First deployment of artificial intelligence recommendations in orthopedic surgery.

Scant research has delved into the non-clinical facets of artificial intelligence (AI), concentrating on leveraging data to enhance the efficiency of healthcare systems and operating rooms. Notably, there is a gap in the literature regarding the implementation and outcomes of AI solutions. The absence of published results demonstrating the practical application and effectiveness of AI in domains beyond clinical settings, particularly in the field of surgery, served as the impetus for our undertaking in this area.

Factors influencing delays and overtime during surgery: a descriptive analytics for high volume arthroplasty procedures.

The aim of this article is to analyze factors influencing delays and overtime during surgery. We utilized descriptive analytics and divided the factors into three levels. In level one, we analyzed each surgical metrics individually and how it may influence the Surgical Success Rate (SSR) of each operating day.

Leveraging machine learning and prescriptive analytics to improve operating room throughput.

Successful days are defined as days when four cases were completed before 3:45pm, and overtime hours are defined as time spent after 3:45pm. Based on these definitions and the 460 unsuccessful days isolated from the dataset, 465 hours, 22 minutes, and 30 seconds total overtime hours were calculated. To reduce the increasing wait lists for hip and knee surgeries, we aim to verify whether it is possible to add a 5th surgery, to the typical 4 arthroplasty surgery per day schedule, without adding extra overtime hours and cost at our clinical institution.

Use of multidisciplinary positive deviance seminars to improve efficiency in a high-volume arthroplasty practice: a pilot study.

Background: Positive deviance (PD) seminars, which have shown excellent results in improving the quality of surgical practices, use individual performance feedback to identify team members who outperform their peers; the strategies from those with exemplary performance are used to improve team members' practices. Our study aimed to use the PD approach with arthroplasty surgeons and nurses to identify multidisciplinary strategies and recommendations to improve operating room (OR) efficiency.

Methods: We recruited 5 surgeons who performed high-volume primary arthroplasty and had participated in 4-joint rooms since 2012, and 29 nurses who had participated in 4-joint rooms and in at least 16 cases in our data set.

Artificial intelligence-driven prescriptive model to optimize team efficiency in a high-volume primary arthroplasty practice.

Purpose: We aimed to improve OR efficiency using machine learning (ML) to find relevant metrics influencing surgery time success and team performance on efficiency to create a model which incorporated team, patient, and surgery-related factors.

Methods: From 2012 to 2020, five surgeons, 44 nurses, and 152 anesthesiologists participated in 1199 four joint days (4796 cases): 1461 THA, 1496 TKA, 652 HR, 242 UKA, and 945 others. Patients were 2461f:2335 m; age, 64.