Understanding Clinicians' Usage Patterns of the CONCERN Early Warning System: Insights from a Multi-Site Pragmatic Cluster Randomized Controlled Trial.

The CONCERN Early Warning System (EWS) uses artificial intelligence (AI) to analyze nursing documentation patterns, predicting hospitalized patients' risk of clinical deterioration. It generates real-time risk scores displayed on the electronic health record (EHR) interface for the inpatient care team, enhancing situational awareness and supporting timely interventions. A recent multi-site pragmatic cluster randomized controlled trial demonstrated a 35.

Evaluating Equity in Usage and Effectiveness of the CONCERN Early Warning System.

The CONCERN Early Warning System (CONCERN EWS) is an artificial intelligence-based clinical decision support system (AI-CDSS) for the prediction of clinical deterioration, leveraging signals from nursing documentation patterns. While a recent multisite randomized controlled trial (RCT) demonstrated its effectiveness in reducing inpatient mortality and length of stay, evaluating implementation outcomes is essential to ensure equitable results across patient populations.This study aims to (1) assess whether clinicians' usage of the CONCERN EWS, as measured by CONCERN Detailed Prediction Screen launches, varied by patient demographic characteristics, including sex, race, ethnicity, and primary language; (2) evaluate whether CONCERN EWS's effectiveness in reducing the risk of in-hospital mortality varied across patient demographic groups.

Influence of the CONCERN Early Warning System on Unanticipated ICU Transfers, In-Hospital Mortality, and Length of Stay: Results from a Multi-site Pragmatic Randomized Controlled Clinical Trial.

Communicating Narrative Concerns Entered by RNs Early Warning System (CONCERN EWS) is a machine-learning predictive model that leverages nursing surveillance documentation patterns to predict deterioration risks for hospitalized patients. In a retrospective cohort study of 1,013 hospital encounters with unanticipated ICU transfers from a multi-site pragmatic randomized controlled trial, we assessed the influence of CONCERN EWS on in-hospital mortality and length of stay following unanticipated ICU transfers. Chi-square tests, t-tests, multivariate logistic regression, and generalized linear models were used.

Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.

The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify deterioration risk. We conducted a 1-year, multisite, pragmatic trial with cluster-randomization of 74 clinical units (37 intervention; 37 usual care) across 2 health systems. Eligible adult hospital encounters were included.

Relationships Between Recent Adverse Childhood Experiences (ACEs) and Somatic Symptoms in Adolescence.

Early identification and interventions are imperative for mitigating the harmful effects of adverse childhood experiences (ACEs). Nonetheless, a substantial barrier persists in identifying adolescents experiencing ACEs. One understudied avenue for early identification of ACEs is through the examination of somatic symptoms endorsed by adolescents.

Estimating the Prevalence of Child Abuse and Neglect Among Adolescents in Primary Care Through Diagnoses Codes and Free-Text EHR Clinical Notes.

Introduction: Adolescents' child abuse and neglect experiences are often under-documented in primary care, leading to missed opportunities for interventions. This study compares the prevalence of child abuse and neglect cases identified by diagnostic codes versus a natural language processing approach of clinical notes.

Method: We retrospectively analyzed data from 8,157 adolescents, using ICD-10 codes and a natural language processing algorithm to identify child abuse and neglect cases and applied topic modeling on clinical notes to extract prevalent topics.

Designing and testing clinical simulations of an early warning system for implementation in acute care settings.

Objectives: Conducting simulation testing with end-users is essential for facilitating successful implementation of new health information technologies. This study designed a standardized simulation testing process with a system prototype prior to implementation to help study teams identify the system's interpretability and feasibility from the end-user perspective and to effectively integrate new innovations into real-world clinical settings and workflows.

Materials And Methods: A clinical simulation model was developed to test a new Clinical Decision Support (CDS) system outside of the clinical environment while maintaining high fidelity.

Variability in Nursing Documentation Patterns across Patients' Hospital Stays.

This study explores the variability in nursing documentation patterns in acute care and ICU settings, focusing on vital signs and note documentation, and examines how these patterns vary across patients' hospital stays, documentation types, and comorbidities. In both acute care and critical care settings, there was significant variability in nursing documentation patterns across hospital stays, by documentation type, and by patients' comorbidities. The results suggest that nurses adapt their documentation practices in response to their patients' fluctuating needs and conditions, highlighting the need to facilitate more individualized care and tailored documentation practices.

A Computational Framework to Evaluate Emergency Department Clinician Task Switching in the Electronic Health Record Using Event Logs.

Workflow fragmentation, defined as task switching, may be one proxy to quantify electronic health record (EHR) documentation burden in the emergency department (ED). Few measures have been operationalized to evaluate task switching at scale. Theoretically grounded in the time-based resource-sharing model (TBRSM) which conceives task switching as proportional to the cognitive load experienced, we describe the functional relationship between cognitive load and the time and effort constructs previously applied for measuring documentation burden.

Improving Patient-Reported Outcomes in Stroke Care using Remote Blood Pressure Monitoring and Telehealth.

Background: Inequities in health care access leads to suboptimal medication adherence and blood pressure (BP) control. Informatics-based approaches may deliver equitable care and enhance self-management. Patient-reported outcomes (PROs) complement clinical measures to assess the impact of illness on patients' well-being in poststroke care.

Using Time Series Clustering to Segment and Infer Emergency Department Nursing Shifts from Electronic Health Record Log Files.

Few computational approaches exist for abstracting electronic health record (EHR) log files into clinically meaningful phenomena like clinician shifts. Because shifts are a fundamental unit of work recognized in clinical settings, shifts may serve as a primary unit of analysis in the study of documentation burden. We conducted a proof- of-concept study to investigate the feasibility of a novel approach using time series clustering to segment and infer clinician shifts from EHR log files.

Understanding the perceived role of electronic health records and workflow fragmentation on clinician documentation burden in emergency departments.

Objective: Understand the perceived role of electronic health records (EHR) and workflow fragmentation on clinician documentation burden in the emergency department (ED).

Methods: From February to June 2022, we conducted semistructured interviews among a national sample of US prescribing providers and registered nurses who actively practice in the adult ED setting and use Epic Systems' EHR. We recruited participants through professional listservs, social media, and email invitations sent to healthcare professionals.

Use of Real-Time Information to Predict Future Arrivals in the Emergency Department.

Study Objective: We aimed to build prediction models for shift-level emergency department (ED) patient volume that could be used to facilitate prediction-driven staffing. We sought to evaluate the predictive power of rich real-time information and understand 1) which real-time information had predictive power and 2) what prediction techniques were appropriate for forecasting ED demand.

Methods: We conducted a retrospective study in an ED site in a large academic hospital in New York City.

A Scoping Review of Integrated Medical Devices and Clinical Decision Support in the Acute Care Setting.

Background: Seamless data integration between point-of-care medical devices and the electronic health record (EHR) can be central to clinical decision support systems (CDSS).

Objective: The objective of this scoping review is to (1) examine the existing evidence related to integrated medical devices, primarily medication pump devices, and associated clinical decision support (CDS) in acute care settings and (2) to identify how acute care clinicians may use device CDS in clinical decision-making. The rationale for this review is that integrated devices are ubiquitous in the acute care setting, and they generate data that may help to contribute to the situational awareness of the clinical team necessary to provide individualized patient care.

Primary Care Nurse Practitioner Work Environments and Hospitalizations and ED Use Among Chronically Ill Medicare Beneficiaries.

Background: Nurse practitioners (NPs) play a critical role in delivering primary care, particularly to chronically ill elderly. Yet, many NPs practice in poor work environments which may affect patient outcomes.

Objective: We investigated the relationship between NP work environments in primary care practices and hospitalizations and emergency department (ED) use among chronically ill elderly.

Factors Influencing Clinician Trust in Predictive Clinical Decision Support Systems for In-Hospital Deterioration: Qualitative Descriptive Study.

Background: Clinician trust in machine learning-based clinical decision support systems (CDSSs) for predicting in-hospital deterioration (a type of predictive CDSS) is essential for adoption. Evidence shows that clinician trust in predictive CDSSs is influenced by perceived understandability and perceived accuracy.

Objective: The aim of this study was to explore the phenomenon of clinician trust in predictive CDSSs for in-hospital deterioration by confirming and characterizing factors known to influence trust (understandability and accuracy), uncovering and describing other influencing factors, and comparing nurses' and prescribing providers' trust in predictive CDSSs.

The mediating role of anxiety/depression symptoms between adverse childhood experiences (ACEs) and somatic symptoms in adolescents.

Introduction: This study examines the relationships among recent adverse childhood experiences (ACEs), somatic symptoms, and anxiety/depression symptoms during adolescence and whether anxiety/depression symptoms mediate the relationship between ACEs and somatic symptoms.

Methods: Longitudinal prospective data from the Longitudinal Studies of Child Abuse and Neglect study of 1354 children and their primary caregivers in the United States was used in this study. A longitudinal cross-lagged path analysis among recent ACEs, anxiety/depression symptoms, and somatic symptoms at three points during adolescence (ages 12, 14, and 16 years) was conducted.

Clinician and Health Care Leaders' Experiences with-and Perceptions of-COVID-19 Documentation Reduction Policies and Practices.

Background: Substantial strategies to reduce clinical documentation were implemented by health care systems throughout the coronavirus disease-2019 (COVID-19) pandemic at national and local levels. This natural experiment provides an opportunity to study the impact of documentation reduction strategies on documentation burden among clinicians and other health professionals in the United States.

Objectives: The aim of this study was to assess clinicians' and other health care leaders' experiences with and perceptions of COVID-19 documentation reduction strategies and identify which implemented strategies should be prioritized and remain permanent post-pandemic.

Characterizing Multitasking and Workflow Fragmentation in Electronic Health Records among Emergency Department Clinicians: Using Time-Motion Data to Understand Documentation Burden.

Background: The impact of electronic health records (EHRs) in the emergency department (ED) remains mixed. Dynamic and unpredictable, the ED is highly vulnerable to workflow interruptions.

Objectives: The aim of the study is to understand multitasking and task fragmentation in the clinical workflow among ED clinicians using clinical information systems (CIS) through time-motion study (TMS) data, and inform their applications to more robust and generalizable measures of CIS-related documentation burden.