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Electric health record (EHR) discontinuity, that is, receiving care outside of a given EHR system, can lead to substantial information bias. We aimed to determine whether a previously described EHR-continuity prediction model can reduce the misclassification of 4 commonly used risk scores in pharmacoepidemiology.

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.

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.

While families have a central role in shaping individual choices and behaviors, healthcare largely focuses on treating individuals or supporting self-care. However, a family is also a health unit. We argue that family informatics is a necessary evolution in scope of health informatics. To deal with the needs of individuals, we must ensure technologies account for the role of their families and may require new classes of digital service. Social [...]

Tumor registries in integrated healthcare systems (IHCS) have high precision for identifying incident cancer but often miss recently diagnosed cancers or those diagnosed outside of the IHCS. We developed an algorithm using the electronic medical record (EMR) to identify people with a history of cancer not captured in the tumor registry to identify adults, aged 40-65 years, with no history of cancer.

Sepsis has a high rate of 30-day unplanned readmissions. Predictive modeling has been suggested as a tool to identify high-risk patients. However, existing sepsis readmission models have low predictive value and most predictive factors in such models are not actionable.

The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing.