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The ability to share nursing data across organizations and electronic health records is a key component of improving care coordination and quality outcomes. Currently, substantial organizational and technical barriers limit the ability to share and compare essential patient data that inform nursing care. Nursing leaders at Kaiser Permanente and the U.S. Department of Veterans Affairs collaborated on the development of an evidence-based information model driven by nursing practice to enable [...]

Electronic reporting of genetic testing results is increasing, but they are often represented in diverse formats and naming conventions. Logical Observation Identifiers Names and Codes (LOINC) is a vocabulary standard that provides universal identifiers for laboratory tests and clinical observations. In genetics, LOINC provides codes to improve interoperability in the midst of reporting style transition, including codes for cytogenetic or mutation analysis tests, specific chromosomal alteration or mutation testing, and [...]

Develop and evaluate a foundational oncology-specific standard for the communication and coordination of care throughout the cancer journey, with early-stage breast cancer as the use case.

The verification of biomedical ontologies is an arduous process that typically involves peer review by subject-matter experts. This work evaluated the ability of crowdsourcing methods to detect errors in SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) and to address the challenges of scalable ontology verification.

There is a lack of recommended models for clinical informatics (CI) governance that can facilitate successful health information technology implementation.

Currently, the processes for harmonizing and extending standards by leveraging the knowledge within local documentation artifacts are not well described. We describe a collaborative project to develop common information models, terminology bindings, and term definitions based on nursing documentation systems, and carry the findings through to the adoption in standards development organizations (SDOs) and technical implementations in clinical applications.

For many literature review tasks, including systematic review (SR) and other aspects of evidence-based medicine, it is important to know whether an article describes a randomized controlled trial (RCT). Current manual annotation is not complete or flexible enough for the SR process. In this work, highly accurate machine learning predictive models were built that include confidence predictions of whether an article is an RCT.

The American Recovery and Reinvestment Act (ARRA) of 2009 clearly articulated the central role that health information technology (HIT) standards would play in improving healthcare quality, safety, and efficiency through the meaningful use of certified, standards based, electronic health record (EHR) technology. In 2012, the Office of the National Coordinator (ONC) asked the Nationwide Health Information Network (NwHIN) Power Team of the Health Information Technology Standards Committee (HITSC) to develop [...]

Radiology reports are usually narrative, unstructured text, a format which hinders the ability to input report contents into decision support systems. In addition, reports often describe multiple lesions, and it is challenging to automatically extract information on each lesion and its relationships to characteristics, anatomic locations, and other information that describes it. The goal of our work is to develop natural language processing (NLP) methods to recognize each lesion in [...]

To integrate data elements from multiple sources for informing comprehensive and standardized collection of family health history (FHH).