Data Management in Forensic Medicine: A Proposal for a Layered Architecture Model.

Area of Science:

• Forensic Medicine
• Digital Forensics
• Information Science

Background:

• Forensic medicine is increasingly data-driven, making data management critical for scientific validity and legal defensibility.
• Current digital forensic workflows face challenges in data integrity, interoperability, and governance.
• A robust data management strategy is essential for modern forensic science.

Purpose of the Study:

• To synthesize current literature on data management in forensic medicine.
• To identify challenges and mitigation strategies in forensic data management.
• To propose a layered data management architecture for digital forensic workflows.

Main Methods:

• Systematic literature review following PRISMA guidelines (Jan 2004 - Aug 2025).
• Searches conducted in PubMed (MEDLINE) and IEEE Xplore using keywords like 'forensic medicine', 'data management', 'data quality', and 'data governance'.
• Development of an eleven-layer data management model based on 12 peer-reviewed publications.

Main Results:

• An eleven-layer data management model for forensic data systems was developed.
• Recurring limitations in forensic data management were identified.
• Mitigation strategies were mapped against identified limitations, demonstrating potential solutions.

Conclusions:

• A well-designed, layered data management architecture with standardized models and privacy controls can enhance forensic data integrity, interoperability, and legal defensibility.
• Systemic weaknesses in forensic data ecosystems can be addressed through deliberate architectural and governance design.
• The proposed model strengthens ethical accountability in forensic processes.