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Driven by beneficial patient-centered outcomes associated with patient portal use and the Affordable Care Act, portal implementation has expanded into safety nets-health systems that offer access to care to a large share of uninsured, Medicaid, and other vulnerable populations. However, little attention has been paid to the factors that affect portal accessibility by the vulnerable patients served by these health systems-including those who are limited English proficient (LEP).

In health informatics, there have been concerns with reuse of electronic health data for research, including potential bias from incorrect or incomplete outcome ascertainment. In this tutorial, we provide a concise review of predictive value-based quantitative bias analysis (QBA), which comprises epidemiologic methods that use estimates of data quality accuracy to quantify the bias caused by outcome misclassification.

To use unsupervised topic modeling to evaluate heterogeneity in sepsis treatment patterns contained within granular data of electronic health records.

The study sought to characterize institution-wide participation in secure messaging (SM) at a large academic health network, describe our experience with electronic medical record (EMR)-based cohort selection, and discuss the potential roles of SM for research recruitment.

Track 1 of the 2018 National NLP Clinical Challenges shared tasks focused on identifying which patients in a corpus of longitudinal medical records meet and do not meet identified selection criteria.

We sought to demonstrate applicability of user stories, progressively elaborated by testable acceptance criteria, as lightweight requirements for agile development of clinical decision support (CDS).

Electronic health records linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. The objective of this study was to develop an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP).

Electronic health records are increasingly utilized for observational and clinical research. Identification of cohorts using electronic health records is an important step in this process. Previous studies largely focused on the methods of cohort selection, but there is little evidence on the impact of underlying vocabularies and mappings between vocabularies used for cohort selection. We aim to compare the cohort selection performance using Australian Medicines Terminology to Anatomical Therapeutic Chemical [...]

The goal of the 2018 n2c2 shared task on cohort selection for clinical trials (track 1) is to identify which patients meet the selection criteria for clinical trials. Cohort selection is a particularly demanding task to which natural language processing and deep learning can make a valuable contribution. Our goal is to evaluate several deep learning architectures to deal with this task.