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Testing individuals for the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen causing the coronavirus disease 2019 (COVID-19), is crucial for curtailing transmission chains. Moreover, rapidly testing many potentially infected individuals is often a limiting factor in controlling COVID-19 outbreaks. Hence, pooling strategies, wherein individuals are grouped and tested simultaneously, are employed. Here, we present a novel pooling strategy that builds on the Bayesian D-optimal experimental design [...]

The lesbian, gay, bisexual, transgender, queer, or questioning (LGBTQ+) community is vulnerable to health-care disparities. Many health-care organizations are working to collect sexual orientation and gender identity in their electronic health records (EHRs), with the goal of providing more inclusive care to their LGBTQ+ patients. There are significant human and technical barriers to making these efforts successful. Based on our 5-year experience at Geisinger (an integrated health system located in [...]

Frailty is a prevalent risk factor for adverse outcomes among patients with chronic lung disease. However, identifying frail patients who may benefit from interventions is challenging using standard data sources. We therefore sought to identify phrases in clinical notes in the electronic health record (EHR) that describe actionable frailty syndromes.

Clinical registries-structured databases of demographic, diagnosis, and treatment information-play vital roles in retrospective studies, operational planning, and assessment of patient eligibility for research, including clinical trials. Registry curation, a manual and time-intensive process, is always costly and often impossible for rare or underfunded diseases. Our goal was to evaluate the feasibility of natural language inference (NLI) as a scalable solution for registry curation.

This article reports a systematic review of studies containing development and validation of models predicting postacute care destination after adult inpatient hospitalization, summarizes clinical populations and variables, evaluates model performance, assesses risk of bias and applicability, and makes recommendations to reduce bias in future models.

BaMaRa allows the secure collection and deidentified centralization of medical data from all patients followed-up in a rare disease expert network in France, based on a minimum data set (SDM-MR). The present article describes BaMaRa information system implementation and development across the whole national territory as well as data access requests through BNDMR, the data warehouse which centralizes all BaMaRa data, during the 2015-2020 period.

Chatbots are software applications to simulate a conversation with a person. The effectiveness of chatbots in facilitating the recruitment of study participants in research, specifically among racial and ethnic minorities, is unknown. The objective of this study is to compare a chatbot versus telephone-based recruitment in enrolling research participants from a predominantly minority patient population at an urban institution. We randomly allocated adults to receive either chatbot or telephone-based outreach [...]

Suboptimal design of health information technology (IT) systems can lead to the introduction of errors in the diagnostic process. We aimed to identify mechanisms that can affect the safety and effectiveness of these systems in hospital settings thus contributing to the building of an explicit and replicable understanding of the variables that can affect the functioning of IT systems.