Universal clinical decision support tool for thromboprophylaxis in hospitalized COVID-19 patients: post hoc analysis of the IMPROVE-DD cluster randomized trial.

Background: Inpatient and extended postdischarge thromboprophylaxis of COVID-19 patients remains suboptimal despite antithrombotic guidelines.

Objectives: To determine whether a novel electronic health record-agnostic clinical decision support (CDS) tool incorporating the International Medical Prevention Registry on Venous Thromboembolism plus D-dimer (IMPROVE-DD) venous thromboembolism (VTE) scores increases appropriate inpatient and extended postdischarge thromboprophylaxis and improves outcomes in COVID-19 inpatients.

Methods: This post hoc analysis of the IMPROVE-DD cluster randomized trial evaluated thromboprophylaxis CDS among COVID-19 inpatients at 4 New York hospitals between December 21, 2020, and January 21, 2022.

Effect of a behavioral nudge on adoption of an electronic health record-agnostic pulmonary embolism risk prediction tool: a pilot cluster nonrandomized controlled trial.

Objective: Our objective was to determine the feasibility and preliminary efficacy of a behavioral nudge on adoption of a clinical decision support (CDS) tool.

Materials And Methods: We conducted a pilot cluster nonrandomized controlled trial in 2 Emergency Departments (EDs) at a large academic healthcare system in the New York metropolitan area. We tested 2 versions of a CDS tool for pulmonary embolism (PE) risk assessment developed on a web-based electronic health record-agnostic platform.

Universal EHRs Clinical Decision Support for Thromboprophylaxis in Medical Inpatients: A Cluster Randomized Trial.

Background: Thromboprophylaxis for medically ill patients during hospitalization and postdischarge remains underutilized. Clinical decision support (CDS) may address this need if embedded within workflow, interchangeable among electronic health records (EHRs), and anchored on a validated model.

Objectives: The purpose of this study was to assess the clinical impact of a universal EHR-integrated CDS tool based on the International Medical Prevention Registry on Venous Thromboembolism plus D-Dimer venous thromboembolism model.

User-Centered Design of a Preference-Driven Patient Activation Tool for Optimizing Depression Treatment in Integrated Primary Care Settings (The Transform DepCare Study).

Background: Few patient engagement tools incorporate the complex patient experiences, contexts, and workflows that limit depression treatment implementation.

Objective: Describe a user-centered design (UCD) process for operationalizing a preference-driven patient activation tool.

Design: Informed by UCD and behavior change/implementation science principles, we designed a preference-driven patient activation prototype for engaging patients in depression treatment.

Reducing prescribing of antibiotics for acute respiratory infections using a frontline nurse-led EHR-Integrated clinical decision support tool: protocol for a stepped wedge randomized control trial.

Background: Overprescribing of antibiotics for acute respiratory infections (ARIs) remains a major issue in outpatient settings. Use of clinical prediction rules (CPRs) can reduce inappropriate antibiotic prescribing but they remain underutilized by physicians and advanced practice providers. A registered nurse (RN)-led model of an electronic health record-integrated CPR (iCPR) for low-acuity ARIs may be an effective alternative to address the barriers to a physician-driven model.

Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study.

Background: Through our work, we have demonstrated how clinical decision support (CDS) tools integrated into the electronic health record (EHR) assist providers in adopting evidence-based practices. This requires confronting technical challenges that result from relying on the EHR as the foundation for tool development; for example, the individual CDS tools need to be built independently for each different EHR.

Objective: The objective of our research was to build and implement an EHR-agnostic platform for integrating CDS tools, which would remove the technical constraints inherent in relying on the EHR as the foundation and enable a single set of CDS tools that can work with any EHR.

The design of studies testing the effectiveness of risk-guided care has many challenges: a scoping review addressing key considerations.

Objectives: Studies evaluating the effectiveness of care based on patients' risk of adverse outcomes (risk-guided care) use a variety of study designs. In this scoping review, using examples, we review characteristics of relevant studies and present key design features to optimize the trustworthiness of results.

Study Design And Setting: We searched five online databases for studies evaluating the effect of risk-guided care among adults on clinical outcomes, process, or cost.

Disseminating child abuse clinical decision support among commercial electronic health records: Effects on clinical practice.

Objectives: The use of electronic health record (EHR)-embedded child abuse clinical decision support (CA-CDS) may help decrease morbidity from child maltreatment. We previously reported on the development of CA-CDS in Epic and Allscripts. The objective of this study was to implement CA-CDS into Epic and Allscripts and determine its effects on identification, evaluation, and reporting of suspected child maltreatment.

Nudging Health Care Providers' Adoption of Clinical Decision Support: Protocol for the User-Centered Development of a Behavioral Economics-Inspired Electronic Health Record Tool.

Background: The improvements in care resulting from clinical decision support (CDS) have been significantly limited by consistently low health care provider adoption. Health care provider attitudes toward CDS, specifically psychological and behavioral barriers, are not typically addressed during any stage of CDS development, although they represent an important barrier to adoption. Emerging evidence has shown the surprising power of using insights from the field of behavioral economics to address psychological and behavioral barriers.

Human-centered design development of mHealth patient-to-peer referral tool in the emergency department.

Background: Given the steady increase of emergency department (ED) visits related to opioid overdoses, this study aims to determine the design and usability of an ED-centered mHealth patient-to-peer referral prototype tool that allows patients to refer peers to comprehensive HIV/HCV and opioid misuse prevention services.

Methods: Two iterative focus group discussion (FDG) sessions and one use-case session were conducted. Eligible participants who were ≥18 years, had a history of injection drug use (IDU), and had utilized the ED in the past year were recruited through the distribution of flyers at the study institution, including the study ED.

A pilot study of a mixed-method approach to design an ED-based peer mHealth referral tool for HIV/HCV and opioid overdose prevention services.

Background: The intersecting epidemics of opioid misuse, injection drug use, and HIV/HCV have resulted in record overdose deaths and sustained high levels of HIV/HCV transmissions. Literature on social networks suggests opportunities to connect people who use drugs (PWUD) and their peers to HIV/HCV and opioid overdose prevention services. However, little evidence exists on how to design such peer referral interventions in emergency department (ED) settings.

Comparison of Chest Radiograph Impressions for Diagnosing Pneumonia: Accounting for Categories of Language Certainty.

Objectives: Uncertain language in chest radiograph (CXR) reports for the diagnosis of pneumonia is prevalent. The purpose of this study is to validate an a priori stratification of CXR results for diagnosing pneumonia based on language of certainty.

Design: Retrospective chart review.

Fulminant and Non-fulminant Clinical COVID-19 Myocarditis in the New York City Area in 2020.

Background: COVID-19 myocarditis is becoming increasingly appreciated as a complication of COVID-19. There are significant hurdles to formal diagnosis with endomyocardial biopsy or cardiac MRI, whether by resource limitations, patient instability, or isolation precautions. Therefore, further exploratory analysis is needed to clinically define the characteristics and spectrum of severity of COVID-19 myocarditis.

Automated Pulmonary Embolism Risk Assessment Using the Wells Criteria: Validation Study.

Background: Computed tomography pulmonary angiography (CTPA) is frequently used in the emergency department (ED) for the diagnosis of pulmonary embolism (PE), while posing risk for contrast-induced nephropathy and radiation-induced malignancy.

Objective: We aimed to create an automated process to calculate the Wells score for pulmonary embolism for patients in the ED, which could potentially reduce unnecessary CTPA testing.

Methods: We designed an automated process using electronic health records data elements, including using a combinatorial keyword search method to query free-text fields, and calculated automated Wells scores for a sample of all adult ED encounters that resulted in a CTPA study for PE at 2 tertiary care hospitals in New York, over a 2-month period.

Comparison of Guidelines for Evaluation of Suspected Pulmonary Embolism in Pregnancy: A Cost-effectiveness Analysis.

Background: Pulmonary embolism (PE) remains a leading cause of maternal mortality, yet diagnosis remains challenging. International diagnostic guidelines vary significantly in their recommendations, making it difficult to determine an optimal policy for evaluation.

Research Question: Which societal-level diagnostic guidelines for evaluation of suspected PE in pregnancy are an optimal policy in terms of its cost-effectiveness?

Study Design And Methods: We constructed a complex Markov decision model to evaluate the cost-effectiveness of each identified societal guidelines for diagnosis of PE in pregnancy.

Pulse-shape discrimination against low-energy Ar-39 beta decays in liquid argon with 4.5 tonne-years of DEAP-3600 data.

The DEAP-3600 detector searches for the scintillation signal from dark matter particles scattering on a 3.3 tonne liquid argon target. The largest background comes from beta decays and is suppressed using pulse-shape discrimination (PSD).

Barriers to the Use of Clinical Decision Support for the Evaluation of Pulmonary Embolism: Qualitative Interview Study.

Background: Clinicians often disregard potentially beneficial clinical decision support (CDS).

Objective: In this study, we sought to explore the psychological and behavioral barriers to the use of a CDS tool.

Methods: We conducted a qualitative study involving emergency medicine physicians and physician assistants.

In-Hospital 30-Day Survival Among Young Adults With Coronavirus Disease 2019: A Cohort Study.

Background: Our objective was to characterize young adult patients hospitalized with coronavirus disease 2019 (COVID-19) and identify predictors of survival at 30 days.

Methods: This retrospective cohort study took place at 12 acute care hospitals in the New York City area. Patients aged 18-39 hospitalized with confirmed COVID-19 between March 1 and April 27, 2020 were included in the study.

External validation of the IMPROVE-DD risk assessment model for venous thromboembolism among inpatients with COVID-19.

There is a need to discriminate which COVID-19 inpatients are at higher risk for venous thromboembolism (VTE) to inform prophylaxis strategies. The IMPROVE-DD VTE risk assessment model (RAM) has previously demonstrated good discrimination in non-COVID populations. We aimed to externally validate the IMPROVE-DD VTE RAM in medical patients hospitalized with COVID-19.

A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation.

Background: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease.

Objective: Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department.

Prevalence and Predictors of Venous Thromboembolism or Mortality in Hospitalized COVID-19 Patients.

Background: We aimed to identify the prevalence and predictors of venous thromboembolism (VTE) or mortality in hospitalized coronavirus disease 2019 (COVID-19) patients.

Methods: A retrospective cohort study of hospitalized adult patients admitted to an integrated health care network in the New York metropolitan region between March 1, 2020 and April 27, 2020. The final analysis included 9,407 patients with an overall VTE rate of 2.