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Complex electronic medical records (EMRs) presenting large amounts of data create risks of cognitive overload. We are designing a Learning EMR (LEMR) system that utilizes models of intensive care unit (ICU) physicians' data access patterns to identify and then highlight the most relevant data for each patient.

Although patients who work and have related health issues are usually first seen in primary care, providers in these settings do not routinely ask questions about work. Guidelines to help manage such patients are rarely used in primary care. Electronic health record (EHR) systems with worker health clinical decision support (CDS) tools have potential for assisting these practices.

Care-management tools are typically utilized for chronic disease management. Sonoma County government agencies employed advanced health information technologies, artificial intelligence (AI), and interagency process improvements to help transform health and health care for socially disadvantaged groups and other displaced individuals.

Registered nurses (RNs) regularly adapt their work to ever-changing situations but routine adaptation transforms into RN strain when service demand exceeds staff capacity and patients are at risk of missed or delayed care. Dynamic monitoring of RN strain could identify when intervention is needed, but comprehensive views of RN work demands are not readily available. Electronic care delivery tools such as nurse call systems produce ambient data that illuminate workplace [...]

Improving the usability of electronic health records (EHR) continues to be a focus of clinicians, vendors, researchers, and regulatory bodies. To understand the impact of usability redesign of an existing, site-configurable feature, we evaluated the user interface (UI) used to screen for depression, alcohol and drug misuse, fall risk, and the existence of advance directive information in ambulatory settings.

Evidence derived from existing health-care data, such as administrative claims and electronic health records, can fill evidence gaps in medicine. However, many claim such data cannot be used to estimate causal treatment effects because of the potential for observational study bias; for example, due to residual confounding. Other concerns include P hacking and publication bias. In response, the Observational Health Data Sciences and Informatics international collaborative launched the Large-scale Evidence [...]

The collection of race, ethnicity, and language (REaL) data from patients is advocated as a first step to identify, monitor, and improve health inequities. As a result, many health care institutions collect patients' preferred languages in their electronic health records (EHRs). These data may be used in clinical care, research, and quality improvement. However, the accuracy of EHR language data are rarely assessed.

This study was aimed to explore the intersection between organizational environment, workflow, and technology in pediatric emergency departments (EDs) and how these factors impact antibiotic prescribing decisions.

Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions.