Barriers to Postdischarge Smartphone App Use Among Patients With Traumatic Rib Fractures.

Rib fractures commonly result from traumatic injury and often require hospitalization for pain control and supportive pulmonary care. Although the use of mobile health technology to share patient-generated health data has increased, it remains limited in patients with traumatic injuries. We sought to assess the feasibility of mobile health tracking in patients with rib fractures by using a smartphone app to monitor postdischarge recovery.

An operational guide to translational clinical machine learning in academic medical centers.

Few published data science tools are ever translated from academia to real-world clinical settings for which they were intended. One dimension of this problem is the software engineering task of turning published academic projects into tools that are usable at the bedside. Given the complexity of the data ecosystem in large health systems, this task often represents a significant barrier to the real-world deployment of data science tools for prospective piloting and evaluation.

Characterization of Post-COVID-19 Definitions and Clinical Coding Practices: Longitudinal Study.

Background: Post-COVID-19 condition (colloquially known as "long COVID-19") characterized as postacute sequelae of SARS-CoV-2 has no universal clinical case definition. Recent efforts have focused on understanding long COVID-19 symptoms, and electronic health record (EHR) data provide a unique resource for understanding this condition. The introduction of the International Classification of Diseases, Tenth Revision (ICD-10) code U09.

Mobilizing data during a crisis: Building rapid evidence pipelines using multi-institutional real world data.

The COVID-19 pandemic generated tremendous interest in using real world data (RWD). Many consortia across the public and private sectors formed in 2020 with the goal of rapidly producing high-quality evidence from RWD to guide medical decision-making, public health priorities, and more. Experiences were gathered from five large consortia on rapid multi-institutional evidence generation during the COVID-19 pandemic.

Postoperative Opioid Prescribing via Rule-Based Guidelines Derived from In-Hospital Consumption: An Assessment of Efficacy Based on Postdischarge Opioid Use.

Background: Many institutions have developed operation-specific guidelines for opioid prescribing. These guidelines rarely incorporate in-hospital opioid consumption, which is highly correlated with consumption. We compare outcomes of several patient-centered approaches to prescribing that are derived from in-hospital consumption, including several experimental, rule-based prescribing guidelines and our current institutional guideline.

Evaluating capabilities of large language models: Performance of GPT-4 on surgical knowledge assessments.

Background: Artificial intelligence has the potential to dramatically alter health care by enhancing how we diagnose and treat disease. One promising artificial intelligence model is ChatGPT, a general-purpose large language model trained by OpenAI. ChatGPT has shown human-level performance on several professional and academic benchmarks.

Leveraging informative missing data to learn about acute respiratory distress syndrome and mortality in long-term hospitalized COVID-19 patients throughout the years of the pandemic.

Electronic health records (EHRs) contain a wealth of information that can be used to further precision health. One particular data element in EHRs that is not only under-utilized but oftentimes unaccounted for is missing data. However, missingness can provide valuable information about comorbidities and best practices for monitoring patients, which could save lives and reduce burden on the healthcare system.

Leveraging informative missing data to learn about acute respiratory distress syndrome and mortality in long-term hospitalized COVID-19 patients throughout the years of the pandemic.

Electronic health records (EHRs) contain a wealth of information that can be used to further precision health. One particular data element in EHRs that is not only under-utilized but oftentimes unaccounted for is missing data. However, missingness can provide valuable information about comorbidities and best practices for monitoring patients, which could save lives and reduce burden on the healthcare system.

Analyzing Surgical Technique in Diverse Open Surgical Videos With Multitask Machine Learning.

Objective: To overcome limitations of open surgery artificial intelligence (AI) models by curating the largest collection of annotated videos and to leverage this AI-ready data set to develop a generalizable multitask AI model capable of real-time understanding of clinically significant surgical behaviors in prospectively collected real-world surgical videos.

Design, Setting, And Participants: The study team programmatically queried open surgery procedures on YouTube and manually annotated selected videos to create the AI-ready data set used to train a multitask AI model for 2 proof-of-concept studies, one generating surgical signatures that define the patterns of a given procedure and the other identifying kinematics of hand motion that correlate with surgeon skill level and experience. The Annotated Videos of Open Surgery (AVOS) data set includes 1997 videos from 23 open-surgical procedure types uploaded to YouTube from 50 countries over the last 15 years.

An Institutional Curriculum for Opioid Prescribing Education: Outcomes From 2017 to 2022.

Introduction: Prescription opioids, including those prescribed after surgery, have greatly contributed to the US opioid epidemic. Educating opioid prescribers is a crucial component of ensuring the safe use of opioids among surgical patients.

Methods: An annual opioid prescribing education curriculum was implemented among new surgical prescribers at our institution between 2017 and 2022.

Comparing Rationale for Opioid Prescribing Decisions after Surgery with Subsequent Patient Consumption: A Survey of the Highest Quartile of Prescribers.

Background: Opioid prescribing patterns, including those after surgery, have been implicated as a significant contributor to the US opioid crisis. A plethora of interventions-from nudges to reminders-have been deployed to improve prescribing behavior, but reasons for persistent outlier behavior are often unknown.

Study Design: Our institution employs multiple prescribing resources and a near real-time, feedback-based intervention to promote appropriate opioid prescribing.

Evaluating Capabilities of Large Language Models: Performance of GPT4 on Surgical Knowledge Assessments.

Background: Artificial intelligence (AI) has the potential to dramatically alter healthcare by enhancing how we diagnosis and treat disease. One promising AI model is ChatGPT, a large general-purpose language model trained by OpenAI. The chat interface has shown robust, human-level performance on several professional and academic benchmarks.

How surgeons use risk calculators and non-clinical factors for informed consent and shared decision making: A qualitative study.

Background: The discussion of risks, benefits, and alternatives to surgery with patients is a defining component of informed consent. As shared-decision making has become central to surgeon-patient communication, risk calculators have emerged as a tool to aid communication and decision-making. To optimize informed consent, it is necessary to understand how surgeons assess and communicate risk, and the role of risk calculators in this process.

Artificial Intelligence-enabled Decision Support in Surgery: State-of-the-art and Future Directions.

Objective: To summarize state-of-the-art artificial intelligence-enabled decision support in surgery and to quantify deficiencies in scientific rigor and reporting.

Background: To positively affect surgical care, decision-support models must exceed current reporting guideline requirements by performing external and real-time validation, enrolling adequate sample sizes, reporting model precision, assessing performance across vulnerable populations, and achieving clinical implementation; the degree to which published models meet these criteria is unknown.

Methods: Embase, PubMed, and MEDLINE databases were searched from their inception to September 21, 2022 for articles describing artificial intelligence-enabled decision support in surgery that uses preoperative or intraoperative data elements to predict complications within 90 days of surgery.

Retention of Knowledge After Opioid Education in Surgical Interns.

Background: In many academic centers, opioid prescribing is managed primarily by residents with little or no formal opioid education. The present study evaluates intern knowledge and comfort with appropriate opioid prescribing 7 months after an organized opioid education effort.

Materials And Methods: A repeat knowledge and attitude survey was sent to surgical interns who had completed an initial opioid education training session 7 months before the study.

Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?

Background: In electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as ​​reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients.

Acute respiratory distress syndrome after SARS-CoV-2 infection on young adult population: International observational federated study based on electronic health records through the 4CE consortium.

Purpose: In young adults (18 to 49 years old), investigation of the acute respiratory distress syndrome (ARDS) after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been limited. We evaluated the risk factors and outcomes of ARDS following infection with SARS-CoV-2 in a young adult population.

Methods: A retrospective cohort study was conducted between January 1st, 2020 and February 28th, 2021 using patient-level electronic health records (EHR), across 241 United States hospitals and 43 European hospitals participating in the Consortium for Clinical Characterization of COVID-19 by EHR (4CE).