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As HIV/AIDS is considered a chronic disease; quality of life (QoL) has become an important focus for researchers and healthcare providers. Technology-mediated interventions have demonstrated improved clinical effectiveness in outcomes, such as viral suppression, for persons living with HIV/AIDS (PLWH). However, the evidence to support the impact of these interventions on QoL is lacking.

Computerized provider order entry (CPOE) is a technology with potential to transform care delivery. While CPOE systems have been studied in adult populations, less is known about the implementation of CPOE in the neonatal intensive care unit (NICU) and perceptions of nurses and physicians using the system.

Patient matching is a key barrier to achieving interoperability. Patient demographic elements must be consistently collected over time and region to be valuable elements for patient matching.

Follow-up with a primary care provider after hospital discharge has been associated with a reduced likelihood of readmission. However, primary care providers are frequently unaware of their patients' hospitalizations. Event notification may be an effective tool for reducing readmissions by notifying primary care providers when their patients have been admitted to and discharged from a hospital.

Despite the widespread adoption of electronic health records (EHRs), usability of many EHRs continues to be suboptimal, with some vendors failing to meet usability standards, resulting in clinician frustration and patient safety hazards. In an effort to increase EHR vendor competition on usability, recommendations have been made and legislation drafted to develop comparison tools that would allow purchasers to better understand the usability of EHR products prior to purchase. Usability [...]

To determine the effect of availability of clinical information from an integrated electronic health record system on pregnancy outcomes at the point of care.

The goal of this study was to develop a practical framework for recognizing and disambiguating clinical abbreviations, thereby improving current clinical natural language processing (NLP) systems' capability to handle abbreviations in clinical narratives.

Phenotyping algorithms are capable of accurately identifying patients with specific phenotypes from within electronic medical records systems. However, developing phenotyping algorithms in a scalable way remains a challenge due to the extensive human resources required. This paper introduces a high-throughput unsupervised feature selection method, which improves the robustness and scalability of electronic medical record phenotyping without compromising its accuracy.