A flexible framework for visualizing and exploring patient misdiagnosis over time

Area of Science:

• Medical Informatics
• Data Visualization
• Clinical Decision Support

Background:

• Diagnosis is a complex process critical for clinical decision-making.
• Manual review of patient diagnostic history is inefficient due to large data volumes.
• Improving diagnostic accuracy and reducing errors requires efficient data analysis.

Purpose of the Study:

• To present a novel visualization and analysis framework for exploring patient diagnostic paths over time.
• To enable efficient data reduction and representation of complex diagnostic histories.
• To provide insights for improving diagnostic accuracy and reducing downstream errors.

Main Methods:

• Developed a flexible visualization and analysis framework.
• Enabled user selection of patients, events, and conditions.
• Implemented data filtering based on various attributes.
• Provided interactive views of patient diagnosis paths.

Main Results:

• Demonstrated a practical application of the framework with a case study on infection-based diagnosis paths.
• Showcased the framework's ability to explore patient diagnostic processes interactively.
• Validated the framework's utility in representing complex patient data.

Conclusions:

• The proposed framework offers a promising approach for analyzing patient diagnostic journeys.
• Visual analytics can effectively address challenges in managing and interpreting large healthcare datasets.
• This tool has the potential to enhance diagnostic quality and patient care.