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The epidemic of coronavirus disease 2019 (COVID-19) broke out in Wuhan, China, in early 2020. In an effort to curb the spread of the epidemic, the government has requisitioned a variety of venues and plant buildings and built more than 20 cabin hospitals to receive patients with mild symptoms within 48 hours. Under this circumstance, we worked out a 5G all-wireless solution to divide the overall network system of the [...]

To develop a comprehensive and current description of what health informatics (HI) professionals do and what they need to know.

Incomplete and static reaction picklists in the allergy module led to free-text and missing entries that inhibit the clinical decision support intended to prevent adverse drug reactions. We developed a novel, data-driven, "dynamic" reaction picklist to improve allergy documentation in the electronic health record (EHR).

This study sought to assess the impact and validity of simulation modeling in informing decision making in a complex area of healthcare delivery: colorectal cancer (CRC) screening.

In this work, we introduce a privacy technique for anonymizing clinical notes that guarantees all private health information is secured (including sensitive data, such as family history, that are not adequately covered by current techniques).

We sought to determine rates of computerized provider order entry (CPOE) patient identity verification and when and where in the ordering process verification occurred.

We evaluated the extent to which studies that tested short message service (SMS)- and application (app)-based interventions for diabetes self-management education and support (DSMES) report on factors that inform both internal and external validity as measured by the RE-AIM (Reach, Efficacy/Effectiveness, Adoption, Implementation, and Maintenance) framework.

Accurate electronic phenotyping is essential to support collaborative observational research. Supervised machine learning methods can be used to train phenotype classifiers in a high-throughput manner using imperfectly labeled data. We developed 10 phenotype classifiers using this approach and evaluated performance across multiple sites within the Observational Health Data Sciences and Informatics (OHDSI) network.