Search AMIA.org

 Leaders in Informatics, Quality, and Systems (LInQS) is a non-ACGME (Accreditation Council for Graduate Medical Education)-accredited 2-year training program developed to enhance training in the fields of health care delivery, quality improvement (QI), clinical informatics, and leadership.

To integrate a computerized adaptive test for depression into the electronic health record (EHR) and establish systems for administering assessments in-clinic and via a patient portal to improve depression care.

 The electronic health record (EHR) has been associated with provider burnout, exacerbated by increasing In-Basket burden.

To enable interactive visualization of the vaginal microbiome across the pregnancy and facilitate discovery of novel insights and generation of new hypotheses.

 Evidence supports using patient-generated blood pressure data for better outcomes in hypertension management. However, obstacles like dealing with home-generated paper data sets and questions of validity slowed the meaningful incorporation of home blood pressure into clinical care. As clinicians value patient data more, reliance on digital health solutions for data collection and shared decision-making grows.

 Experiential learning through simulation allows students to apply didactic knowledge to real-world situations. Tabletop simulation allows for the exploration of a variety of topics, including cybersecurity in health care. Due to its low frequency, yet high-risk nature, simulation is a perfect educational modality to practice responding to a cybersecurity attack. As such, the authors designed and executed a tabletop cybersecurity simulation consisting of a prebriefing, four rounds of injects detailing [...]

The Multiple Chronic Conditions (MCCs) Electronic Care (e-Care) Plan project aims to establish care planning data standards for individuals living with MCCs. This article reports on the portion of the project focused on long COVID and presents the process of identifying and modeling data elements using the HL7 Fast Healthcare Interoperability Resources (FHIR) standard.

 Telemetry monitoring is crucial for high-risk patients but excessive use beyond practice standards increases costs. Prior studies have shown that electronic health record (EHR) alerts reduce low-value telemetry monitoring. However, specific components of these alerts that contribute to effectiveness are unknown.

Accelerating demand for patient messaging has impacted the practice of many providers. Messages are not recommended for urgent medical issues, but some do require rapid attention. This presents an opportunity for artificial intelligence (AI) methods to prioritize review of messages. Our study aimed to highlight some patient portal messages for prioritized review using a custom AI system integrated into the electronic health record (EHR).