No abstract

Background: Operating rooms are the core of hospitals. They are a primary source of revenue and are often seen as one of the bottlenecks in the medical system. Many efforts are made to increase throughput, reduce costs, and maximize incomes, as well as optimize clinical outcomes and patient satisfaction. We trained a predictive model on the length of surgeries to improve the productivity and utility of operative rooms in general hospitals. Methods: We collected clinical and administrative data for the last 10 years from two large general public hospitals in Israel. We trained a machine learning model to give the expected length of surgery using pre-operative data. These data included diagnoses, laboratory tests, risk factors, demographics, procedures, anesthesia type, and the main surgeon’s level of experience. We compared our model to a naïve model that represented current practice. Findings: Our prediction model achieved better performance than the naïve model and explained almost 70% of the variance in surgery durations. Interpretation: A machine learning-based model can be a useful approach for increasing operating room utilization. Among the most important factors were the type of procedures and the main surgeon’s level of experience. The model enables the harmonizing of hospital productivity through wise scheduling and matching suitable teams for a variety of clinical procedures for the benefit of the individual patient and the system as a whole.

A method of laparoscopic diagnostic peritoneal lavage (L-DPL) in hemodynamically stable patients with penetrating lower thoracic or abdominal stab wounds is described. The method is especially applicable for trauma surgeons with only basic experience in laparoscopic technique. This procedure is used to obtain conclusive evidence of significant intra-abdominal injury, confirm peritoneal penetration, control intra-abdominal bleeding, and repair lacerations to the diaphragm and abdominal wall. The combination of laparoscopy and DPL afforded by the L-DPL method adds to the sensitivity and specificity of DPL, and avoids under or over sesitivty, that have limited the use of DPL in the hemodynamically stable trauma patients with suspicious or proven peritoneal penetration.

Objective To describe the challenges facing the obstetric division following a cyberattack and discuss ways of preparing for and overcoming another one. Methods A retrospective descriptive study conducted in a mid‐sized medical center. Division activities, including the number of deliveries, cesarean sections, emergency room visits, admissions, maternal–fetal medicine department occupancy, and ambulatory encounters, from 2 weeks before the attack to 8 weeks following it (a total of 11 weeks), were compared with the retrospective period in 2019 (pre‐COVID‐19). In addition, we present the challenges and adaptation measures taken at the division and hospital levels leading up to the resumption of full division activity. Results On the day of the cyberattack, critical decisions were made. The media announced the event, calling on patients not to come to our hospital. Also, all elective activities other than cesarean deliveries were stopped. The number of deliveries, admissions, and both emergency room and ambulatory clinic visits decreased by 5%–10% overall for 11 weeks, reflecting the decrease in division activity. Nevertheless, in all stations, there were sufficient activities and adaptation measures to ensure patient safety, decision‐making, and workflow of patients were accounted for. Conclusions The risk of ransomware cyberattacks is growing. Healthcare systems at all levels should recognize this threat and have protocols for dealing with them once they occur.