Laparoscopic diagnostic peritoneal lavage (L-DPL): A method for evaluation of penetrating abdominal stab wounds

BACKGROUND: The management of penetrating abdominal stab wounds has been the subject of continued reappraisal and controversy. In the present study a novel method which combines the use of diagnostic laparoscopy and DPL, termed laparoscopic diagnostic peritoneal lavage (L-DPL) is described METHOD: Five trauma patients with penetrating injuries to the lower chest or abdomen were included. Standard videoscopic equipment is utilized for the laparoscopic trauma evaluation of the injured patient. When no significant injury is detected, the videoscope is withdrawn and 1000 mL of normal saline is infused through the abdominal trochar into the peritoneal cavity, and the effluent fluid studied for RBCs, WBC, amylase debry, bile as it is uced in regular diagnostic peritoneal lavage RESULTS: Laparoscopic peritoneal lavage (L-DPL) was then performed and proved to be negative in all 5 patients. RBC lavage counts above 100,000/mcrl were not considered as a positive lavage result, because the bleeding source was directly observed and controlled laparoscopically. All patients recovered uneventfully and were released within 3 days. This procedure combines the visual advantages of laparoscopy together with the sensitivity and specificty of DPL for the diagnosis of significant penetrating intra-abdominal injury, when the diagnostic strategy of selective consevatism for abdominal stab wounds is adopted. CONCLUSION: 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

When all computers shut down: the clinical impact of a major cyber-attack on a general hospital

ImportanceHealthcare organizations operate in a data-rich environment and depend on digital computerized systems; thus, they may be exposed to cyber threats. Indeed, one of the most vulnerable sectors to hacks and malware is healthcare. However, the impact of cyberattacks on healthcare organizations remains under-investigated.ObjectiveThis study aims to describe a major attack on an entire medical center that resulted in a complete shutdown of all computer systems and to identify the critical actions required to resume regular operations.SettingThis study was conducted on a public, general, and acute care referral university teaching hospital.MethodsWe report the different recovery measures on various hospital clinical activities and their impact on clinical work.ResultsThe system malfunction of hospital computers did not reduce the number of heart catheterizations, births, or outpatient clinic visits. However, a sharp drop in surgical activities, emergency room visits, and total hospital occupancy was observed immediately and during the first postattack week. A gradual increase in all clinical activities was detected starting in the second week after the attack, with a significant increase of 30% associated with the restoration of the electronic medical records (EMR) and laboratory module and a 50% increase associated with the return of the imaging module archiving. One limitation of the present study is that, due to its retrospective design, there were no data regarding the number of elective internal care hospitalizations that were considered crucial.Conclusions and relevanceThe risk of ransomware cyberattacks is growing. Healthcare systems at all levels of the hospital should be aware of this threat and implement protocols should this catastrophic event occur. Careful evaluation of steady computer system recovery weekly enables vital hospital function, even under a major cyberattack. The restoration of EMR, laboratory systems, and imaging archiving modules was found to be the most significant factor that allowed the return to normal clinical hospital work

Optimizing Operation Room Utilization—A Prediction Model

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

Optimizing Operation Room Utilization—A Prediction Model

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