Development of a machine learning model to predict intraoperative transfusion and guide type and screen ordering.

Study Objective: To develop an algorithm to predict intraoperative Red Blood Cell (RBC) transfusion from preoperative variables contained in the electronic medical record of our institution, with the goal of guiding type and screen ordering.

Design: Machine Learning model development on retrospective single-center hospital data.

Setting: Preoperative period and operating room.

Visualization of aggregate perioperative data improves anesthesia case planning: A randomized, cross-over trial.

Study Objective: A challenge in reducing unwanted care variation is effectively managing the wide variety of performed surgical procedures. While an organization may perform thousands of types of cases, privacy and logistical constraints prevent review of previous cases to learn about prior practices. To bridge this gap, we developed a system for extracting key data from anesthesia records.

Emergency Clinical Procedure Detection With Deep Learning.

Information about a patient's state is critical for hospitals to provide timely care and treatment. Prior work on improving the information flow from emergency medical services (EMS) to hospitals demonstrated the potential of using automated algorithms to detect clinical procedures. However, prior work has not made effective use of video sources that might be available during patient care.