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Information overload remains a challenge for patients seeking clinical trials. We present a novel system (DQueST) that reduces information overload for trial seekers using dynamic questionnaires.

Mobile health (mHealth) interventions have demonstrated promise in improving outcomes by motivating patients to adopt and maintain healthy lifestyle changes as well as improve adherence to guideline-directed medical therapy. Early results combining behavioral economic strategies with mHealth delivery have demonstrated mixed results. In reviewing these studies, we propose that the success of a mHealth intervention links more strongly with how well it connects patients back to routine clinical care, rather [...]

Clinical decision support (CDS) systems are prevalent in electronic health records and drive many safety advantages. However, CDS systems can also cause unintended consequences. Monitoring programs focused on alert firing rates are important to detect anomalies and ensure systems are working as intended. Monitoring efforts do not generally include system load and time to generate decision support, which is becoming increasingly important as more CDS systems rely on external, web-based [...]

Despite the widespread and increasing use of electronic health records (EHRs), the quality of EHRs is problematic. Efforts have been made to address reasons for poor EHR documentation quality. Previous systematic reviews have assessed intervention effectiveness within the outpatient setting or paper documentation. The purpose of this systematic review was to assess the effectiveness of interventions seeking to improve EHR documentation within an inpatient setting.

We assessed whether machine learning can be utilized to allow efficient extraction of infectious disease activity information from online media reports.

Prospective enrollment of research subjects in the fast-paced emergency department (ED) is challenging. We sought to develop a software application to increase real-time clinical trial enrollment during an ED visit. The Prospective Intelligence System for Clinical Emergency Services (PISCES) scans the electronic health record during ED encounters for preselected clinical characteristics of potentially eligible study participants and notifies the treating physician via mobile phone text alerts. PISCES alerts began 3 [...]

Identifying patients who meet selection criteria for clinical trials is typically challenging and time-consuming. In this article, we describe our clinical natural language processing (NLP) system to automatically assess patients' eligibility based on their longitudinal medical records. This work was part of the 2018 National NLP Clinical Challenges (n2c2) Shared-Task and Workshop on Cohort Selection for Clinical Trials.

This article presents a novel method of semisupervised learning using convolutional autoencoders for optical endomicroscopic images. Optical endomicroscopy (OE) is a newly emerged biomedical imaging modality that can support real-time clinical decisions for the grade of dysplasia. To enable real-time decision making, computer-aided diagnosis (CAD) is essential for its high speed and objectivity. However, traditional supervised CAD requires a large amount of training data. Compared with the limited number of [...]