The COVID-19 Pandemic's Effect on Preventive Imaging.

Introduction: This study assessed the effect of the COVID-19 pandemic on preventive care imaging and potential disparities because preventive care may be perceived as nonurgent. The objective was to identify the associations between the COVID-19 pandemic and changes in preventive imaging volumes for patients in general and as affected by race and ethnicities.

Methods: The authors performed a retrospective observational study by extracting the weekly volumes of all imaging studies between January 7, 2019 and May 1, 2022 from a radiology data warehouse at a tertiary care medical center (=92,105 preventive imaging studies and 3,493,063 total radiology imaging studies) and compared preshutdown with postshutdown periods using a 2-sample -test.

Impact of team performance on the surgical safety checklist on patient outcomes: an operating room black box analysis.

Background: Surgical safety checklists reduce adverse events, but monitoring adherence to checklists is confounded by observation bias. The ORBB platform can monitor checklist compliance and correlate compliance with outcomes. This study aims to evaluate the association between checklist compliance and patient outcomes using the ORBB platform.

Specific Gravity Improves Identification of Clinically Significant Quantitative Proteinuria from the Dipstick Urinalysis.

Key Points: Urine albumin-to-creatinine ratio and urine protein-to-creatinine ratio are frequently obtained and represent possible tools for screening for proteinuria and thus early CKD. Adding specific gravity to dipstick proteinuria improves the ability to screen patients with clinically significant proteinuria and can be used to identify patients with early CKD.

Background: CKD is often underdiagnosed during early stages when GFR is preserved because of underutilization of testing for quantitative urine albumin-to-creatinine ratio (UACR) or urine protein-to-creatinine ratio (UPCR).

Validation of a predictive model for hospital-acquired acute kidney injury with emergence of SARS-CoV-2 variants.

We previously developed and validated a model to predict acute kidney injury (AKI) in hospitalized coronavirus disease 2019 (COVID-19) patients and found that the variables with the highest importance included a history of chronic kidney disease and markers of inflammation. Here, we assessed model performance during periods when COVID-19 cases were attributable almost exclusively to individual variants. Electronic Health Record data were obtained from patients admitted to 19 hospitals.

Real-Time Patient Portal Use Among Emergency Department Patients: An Open Results Study.

Objectives: We characterized real-time patient portal test result viewing among emergency department (ED) patients and described patient characteristics overall and among those not enrolled in the portal at ED arrival.

Methods: Our observational study at an academic ED used portal log data to trend the proportion of adult patients who viewed results during their visit from May 04, 2021 to April 04, 2022. Correlation was assessed visually and with Kendall's τ.

Risk Prediction for Acute Kidney Injury in Patients Hospitalized With COVID-19.

Rationale & Objective: Acute kidney injury (AKI) is common in patients hospitalized with COVID-19, but validated, predictive models for AKI are lacking. We aimed to develop the best predictive model for AKI in hospitalized patients with coronavirus disease 2019 and assess its performance over time with the emergence of vaccines and the Delta variant.

Study Design: Longitudinal cohort study.

Using dipstick urinalysis to predict development of acute kidney injury in patients with COVID-19.

Background: Acute kidney injury (AKI) is a common complication in patients hospitalized with COVID-19 and may require renal replacement therapy (RRT). Dipstick urinalysis is frequently obtained, but data regarding the prognostic value of hematuria and proteinuria for kidney outcomes is scarce.

Methods: Patients with positive severe acute respiratory syndrome-coronavirus 2 (SARS-CoV2) PCR, who had a urinalysis obtained on admission to one of 20 hospitals, were included.

Rolling Up the Sleeve: Equitable, Efficient, and Safe COVID-19 Mass Immunization for Academic Medical Center Employees.

Background: Novel coronavirus disease 2019 (COVID-19) vaccine administration has faced distribution barriers across the United States. We sought to delineate our vaccine delivery experience in the first week of vaccine availability, and our effort to prioritize employees based on risk with a goal of providing an efficient infrastructure to optimize speed and efficiency of vaccine delivery while minimizing risk of infection during the immunization process.

Objective: This article aims to evaluate an employee prioritization/invitation/scheduling system, leveraging an integrated electronic health record patient portal framework for employee COVID-19 immunizations at an academic medical center.

Electronic Health Records-Based Cardio-Oncology Registry for Care Gap Identification and Pragmatic Research: Procedure and Observational Study.

Background: Professional society guidelines are emerging for cardiovascular care in cancer patients. However, it is not yet clear how effectively the cancer survivor population is screened and treated for cardiomyopathy in contemporary clinical practice. As electronic health records (EHRs) are now widely used in clinical practice, we tested the hypothesis that an EHR-based cardio-oncology registry can address these questions.

Algorithmic Detection of Boolean Logic Errors in Clinical Decision Support Statements.

Objective: Clinical decision support (CDS) can contribute to quality and safety. Prior work has shown that errors in CDS systems are common and can lead to unintended consequences. Many CDS systems use Boolean logic, which can be difficult for CDS analysts to specify accurately.

Impact of a problem-oriented view on clinical data retrieval.

Objective: The electronic health record (EHR) data deluge makes data retrieval more difficult, escalating cognitive load and exacerbating clinician burnout. New auto-summarization techniques are needed. The study goal was to determine if problem-oriented view (POV) auto-summaries improve data retrieval workflows.

The use of automated data extraction tools to develop a solid organ transplant registry: Proof of concept study of bloodstream infections.

Background: We created an electronic health record-based registry using automated data extraction tools to study the epidemiology of bloodstream infections (BSI) in solid organ transplant recipients. The overarching goal was to determine the usefulness of an electronic health record-based registry using data extraction tools for clinical research in solid organ transplantation.

Methods: We performed a retrospective single-center cohort study of adult solid organ transplant recipients from 2010 to 2015.

User stories as lightweight requirements for agile clinical decision support development.

Objective: We sought to demonstrate applicability of user stories, progressively elaborated by testable acceptance criteria, as lightweight requirements for agile development of clinical decision support (CDS).

Materials And Methods: User stories employed the template: As a [type of user], I want [some goal] so that [some reason]. From the "so that" section, CDS benefit measures were derived.

"D/C the CC (Carbon Copy)" - Improving the EHR Signal-to-Noise Ratio for Clinicians by Selective Feature De-Implementation.

De-implementation of a 10-year EHR configuration resulted in over 50% decrease in the volume of the most-common InBasket message type received by PCPs. Pro-actively seeking out ways to not only (a) implement helpful new EHR features but (b) de-implement detrimental ones offers an opportunity to accelerate improvement in the S/N ratio and reduce clinician frustration and dissatisfaction with the EHR. Balancing governance decision agendas with de-implementation opportunities can enhance the clinician experience.

Correction: SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets.

[This corrects the article DOI: 10.2196/11487.].

Count me in: using a patient portal to minimize implicit bias in clinical research recruitment.

Objective: Determine whether women and men differ in volunteering to join a Research Recruitment Registry when invited to participate via an electronic patient portal without human bias.

Materials And Methods: Under-representation of women and other demographic groups in clinical research studies could be due either to invitation bias (explicit or implicit) during screening and recruitment or by lower rates of deciding to participate when offered. By making an invitation to participate in a Research Recruitment Registry available to all patients accessing our patient portal, regardless of demographics, we sought to remove implicit bias in offering participation and thus independently assess agreement rates.

SNOMED CT Concept Hierarchies for Computable Clinical Phenotypes From Electronic Health Record Data: Comparison of Intensional Versus Extensional Value Sets.

Background: Defining clinical phenotypes from electronic health record (EHR)-derived data proves crucial for clinical decision support, population health endeavors, and translational research. EHR diagnoses now commonly draw from a finely grained clinical terminology-either native SNOMED CT or a vendor-supplied terminology mapped to SNOMED CT concepts as the standard for EHR interoperability. Accordingly, electronic clinical quality measures (eCQMs) increasingly define clinical phenotypes with SNOMED CT value sets.

Agile Co-Development for Clinical Adoption and Adaptation of Innovative Technologies.

Even the most innovative healthcare technologies provide patient benefits only when adopted by clinicians and/or patients in actual practice. Yet realizing optimal positive impact from a new technology for the widest range of individuals who would benefit remains elusive. In software and new product development, iterative rapid-cycle "agile" methods more rapidly provide value, mitigate failure risks, and adapt to customer feedback.

SNOMED CT Concept Hierarchies for Sharing Definitions of Clinical Conditions Using Electronic Health Record Data.

Background: Defining clinical conditions from electronic health record (EHR) data underpins population health activities, clinical decision support, and analytics. In an EHR, defining a condition commonly employs a diagnosis value set or "grouper." For constructing value sets, Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) offers high clinical fidelity, a hierarchical ontology, and wide implementation in EHRs as the standard interoperability vocabulary for problems.

Agile Acceptance Test-Driven Development of Clinical Decision Support Advisories: Feasibility of Using Open Source Software.

Background: Moving to electronic health records (EHRs) confers substantial benefits but risks unintended consequences. Modern EHRs consist of complex software code with extensive local configurability options, which can introduce defects. Defects in clinical decision support (CDS) tools are surprisingly common.

Rapid Development of Specialty Population Registries and Quality Measures from Electronic Health Record Data*. An Agile Framework.

Background: Creation of a new electronic health record (EHR)-based registry often can be a "one-off" complex endeavor: first developing new EHR data collection and clinical decision support tools, followed by developing registry-specific data extractions from the EHR for analysis. Each development phase typically has its own long development and testing time, leading to a prolonged overall cycle time for delivering one functioning registry with companion reporting into production. The next registry request then starts from scratch.

Agile Model Driven Development of Electronic Health Record-Based Specialty Population Registries.

The transformation of the American healthcare payment system from fee-for-service to value-based care increasingly makes it valuable to develop patient registries for specialized populations, to better assess healthcare quality and costs. Recent widespread adoption of Electronic Health Records (EHRs) in the U.S.

Illness risk perceptions and trust: the association with blood pressure self-measurement.

Objective: To determine the prevalence of blood pressure selfmeasurement among those with hypertension and examine how this behavior may be associated with illness perceptions, risk perceptions, and attitudes about care.

Methods: Cross-sectional data from a population-based study of cardiovascular disease (n = 656).

Results: The prevalence of self-measurement was 26.

Level of acculturation and hypertension among Dallas County Hispanics: findings from the Dallas Heart Study.

Purpose: The purpose of this study is to examine whether the prevalence of hypertension differs by acculturation status among Hispanics in Dallas County, Texas. The authors test the hypothesis that compared with those of low acculturation, those of mid- and high-level acculturation will be at greater risk for having hypertension.

Methods: Conducted from July 2000 through October 2002, the Dallas Heart Study (DHS) is a general population cross-sectional study of cardiovascular risk factors among Dallas County residents.