Real-World Clinical Datasets in Practice: Applications for Learners, Clinician-Educators, and Health Services Teams

Area of Science:

• Health Services Research
• Clinical Informatics
• Data Science in Healthcare

Background:

• Real-world clinical databases offer accessible avenues for population health, quality improvement, and outcomes research.
• De-identified, large-scale datasets facilitate investigations into healthcare delivery, costs, treatment effectiveness, and clinical outcomes.
• These resources eliminate the need for patient recruitment and extensive funding, democratizing research.

Purpose of the Study:

• To review and compare four major real-world clinical databases: HCUP, MIMIC-IV, TriNetX, and Epic Cosmos.
• To highlight the structure, data types, access requirements, and use cases of each database.
• To provide guidance for researchers in selecting appropriate datasets and formulating research questions.

Main Methods:

• A review and synopsis of four widely utilized real-world clinical databases.
• Comparative analysis of database features, including structure, data types, and access.
• Discussion of ideal use cases and guidance for research team engagement.

Main Results:

• Overview of Healthcare Cost and Utilization Project (HCUP), Medical Information Mart for Intensive Care (MIMIC-IV), TriNetX, and Epic Cosmos.
• Detailed comparison of data accessibility (low-cost, open, subscription, institutional).
• Identification of strengths and weaknesses for various research applications.

Conclusions:

• Real-world clinical databases are crucial for advancing healthcare research and improving patient outcomes.
• Guidance is provided for learners and research teams to effectively utilize these databases.
• Mentorship and strategic dataset selection are key to successful data-driven research.