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To propose a paradigm for a scalable time-aware clinical data search, and to describe the design, implementation and use of a search engine realizing this paradigm.

The growing use of artificial intelligence (AI) in health care has raised questions about who should be held liable for medical errors that result from care delivered jointly by physicians and algorithms. In this survey study comparing views of physicians and the U.S. public, we find that the public is significantly more likely to believe that physicians should be held responsible when an error occurs during care delivered with medical [...]

The prevalence of social media for sharing personal thoughts makes it a viable platform for the assessment of suicide risk. However, deep learning models are not able to capture the diverse nature of linguistic choices and temporal patterns that can be exhibited by a suicidal user on social media and end up overfitting on specific cues that are not generally applicable. We propose Adversarial Suicide assessment Hierarchical Attention (ASHA), a [...]

Artificial intelligence (AI) is critical to harnessing value from exponentially growing health and healthcare data. Expectations are high for AI solutions to effectively address current health challenges. However, there have been prior periods of enthusiasm for AI followed by periods of disillusionment, reduced investments, and progress, known as "AI Winters." We are now at risk of another AI Winter in health/healthcare due to increasing publicity of AI solutions that are [...]

Medication list discrepancies between outpatient clinics and pharmacies can lead to medication errors. Within the last decade, a new health information technology (IT), CancelRx, emerged to send a medication cancellation message from the clinic's electronic health record (EHR) to the outpatient pharmacy's software. The objective of this study was to measure the impact of CancelRx on reducing medication discrepancies between the EHR and pharmacy dispensing software.

This study examined the perspectives on the use of data visualizations and identified key features seriously ill children, their parents, and clinicians prefer to see when visualizing symptom data obtained from mobile health technologies (an Apple Watch and smartphone symptom app).

The Global Digital Exemplar (GDE) Program is a national attempt to accelerate digital maturity in healthcare providers through promoting knowledge transfer across the English National Health Service (NHS). "Blueprints"-documents capturing implementation experience-were intended to facilitate this knowledge transfer. Here we explore how Blueprints have been conceptualized, produced, and used to promote interorganizational knowledge transfer across the NHS.

Automated analysis of vaccine postmarketing surveillance narrative reports is important to understand the progression of rare but severe vaccine adverse events (AEs). This study implemented and evaluated state-of-the-art deep learning algorithms for named entity recognition to extract nervous system disorder-related events from vaccine safety reports.

Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020.