Usability Testing a Potentially Inappropriate Medication Dashboard: A Core Component of the Dashboard Development Process.

Background: With the increased usage of dashboard reporting systems to monitor and track patient panels by clinical users, developers must ensure that the information displays they produce are accurate and intuitive. When evaluating usability of a clinical dashboard among potential end users, developers oftentimes rely on methods such as questionnaires as opposed to other, more time-intensive strategies that incorporate direct observation.

Objectives: Prior to release of the potentially inappropriate medication (PIM) clinical dashboard, designed to facilitate completion of a quality improvement project by clinician scholars enrolled in the Veterans Affairs (VA) workforce development Geriatric Scholars Program (GSP), we evaluated the usability of the system.

An increased mortality risk is associated with abnormal iron status in diabetic and non-diabetic Veterans with predialysis chronic kidney disease.

Iron parameters have not been well characterized in pre-dialysis patients with chronic kidney disease (CKD), and it remains unclear if abnormal iron balance is associated with increased mortality. Therefore, we performed a historical cohort study using data from the Veterans Affairs Corporate Data Warehouse to evaluate the relationship between iron status and mortality. We identified a pre-dialysis CKD cohort with at least one set of iron indices between 2006-2015.

Validation of Algorithms Used to Identify Red Blood Cell Transfusion Related Admissions in Veteran Patients with End Stage Renal Disease.

Background: The goal of this study was to compare the performance of several database algorithms designed to identify red blood cell (RBC) Transfusion Related hospital Admissions (TRAs) in Veterans with end stage renal disease (ESRD).

Methods: Hospitalizations in Veterans with ESRD and evidence of dialysis between 01/01/2008 and 12/31/2013 were screened for TRAs using a clinical algorithm (CA) and four variations of claims-based algorithms (CBA 1-4). Criteria were implemented to exclude patients with non-ESRD-related anemia (e.

Learning to detect and understand drug discontinuation events from clinical narratives.

Objective: Identifying drug discontinuation (DDC) events and understanding their reasons are important for medication management and drug safety surveillance. Structured data resources are often incomplete and lack reason information. In this article, we assessed the ability of natural language processing (NLP) systems to unlock DDC information from clinical narratives automatically.

Predicting Psychiatric Hospitalizations among Elderly Veterans with a History of Mental Health Disease.

Introduction: Patient Aligned Care Team (PACT) care managers are tasked with identifying aging Veterans with psychiatric disease in attempt to prevent psychiatric crises. However, few resources exist that use real-time information on patient risk to prioritize coordinating appropriate care amongst a complex aging population.

Objective: To develop and validate a model to predict psychiatric hospital admission, during a 90-day risk window, in Veterans ages 65 or older with a history of mental health disease.