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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.

This review summarizes past and current informatics activities at the Centers for Disease Control and Prevention National Program of Cancer Registries to inform readers about efforts to improve, standardize, and automate reporting to public health cancer registries.

The 2019 novel coronavirus disease (COVID-19) outbreak progressed rapidly from a public health (PH) emergency of international concern (World Health Organization [WHO], 30 January 2020) to a pandemic (WHO, 11 March 2020). The declaration of a national emergency in the United States (13 March 2020) necessitated the addition and modification of terminology related to COVID-19 and development of the disease's case definition. During this period, the Centers for Disease Control [...]

Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest-based algorithm to identify hospitalized patients at high risk for delirium, and evaluated its performance in a clinical setting.

Delay or failure to view test results in a hospital setting can lead to delayed diagnosis, risk of patient harm, and represents inefficiency. Factors influencing this were investigated to identify how timeliness and completeness of test review could be improved through an evidence-based redesign of the use of clinical test review software.

The objective of this study was to assess the clinical and financial impact of a quality improvement project that utilized a modified Early Warning Score (mEWS)-based clinical decision support intervention targeting early recognition of sepsis decompensation.

Our primary objectives were to examine adherence rates across two technologies (e-prescribing software and smart pill bottle) with cross-validation from alert-triggered messaging within the patient electronic health record (EHR) portal and to explore the benefits and challenges faced by atrial fibrillation (AF) patients in using a smart pill bottle.

Cross-institutional distributed healthcare/genomic predictive modeling is an emerging technology that fulfills both the need of building a more generalizable model and of protecting patient data by only exchanging the models but not the patient data. In this article, the implementation details are presented for one specific blockchain-based approach, ExplorerChain, from a software development perspective. The healthcare/genomic use cases of myocardial infarction, cancer biomarker, and length of hospitalization after surgery are [...]

To develop scalable natural language processing (NLP) infrastructure for processing the free text in electronic health records (EHRs).

There is little known about how academic medical centers (AMCs) in the US develop, implement, and maintain predictive modeling and machine learning (PM and ML) models. We conducted semi-structured interviews with leaders from AMCs to assess their use of PM and ML in clinical care, understand associated challenges, and determine recommended best practices. Each transcribed interview was iteratively coded and reconciled by a minimum of 2 investigators to identify key [...]