Developing and Evaluating Large Language Model-Generated Emergency Medicine Handoff Notes.

Importance: An emergency medicine (EM) handoff note generated by a large language model (LLM) has the potential to reduce physician documentation burden without compromising the safety of EM-to-inpatient (IP) handoffs.

Objective: To develop LLM-generated EM-to-IP handoff notes and evaluate their accuracy and safety compared with physician-written notes.

Design, Setting, And Participants: This cohort study used EM patient medical records with acute hospital admissions that occurred in 2023 at NewYork-Presbyterian/Weill Cornell Medical Center.

Linking Patient Encounters across Primary and Ancillary Electronic Health Record Systems: A Comparison of Two Approaches.

Background: To achieve scientific goals, researchers often require integration of data from a primary electronic health record (EHR) system and one or more ancillary EHR systems used during the same patient care encounter. Although studies have demonstrated approaches for linking patient identity records across different EHR systems, little is known about linking patient encounter records across primary and ancillary EHR systems.

Objectives: We compared a patients-first approach versus an encounters-first approach for linking patient encounter records across multiple EHR systems.

Comparative Merits of Available Mortality Data Sources for Clinical Research.

Obtaining reliable data on patient mortality is a critical challenge facing observational researchers seeking to conduct studies using real-world data. As these analyses are conducted more broadly using newly-available sources of real-world evidence, missing data can serve as a rate-limiting factor. We conducted a comparison of mortality data sources from different stakeholder perspectives - academic medical center (AMC) informatics service providers, AMC research coordinators, industry analytics professionals, and academics - to understand the strengths and limitations of differing mortality data sources: locally generated data from sites conducting research, data provided by governmental sources, and commercially available data sets.

Implementation of a commercial federated network of electronic health record data to enable sponsor-initiated clinical trials at an academic medical center.

Background: A commercial federated network called TriNetX has connected electronic health record (EHR) data from academic medical centers (AMCs) with biopharmaceutical sponsors in a privacy-preserving manner to promote sponsor-initiated clinical trials. Little is known about how AMCs have implemented TriNetX to support clinical trials.

Findings: At our AMC over a six-year period, TriNetX integrated into existing institutional workflows enabled 402 requests for sponsor-initiated clinical trials, 14 % (n = 56) of which local investigators expressed interest in conducting.

Liver Severity Score-Based Modeling to Predict Six-Week Mortality Risk Among Hospitalized Cirrhosis Patients With Upper Gastrointestinal Bleeding.

Background: Patients with cirrhosis who have gastrointestinal bleeding have high short-term mortality, but the best modality for risk calculation remains in debate. Liver severity indices, such as Child-Turcotte-Pugh (CTP) and Model-for-End-Stage-Liver Disease (MELD) score, are well-studied in portal hypertensive bleeding, but there is a paucity of data confirming their accuracy in non-portal hypertensive bleeding and overall acute upper gastrointestinal bleeding (UGIB), unrelated to portal hypertension.

Aims: This study aims to better understand the accuracy of current mortality risk calculators in predicting mortality for patients with any type of UGIB, which could allow for earlier risk stratification and targeted intervention prior to endoscopy to identify the bleeding source.

A method to automate the discharge summary hospital course for neurology patients.

Objective: Generation of automated clinical notes has been posited as a strategy to mitigate physician burnout. In particular, an automated narrative summary of a patient's hospital stay could supplement the hospital course section of the discharge summary that inpatient physicians document in electronic health record (EHR) systems. In the current study, we developed and evaluated an automated method for summarizing the hospital course section using encoder-decoder sequence-to-sequence transformer models.

Impact of Race and Neighborhood Socioeconomic Characteristics on Liver Cancer Diagnosis in Patients with Viral Hepatitis and Cirrhosis.

Background: Concerning data have revealed that viral hepatitis and hepatocellular carcinoma (HCC) disproportionally impact non-White patients and those from lower socioeconomic status. A recent study found that HCC clusters were more likely to be in high poverty areas in New York City.

Aims: We aim to investigate the impacts of neighborhood characteristics on those with viral hepatitis and cirrhosis, particularly with advanced HCC diagnosis.

Design and validation of a FHIR-based EHR-driven phenotyping toolbox.

Objectives: To develop and validate a standards-based phenotyping tool to author electronic health record (EHR)-based phenotype definitions and demonstrate execution of the definitions against heterogeneous clinical research data platforms.

Materials And Methods: We developed an open-source, standards-compliant phenotyping tool known as the PhEMA Workbench that enables a phenotype representation using the Fast Healthcare Interoperability Resources (FHIR) and Clinical Quality Language (CQL) standards. We then demonstrated how this tool can be used to conduct EHR-based phenotyping, including phenotype authoring, execution, and validation.

Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient Mortality.

Utilizing clinical texts in survival analysis is difficult because they are largely unstructured. Current automatic extraction models fail to capture textual information comprehensively since their labels are limited in scope. Furthermore, they typically require a large amount of data and high-quality expert annotations for training.

Prolonged Unconsciousness is Common in COVID-19 and Associated with Hypoxemia.

Objective: The purpose of this study was to estimate the time to recovery of command-following and associations between hypoxemia with time to recovery of command-following.

Methods: In this multicenter, retrospective, cohort study during the initial surge of the United States' pandemic (March-July 2020) we estimate the time from intubation to recovery of command-following, using Kaplan Meier cumulative-incidence curves and Cox proportional hazard models. Patients were included if they were admitted to 1 of 3 hospitals because of severe coronavirus disease 2019 (COVID-19), required endotracheal intubation for at least 7 days, and experienced impairment of consciousness (Glasgow Coma Scale motor score <6).

An architecture for research computing in health to support clinical and translational investigators with electronic patient data.

Objective: Obtaining electronic patient data, especially from electronic health record (EHR) systems, for clinical and translational research is difficult. Multiple research informatics systems exist but navigating the numerous applications can be challenging for scientists. This article describes Architecture for Research Computing in Health (ARCH), our institution's approach for matching investigators with tools and services for obtaining electronic patient data.

Comparing automated vs. manual data collection for COVID-specific medications from electronic health records.

Introduction: Data extraction from electronic health record (EHR) systems occurs through manual abstraction, automated extraction, or a combination of both. While each method has its strengths and weaknesses, both are necessary for retrospective observational research as well as sudden clinical events, like the COVID-19 pandemic. Assessing the strengths, weaknesses, and potentials of these methods is important to continue to understand optimal approaches to extracting clinical data.

Automated Extraction of Tumor Staging and Diagnosis Information From Surgical Pathology Reports.

Purpose: Typically stored as unstructured notes, surgical pathology reports contain data elements valuable to cancer research that require labor-intensive manual extraction. Although studies have described natural language processing (NLP) of surgical pathology reports to automate information extraction, efforts have focused on specific cancer subtypes rather than across multiple oncologic domains. To address this gap, we developed and evaluated an NLP method to extract tumor staging and diagnosis information across multiple cancer subtypes.

Hyperglycemia in acute COVID-19 is characterized by insulin resistance and adipose tissue infectivity by SARS-CoV-2.

Individuals infected with SARS-CoV-2 who also display hyperglycemia suffer from longer hospital stays, higher risk of developing acute respiratory distress syndrome (ARDS), and increased mortality. Nevertheless, the pathophysiological mechanism of hyperglycemia in COVID-19 remains poorly characterized. Here, we show that hyperglycemia is similarly prevalent among patients with ARDS independent of COVID-19 status.

Critical carE Database for Advanced Research (CEDAR): An automated method to support intensive care units with electronic health record data.

Patients treated in an intensive care unit (ICU) are critically ill and require life-sustaining organ failure support. Existing critical care data resources are limited to a select number of institutions, contain only ICU data, and do not enable the study of local changes in care patterns. To address these limitations, we developed the Critical carE Database for Advanced Research (CEDAR), a method for automating extraction and transformation of data from an electronic health record (EHR) system.

Closed-loop vagal nerve stimulation for intractable epilepsy: A single-center experience.

Purpose: A new class of heart-rate sensing, closed-loop vagal nerve stimulator (VNS) devices for refractory epilepsy may improve seizure control by using pre-ictal autonomic changes as an indicator for stimulation. We compared our experience with closed- versus open-loop stimulator implantation at a single institution.

Methods: We conducted a retrospective chart review of consecutive VNS implantations performed from 2004 to 2018.

Hyperglycemia in Acute COVID-19 is Characterized by Adipose Tissue Dysfunction and Insulin Resistance.

COVID-19 has proven to be a metabolic disease resulting in adverse outcomes in individuals with diabetes or obesity. Patients infected with SARS-CoV-2 and hyperglycemia suffer from longer hospital stays, higher risk of developing acute respiratory distress syndrome (ARDS), and increased mortality compared to those who do not develop hyperglycemia. Nevertheless, the pathophysiological mechanism(s) of hyperglycemia in COVID-19 remains poorly characterized.

Shotgun transcriptome, spatial omics, and isothermal profiling of SARS-CoV-2 infection reveals unique host responses, viral diversification, and drug interactions.

In less than nine months, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) killed over a million people, including >25,000 in New York City (NYC) alone. The COVID-19 pandemic caused by SARS-CoV-2 highlights clinical needs to detect infection, track strain evolution, and identify biomarkers of disease course. To address these challenges, we designed a fast (30-minute) colorimetric test (LAMP) for SARS-CoV-2 infection from naso/oropharyngeal swabs and a large-scale shotgun metatranscriptomics platform (total-RNA-seq) for host, viral, and microbial profiling.

A Method to Improve Availability and Quality of Patient Race Data in an Electronic Health Record System.

Background: Although federal regulations mandate documentation of structured race data according to Office of Management and Budget (OMB) categories in electronic health record (EHR) systems, many institutions have reported gaps in EHR race data that hinder secondary use for population-level research focused on underserved populations. When evaluating race data available for research purposes, we found our institution's enterprise EHR contained structured race data for only 51% (1.6 million) of patients.

Using electronic health records for population health sciences: a case study to evaluate the associations between changes in left ventricular ejection fraction and the built environment.

Objective: Electronic health record (EHR) data linked with address-based metrics using geographic information systems (GIS) are emerging data sources in population health studies. This study examined this approach through a case study on the associations between changes in ejection fraction (EF) and the built environment among heart failure (HF) patients.

Materials And Methods: We identified 1287 HF patients with at least 2 left ventricular EF measurements that are minimally 1 year apart.

Utilizing electronic health data and machine learning for the prediction of 30-day unplanned readmission or all-cause mortality in heart failure.

Background: Existing risk assessment tools for heart failure (HF) outcomes use structured databases with static, single-timepoint clinical data and have limited accuracy.

Objective: The purpose of this study was to develop a comprehensive approach for accurate prediction of 30-day unplanned readmission and all-cause mortality (ACM) that integrates clinical and physiological data available in the electronic health record system.

Methods: Three predictive models for 30-day unplanned readmissions or ACM were created using an extreme gradient boosting approach: (1) index admission model; (2) index discharge model; and (3) feature-aggregated model.