Large Language Models for More Efficient Reporting of Hospital Quality Measures.

Hospital quality measures are a vital component of a learning health system, yet they can be costly to report, statistically underpowered, and inconsistent due to poor interrater reliability. Large language models (LLMs) have recently demonstrated impressive performance on health care-related tasks and offer a promising way to provide accurate abstraction of complete charts at scale. To evaluate this approach, we deployed an LLM-based system that ingests Fast Healthcare Interoperability Resources data and outputs a completed Severe Sepsis and Septic Shock Management Bundle (SEP-1) abstraction.

Leveraging Lean Methodology to Improve Compliance With Work-Hour Restrictions.

Importance: Since work-hour restrictions were instituted in 2003, sustainably complying with duty-hour regulations remains a challenge for general surgery residency programs across the nation.

Objective: To determine whether industry-based process improvement techniques could be leveraged to increase compliance with work-hour restrictions within a general surgery residency.

Design, Setting, And Participants: This quality improvement project using Lean methodology was conducted from October to November of the 2021 to 2022 academic year.

Antibody Levels from High-Throughput Variant-Specific SARS-CoV-2 Anti-Spike IgG and ACE2 Neutralization Assays Correlate with COVID Infection Risk in a Large Population.

Background: SARS-CoV-2 antibody levels have been proposed as a correlate of protection (CoP) from infection. Yet, large-scale prospective studies of cost-effective scalable antibody measures as predictors of infection under real-world conditions are limited. We examined whether antibody levels measured using high-throughput variant-specific SARS-CoV-2 anti-spike immunoglobulin G (IgG) and ACE2-neutralization assays correlate with cell-based neutralizing antibody (NAb) measurements, and whether they can serve as a reasonable CoP from SARS-CoV-2 infection.

Patient-Clinician Communication Interventions Across Multiple Primary Care Sites: A Cluster Randomized Clinical Trial.

Importance: Despite various attempts to improve patient-clinician communication, there has been limited head-to-head comparison of these efforts.

Objective: To assess whether clinician coaching (mobile application or in-person) is more effective than reminder posters in examination rooms and whether mobile app use is noninferior to in-person coaching.

Design, Setting, And Participants: A cluster randomized clinical trial with 3 arms.

Diagnostic Performance of a Next-Generation Virtual/Augmented Reality Headset: A Pilot Study of Diverticulitis on CT.

Next-generation virtual/augmented reality (VR/AR) headsets may rival the desktop computer systems that are approved for clinical interpretation of radiologic images, but require validation for high-resolution low-luminance diagnoses like diverticulitis. The primary aim of this study is to compare diagnostic performance for detecting diverticulitis on CT between radiologists using a headset versus a desktop. The secondary aim is to survey participating radiologists about the usage of both devices.

The Long Haul to Surgery: Long COVID Has Minimal Burden on Surgical Departments.

Many patients infected with the SARS-CoV-2 virus (COVID-19) continue to experience symptoms for weeks to years as sequelae of the initial infection, referred to as "Long COVID". Although many studies have described the incidence and symptomatology of Long COVID, there are little data reporting the potential burden of Long COVID on surgical departments. A previously constructed database of survey respondents who tested positive for COVID-19 was queried, identifying patients reporting experiencing symptoms consistent with Long COVID.

Longitudinal assessment of the impact of COVID-19 infection on mask-wearing behaviors.

Background: Wearing a mask was a crucial component in slowing the COVID-19 pandemic. However, little is known about the intersectionality between mask usage, risk perception, and infection. The purpose of this study was to investigate whether risk perceptions and masking behaviors are associated with contracting SARS-CoV-2 and how contracting SARS-CoV-2 subsequently changes masking behaviors in specific situations.

Integrating clinical research into electronic health record workflows to support a learning health system.

Objective: Integrating clinical research into routine clinical care workflows within electronic health record systems (EHRs) can be challenging, expensive, and labor-intensive. This case study presents a large-scale clinical research project conducted entirely within a commercial EHR during the COVID-19 pandemic.

Case Report: The UCSD and UCSDH COVID-19 NeutraliZing Antibody Project (ZAP) aimed to evaluate antibody levels to SARS-CoV-2 virus in a large population at an academic medical center and examine the association between antibody levels and subsequent infection diagnosis.

AI-Generated Draft Replies Integrated Into Health Records and Physicians' Electronic Communication.

Importance: Timely tests are warranted to assess the association between generative artificial intelligence (GenAI) use and physicians' work efforts.

Objective: To investigate the association between GenAI-drafted replies for patient messages and physician time spent on answering messages and the length of replies.

Design, Setting, And Participants: Randomized waiting list quality improvement (QI) study from June to August 2023 in an academic health system.

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.

Generative artificial intelligence responses to patient messages in the electronic health record: early lessons learned.

Background: Electronic health record (EHR)-based patient messages can contribute to burnout. Messages with a negative tone are particularly challenging to address. In this perspective, we describe our initial evaluation of large language model (LLM)-generated responses to negative EHR patient messages and contend that using LLMs to generate initial drafts may be feasible, although refinement will be needed.

Call me Ishmael: addressing the white whale of team communication in the operating room with labelled surgical caps at an academic medical centre.

Introduction: Effective communication in the operating room (OR) is crucial. Addressing a colleague by their name is respectful, humanising, entrusting and associated with improved clinical outcomes. We aimed to enhance team communication in the perioperative environment by offering personalised surgical caps labelled with name and provider role to all OR team members at a large academic medical centre.

Implementation of an electronic health record-integrated instant messaging system in an academic health system.

Objectives: Effective communication amongst healthcare workers simultaneously promotes optimal patient outcomes when present and is deleterious to outcomes when absent. The advent of electronic health record (EHR)-embedded secure instantaneous messaging systems has provided a new conduit for provider communication. This manuscript describes the experience of one academic medical center with deployment of one such system (Secure Chat).

An Automated System for Physician Trainee Procedure Logging via Electronic Health Records.

Importance: Procedural proficiency is a core competency for graduate medical education; however, procedural reporting often relies on manual workflows that are duplicative and generate data whose validity and accuracy are difficult to assess. Failure to accurately gather these data can impede learner progression, delay procedures, and negatively impact patient safety.

Objective: To examine accuracy and procedure logging completeness of a system that extracts procedural data from an electronic health record system and uploads these data securely to an application used by many residency programs for accreditation.

Integrated Genomic and Social Network Analyses of SARS-CoV-2 Transmission in the Healthcare Setting.

Background: Infection prevention (IP) measures are designed to mitigate the transmission of pathogens in healthcare. Using large-scale viral genomic and social network analyses, we determined if IP measures used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were adequate in protecting healthcare workers (HCWs) and patients from acquiring SARS-CoV-2.

Methods: We performed retrospective cross-sectional analyses of viral genomics from all available SARS-CoV-2 viral samples collected at UC San Diego Health and social network analysis using the electronic medical record to derive temporospatial overlap of infections among related viromes and supplemented with contact tracing data.

Surgery and Anesthesia Preoperative "Virtual Huddle": A Pilot Trial to Enhance Communication across the Drape.

Objectives:  Effective communication between surgeons and anesthesiologists is critical for high-quality, safe, and efficient perioperative patient care. Despite widespread implementation of surgical safety checklists and time-outs, ineffective team communication remains a leading cause of patient safety events in the operating room. To promote effective communication, we conducted a pilot trial of a "virtual huddle" between anesthesiologists and surgeons.

Association of physician burnout with perceived EHR work stress and potentially actionable factors.

Objective: Physicians of all specialties experienced unprecedented stressors during the COVID-19 pandemic, exacerbating preexisting burnout. We examine burnout's association with perceived and actionable electronic health record (EHR) workload factors and personal, professional, and organizational characteristics with the goal of identifying levers that can be targeted to address burnout.

Materials And Methods: Survey of physicians of all specialties in an academic health center, using a standard measure of burnout, self-reported EHR work stress, and EHR-based work assessed by the number of messages regarding prescription reauthorization and use of a staff pool to triage messages.

Developing a highly-reliable learning health system.

Multiple independent frameworks to support continuous improvement have been proposed to guide healthcare organizations. Two of the most visible are High-reliability Health care, (Chassin et al., 2013) which is emphasized by The Joint Commission, and Learning Health Systems, (Institute of Medicine, 2011) highlighted by the National Academy of Medicine.

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.

Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum.

Importance: The rapid expansion of virtual health care has caused a surge in patient messages concomitant with more work and burnout among health care professionals. Artificial intelligence (AI) assistants could potentially aid in creating answers to patient questions by drafting responses that could be reviewed by clinicians.

Objective: To evaluate the ability of an AI chatbot assistant (ChatGPT), released in November 2022, to provide quality and empathetic responses to patient questions.