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The American Medical Informatics Association convened the 2014 Health Policy Invitational Meeting to develop recommendations for updates to current policies and to establish an informatics research agenda for personalizing medicine. In particular, the meeting focused on discussing informatics challenges related to personalizing care through the integration of genomic or other high-volume biomolecular data with data from clinical systems to make health care more efficient and effective. This report summarizes the [...]

Develop a device-agnostic cloud platform to host diabetes device data and catalyze an ecosystem of software innovation for type 1 diabetes (T1D) management.

Given the clinical and public health benefits of routine Human Immunodeficiency Virus (HIV) testing in the emergency department (ED) and Centers for Disease Control and Prevention recommendations, Maricopa Medical Center, as part of Maricopa Integrated Health System, started Test, Educate, Support, and Treat Arizona (TESTAZ) and became the first and, to-date, only hospital in Arizona to implement routine, non-targeted, opt-out, rapid HIV screening in the ED. The authors describe the [...]

To assess whether integrating critical result management software--Alert Notification of Critical Results (ANCR)--with an electronic health record (EHR)-based results management application impacts closed-loop communication and follow-up of nonurgent, clinically significant radiology results by primary care providers (PCPs).

ClinicalTrials.gov serves critical functions of disseminating trial information to the public and helping the trials recruit participants. This study assessed the readability of trial descriptions at ClinicalTrials.gov using multiple quantitative measures.

Through colonoscopy, polyps can be identified and removed to reduce colorectal cancer incidence and mortality. Appropriate use of surveillance colonoscopy, post polypectomy, is a focus of healthcare reform.

This study analyzes reports to the Egyptian medication error (ME) reporting system from June to December 2014.

This paper presents an automatic, active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort and (2) the robustness of incremental active learning framework across different selection criteria and data sets are determined.

Variations of clinical terms are very commonly encountered in clinical texts. Normalization methods that use similarity measures or hand-coded approximation rules for matching clinical terms to standard terminologies have limited accuracy and coverage.