Garbage In, Garbage Out? Negative Impact of Physiological Waveform Artifacts in a Hospital Clinical Data Warehouse.

Hospitals around the world are deploying increasingly advanced systems to collect and store high-resolution physiological patient data for quality improvement and research. However, data accuracy, completeness, consistency, and contextual validity remain issues. This report highlights a data artifact known as waveform clipping in our hospital's physiological data capture system that went unnoticed for years, limiting data analysis and delaying several research projects.

Enhancement of a social risk score in the electronic health record to identify social needs among medically underserved patients: using structured data and free-text provider notes.

Objective: To improve the performance of a social risk score (a predictive risk model) using electronic health record (EHR) structured and unstructured data.

Materials And Methods: We used EPIC-based EHR data from July 2016 to June 2021 and linked it to community-level data from the US Census American Community Survey. We identified predictors of interest within the EHR structured data and applied natural language processing (NLP) techniques to identify patients' social needs in the EHR unstructured data.

Comparing ChatGPT and a Single Anesthesiologist's Responses to Common Patient Questions: An Exploratory Cross-Sectional Survey of a Panel of Anesthesiologists.

Increased patient access to electronic medical records and resources has resulted in higher volumes of health-related questions posed to clinical staff, while physicians' rising clinical workloads have resulted in less time for comprehensive, thoughtful responses to patient questions. Artificial intelligence chatbots powered by large language models (LLMs) such as ChatGPT could help anesthesiologists efficiently respond to electronic patient inquiries, but their ability to do so is unclear. A cross-sectional exploratory survey-based study comprised of 100 anesthesia-related patient question/response sets based on two fictitious simple clinical scenarios was performed.

Using machine learning to predict five-year transplant-free survival among infants with hypoplastic left heart syndrome.

Hypoplastic left heart syndrome (HLHS) is a congenital malformation commonly treated with palliative surgery and is associated with significant morbidity and mortality. Risk stratification models have often relied upon traditional survival analyses or outcomes data failing to extend beyond infancy. Individualized prediction of transplant-free survival (TFS) employing machine learning (ML) based analyses of outcomes beyond infancy may provide further valuable insight for families and healthcare providers along the course of a staged palliation.

Psychological resilience, emotional symptoms, and recovery duration in adolescent sport-related concussion.

Objective: This study aimed to explore the relation between resilience, emotional changes following injury, and recovery duration in sport-related concussion.

Methods: Thirty-one high school student-athletes (ages 14-18) with sports-related injuries (concussion,  = 17 orthopedic injury,  = 14) were recruited from a pediatric sports medicine clinic. Participants completed self-report resilience ratings and self- and parent-reported post-concussion symptoms as part of a neuropsychological test battery.

Precision Anesthesia in 2050.

Over the last few decades, the field of anesthesia has advanced far beyond its humble beginnings. Today's anesthetics are better and safer than ever, thanks to innovations in drugs, monitors, equipment, and patient safety.1-4 At the same time, we remain limited by our herd approach to medicine.

Effects of Cumulative Head Impact Exposure in Adolescent Male Contact and Collision Sport Student Athletes.

Objective: This prospective cohort study aimed to investigate the association between head impact exposure (HIE) and neuropsychological sequelae in high school football and ice hockey players over 1 year.

Setting: Community sample.

Participants: A cohort of 52 adolescent American football and ice hockey players were enrolled in the study, with a final study sample of 35 included in analyses.

Application of natural language processing to identify social needs from patient medical notes: development and assessment of a scalable, performant, and rule-based model in an integrated healthcare delivery system.

Objectives: To develop and test a scalable, performant, and rule-based model for identifying 3 major domains of social needs (residential instability, food insecurity, and transportation issues) from the unstructured data in electronic health records (EHRs).

Materials And Methods: We included patients aged 18 years or older who received care at the Johns Hopkins Health System (JHHS) between July 2016 and June 2021 and had at least 1 unstructured (free-text) note in their EHR during the study period. We used a combination of manual lexicon curation and semiautomated lexicon creation for feature development.

Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects.

Machine vision describes the use of artificial intelligence to interpret, analyze, and derive predictions from image or video data. Machine vision-based techniques are already in clinical use in radiology, ophthalmology, and dermatology, where some applications currently equal or exceed the performance of specialty physicians in areas of image interpretation. While machine vision in anesthesia has many potential applications, its development remains in its infancy in our specialty.

Survival analysis for pediatric heart transplant patients using a novel machine learning algorithm: A UNOS analysis.

Background: Impact of pretransplantation risk factors on mortality in the first year after heart transplantation remains largely unknown. Using machine learning algorithms, we selected clinically relevant identifiers that could predict 1-year mortality after pediatric heart transplantation.

Methods: Data were obtained from the United Network for Organ Sharing Database for years 2010-2020 for patients 0-17 years receiving their first heart transplant (N = 4150).

A machine-learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset.

Background: Pediatric anesthesia has evolved to a high level of patient safety, yet a small chance remains for serious perioperative complications, even in those traditionally considered at low risk. In practice, prediction of at-risk patients currently relies on the American Society of Anesthesiologists Physical Status (ASA-PS) score, despite reported inconsistencies with this method.

Aims: The goal of this study was to develop predictive models that can classify children as low risk for anesthesia at the time of surgical booking and after anesthetic assessment on the procedure day.

Longitudinal Outcomes of Cumulative Impact Exposure on Oculomotor Functioning in Professional Motorsport Drivers.

Importance: Professional motorsport drivers are regularly exposed to biomechanical forces comparable with those experienced by contact and collision sport athletes, and little is known about the potential short-term and long-term neurologic sequelae.

Objective: To determine whether cumulative impact exposure is associated with oculomotor functioning in motorsport drivers from the INDYCAR professional open-wheel automobile racing series.

Design, Setting, And Participants: This is a longitudinal retrospective cohort study conducted across 3 racing seasons (2017-2019).

How do we measure organisational wellness? Development of a comprehensive patient-centred and employee-centred visual analytical solution.

Background: Dashboards are visual information systems frequently employed by healthcare organisations to track key quality improvement and patient safety performance metrics. The typical healthcare dashboard focuses on specific metrics, disease processes or units within a larger healthcare organisation. Here, we describe the development of a visual analytical solution (keystone dashboard) for monitoring an entire healthcare organisation.

Examining Implicit Bias Differences in Pediatric Surgical Fellowship Letters of Recommendation Using Natural Language Processing.

Objective: We analyzed the prevalence and type of bias in letters of recommendation (LOR) for pediatric surgical fellowship applications from 2016-2021 using natural language processing (NLP) at a quaternary care academic hospital.

Design: Demographics were extracted from submitted applications. The Valence Aware Dictionary for sEntiment Reasoning (VADER) model was used to calculate polarity scores.

Performance Characteristics of a Machine-Learning Tool to Predict 7-Day Hospital Readmissions.

Objectives: To develop an institutional machine-learning (ML) tool that utilizes demographic, socioeconomic, and medical information to stratify risk for 7-day readmission after hospital discharge; assess the validity and reliability of the tool; and demonstrate its discriminatory capacity to predict readmissions.

Patients And Methods: We performed a combined single-center, cross-sectional, and prospective study of pediatric hospitalists assessing the face and content validity of the developed readmission ML tool. The cross-sectional analyses used data from questionnaire Likert scale responses regarding face and content validity.

Feasibility of Conducting Long-term Health and Behaviors Follow-up in Adolescents: Longitudinal Observational Study.

Background: Machine learning uses algorithms that improve automatically through experience. This statistical learning approach is a natural extension of traditional statistical methods and can offer potential advantages for certain problems. The feasibility of using machine learning techniques in health care is predicated on access to a sufficient volume of data in a problem space.

Assessing Preseason Baseline Cognitive Functioning in High School Student Athletes: A "Hybrid" Approach.

Objective: International consensus statements highlight the value of neuropsychological testing for sport-related concussion. Computerized measures are the most frequently administered assessments of pre-injury baseline and post-injury cognitive functioning, despite known measurement limitations. To our knowledge, no studies have explored the convergent validity of computerized Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) and traditional, well-validated paper and pencil (P&P) neuropsychological tests in high school student athletes.

Impact of Coronavirus-2019 On Pediatric and Adult Heart Transplantation Waitlist Activity and Mortality in The United States: A Descriptive Approach.

Background: Transplant centers saw a substantial reduction in deceased donor solid organ transplantation since the beginning of the coronavirus 2019 (COVID-19) pandemic in the United States. There is limited data on the impact of COVID-19 on adult and pediatric heart transplant volume and variation in transplant practices. We hypothesized that heart transplant activity decreased during COVID-19 with associated increased waitlist mortality.

Socioeconomic inequalities in low birth weight risk before and during the COVID-19 pandemic in Argentina: A cross-sectional study.

Background: The coronavirus disease 2019 (COVID-19) pandemic may have exacerbated existing socioeconomic inequalities in health. In Argentina, public hospitals serve the poorest uninsured segment of the population, while private hospitals serve patients with health insurance. This study aimed to assess whether socioeconomic inequalities in low birth weight (LBW) risk changed during the first wave of the COVID-19 pandemic.

"P": an adaptive modeling tool for post-COVID-19 restart of surgical services.

Objective: To develop a predictive analytics tool that would help evaluate different scenarios and multiple variables for clearance of surgical patient backlog during the COVID-19 pandemic.

Materials And Methods: Using data from 27 866 cases (May 1 2018-May 1 2020) stored in the Johns Hopkins All Children's data warehouse and inputs from 30 operations-based variables, we built mathematical models for (1) time to clear the case backlog (2), utilization of personal protective equipment (PPE), and (3) assessment of overtime needs.

Results: The tool enabled us to predict desired variables, including number of days to clear the patient backlog, PPE needed, staff/overtime needed, and cost for different backlog reduction scenarios.

[Interdisciplinary approach to endogenous neonatal endophthalmitis. Case report].

Neonatal endogenous endophthalmitis is a rare condition that can cause serious eye injuries. It can manifest in patients with comorbidities, such as preterm birth, low birth weight, postsurgical perinatal complications, or sepsis. This case report documents a preterm patient who underwent multiple abdominal surgeries.

Audit of blood product utilization in the care of injured children.

Background: Blood product utilization in injured children is poorly characterized; the decision to prepare products or transfuse patients can be difficult due to a lack of reliable evidence of transfusion needs across pediatric age-groups and injury types. We conducted an audit of transfusion practices in pediatric trauma based on age, injuries, and mechanism of injury.

Methods: We reviewed and cross-referenced blood product transfusion practice data from the trauma registry and the anesthesia transfusion record database at a level 1 pediatric trauma center over a 10-year period.