Detailed clinical models: representing knowledge, data and semantics in healthcare information technology

Area of Science:

• Health Informatics
• Clinical Knowledge Management

Background:

• Clinical knowledge modeling is essential for effective Electronic Health Records (EHR) management and preservation.
• Lack of standardized clinical knowledge modeling leads to information loss during EHR data exchange.
• Common data elements and terminology binding are key to capturing and managing clinical information across time and technology.

Purpose of the Study:

• To present an overview of harmonizing clinical knowledge modeling using Detailed Clinical Models (DCMs).
• To explain the contribution of DCMs to the preservation of EHR data.
• To highlight the need for standardized approaches in clinical knowledge representation.

Main Methods:

• Reviewing past attempts at clinical knowledge modeling for semantic interoperability.
• Analyzing the differences in conceptual, logical, and technical expressions across various modeling approaches.
• Identifying synergies within the Clinical Information Modeling Initiative (CIMI) for a harmonized reference model.

Main Results:

• Multiple past projects aimed at semantic interoperability through standardized data representation and common terminologies.
• Existing clinical knowledge modeling approaches differ significantly in their conceptual, logical, and technical implementations.
• The Clinical Information Modeling Initiative (CIMI) is actively working towards a harmonized reference model for clinical knowledge.

Conclusions:

• CIMI aims to establish a reference model and formalisms, potentially based on Detailed Clinical Models (DCMs).
• The development of a global repository for DCMs is a potential future outcome.
• Harmonized clinical knowledge modeling is key to ensuring the long-term accessibility and usability of EHR data.