Selecting venues for AMIA events and conferences: guiding ethical principles.

A discussion and debate on the American Medical Informatics Association's (AMIA) Ethical, Legal, and Social Issues (ELSI) Working Group listserv in 2021 raised important issues related to a forthcoming conference in Texas. Texas had recently enacted a restrictive abortion law and restricted voting rights. Several AMIA members advocated for a boycott of the state and the scheduled conference.

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.

COVID-19 Vaccination Gap in Admitted Trauma Patients: A Critical Opportunity.

Background: Coronavirus disease 2019 (COVID-19) vaccination is the core strategy for pandemic management. We hypothesized that a vaccination gap might exist between emergency department (ED) patients admitted for trauma and other ED patients.

Study Design: This was an observational quality improvement study using electronic health record data at an academic level-1 trauma center.

A tribute to Karen Greenwood and her contributions to the American Medical Informatics Association.

After 25 years of service to the American Medical Informatics Association (AMIA), Ms Karen Greenwood, the Executive Vice President and Chief Operating Officer, is leaving the organization. In this perspective, we reflect on her accomplishments and her effect on the organization and the field of informatics nationally and globally. We also express our appreciation and gratitude for Ms Greenwood's role at AMIA.

Developing a COVID-19 WHO Clinical Progression Scale inpatient database from electronic health record data.

Objective: There is a need for a systematic method to implement the World Health Organization's Clinical Progression Scale (WHO-CPS), an ordinal clinical severity score for coronavirus disease 2019 patients, to electronic health record (EHR) data. We discuss our process of developing guiding principles mapping EHR data to WHO-CPS scores across multiple institutions.

Materials And Methods: Using WHO-CPS as a guideline, we developed the technical blueprint to map EHR data to ordinal clinical severity scores.

Factors associated with initial tidal volume selection during neonatal volume-targeted ventilation in two NICUs: a retrospective cohort study.

Objective: To quantify initial tidal volume (VT) during neonatal volume-targeted ventilation (VTV) and to characterize the agreement of initial VT with the limited-evidence available.

Study Design: We performed a multi-center retrospective observational cohort study in two Neonatal Intensive Care Units evaluating 313 infants who received VTV as the initial ventilation modality prior to postnatal day 14. We generated descriptive statistics and performed multivariable logistic regression analysis to determine factors associated with initial VT use that agreed with available literature.

Searching Full-Text Anatomic Pathology Reports Using Business Intelligence Software.

Although the laboratory information system has largely solved the problem of storing anatomic pathology reports and disseminating their contents across the healthcare system, the retrospective query of anatomic pathology reports remains an area for improvement across laboratory information system vendors. Our institution desired the ability to query our repository of anatomic pathology reports for clinical, operational, research, and educational purposes. To address this need, we developed a full-text anatomic pathology search tool using the business intelligence software, Tableau.

Defining AMIA's artificial intelligence principles.

Recent advances in the science and technology of artificial intelligence (AI) and growing numbers of deployed AI systems in healthcare and other services have called attention to the need for ethical principles and governance. We define and provide a rationale for principles that should guide the commission, creation, implementation, maintenance, and retirement of AI systems as a foundation for governance throughout the lifecycle. Some principles are derived from the familiar requirements of practice and research in medicine and healthcare: beneficence, nonmaleficence, autonomy, and justice come first.

Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study.

Objective: To create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing.

Design: Retrospective cohort study.

Setting: One US hospital during 2015-21 was used for model training and internal validation.

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.

cosinoRmixedeffects: an R package for mixed-effects cosinor models.

Background: Wearable devices enable monitoring and measurement of physiological parameters over a 24-h period, and some of which exhibit circadian rhythm characteristics. However, the currently available R package cosinor could only analyze daily cross-sectional data and compare the parameters between groups with two levels. To evaluate longitudinal changes in the circadian patterns, we need to extend the model to a mixed-effect model framework, allowing for random effects and interaction between COSINOR parameters and time-varying covariates.

Major adverse cardiac events after emergency department evaluation of chest pain patients with advanced testing: Systematic review and meta-analysis.

Objectives: Our primary objective was to describe the risk of major adverse cardiac events (MACE) at 1, 6, and 12 months after a negative coronary computed tomography angiogram (cCTA), electrocardiogram (ECG) stress test, stress echocardiography, and myocardial perfusion scintigraphy (MPS) in low- to intermediate-risk patients.

Methods: Initially, 952 articles were identified for screening, 81 met criteria for full-text review, and once risk of bias was assessed, 33 articles were included in this meta-analysis. We utilized a random-effects model to assess pooled MACE event proportion for patients undergoing evaluation of acute coronary syndrome (ACS) when risk stratified to a low- to intermediate-risk category after undergoing standard testing.

Association of Healthcare Access With Intensive Care Unit Utilization and Mortality in Patients of Hispanic Ethnicity Hospitalized With COVID-19.

Background: Racial and ethnic minority groups in the United States experience a disproportionate burden of COVID-19 deaths.

Objective: To evaluate whether outcome differences between Hispanic and non-Hispanic COVID-19 hospitalized patients exist and, if so, to identify the main malleable contributing factors.

Design, Setting, Participants: Retrospective, cross-sectional, observational study of 6097 adult COVID-19 patients hospitalized within a single large healthcare system from March to November 2020.

Incorporation of natriuretic peptides with clinical risk scores to predict heart failure among individuals with dysglycaemia.

Aims: To evaluate the performance of the WATCH-DM risk score, a clinical risk score for heart failure (HF), in patients with dysglycaemia and in combination with natriuretic peptides (NPs).

Methods And Results: Adults with diabetes/pre-diabetes free of HF at baseline from four cohort studies (ARIC, CHS, FHS, and MESA) were included. The machine learning- [WATCH-DM(ml)] and integer-based [WATCH-DM(i)] scores were used to estimate the 5-year risk of incident HF.

Before-Visit Questionnaire: A Tool to Augment Communication and Decrease Provider Documentation Burden in Pediatric Diabetes.

Objective: To develop and evaluate an electronic tool that collects interval history and incorporates it into a provider summary note.

Methods: A parent-facing online before-visit questionnaire (BVQ) collected information from parents and caregivers of pediatric diabetes patients prior to a clinic encounter. This information was related to interval history and perceived self-management barriers.

Harnessing the Electronic Health Record and Computerized Provider Order Entry Data for Resource Management During the COVID-19 Pandemic: Development of a Decision Tree.

Background: The COVID-19 pandemic has resulted in shortages of diagnostic tests, personal protective equipment, hospital beds, and other critical resources.

Objective: We sought to improve the management of scarce resources by leveraging electronic health record (EHR) functionality, computerized provider order entry, clinical decision support (CDS), and data analytics.

Methods: Due to the complex eligibility criteria for COVID-19 tests and the EHR implementation-related challenges of ordering these tests, care providers have faced obstacles in selecting the appropriate test modality.

COVID-19 Mass Vaccination Resource Calculator.

Background: Despite the recent emergency use authorization of two vaccines for the prevention of the 2019 novel coronavirus (COVID-19) disease, vaccination rates are lower than expected. Vaccination efforts may be hampered by supply, delivery, storage, patient prioritization, administration infrastructure or logistics problems. To address the last issue, our institution is sharing publically a calculator to optimize the management of staffing and facility resources in an outpatient mass vaccination effort.

The Effect of Electronic Health Record Burden on Pediatricians' Work-Life Balance and Career Satisfaction.

Objectives: To examine pediatricians' perspectives on administrative tasks including electronic health record (EHR) documentation burden and their effect on work-life balance and life and career satisfaction.

Methods: We analyzed 2018 survey data from the American Academy of Pediatrics (AAP) Pediatrician Life and Career Experience Study (PLACES), a longitudinal cohort study of early and midcareer pediatricians. Cohorts graduated from residency between 2002 and 2004 or 2009 and 2011.