Multi-dimensional knowledge translation: enabling health informatics capacity audits using patient journey models

Area of Science:

• Health Informatics
• Knowledge Translation
• Data Modeling

Background:

• Information Technology (IT) adoption in Neonatal Intensive Care Units (NICUs) presents complex data challenges.
• Existing methods for evaluating IT uptake lack a structured approach to translate raw data into actionable insights.

Purpose of the Study:

• To present a multi-dimensional knowledge translation framework.
• To convert survey data on IT uptake in NICUs into health informatics capacity audits.
• To develop a structured data modeling approach for patient journey analysis.

Main Methods:

• Utilized a structured data modeling approach to translate multi-dimensional survey data.
• Developed patient journey models based on a pre-defined Patient Journey Model architecture (PaJMa).
• Analyzed PaJMa models to construct comprehensive health informatics capacity audits.

Main Results:

• Successfully translated IT uptake survey data into patient journey models.
• Generated capacity audits providing detailed insights into IT integration (when, how, why).
• Demonstrated the capability to assess IT support for patient information flow and impact on patient journeys.

Conclusions:

• The proposed approach enables sophisticated health informatics capacity audits.
• This framework enhances the understanding of IT's role and integration within patient care pathways.
• The methodology provides a robust tool for evaluating health IT effectiveness in NICUs.