Visualizing Patient Pathways and Identifying Data Repositories in a UK Neurosciences Center: Exploratory Study

Area of Science:

• Health Informatics
• Clinical Data Management
• Neuroscience Research

Background:

• Health and clinical activity data are crucial for research, patient care, and service efficiency.
• Understanding clinical pathways is essential for managing complex health data.
• Effective use of health data depends on identifying relevant datasets.

Purpose of the Study:

• To apply Business Process Modeling Notation (BPMN) for representing clinical pathways.
• To map clinical activity and data flows within a UK neurosciences center.
• To identify electronic health record (EHR) data and nonstandard data repositories.

Main Methods:

• Utilized BPMN to map and visualize patient journeys and data movement.
• Identified datasets stored outside standard applications.
• Collected information on these external datasets via questionnaires.

Main Results:

• Identified 13 standard applications capturing neurology clinical activity within the EHR.
• Discovered 22 distinct datasets managed outside standard applications for direct patient care.
• These external datasets tracked patient blood results, home visits, and triage status.

Conclusions:

• Mapping data flows identified gaps where EHRs do not meet daily patient care needs.
• Data stored outside standard applications pose risks of duplication and being overlooked.
• Future work should focus on unmet data needs for better data capture and centralization.