CannonDesign receives research grant from CCIRF to study military surgical team performance

CannonDesign was recently awarded $27,000 from the Competency & Credentialing Institute Research Foundation (CCIRF), part of a total grant of just over $41,000. The funding supports a study titled, "Exploring How Team Familiarity and Spatial Topology Influence Surgical Team Performance in a Military Medical Center," led by Principal Investigator Colonel Christopher Stucky, PhD, of the US Army. Colonel Stucky is based at the Landstuhl Regional Medical Center (LRMC) in Germany and also serves as the Perioperative Consultant to the Army Surgeon-General. 

In July, spatial network analyses will be conducted as part of this study. Subsequently, on-site data review and analysis will take place at LRMC in August, ensuring the confidentiality and security of the data. 

“We are thrilled at this opportunity to leverage our research capabilities with this grant,” says Felix Kabo, Ph.D., M.Arch., M.S., Research Director at CannonDesign. “We are looking forward to conducting the additional research this summer at the LRMC to help deliver healthcare more effectively to the men and women in our armed forces.”  

The ongoing research aims to enhance the understanding of how surgical team familiarity and the spatial layout of medical facilities impact team performance and surgical outcomes. This study is underway and has already resulted in key findings that will be shared in a manuscript titled "Surgical Control Time Underestimation: Implications for Medical Systems and the Future Integration of AI and ML Models." The manuscript will soon be submitted to a medical journal. 

The study findings focus on the accurate estimation of surgical procedure times, which is vital for optimizing healthcare access, patient outcomes, and cost-effectiveness. Efficient operating room utilization hinges on precise predictions of surgical control times (SCTs). The study analyzed 14,438 surgical cases across 13 specialties from January 2019 to January 2023, examining the discrepancies between preoperatively predicted SCTs and actual SCTs. 

Key findings include: 

• On average, surgeries took 12.3% longer than predicted, with a mean underestimation of 10.4 minutes. 
• SCTs accounted for 78% of the total operative time. 
• Eleven of the thirteen specialties consistently underestimated SCTs. 
• Pain management, neurosurgery, and orthopedics had the largest underestimations. 
• General surgery and podiatry had the most accurate predictions. 

The study's implications are far-reaching, suggesting that integrating artificial intelligence (AI) and machine learning (ML) models could greatly improve the accuracy of surgical time predictions, ultimately aiding in better resource allocation and healthcare delivery. 

For more details on the study and its implications, stay tuned for the upcoming journal publication.