First deployment of artificial intelligence recommendations in orthopedic surgery

Area of Science:

• Healthcare Management
• Artificial Intelligence in Surgery
• Health Informatics

Background:

• Limited research exists on non-clinical applications of artificial intelligence (AI) in healthcare.
• A significant gap in the literature pertains to the practical implementation and measured outcomes of AI solutions in surgical settings.
• Existing studies primarily focus on clinical AI applications, neglecting operational enhancements.

Purpose of the Study:

• To address the gap in understanding AI's non-clinical impact on surgical efficiency.
• To evaluate the effectiveness of implementing a prescriptive analytics system (PAS) in an operating room (OR) setting.
• To quantify improvements in OR efficiency defined as completing four uncomplicated arthroplasty surgeries within an 8-hour timeframe.

Main Methods:

• A Community Case Study was conducted involving 228 patient arthroplasty surgeries.
• A prescriptive analytics system (PAS) utilizing supervised machine learning techniques was implemented.
• Operating room efficiency was measured before and after PAS implementation.

Main Results:

• Operating room efficiency increased substantially from 39% to 93% after implementing the PAS.
• The AI-driven PAS demonstrated a significant positive impact on surgical workflow optimization.
• The study successfully achieved its goal of enhancing OR efficiency through AI.

Conclusions:

• Artificial intelligence, specifically through PAS, can dramatically improve non-clinical operating room efficiency.
• The findings highlight the potential of AI to optimize surgical processes and resource utilization.
• This study provides evidence for the practical application and benefits of AI in enhancing healthcare operations beyond direct patient care.