Effect of a Default Order vs an Alert in the Electronic Health Record on Hepatitis C Virus Screening Among Hospitalized Patients: A Stepped-Wedge Randomized Clinical Trial.

Importance: Hepatitis C virus (HCV) screening has been recommended for patients born between 1945 and 1965, but rates remain low.

Objective: To evaluate whether a default order within the admission order set increases HCV screening compared with a preexisting alert within the electronic health record.

Design, Setting, And Participants: This stepped-wedge randomized clinical trial was conducted from June 23, 2020, to April 10, 2021, at 2 hospitals within an academic medical center.

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.

Design, Implementation, and Validation of an Automated, Algorithmic COVID-19 Triage Tool.

Objective: We describe the design, implementation, and validation of an online, publicly available tool to algorithmically triage patients experiencing severe acute respiratory syndrome coronavirus (SARS-CoV-2)-like symptoms.

Methods: We conducted a chart review of patients who completed the triage tool and subsequently contacted our institution's phone triage hotline to assess tool- and clinician-assigned triage codes, patient demographics, SARS-CoV-2 (COVID-19) test data, and health care utilization in the 30 days post-encounter. We calculated the percentage of concordance between tool- and clinician-assigned triage categories, down-triage (clinician assigning a less severe category than the triage tool), and up-triage (clinician assigning a more severe category than the triage tool) instances.

Transitions from One Electronic Health Record to Another: Challenges, Pitfalls, and Recommendations.

Objective: We address the challenges of transitioning from one electronic health record (EHR) to another-a near ubiquitous phenomenon in health care. We offer mitigating strategies to reduce unintended consequences, maximize patient safety, and enhance health care delivery.

Methods: We searched PubMed and other sources to identify articles describing EHR-to-EHR transitions.

Reducing Alert Burden in Electronic Health Records: State of the Art Recommendations from Four Health Systems.

Background: Electronic health record (EHR) alert fatigue, while widely recognized as a concern nationally, lacks a corresponding comprehensive mitigation plan.

Objectives: The goal of this manuscript is to provide practical guidance to clinical informaticists and other health care leaders who are considering creating a program to manage EHR alerts.

Methods: This manuscript synthesizes several approaches and recommendations for better alert management derived from four U.

Computerized prescriber order entry-related patient safety reports: analysis of 2522 medication errors.

Objective: To examine medication errors potentially related to computerized prescriber order entry (CPOE) and refine a previously published taxonomy to classify them.

Materials And Methods: We reviewed all patient safety medication reports that occurred in the medication ordering phase from 6 sites participating in a United States Food and Drug Administration-sponsored project examining CPOE safety. Two pharmacists independently reviewed each report to confirm whether the error occurred in the ordering/prescribing phase and was related to CPOE.

Order sets in electronic health records: principles of good practice.

Electronic health record (EHR) order sets are common. Order sets represent one clinical decision support (CDS) tool within computerized provider order entry systems that may promote safe, efficient, and evidence-based patient care. A small number of order sets account for the vast majority of use, suggesting that some order sets have a higher value to clinicians than others.