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Patient-generated health data (PGHD), such as patient-reported outcomes and mobile health data, have been increasingly used to improve health care delivery and outcomes. Integrating PGHD into electronic health records (EHRs) further expands the capacities to monitor patients' health status without requiring office visits or hospitalizations. By reviewing and discussing PGHD with patients remotely, clinicians could address the clinical issues efficiently outside of clinical settings. However, EHR-integrated PGHD may create a [...]

Health and biomedical informatics graduate-level degree programs have proliferated across the United States in the last 10 years. To help inform programs on practices in teaching and learning, a survey of master's programs in health and biomedical informatics in the United States was conducted to determine the national landscape of culminating experiences including capstone projects, research theses, internships, and practicums. Almost all respondents reported that their programs required a culminating [...]

The purpose of the study was to determine if association exists between evidence-based provider training and clinician proficiency in electronic health record (EHR) use and if so, which EHR use metrics and vendor-defined indices exhibited association.

Pressure injuries are common and serious complications for hospitalized patients. The pressure injury rate is an important patient safety metric and an indicator of the quality of nursing care. Timely and accurate prediction of pressure injury risk can significantly facilitate early prevention and treatment and avoid adverse outcomes. While many pressure injury risk assessment tools exist, most were developed before there was access to large clinical datasets and advanced statistical [...]

The rapidly evolving science about the Coronavirus Disease 2019 (COVID-19) pandemic created unprecedented health information needs and dramatic changes in policies globally. We describe a platform, Watson Assistant (WA), which has been used to develop conversational agents to deliver COVID-19 related information. We characterized the diverse use cases and implementations during the early pandemic and measured adoption through a number of users, messages sent, and conversational turns (ie, pairs of [...]

IBM(R) Watson for Oncology (WfO) is a clinical decision-support system (CDSS) that provides evidence-informed therapeutic options to cancer-treating clinicians. A panel of experienced oncologists compared CDSS treatment options to treatment decisions made by clinicians to characterize the quality of CDSS therapeutic options and decisions made in practice.

Accurate risk prediction is important for evaluating early medical treatment effects and improving health care quality. Existing methods are usually designed for dynamic medical data, which require long-term observations. Meanwhile, important personalized static information is ignored due to the underlying uncertainty and unquantifiable ambiguity. It is urgent to develop an early risk prediction method that can adaptively integrate both static and dynamic health data.

We aim to develop a hybrid model for earlier and more accurate predictions for the number of infected cases in pandemics by (1) using patients' claims data from different counties and states that capture local disease status and medical resource utilization; (2) utilizing demographic similarity and geographical proximity between locations; and (3) integrating pandemic transmission dynamics into a deep learning model.