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To discuss and illustrate the utility of two open collaborative data science platforms, and how they would benefit data science and informatics education.

Standards such as the Logical Observation Identifiers Names and Codes (LOINC®) are critical for interoperability and integrating data into common data models, but are inconsistently used. Without consistent mapping to standards, clinical data cannot be harmonized, shared, or interpreted in a meaningful context. We sought to develop an automated machine learning pipeline that leverages noisy labels to map laboratory data to LOINC codes.

Medication adherence is an important aspect of chronic disease management. Electronic health record (EHR) data are often not linked to dispensing data, limiting clinicians' understanding of which of their patients fill their medications, and how to tailor care appropriately. We aimed to develop an algorithm to link EHR prescribing to claims-based dispensing data and use the results to quantify how often patients with diabetes filled prescribed chronic disease medications.

This quality improvement project evaluated the impact of a tailored, evidence-based training strategy on advanced electronic medical record (EMR) use for Veterans Administration (VA) clinicians experienced in using the EMR. After developing the curriculum, an online needs assessment tool evaluated 20 clinicians' competency gaps. Responses were used to prioritize clinicians' training needs. Clinician informaticists then provided 2-4 h of tailored training to groups of 1-5 clinicians. Compared with baseline scores [...]

Healthcare organizations use research data models supported by projects and tools that interest them, which often means organizations must support the same data in multiple models. The healthcare research ecosystem would benefit if tools and projects could be adopted independently from the underlying data model. Here, we introduce the concept of a reusable application programming interface (API) for healthcare and show that the i2b2 API can be adapted to support [...]

To conduct a systematic review of deep learning models for electronic health record (EHR) data, and illustrate various deep learning architectures for analyzing different data sources and their target applications. We also highlight ongoing research and identify open challenges in building deep learning models of EHRs.

The gold standard for diagnosing sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during sleep. We developed the National Sleep Research Resource (NSRR), a comprehensive system for sharing sleep data. The NSRR embodies elements of a data commons aimed at accelerating research to address critical questions about the impact of sleep disorders on important health outcomes.

We explored how judgements provided by physicians can be used to learn relevance models that enhance the quality of patient cohorts retrieved from Electronic Health Records (EHRs) collections.

As patient portals are increasingly used for research recruitment, it is important to examine the demographic makeup of research registries that are populated via portals and the factors that influence participation in these registries.

Obtaining patient permissions for research contact and for surplus tissue use as part of routine clinical practice can improve research participation. This study aims to investigate the difference in patient permissions for use of surplus tissues, and for direct contact for research, using 2 different methods of solicitation.