Toward Real-World Reproducibility: Verifying Value Sets for Clinical Research.

Standardized operational definitions are an important tool to improve reproducibility of research using secondary real-world healthcare data. This approach was leveraged for studies evaluating the effectiveness of AZD7442 as COVID-19 pre-exposure prophylaxis across multiple healthcare systems. Value sets were defined, grouped, and mapped.

Risperdal CONSTA Needle Detachment. Incidence Rates Before and After Kit Redesign: A Retrospective Study using Electronic Health Records and Natural Language Processing in the Department of Veterans Affairs.

Introduction: Janssen received reports of needle detachments for Risperdal CONSTA and, in response, redesigned the kit.

Objective: The study objective was to estimate the rate of Risperdal CONSTA needle detachments prior to and after the introduction of a redesigned kit.

Methods: This retrospective study used record abstraction in the US Department of Veterans Affairs (VA).

Identifying Patients With Relapsing-Remitting Multiple Sclerosis Using Algorithms Applied to US Integrated Delivery Network Healthcare Data.

Background: Relapsing-remitting multiple sclerosis (RRMS) has a major impact on affected patients; therefore, improved understanding of RRMS is important, particularly in the context of real-world evidence.

Objectives: To develop and validate algorithms for identifying patients with RRMS in both unstructured clinical notes found in electronic health records (EHRs) and structured/coded health care claims data.

Methods: US Integrated Delivery Network data (2010-2014) were queried for study inclusion criteria (possible multiple sclerosis [MS] base cohort): one or more MS diagnosis code, patients aged 18 years or older, 1 year or more baseline history, and no other demyelinating diseases.

Characterization and survival of patients with metastatic basal cell carcinoma in the Department of Veterans Affairs: a retrospective electronic health record review.

Available descriptive statistics for patients with metastatic basal cell carcinoma (mBCC) are limited. To describe disease characteristics, treatment patterns, survival outcomes, and prognostic factors of patients with mBCC, we conducted a retrospective review of electronic health records in the Department of Veterans Affairs (VA). The primary outcome was survival.

Determining Multiple Sclerosis Phenotype from Electronic Medical Records.

Background: Multiple sclerosis (MS), a central nervous system disease in which nerve signals are disrupted by scarring and demyelination, is classified into phenotypes depending on the patterns of cognitive or physical impairment progression: relapsing-remitting MS (RRMS), primary-progressive MS (PPMS), secondary-progressive MS (SPMS), or progressive-relapsing MS (PRMS). The phenotype is important in managing the disease and determining appropriate treatment. The ICD-9-CM code 340.

Characteristics and use of treatment modalities of patients with binge-eating disorder in the Department of Veterans Affairs.

Objective: In 2013 binge-eating disorder (BED) was recognized as a formal diagnosis, but was historically included under the diagnosis code for eating disorder not otherwise specified (EDNOS). This study compared the characteristics and use of treatment modalities in BED patients to those with EDNOS without BED (EDNOS-only) and to matched-patients with no eating disorders (NED).

Methods: Patients were identified for this study from electronic health records in the Department of Veterans Affairs from 2000 to 2011.

Multiple Sclerosis and Risk of Infection-Related Hospitalization and Death in US Veterans.

Background: This study estimated the risk of infection-related hospitalizations and death in patients with and without multiple sclerosis (MS).

Methods: We identified adults with MS in the US Department of Veterans Affairs (VA) system between 1999 and 2010. Each veteran with MS was matched, on age and sex, with up to four veterans without MS.

Healthcare costs and resource utilization of patients with binge-eating disorder and eating disorder not otherwise specified in the Department of Veterans Affairs.

Objective: The objective of this study was to compare the one-year healthcare costs and utilization of patients with binge-eating disorder (BED) to patients with eating disorder not otherwise specified without BED (EDNOS-only) and to matched patients without an eating disorder (NED).

Methods: A natural language processing (NLP) algorithm identified adults with BED from clinical notes in the Department of Veterans Affairs (VA) electronic health record database from 2000 to 2011. Patients with EDNOS-only were identified using ICD-9 code (307.

Automated identification of patients with a diagnosis of binge eating disorder from narrative electronic health records.

Binge eating disorder (BED) does not have an International Classification of Diseases, 9th or 10th edition code, but is included under 'eating disorder not otherwise specified' (EDNOS). This historical cohort study identified patients with clinician-diagnosed BED from electronic health records (EHR) in the Department of Veterans Affairs between 2000 and 2011 using natural language processing (NLP) and compared their characteristics to patients identified by EDNOS diagnosis codes. NLP identified 1487 BED patients with classification accuracy of 91.

Requirements for an efficient biomedical terminology browser.

Biomedical ontologies provide knowledge in support of health care applications. Knowledge engineers require tools to develop and manage a rich biomedical ontology. An efficient terminology browser is necessary for knowledge engineers to develop and manage a rich biomedical ontology that supports a variety of health care applications.

Building knowledge: a system for batch loading biomedical terminologies.

Ontologies provide knowledge that supports health care applications. Biomedical ontologies must include a vast number of both standard and proprietary terminology concepts. Conventional loading methods are labor-intensive and inefficient.

Informatics in radiology: IHE teaching file and clinical trial export integration profile: functional examples.

The digital revolution in radiology introduced the need for electronic export of medical images. However, the current export process is complicated and time consuming. In response to this continued difficulty, the Integrating the Healthcare Enterprise (IHE) initiative published the Teaching File and Clinical Trial Export (TCE) integration profile.

Using Java to generate globally unique identifiers for DICOM objects.

Digital imaging and communication in medicine (DICOM) specifies that all DICOM objects have globally unique identifiers (UIDs). Creating these UIDs can be a difficult task due to the variety of techniques in use and the requirement to ensure global uniqueness. We present a simple technique of combining a root organization identifier, assigned descriptive identifiers, and JAVA generated unique identifiers to construct DICOM compliant UIDs.

Translating the IHE Teaching File and Clinical Trial Export (TCE) profile document templates into functional DICOM structured report objects.

The Integrating the Healthcare Enterprise (IHE) Teaching File and Clinical Trial Export (TCE) integration profile describes a standard workflow for exporting key images from an image manager/archive to a teaching file, clinical trial, or electronic publication application. Two specific digital imaging and communication in medicine (DICOM) structured reports (SR) reference the key images and contain associated case information. This paper presents step-by-step instructions for translating the TCE document templates into functional and complete DICOM SR objects.

Using applet-servlet communication for optimizing window, level and crop for DICOM to JPEG conversion.

In the creation of interesting radiological cases in a digital teaching file, it is necessary to adjust the window and level settings of an image to effectively display the educational focus. The web-based applet described in this paper presents an effective solution for real-time window and level adjustments without leaving the picture archiving and communications system workstation. Optimized images are created, as user-defined parameters are passed between the applet and a servlet on the Health Insurance Portability and Accountability Act-compliant teaching file server.

Academic podcasting: quality media delivery.

A video podcast of the CME-approved University of Utah Department of Biomedical Informatics seminar was created in order to address issues with streaming video quality, take advantage of popular web-based syndication methods, and make the files available for convenient, subscription-based download. An RSS feed, which is automatically generated, contains links to the media files and allows viewers to easily subscribe to the weekly seminars in a format that guarantees consistent video quality.

Informatics in radiology (infoRAD): Vendor-neutral case input into a server-based digital teaching file system.

Although digital teaching files are important to radiology education, there are no current satisfactory solutions for export of Digital Imaging and Communications in Medicine (DICOM) images from picture archiving and communication systems (PACS) in desktop publishing format. A vendor-neutral digital teaching file, the Radiology Interesting Case Server (RadICS), offers an efficient tool for harvesting interesting cases from PACS without requiring modifications of the PACS configurations. Radiologists push imaging studies from PACS to RadICS via the standard DICOM Send process, and the RadICS server automatically converts the DICOM images into the Joint Photographic Experts Group format, a common desktop publishing format.