Evaluation of clinical information modeling tools.

Area of Science:

• Health Informatics
• Software Engineering
• Data Modeling

Background:

• Clinical information models are crucial for structuring data in Electronic Health Records (EHRs).
• Existing software tools support the creation and management of these models.
• A need exists for standardized evaluation metrics to assess these tools.

Purpose of the Study:

• To define, test, and validate evaluation metrics for software tools used in clinical information modeling.
• To assess the current landscape of clinical information modeling tools based on defined criteria.
• To guide decision-making for selecting appropriate tools in healthcare organizations.

Main Methods:

• Developed a framework based on 20 functional requirements and 50 conformance criteria.
• Evaluated 9 out of 11 identified tools for clinical information modeling.
• Applied the defined metrics to assess tool performance in various aspects of model management and implementation.

Main Results:

• Functionalities for data types, specifications, metadata, and terminology bindings show good adoption.
• Areas such as displaying semantic relationships and terminology server communication have low adoption rates.
• Identified a need for enhanced tool support in governance, clinician involvement, and technical validation processes.

Conclusions:

• The developed evaluation metrics are effective for assessing clinical information modeling tools.
• Improvements are needed in tools to better support information modeling and software development lifecycles.
• The metrics aid decision-makers in selecting optimal tools for their specific organizational needs.