Development and application of a non-technical skills coaching intervention framework for surgeons: A pilot quality improvement initiative.

Non-technical skills (NTS) challenges experienced by surgeons may degrade performance, ultimately impacting the safety and quality of care delivered to patients. The objectives of this work were to develop a framework for NTS coaching for surgeons and implement a coaching program utilizing the developed NTS coaching framework. Leveraging adult learning and self-determination theories, a specialty-agnostic NTS coaching framework was developed for individual coaching sessions with robotic surgeons.

Nontechnical Skills Assessment in Acute Care Trauma Simulations: A Mixed Methods Approach Using Eye Tracking and Behavioral Marker Systems.

Introduction: The condition of trauma patients and the urgent need for timely resuscitation present unique challenges to trauma teams. These difficulties are exacerbated for military trauma teams in combat environments. Consequently, there is a need for continued improvement of nontechnical skills (NTS) training for trauma teams.

Eye tracking and audio sensors to evaluate surgeon's non-technical skills: An empirical study.

Non-Technical Skills (NTS) of medical teams are currently measured using subjective and resource-intensive ratings given by experts. This study explores if objective NTS assessment approaches with eye-tracking and audio sensors can measure teamwork and communication skills in surgery. Eight surgeons participated in a simulated two-phase surgical scenario developed to assess their NTS.

A sensor-based framework for layout and workflow assessment in operating rooms.

Due to their large sizes and impediments to personnel workflows, integrating robotic technologies into the existing operating rooms (OR) is a challenge. In this study, we developed an ultra-wideband sensor-based human-machine-environment framework for layout and workflow assessments within the OR. In addition to providing best practices for use of the framework, we also demonstrated its effectiveness in understanding layout and workflow inefficiencies in 12 robotic-assisted surgeries (RAS) across 4 different surgical specialties.

Detecting Unusual Intravenous Infusion Alerting Patterns with Machine Learning Algorithms.

Objective: To detect unusual infusion alerting patterns using machine learning (ML) algorithms as a first step to advance safer inpatient intravenous administration of high-alert medications.

Materials And Methods: We used one year of detailed propofol infusion data from a hospital. Interpretable and clinically relevant variables were feature engineered, and data points were aggregated per calendar day.

Quantifying the Impact of Infusion Alerts and Alarms on Nursing Workflows: A Retrospective Analysis.

Background: Smart infusion pumps affect workflows as they add alerts and alarms in an information-rich clinical environment where alarm fatigue is already a major concern. An analytic approach is needed to quantify the impact of these alerts and alarms on nursing workflows and patient safety.

Objectives: To analyze a detailed infusion dataset from a smart infusion pump system and identify contributing factors for infusion programming alerts, operational alarms, and alarm resolution times.