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Accurate extraction of breast cancer patients' phenotypes is important for clinical decision support and clinical research. This study developed and evaluated cancer domain pretrained CancerBERT models for extracting breast cancer phenotypes from clinical texts. We also investigated the effect of customized cancer-related vocabulary on the performance of CancerBERT models.

To understand the nature of health consumer self-management workarounds during the COVID-19 pandemic; to classify these workarounds using the Substitution, Augmentation, Modification, and Redefinition (SAMR) framework; and to see how digital tools had assisted these workarounds.

This systematic review aims to assess how information from unstructured text is used to develop and validate clinical prognostic prediction models. We summarize the prediction problems and methodological landscape and determine whether using text data in addition to more commonly used structured data improves the prediction performance.

To assess whether previously observed differences in interoperable exchange by physician practice size persisted in 2019 and identify the role of 3 factors shaping interoperable exchange among physicians in practices of varying sizes: Federal incentive programs designed to encourage health IT use, value-based care, and selection of electronic health record (EHR) developer.

Electronic health record (EHR)-derived data are extensively used in health research. However, the pattern of patient interaction with the healthcare system can result in informative presence bias if those who have poorer health have more data recorded than healthier patients. We aimed to determine how informative presence affects bias across multiple scenarios informed by real-world healthcare utilization patterns.

A participant's medical history is important in clinical research and can be captured from electronic health records (EHRs) and self-reported surveys. Both can be incomplete, EHR due to documentation gaps or lack of interoperability and surveys due to recall bias or limited health literacy. This analysis compares medical history collected in the All of Us Research Program through both surveys and EHRs.

Emerging technologies (eg, wearable devices) have made it possible to collect data directly from individuals (eg, time-series), providing new insights on the health and well-being of individual patients. Broadening the access to these data would facilitate the integration with existing data sources (eg, clinical and genomic data) and advance medical research. Compared to traditional health data, these data are collected directly from individuals, are highly unique and provide fine-grained information [...]

We conducted a horizon scan to (1) identify challenges in patient-centered clinical decision support (PC CDS) and (2) identify future directions for PC CDS.

To combine machine efficiency and human intelligence for converting complex clinical trial eligibility criteria text into cohort queries.

The systematic documentation of sexual orientation and gender identity data in electronic health records can improve patient-centered care and help to identify and address health disparities affecting sexual and gender minority populations. Although there are existing guidelines for sexual orientation and gender identity data among adult patients, there are not yet standard recommendations for pediatric patients. In this article, we discuss methods that pediatric primary care organizations can use to [...]