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The US Department of Veterans Affairs (VA) is using an automated short message service application named "Annie" as part of its coronavirus disease 2019 (COVID-19) response with a protocol for coronavirus precautions, which can help the veteran monitor symptoms and can advise the veteran when to contact his or her VA care team or a nurse triage line. We surveyed 1134 veterans on their use of the Annie application and [...]

As coronavirus disease 2019 (COVID-19) started its rapid emergence and gradually transformed into an unprecedented pandemic, the need for having a knowledge repository for the disease became crucial. To address this issue, a new COVID-19 machine-readable dataset known as the COVID-19 Open Research Dataset (CORD-19) has been released. Based on this, our objective was to build a computable co-occurrence network embeddings to assist association detection among COVID-19-related biomedical entities.

The study sought to evaluate the overall performance of hospitals that used the Computerized Physician Order Entry Evaluation Tool in both 2017 and 2018, along with their performance against fatal orders and nuisance orders.

A major bottleneck hindering utilization of electronic health record data for translational research is the lack of precise phenotype labels. Chart review as well as rule-based and supervised phenotyping approaches require laborious expert input, hampering applicability to studies that require many phenotypes to be defined and labeled de novo. Though International Classification of Diseases codes are often used as surrogates for true labels in this setting, these sometimes suffer from [...]

The study sought to evaluate early lessons from a remote patient monitoring engagement and education technology solution for patients with coronavirus disease 2019 (COVID-19) symptoms.

The US National Library of Medicine regularly collects summary data on direct use of Unified Medical Language System (UMLS) resources. The summary data sources include UMLS user registration data, required annual reports submitted by registered users, and statistics on downloads and application programming interface calls. In 2019, the National Library of Medicine analyzed the summary data on 2018 UMLS use. The library also conducted a scoping review of the literature [...]

Recent studies on electronic health records (EHRs) started to learn deep generative models and synthesize a huge amount of realistic records, in order to address significant privacy issues surrounding the EHR. However, most of them only focus on structured records about patients' independent visits, rather than on chronological clinical records. In this article, we aim to learn and synthesize realistic sequences of EHRs based on the generative autoencoder.

Crowdsourced ratings have driven increased performance transparency between consumers and suppliers. While many industries have benefitted from such transparency, crowdsourced ratings have struggled to scale in the healthcare domain. In theory, interoperability services offer an ideal setting for crowdsourced ratings: costs are high, performance is variable, and information asymmetries between provider organizations (customers) and vendors offering interoperability solutions exist. Via a Cooperative Agreement between the Office of the National Coordinator [...]

We sought to predict if patients with type 2 diabetes mellitus (DM2) would develop 10 selected complications. Accurate prediction of complications could help with more targeted measures that would prevent or slow down their development.