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Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data.

In contrast to efficacy, safety hypotheses of clinical trials are not always pre-specified, and therefore, the safety interpretation work of a trial tends to be more exploratory, often reactive, and the analysis more statistically and graphically challenging.

Microbiology laboratory results are complex and cumbersome to review. We sought to develop a new review tool to improve the ease and accuracy of microbiology results review.

To build effective applications, technology designers must understand consumer health needs. Pregnancy is a common health condition, and expectant families have unanswered questions. This study examined consumer health-related needs in pregnant women and caregivers and determined the types of needs that were not met.

Venous thromboembolism (VTE) prophylaxis is an important consideration for hospitalized older adults, and the Padua Prediction Score (PPS) is a risk prediction tool used to prioritize patient selection. We developed an automated PPS (APPS) algorithm using electronic health record (EHR) data. This study examines the accuracy of APPS and its individual components versus manual data extraction.

Over the last decade, electronic health records (EHRs) have shaped clinical practice. In this article, we investigated the perceived effects of EHR use on clinical workflow and meaningful use (MU) performance metrics.

Inhospital pediatric trauma care typically spans multiple locations, which influences the use of resources, that could be improved by gaining a better understanding of the inhospital flow of patients and identifying opportunities for improvement.

U.S. poison control centers pose a special case for patient identity matching because they collect only minimal patient identifying information.

To explore perceptions of critical care providers about a novel collaborative inpatient health information technology (HIT) in a pediatric intensive care unit (PICU) setting.