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Electronic health records (EHRs) provide opportunities for the development of computable predictive tools. Conventional machine learning methods and deep learning methods have been widely used for this task, with the approach of usually designing one tool for one clinical outcome. Here we developed PheW2P2V, a Phenome-Wide prediction framework using Weighted Patient Vectors. PheW2P2V conducts tailored predictions for phenome-wide phenotypes using numeric representations of patients' past medical records weighted based on [...]

This study aimed to investigate the predictive capabilities of historical patient records to predict patient adverse outcomes such as mortality, readmission, and prolonged length of stay (PLOS).

 This study aimed to assess the desirability, feasibility, and sustainability of integrating a project-based capstone course with the course-based curriculum of an interdisciplinary MSc Health Informatics program guided by a student-partnered steering committee and student-centered approach.

To report lessons from integrating the methods and perspectives of clinical informatics (CI) and implementation science (IS) in the context of Improving the Management of symPtoms during and following Cancer Treatment (IMPACT) Consortium pragmatic trials.

Firearm injuries constitute a public health crisis. At the healthcare encounter level, they are, however, rare events.

Clinical research networks facilitate collaborative research, but data sharing remains a common barrier.

Reinforcement learning (RL) represents a pivotal avenue within natural language processing (NLP), offering a potent mechanism for acquiring optimal strategies in task completion. This literature review studies various NLP applications where RL has demonstrated efficacy, with notable applications in healthcare settings.

 Clinical Decision Support (CDS) tools have a mixed record of effectiveness, often due to inadequate alignment with clinical workflows and poor usability. While there is a consensus that usability testing methods address these issues, in practice, usability testing is generally only used for selected projects (such as funded research studies). There is a critical need for CDS operations to apply usability testing to all CDS implementations.

 Social media networks have been found to provide emotional, instrumental, and social support, which may contribute to improved adherence to postbariatric surgery care recommendations.

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).