Case-based visualization of a patient cohort using SEER epidemiologic data.

Area of Science:

• Oncology
• Public Health
• Health Informatics

Background:

• Cancer registry data are crucial for epidemiological tracking and informing clinical decisions.
• Limited integration exists between public cancer datasets and electronic patient records for clinical use.
• Electronic patient records lack direct access to comprehensive cancer registry information.

Purpose of the Study:

• To implement a proof-of-concept integration of the SEER (Surveillance, Epidemiology and End Results) dataset with a digital breast cancer tumor board.
• To evaluate the utility of this integration within a German university hospital's clinical setting.
• To explore dynamic data visualization for cohort composition and survival analysis.

Main Methods:

• Integration of the public SEER dataset with a digital breast cancer tumor board system.
• Utilizing routine documentation for data input and dynamic visualization.
• Generating cohort composition and Kaplan-Meier survival plots.
• Conducting an evaluation of the implemented system.

Main Results:

• Successful proof-of-concept integration was established.
• Dynamic visualizations of cohort composition and survival plots were generated.
• Favorable feedback was received on the concept and implementation.
• Key data elements, such as receptor status, were identified as missing in the SEER dataset, limiting clinical utility.

Conclusions:

• The integration of SEER data into clinical workflows is feasible and conceptually valuable.
• The current SEER dataset's limitations, particularly missing crucial clinical data, restrict its direct application in individual patient diagnostic and treatment decisions.
• Further development is needed to enhance the completeness of registry data for improved clinical utility.