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Little has been written about physician stress that may be associated with electronic medical records (EMR).

Health information exchange (HIE) is a significant component of healthcare transformation strategies at both the state and national levels. HIE is expected to improve care coordination, and advance public health, but implementation is massively complex and involves significant risk. In New York, three regional health information organizations (RHIOs) implemented an HIE use case for public health reporting by demonstrating capability to deliver accurate responses to electronic queries via a set [...]

Electronic health record (EHR) use in ambulatory care can improve safety and quality; however, problems with design, implementation, and poor interface with other systems lead users to develop 'workarounds', or behaviors users adopt to overcome perceived limitations in a technical system. We documented workarounds used in independent, community-based primary care practices, and developed a typology of their key features.

To evaluate if electronic health records (EHR) with prior clinical information have observable effects for patients with diabetes presenting to emergency departments (ED), we examined measures of quality and resource utilization.

To create a clinical decision support (CDS) system that is shareable across healthcare delivery systems and settings over large geographic regions.

To study and report on the use of open source electronic health records (EHR) to assist with chronic care management within safety net medical settings, such as community health centers (CHC).

To evaluate dosing alert appropriateness, categorize orders with alerts, and compare the appropriateness of alerts due to customized and non-customized dose ranges at a pediatric hospital.

There is little evidence that readability formula outcomes relate to text understanding. The potential cause may lie in their strong reliance on word and sentence length. We evaluated word familiarity rather than word length as a stand-in for word difficulty. Word familiarity represents how well known a word is, and is estimated using word frequency in a large text corpus, in this work the Google web corpus. We conducted a [...]

Electronic health records possess critical predictive information for machine-learning-based diagnostic aids. However, many traditional machine learning methods fail to simultaneously integrate textual data into the prediction process because of its high dimensionality. In this paper, we present a supervised method using Laplacian Eigenmaps to enable existing machine learning methods to estimate both low-dimensional representations of textual data and accurate predictors based on these low-dimensional representations at the same time.