Designing An electronic patient record with multiple real time decision support modules for managing diseases

Area of Science:

• Oral Health Informatics
• Clinical Decision Support Systems
• Dental Public Health

Background:

• Physician adherence to clinical guidelines for caries management remains low (30-50%).
• There is a growing need for integrated clinical decision support systems (CDSS) to improve guideline compliance.
• Existing systems often lack scalability and interdisciplinary applicability.

Purpose of the Study:

• To design and develop a scalable, interdisciplinary electronic oral health record (eOHR) with an integrated CDSS for caries management.
• To create a system capable of automatic risk assessment and treatment recommendation generation.
• To facilitate seamless data access for multiple users across different healthcare settings.

Main Methods:

• Developed an eOHR using Authorware, a multimedia program generator, for cross-platform compatibility (PCs, Macs, internet).
• Implemented an open-architecture database with SQL calls for multi-user access.
• Integrated modules for patient demographics, medical/dental/social histories, physical exams, vital signs, chief complaints, and caries/periodontal disease recording.
• Incorporated automatic data surveillance for caries risk factor identification and classification (low, medium, high).
• Automated generation of management strategies at both systemic and local lesion levels.

Main Results:

• The developed eOHR system features comprehensive data modules and automated risk assessment capabilities.
• The system provides clinicians with automatically generated management strategies for caries.
• The platform is designed for scalability and interdisciplinary use in primary, secondary, and tertiary care settings.
• The system is currently undergoing beta testing with Delta Dental Insurance and the Veterans' Administration.

Conclusions:

• The developed eOHR with an integrated CDSS offers a promising solution to improve caries management and guideline adherence.
• The system's scalability and interdisciplinary design support its potential for widespread adoption in diverse healthcare environments.
• Automated risk assessment and treatment recommendations can enhance clinical decision-making in oral health.
• Beta testing results will be crucial for further refinement and validation of the system's efficacy.