Requirements of Health Data Management Systems for Biomedical Care and Research: Scoping Review

Area of Science:

• Health Informatics
• Biomedical Research
• Data Management

Background:

• Disruptive advancements in clinical and biomedical research have necessitated major changes in health data management.
• The integration of big data analytics and the Internet of Things (IoT) is crucial for real-time smart health information systems.
• Evolving patient care needs demand more accurate prognoses and diagnoses, driving system development.

Purpose of the Study:

• To demonstrate the need for secure, efficient health data management systems enabling decentralized record updates and data analysis.
• To analyze the limitations of current health data management systems.
• To support precise diagnoses, prognoses, and public health insights through improved data analysis.

Main Methods:

• A comprehensive literature search was conducted using major scientific databases (IEEE, ACM, Elsevier, MEDLINE, PubMed, Scopus, Web of Science).
• Research articles and information on medical lawsuits, health regulations, and acts were reviewed.
• The study focused on the temporal evolution and requirements of health data management systems.

Main Results:

• Health data management systems have transformed from paper-based to advanced systems including cloud, IoT, big data analytics, and blockchain.
• Key requirements identified include: medical record data, real-time access, patient participation, data sharing, data security, patient identity privacy, and public insights.
• This is the first analysis to review the temporal evolution of health data management systems against these seven core requirements.

Conclusions:

• A comprehensive, real-time health data management system is essential for physicians, patients, and external users.
• Integrating big data analytics will enhance disease prognosis, diagnosis, and prediction, aiding in prevention plan development.