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Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

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Data Warehouse to Support Clinical Trials in Dentistry.

Alessandra Introvaia¹, Noemi Giordano¹, Samanta Rosati¹

¹Department of Electronics and Telecommunications - Politecnico di Torino, Italy.

Studies in Health Technology and Informatics

|May 23, 2026

Summary

This study introduces a relational data warehouse (DW) designed for periodontal clinical data integration and quality control. The DW effectively manages patient information and related clinical details, proving useful in research settings.

Keywords:

Data Quality Data Warehouse Dentistry Trial Data Management

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Area of Science:

Biomedical Informatics
Clinical Research Data Management

Background:

Digitalization in clinical research necessitates robust data integration and quality-checking infrastructure.
Managing large volumes of complex clinical data, particularly periodontal data, presents significant challenges.

Purpose of the Study:

To design and present a relational data warehouse (DW) specifically for collecting and managing periodontal clinical data.
To establish a structured data model centered around a 'Patient' table for efficient data organization.

Main Methods:

Development of a relational data warehouse (DW) architecture.
Implementation of a core 'Patient' table with linked related tables for comprehensive data storage.
Utilizing appropriate relationships to connect different clinical information tables.

Main Results:

Successful design of a relational data warehouse for periodontal clinical data.
Demonstrated effectiveness of the DW using 95 real-world records from the Gum-Gut Project.
The DW structure facilitated the integration and organization of data from periodontitis patients and controls.

Conclusions:

The proposed relational data warehouse provides a viable infrastructure for integrating and quality-checking periodontal clinical data.
The DW design, with its central 'Patient' table and relational links, is effective for managing complex clinical datasets.
This approach supports efficient data handling in digitalized clinical research, as evidenced by the Gum-Gut Project data.