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Wrong patient selection errors may be tracked by retract-reorder (RAR) events. The aim of this quality improvement study was to assess the impact of reducing the number of concurrently open electronic health records from 4 to 2 on RAR errors generated by a tele-critical care service.

To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online.

While patients often contribute data for research, they want researchers to protect their data. As part of a participatory design of privacy-enhancing software, this study explored patients' perceptions of privacy protection in research using their healthcare data.

To assess primary care teams' perceptions of a health information exchange (HIE) event notification intervention for geriatric patients in 2 Veterans Health Administration (VHA) medical centers.

The study sought to assess the clinical performance of a machine learning model aiming to identify unusual medication orders.

To measure nurse-perceived electronic health records (EHR) usability with a standardized metric of technology usability and evaluate its association with professional burnout.

Accessing medical data from multiple institutions is difficult owing to the interinstitutional diversity of vocabularies. Standardization schemes, such as the common data model, have been proposed as solutions to this problem, but such schemes require expensive human supervision. This study aims to construct a trainable system that can automate the process of semantic interinstitutional code mapping.

The Global Digital Exemplar (GDE) Program is a national attempt to accelerate digital maturity in healthcare providers through promoting knowledge transfer across the English National Health Service (NHS). "Blueprints"-documents capturing implementation experience-were intended to facilitate this knowledge transfer. Here we explore how Blueprints have been conceptualized, produced, and used to promote interorganizational knowledge transfer across the NHS.

Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020.

Routine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias.