Big data in facial plastic and reconstructive surgery: from large databases to registries.

Area of Science:

• Medical research methodology
• Health informatics
• Plastic surgery outcomes

Background:

• Clinical research in facial plastic and reconstructive surgery (FPRS) faces significant limitations, including challenges with randomization and sample size.
• The evolving healthcare landscape emphasizes outcomes measurement and value, necessitating innovative research approaches.
• Existing big data tools from other medical fields can serve as models for FPRS research.

Purpose of the Study:

• To explore alternative research avenues in FPRS using big data, specifically administrative databases and registries.
• To address the persistent gaps in high-level evidence within FPRS research.
• To identify potential models for FPRS outcomes research.

Main Methods:

• Review of recent literature on large-scale outcomes research in FPRS over the past five years.
• Analysis of key administrative databases utilized in FPRS research.
• Evaluation of the strengths and weaknesses of various big data sources.

Main Results:

• FPRS literature employing large-scale outcomes research using administrative databases has shown steady growth in the last five years.
• Key administrative databases relevant to FPRS were identified, along with their respective advantages and disadvantages.
• A registry specifically designed with FPRS-defined outcomes emerged as having the highest potential for advancing outcomes research.

Conclusions:

• Despite a trend towards evidence-based approaches, gaps in FPRS research persist.
• Large administrative databases and registries present viable solutions to fill existing voids in FPRS outcomes research.
• Implementing big data strategies is crucial for enhancing the evidence base in facial plastic and reconstructive surgery.