Association of amisulpride and recovery room length of stay among patients with postoperative nausea and vomiting following outpatient surgery.

Study Objective: Postoperative nausea and vomiting (PONV) is a common sequela of surgery in patients undergoing general anesthesia. Amisulpride has shown promise in its ability to treat PONV. The objective of this study was to determine if amisulpride is associated with significant changes in PACU efficiency within a fast-paced ambulatory surgery center.

Design and Pilot Study of a High-Fidelity Medical Simulation of a Hospital-Wide Cybersecurity Attack.

Background: Cybersecurity incidents affecting hospitals have grown in prevalence and consequence over the last two decades, increasing the importance of cybersecurity preparedness and response training to minimize clinical disruptions. This work describes the development, execution, and post-exercise assessment of a novel simulation scenario consisting of four interlocking intensive care unit (ICU) patient scenarios. This simulation was designed to demonstrate the management of acute pathologies without access to conventional treatment methods during a cybersecurity incident in order to raise clinician awareness of the increasing incidence and patient safety implications of such events.

Constructing synthetic datasets with generative artificial intelligence to train large language models to classify acute renal failure from clinical notes.

Objectives: To compare performances of a classifier that leverages language models when trained on synthetic versus authentic clinical notes.

Materials And Methods: A classifier using language models was developed to identify acute renal failure. Four types of training data were compared: (1) notes from MIMIC-III; and (2, 3, and 4) synthetic notes generated by ChatGPT of varied text lengths of 15 (GPT-15 sentences), 30 (GPT-30 sentences), and 45 (GPT-45 sentences) sentences, respectively.

Ransomware Cyberattack Associated With Cardiac Arrest Incidence and Outcomes at Untargeted, Adjacent Hospitals.

Objectives: Healthcare ransomware cyberattacks have been associated with major regional hospital disruptions, but data reporting patient-oriented outcomes in critical conditions such as cardiac arrest (CA) are limited. This study examined the CA incidence and outcomes of untargeted hospitals adjacent to a ransomware-infected healthcare delivery organization (HDO).

Design Setting And Patients: This cohort study compared the CA incidence and outcomes of two untargeted academic hospitals adjacent to an HDO under a ransomware cyberattack during the pre-attack (April 3-30, 2021), attack (May 1-28, 2021), and post-attack (May 29, 2021-June 25, 2021) phases.

Machine Learning Prediction Models to Reduce Length of Stay at Ambulatory Surgery Centers Through Case Resequencing.

The post-anesthesia care unit (PACU) length of stay is an important perioperative efficiency metric. The aim of this study was to develop machine learning models to predict ambulatory surgery patients at risk for prolonged PACU length of stay - using only pre-operatively identified factors - and then to simulate the effectiveness in reducing the need for after-hours PACU staffing. Several machine learning classifier models were built to predict prolonged PACU length of stay (defined as PACU stay ≥ 3 hours) on a training set.

Ransomware Attack Associated With Disruptions at Adjacent Emergency Departments in the US.

Importance: Cyberattacks on health care delivery organizations are increasing in frequency and sophistication. Ransomware infections have been associated with significant operational disruption, but data describing regional associations of these cyberattacks with neighboring hospitals have not been previously reported, to our knowledge.

Objective: To examine an institution's emergency department (ED) patient volume and stroke care metrics during a month-long ransomware attack on a geographically proximal but separate health care delivery organization.

An Ensemble Learning Approach to Improving Prediction of Case Duration for Spine Surgery: Algorithm Development and Validation.

Background: Estimating surgical case duration accurately is an important operating room efficiency metric. Current predictive techniques in spine surgery include less sophisticated approaches such as classical multivariable statistical models. Machine learning approaches have been used to predict outcomes such as length of stay and time returning to normal work, but have not been focused on case duration.

Obesity and outcomes in patients undergoing upper airway surgery for obstructive sleep apnea.

Objective: Obesity is frequently debated as a factor associated with increased postoperative complications. Specifically, upper airway surgeries for obstructive sleep apnea (OSA), a common comorbidity among obese patients, may be complicated by obesity's impact on intraoperative ventilation. The aim of this retrospective study was to analyze the association of various degrees of obesity with postoperative outcomes in patients undergoing surgery for OSA.

Identification of Preanesthetic History Elements by a Natural Language Processing Engine.

Background: Methods that can automate, support, and streamline the preanesthesia evaluation process may improve resource utilization and efficiency. Natural language processing (NLP) involves the extraction of relevant information from unstructured text data. We describe the utilization of a clinical NLP pipeline intended to identify elements relevant to preoperative medical history by analyzing clinical notes.

Cybersecurity Challenges and the Academic Health Center: An Interactive Tabletop Simulation for Executives.

Problem: Academic health centers (AHCs) face cybersecurity vulnerabilities that have potential costs to an institution's finances, reputation, and ability to deliver care. Yet many AHC executives may not have sufficient knowledge of the potential impact of cyberattacks on institutional missions such as clinical care, research, and education. Improved cybersecurity awareness and education are areas of opportunity for many AHCs.

Connected Medical Technology and Cybersecurity Informed Consent: A New Paradigm.

Background: Connected medical technology is increasingly prevalent and offers both a host of new therapeutic potentials and cybersecurity-related considerations. Current practice largely does not include discussions of cybersecurity issues when clinicians obtain informed consent.

Objective: This paper aims to raise awareness about cybersecurity considerations for connected medical technology as they relate to informed consent discussions between patients and clinicians.

Wave of Wearables: Clinical Management of Patients and the Future of Connected Medicine.

The future of connected health care will involve the collection of patient data or enhancement of clinician workflows through various biosensors and displays found on wearable electronic devices, many of which are marketed directly to consumers. The adoption of wearables in health care is being driven by efforts to reduce health care costs, improve care quality, and increase clinician efficiency. Wearables have significant potential to achieve these goals but are currently limited by lack of widespread integrations into electronic health records, biosensor data collection types, and a lack of scientifically rigorous literature showing benefit.

Hacking 9-1-1: Infrastructure Vulnerabilities and Attack Vectors.

9-1-1 call centers are a critical component of prehospital care: they accept emergency calls, dispatch field responders such as emergency medical services, and provide callers with emergency medical instructions before their arrival. The aim of this study was to describe the technical structure of the 9-1-1 call-taking system and to describe its vulnerabilities that could lead to compromised patient care. 9-1-1 calls answered from mobile phones and landlines use a variety of technologies to provide information about caller location and other information.

Clinical Cybersecurity Training Through Novel High-Fidelity Simulations.

Background: Cybersecurity risks in health care systems have traditionally been measured in data breaches of protected health information, but compromised medical devices and critical medical infrastructure present risks of disruptions to patient care. The ubiquitous prevalence of connected medical devices and systems may be associated with an increase in these risks.

Objective: This article details the development and execution of three novel high-fidelity clinical simulations designed to teach clinicians to recognize, treat, and prevent patient harm from vulnerable medical devices.

Recording medical students' encounters with standardized patients using Google Glass: providing end-of-life clinical education.

Problem: Medical education today frequently includes standardized patient (SP) encounters to teach history-taking, physical exam, and communication skills. However, traditional wall-mounted cameras, used to record video for faculty and student feedback and evaluation, provide a limited view of key nonverbal communication behaviors during clinical encounters.

Approach: In 2013, 30 second-year medical students participated in an end-of-life module that included SP encounters in which the SPs used Google Glass to record their first-person perspective.

Ultrasound-guided central venous access using Google Glass.

Background: The use of ultrasound during invasive bedside procedures is quickly becoming the standard of care. Ultrasound machine placement during procedures often requires the practitioner to turn their head during the procedure to view the screen. Such turning has been implicated in unintentional hand movements in novices.

A standardized template for measuring and reporting telephone pre-arrival cardiopulmonary resuscitation instructions.

Background: Bystander cardiopulmonary resuscitation (CPR) improves out-of-hospital cardiac arrest (OHCA) survival. Telephone CPR (TCPR) comprises CPR instruction given by emergency dispatchers to bystanders responding to OHCA and the CPR performed as a result. TCPR instructions improve bystander CPR rates, but the quality of the instructions varies widely.