Search AMIA.org

We propose to use segment graph convolutional and recurrent neural networks (Seg-GCRNs), which use only word embedding and sentence syntactic dependencies, to classify relations from clinical notes without manual feature engineering. In this study, the relations between 2 medical concepts are classified by simultaneously learning representations of text segments in the context of sentence syntactic dependency: preceding, concept1, middle, concept2, and succeeding segments. Seg-GCRN was systematically evaluated on the i2b2/VA [...]

The aim of this study was to generate synthetic electronic health records (EHRs). The generated EHR data will be more realistic than those generated using the existing medical Generative Adversarial Network (medGAN) method.

CancelRx allows prescribers to send electronic cancellation messages to pharmacies when medications are discontinued. Little is known about its functionality and impact on clinical workflows.

Thirty-day hospital readmissions are a quality metric for health care systems. Predictive models aim to identify patients likely to readmit to more effectively target preventive strategies. Many risk of readmission models have been developed on retrospective data, but prospective validation of readmission models is rare. To the best of our knowledge, none of these developed models have been evaluated or prospectively validated in a military hospital.

In a time-constrained clinical environment, physicians cannot feasibly document all aspects of an office visit in the electronic health record (EHR). This is especially true for patients with multiple chronic conditions requiring complex clinical reasoning. It is unclear how physicians prioritize the documentation of health information in the EHR.

The purpose of this article is to describe neonatal intensive care unit clinician perceptions of a continuous predictive analytics technology and how those perceptions influenced clinician adoption. Adopting and integrating new technology into care is notoriously slow and difficult; realizing expected gains remain a challenge.

Heart failure is one of the serious cardiovascular diseases, which poses a global pandemic and places a heavy burden on health care systems worldwide. The incidence of this disease in Iran is higher than in other Asian countries. To reduce patients' complications, readmission rates, and health care expenditures, it is necessary to design interventions, which are culturally appropriate and based on community needs.

With the widespread adoption of vendor-supplied electronic health record (EHR) systems, clinical decision support (CDS) customization efforts beyond those anticipated by the vendor may require the use of technologies external to the EHR such as web services. Pursuing such customizations, however, is not without risk. Validating the expected behavior of a customized CDS system in the high-volume, complex environment of the live EHR is a challenging problem.