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Shared decision-making (SDM) is an approach in which patients and clinicians act as partners in making medical decisions. Patients receive the information needed to decide and are encouraged to balance risks, benefits, and preferences. Informative materials are vital to SDM. Atrial fibrillation (AF) is the most common cardiac arrhythmia and responsible for 10% of ischemic strokes, however 1/3 of patients are not on appropriate anticoagulation. Decision sharing may facilitate treatment [...]

Tumor registries are a rich source of real-world data which can be used to test important hypotheses that inform clinical care. Exploratory data analysis at the level of individual subjects, when enhanced by interactive data visualizations, has the potential to provide novel insights and generate new hypothesis.

Situational awareness and anticipatory guidance for nurses receiving a patient after surgery are keys to patient safety. Little work has defined the role of artificial intelligence (AI) to support these functions during nursing handoff communication or patient assessment. We used interviews to better understand how AI could work in this context.

Modern healthcare data reflect massive multi-level and multi-scale information collected over many years. The majority of the existing phenotyping algorithms use case-control definitions of disease. This paper aims to study the time to disease onset and progression and identify the time-varying risk factors that drive them.

To provide a method for evaluating the interoperability of Fast Healthcare Interoperability Resources (FHIR®) clients and servers supporting different FHIR resources, profiles, and versions, and to determine the feasibility of FHIR servers supporting multiple FHIR Implementation Guides (IGs).

Problem-oriented documentation is an accepted method of note construction which facilitates clinical thought processes. However, problem-oriented documentation is challenging to put into practice using commercially available electronic health record (EHR) systems.

The objective of this study was to systematically review all literature studying the effect of patient education on patient engagement through patient portals.

The objective of this study is to describe application of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to support medical device real-world evaluation in a National Evaluation System for health Technology Coordinating Center (NESTcc) Test-Case involving 2 healthcare systems, Mercy Health and Mayo Clinic. CDM implementation was coordinated across 2 healthcare systems with multiple hospitals to aggregate both medical device data from supply chain databases and patient [...]

The aim of this study was to test the feasibility of PICO (participants, interventions, comparators, outcomes) entity extraction using weak supervision and natural language processing.

A shallow convolutional neural network (CNN), TextCNN, has become nearly ubiquitous for classification among clinical and medical text. This research presents a novel eXplainable-AI (X-AI) software, Red Flag/Blue Flag (RFBF), designed for binary classification with TextCNN. RFBF visualizes each convolutional filter's discriminative capability. This is a more informative approach than direct assessment of logit contribution, features that overfit to train set nuances on smaller datasets may indiscriminately activate large logits [...]