SmartChart Suite: a Fast Healthcare Interoperability Resources-based framework for longitudinal syphilis surveillance using structured and unstructured data.

Objective: The resurgence of syphilis in the United States presents a significant public health challenge. Much of the information needed for syphilis surveillance resides in electronic health records (EHRs). In this manuscript, we describe a surveillance platform for automating the extraction of EHR data, known as SmartChart Suite, and the results from a pilot.

Extending an open-source tool to measure data quality: case report on Observational Health Data Science and Informatics (OHDSI).

Introduction: As the health system seeks to leverage large-scale data to inform population outcomes, the informatics community is developing tools for analysing these data. To support data quality assessment within such a tool, we extended the open-source software Observational Health Data Sciences and Informatics (OHDSI) to incorporate new functions useful for population health.

Methods: We developed and tested methods to measure the completeness, timeliness and entropy of information.

Risk of angioedema associated with levetiracetam compared with phenytoin: Findings of the observational health data sciences and informatics research network.

Recent adverse event reports have raised the question of increased angioedema risk associated with exposure to levetiracetam. To help address this question, the Observational Health Data Sciences and Informatics research network conducted a retrospective observational new-user cohort study of seizure patients exposed to levetiracetam (n = 276,665) across 10 databases. With phenytoin users (n = 74,682) as a comparator group, propensity score-matching was conducted and hazard ratios computed for angioedema events by per-protocol and intent-to-treat analyses.

Characterizing treatment pathways at scale using the OHDSI network.

Observational research promises to complement experimental research by providing large, diverse populations that would be infeasible for an experiment. Observational research can test its own clinical hypotheses, and observational studies also can contribute to the design of experiments and inform the generalizability of experimental research. Understanding the diversity of populations and the variance in care is one component.

Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers.

The vision of creating accessible, reliable clinical evidence by accessing the clincial experience of hundreds of millions of patients across the globe is a reality. Observational Health Data Sciences and Informatics (OHDSI) has built on learnings from the Observational Medical Outcomes Partnership to turn methods research and insights into a suite of applications and exploration tools that move the field closer to the ultimate goal of generating evidence about all aspects of healthcare to serve the needs of patients, clinicians and all other decision-makers around the world.

An Interactive User Interface for Drug Labeling to Improve Readability and Decision-Making.

FDA-approved prescribing information (also known as product labeling or labels) contain critical safety information for health care professionals. Drug labels have often been criticized, however, for being overly complex, difficult to read, and rife with overwarning, leading to high cognitive load. In this project, we aimed to improve the usability of drug labels by increasing the 'signal-to-noise ratio' and providing meaningful information to care providers based on patient-specific comorbidities and concomitant medications.

Regenstrief Institute's Medical Gopher: a next-generation homegrown electronic medical record system.

Objective: Regenstrief Institute developed one of the seminal computerized order entry systems, the Medical Gopher, for implementation at Wishard Hospital nearly three decades ago. Wishard Hospital and Regenstrief remain committed to homegrown software development, and over the past 4 years we have fully rebuilt Gopher with an emphasis on usability, safety, leveraging open source technologies, and the advancement of biomedical informatics research. Our objective in this paper is to summarize the functionality of this new system and highlight its novel features.

Speaking the same language? International variations in the safety information accompanying top-selling prescription drugs.

Background: The official prescribing information document distributed with a prescription drug is a key source of safety information, but it may include excessive or insufficient details.

Objectives: To compare prescribing information approved by the US Food and Drug Administration with the UK, Canada and Australia to identify content differences in safety warnings.

Methods: For 20 top-selling prescription drugs, we used an automated natural language processing tool to calculate the number and severity of reported adverse drug reactions (ADRs).

Adherence to drug-drug interaction alerts in high-risk patients: a trial of context-enhanced alerting.

Objective: Drug-drug interaction (DDI) alerting is an important form of clinical decision support, yet physicians often fail to attend to critical DDI warnings due to alert fatigue. We previously described a model for highlighting patients at high risk of a DDI by enhancing alerts with relevant laboratory data. We sought to evaluate the effect of this model on alert adherence in high-risk patients.

Literature based drug interaction prediction with clinical assessment using electronic medical records: novel myopathy associated drug interactions.

Drug-drug interactions (DDIs) are a common cause of adverse drug events. In this paper, we combined a literature discovery approach with analysis of a large electronic medical record database method to predict and evaluate novel DDIs. We predicted an initial set of 13197 potential DDIs based on substrates and inhibitors of cytochrome P450 (CYP) metabolism enzymes identified from published in vitro pharmacology experiments.

A successful model and visual design for creating context-aware drug-drug interaction alerts.

Evaluating the potential harm of a drug-drug interaction (DDI) requires knowledge of a patient's relevant co-morbidities and risk factors. Current DDI alerts lack such patient-specific contextual data. In this paper, we present an efficient model for integrating pertinent patient data into DDI alerts.

ADESSA: A Real-Time Decision Support Service for Delivery of Semantically Coded Adverse Drug Event Data.

Evaluating medications for potential adverse events is a time-consuming process, typically involving manual lookup of information by physicians. This process can be expedited by CDS systems that support dynamic retrieval and filtering of adverse drug events (ADE's), but such systems require a source of semantically-coded ADE data. We created a two-component system that addresses this need.

Data visualization speeds review of potential adverse drug events in patients on multiple medications.

Patients on multiple medications are at increased risk for adverse drug events. While physicians can reduce this risk by regularly reviewing the side-effect profiles of their patients' medications, this process can be time-consuming. We created a decision support system designed to expedite reviewing potential adverse reactions through information visualization.

Electronic medical billing records and public health surveillance: comparison of two systems used during the 1996 Atlanta Olympic Games.

For selected diagnoses of public health interest during the 1996 Olympic Games, the authors compared data concurrently obtained on the same patient population by two separate surveillance systems: (1) an existing hospital electronic medical billing records system and (2) a system based on manual record abstraction. Counts of total patient visits closely agreed, though the two systems differed considerably in some diagnostic categories, especially injuries. The authors concluded that while causation, risk factors, and illness severity are not reflected directly in standard International Classification of Diseases (ICD) codes, and "E" codes to indicate causation may not be used, special-purpose surveillance systems based on existing computerized medical records may be as effective as manual data abstracting.