Multi-State Modeling of Pressure Injury Staging Transition Trajectories to Inform Next-Generation Clinical Decision Support.

This study aimed to evaluate the location-specific and time-sensitive trajectories of pressure injuries (PrIs) stages using real-world electronic health record (EHR) datasets. Using a dataset of 29,475 patients with records of PrIs documented from 2015 to 2023, we developed four PrI patient sub-cohorts with common PrI locations, including coccyx, buttocks, sacrum and heel. We estimated transition intensities between three PrI states: stage 1, stage 2, and a severe stage in each group.

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.

Patterns of Co-Occurring Pressure Injuries: A Data-Driven Study Using Real-World Clinical Records.

This study aimed to investigate patterns and risk factors associated with co-occurring pressure injuries (PrIs) using real-world clinical data. This retrospective cohort study analyzed electronic health records (EHRs) of adult patients with PrIs from 2015 to 2023 across five hospitals within a large U.S.

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.

Using a Healthcare Process Modeling Approach to Understand Electronic Health Records-based Pressure Injury Data and to Support Development of a Standardized Pressure Injury Phenotyping Pipeline.

The complexity of health care processes present significant challenges for using Electronic Health Records (EHR) data to build high fidelity phenotypes. This study leverages a healthcare process modeling (HPM) approach to enable understanding of EHR-based pressure injury (PrI) data patterns needed for building a standardized PrI phenotyping pipeline. The PrI HPM was developed and validated using mixed methods, including exploratory sequential design, through interdisciplinary collaboration among clinical experts, data scientists, database analysts, and informaticians.

Toward Identifying New Risk Aversions and Subsequent Limitations and Biases When Making De-identified Structured Data Sets Openly Available in a Post-LLM world.

Making clinical datasets openly available is critical to promote reproducibility and transparency of scientific research. Currently, few datasets are accessible to the public. To support the open science initiative, we plan to release the structured clinical datasets from the CONCERN study.

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.

The Optimized Use of a Contact-Free Continuous Monitoring System on Clinical Outcomes During COVID-19.

Objectives: The purpose of this study was to examine the impact of a contact-free continuous monitoring system on clinical outcomes including unplanned intensive care unit (ICU) transfer (primary), length of stay (LOS), code blue, and mortality. A secondary aim was to evaluate the return on investment associated with implementing the contact-free continuous monitoring program during the COVID public health emergency.

Methods: An interrupted time series evaluation was conducted to examine the association between the use of contact-free continuous monitoring and clinical outcomes.

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.

Identifying Barriers to The Implementation of Communicating Narrative Concerns Entered by Registered Nurses, An Early Warning System SmartApp.

Background:  Nurses are at the frontline of detecting patient deterioration. We developed Communicating Narrative Concerns Entered by Registered Nurses (CONCERN), an early warning system for clinical deterioration that generates a risk prediction score utilizing nursing data. CONCERN was implemented as a randomized clinical trial at two health systems in the Northeastern United States.

The Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) Clinical Decision Support Early Warning System: Protocol for a Cluster Randomized Pragmatic Clinical Trial.

Background: Every year, hundreds of thousands of inpatients die from cardiac arrest and sepsis, which could be avoided if those patients' risk for deterioration were detected and timely interventions were initiated. Thus, a system is needed to convert real-time, raw patient data into consumable information that clinicians can utilize to identify patients at risk of deterioration and thus prevent mortality and improve patient health outcomes. The overarching goal of the COmmunicating Narrative Concerns Entered by Registered Nurses (CONCERN) study is to implement and evaluate an early warning score system that provides clinical decision support (CDS) in electronic health record systems.

Reducing ICU Utilization, Length of Stay, and Cost by Optimizing the Clinical Use of Continuous Monitoring System Technology in the Hospital.

Background: Continuous monitoring system technology (CMST) aids in earlier detection of deterioration of hospitalized patients, but whether improved outcomes are sustainable is unknown.

Methods: This interrupted time series evaluation explored whether optimized clinical use of CMST was associated with sustained improvement in intensive care unit (ICU) utilization, hospital length of stay, cardiac arrest rates, code blue events, mortality, and cost across multiple adult acute care units.

Results: A total of 20,320 patients in the postoptimized use cohort compared with 16,781 patients in the preoptimized use cohort had a significantly reduced ICU transfer rate (1.

Identifying nursing documentation patterns associated with patient deterioration and recovery from deterioration in critical and acute care settings.

Objectives: Nursing documentation behavior within electronic health records may reflect a nurse's concern about a patient and can be used to predict patient deterioration. Our study objectives were to quantify variations in nursing documentation patterns, confirm those patterns and variations with clinicians, and identify which patterns indicate patient deterioration and recovery from clinical deterioration events in the critical and acute care settings.

Methods: We collected patient data from electronic health records and conducted a regression analysis to identify different nursing documentation patterns associated with patient outcomes resulting from clinical deterioration events in the intensive care unit (ICU) and acute care unit (ACU).

Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework.

Objective: There are signals of clinicians' expert and knowledge-driven behaviors within clinical information systems (CIS) that can be exploited to support clinical prediction. Describe development of the Healthcare Process Modeling Framework to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals).

Materials And Methods: We employed an iterative framework development approach that combined data-driven modeling and simulation testing to define and refine a process for phenotyping clinician behaviors.

An Implementation Science Approach to Promote Optimal Implementation, Adoption, Use, and Spread of Continuous Clinical Monitoring System Technology.

Objectives: This study aimed to apply implementation science tenets to guide the deployment and use of in-hospital Clinical Monitoring System Technology (CMST) and to develop a toolkit to promote optimal implementation, adoption, use, and spread of CMST.

Methods: Six steps were carried out to (1) establish leadership support; (2) identify, educate, and sustain champions; (3) enlist clinical staff users to learn barriers and facilitators; (4) examine initial qualitative data from 11 clinician group interviews; (5) validate barriers/facilitators to CMST use and toolkit content; and (6) propose a toolkit to promote utilization. Clinical Monitoring System Technology output before and after implementation were compared.