clinical practice and research.
Area of Science:
- Health Informatics
- Data Management
- Clinical Research
Context:
- Medical records contain vast amounts of data crucial for clinical medicine.
- Evidence-based medicine (EBM) principles are widely adopted, but hospital information systems lag in leveraging EBM.
- Existing data warehouse (DWH) systems face challenges with data reliability, high-dimensional searches, and data fragmentation due to evolving testing methods.
Purpose:
- To develop a novel system infrastructure for managing and analyzing long time-series medical data.
- To address data shifting and fragmentation issues caused by changes in laboratory testing methods.
- To create flexible DWH environments for diverse information retrieval demands.
Summary:
- A new system infrastructure is proposed that facilitates data exchange to accommodate shifts caused by evolving testing methods.
- This system enables the seamless analysis of long time-series data within DWH environments.
- It supports recording comprehensive analyses of laboratory diagnostic characteristics in knowledge databases.
Impact:
- Facilitates the seamless analysis of long time-series data, enhancing the utility of medical records.
- Enables the creation of knowledge databases from comprehensive analyses for educational, research, and clinical applications.
- Improves data reliability and search capabilities in hospital information systems, supporting evidence-based medicine.
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