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To facilitate high-quality inpatient care for stroke patients, we built a system within our electronic health record (EHR) to identify stroke patients while they are in the hospital; capture necessary data in the EHR to minimize the burden of manual abstraction for stroke performance measures, decreasing daily time requirement from 2 hours to 15 minutes; generate reports using an automated process; and electronically transmit data to third parties. Provider champions [...]

To assess information sources that may elucidate errors related to radiologic diagnostic imaging, quantify the incidence of potential safety events from each source, and quantify the number of steps involved from diagnostic imaging chain and socio-technical factors.

Clinical vocabularies allow for standard representation of clinical concepts, and can also contain knowledge structures, such as hierarchy, that facilitate the creation of maintainable and accurate clinical decision support (CDS). A key architectural feature of clinical hierarchies is how they handle parent-child relationships - specifically whether hierarchies are strict hierarchies (allowing a single parent per concept) or polyhierarchies (allowing multiple parents per concept). These structures handle subsumption relationships (ie, ancestor [...]

Online platforms have created a variety of opportunities for breast patients to discuss their hormonal therapy, a long-term adjuvant treatment to reduce the chance of breast cancer occurrence and mortality. The goal of this investigation is to ascertain the extent to which the messages breast cancer patients communicated through an online portal can indicate their potential for discontinuing hormonal therapy.

Globally, 36% of deaths among children can be attributed to environmental factors. However, no comprehensive list of environmental exposures exists. We seek to address this gap by developing a literature-mining algorithm to catalog prenatal environmental exposures.

To illustrate the need for wider implementation of the CancelRx message by quantifying and characterizing the inappropriate usage of new electronic prescription (NewRx) messages for communicating discontinuation instructions to pharmacies.

Develop an approach, One-class-at-a-time, for triaging psychiatric patients using machine learning on textual patient records. Our approach aims to automate the triaging process and reduce expert effort while providing high classification reliability.

Data derived from primary care electronic medical records (EMRs) are being used for research and surveillance. Case definitions are required to identify patients with specific conditions in EMR data with a degree of accuracy. The purpose of this study is to identify and provide a summary of case definitions that have been validated in primary care EMR data.

Medication reconciliation (MedRec) can improve patient safety by resolving medication discrepancies. Because information technology (IT) and patient engagement are promising approaches to optimizing MedRec, the SEAMPAT project aims to develop a MedRec IT platform based on two applications: the "patient app" and the "MedRec app." This study evaluates three dimensions of the usability (efficiency, satisfaction, and effectiveness) and usefulness of the patient app.

Electronic health record (EHR) algorithms for defining patient cohorts are commonly shared as free-text descriptions that require human intervention both to interpret and implement. We developed the Phenotype Execution and Modeling Architecture (PhEMA, http://projectphema.org) to author and execute standardized computable phenotype algorithms. With PhEMA, we converted an algorithm for benign prostatic hyperplasia, developed for the electronic Medical Records and Genomics network (eMERGE), into a standards-based computable format. Eight sites (7 [...]