A Toolchain for Big Data Analyses in the Intelligent Cognitive Operating Room.

Area of Science:

• Computer Science
• Medical Informatics
• Data Science

Background:

• Big data technologies offer potential for enhancing healthcare operations.
• The peri-operative setting presents unique challenges for data system implementation.

Purpose of the Study:

• To evaluate big data infrastructures for operating room applicability.
• To develop requirements for context-sensitive systems supporting medical staff.
• To compare data mining technologies, interfaces, and infrastructures for peri-operative use.

Main Methods:

• Evaluation of existing big data infrastructures.
• Generation of system design requirements.
• Comparative analysis of data mining technologies and software architectures.
• Selection of the lambda architecture for the proposed system.

Main Results:

• Identified requirements for context-sensitive systems in the operating room.
• Compared various data mining technologies and software infrastructures.
• Selected the lambda architecture as a suitable model for peri-operative big data integration.

Conclusions:

• Big data infrastructures can be adapted for the operating room.
• The lambda architecture provides a robust framework for real-time and postoperative data analysis in surgery.