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To improve timely access to quality HIV research data, the Rakai Health Sciences Program (RHSP) Data Mart was developed to store cohort study data from a legacy database platform in a modernized system using standard data management processes. The RHSP Data Mart was developed on a Microsoft SQL Server platform using Microsoft SQL Server Integration Services with custom data mappings and queries. The data mart stores 20+ years of longitudinal [...]

Machine learning has the potential to improve identification of patients for appropriate diagnostic testing and treatment, including those who have rare diseases for which effective treatments are available, such as acute hepatic porphyria (AHP). We trained a machine learning model on 205 571 complete electronic health records from a single medical center based on 30 known cases to identify 22 patients with classic symptoms of AHP that had neither been diagnosed [...]

The International Classification of Childhood Cancer (ICCC) facilitates the effective classification of a heterogeneous group of cancers in the important pediatric population. However, there has been no development of machine learning models for the ICCC classification. We developed deep learning-based information extraction models from cancer pathology reports based on the ICD-O-3 coding standard. In this article, we describe extending the models to perform ICCC classification.

Vulnerable populations face numerous barriers in managing chronic disease(s). As healthcare systems work toward integrating social risk factors into electronic health records and healthcare delivery, we need better understanding of the interrelated nature of social needs within patients' everyday lives to inform effective informatics interventions to advance health equity.

To implement a dynamic data management and control framework that meets the multiple demands of high data quality, rigorous information technology security, and flexibility to continuously incorporate new methodology for a large disease registry.

Structured real-world data (RWD), such as those found in cancer registries, provide a rich source of information regarding the natural history of cancer. Interactive data visualizations of cancer lesions can provide insights into certain clinical tumor characteristics (CTC). Software that can be integrated into an oncological data collection effort and generate anatomical data visualizations of CTC are limited.

Physician burnout is at epidemic proportions, impacts clinical outcomes, and is very costly. Although there is emerging data about effective interventions, most physicians at risk of burnout do not seek help. Survey-based measures exist which can quantify burnout within populations, but these are usually only administered episodically. We hypothesized that a novel modality for real-time measurement of happiness and stressors would be acceptable, scalable, and could provide new actionable insights.

The coronavirus disease 2019 pandemic has resulted in large-scale changes to incorporate telemedicine for the delivery of care. People with cystic fibrosis (CF) have care considerations that pose challenges to telemedicine; they include frequent visits for pulmonary disease progression, medication management, and evaluation by a multidisciplinary team of providers. We share our center's experience with video visits replacing in-person clinic evaluation, using quality improvement strategies to create a replicable workflow [...]

We designed an mHealth application (app) user interface (UI) prototype informed by participatory design sessions, persuasive systems design (PSD) principles, and Lorig and Holman's self-management behavior framework to support self-management activities of Hispanic informal dementia caregivers and assessed their perceptions and preferences regarding features and functions of the app.

The Alzheimer's Therapeutic Research Institute (ATRI) developed a novel clinical data management system, the ATRI electronic data capture system (ATRI EDC), to address the complex regulatory, operational, and data requirements that arise in the conduct of multicenter Alzheimer's disease and Alzheimer's disease-related dementias (AD/ADRDs) clinical trials. We describe the system, its utility, and the broader implications for the field of clinical trials and clinical research informatics.