Over the past two years we have seen digital tools and technology accelerate the push of innovation into (and out of) the classroom, from K-12 and higher ed to corporate training and lifelong learning. With the proliferation of tablets and cloud-based content and delivery, online learning is quickly becoming pervasive.
Biomedical research has and will continue to generate large amounts of data (termed ‘big data’) in many formats and at all levels. Consequently, there is an increasing need to better understand and mine the data to further knowledge and foster new discovery. The National Institutes of Health (NIH) has initiated
The rise of electronic medical records promotes the collection and aggregation of medical data. These data have tremendous potential utility for health policy and public health; yet there are gaps in the scholarly literature. No articles in the medical or legal literature have mapped the “information flows” from patient to
Healthcare organizations should adopt a standardized framework for data governance if they want to harness the power of big data, says a new report. But governance is but one element in the highly complex world of healthcare information, where many long-held practices must change, says the report, from the Institute
"analytics is predicated on “big data” but in education, big data will not exist until we sort out the current failure of interoperability"
By spotting patterns in the data produced by students’ online learning activity, learning analytics systems should be able to help:
predict student progress;
inform adaptive learning strategies (sequencing digital learning activities
A b s t r a c t Objective: To determine whether natural language processing (NLP) can effectively detect adverse events defined in the New York Patient Occurrence Reporting and Tracking System (NYPORTS) using discharge summaries.
Objective (1) To evaluate a state-of-the-art natural language processing (NLP)-based approach to automatically de-identify a large set of diverse clinical notes. (2) To measure the impact of de-identification on the performance of information extraction algorithms on the de-identified documents.
Objective: To assess whether connections between physicians based on shared patients in administrative data correspond with professional relationships between physicians.
Data Sources/Study Setting: Survey of physicians affiliated with a large academic and community physicians’ organization and 2006 Medicare data from a 100 percent sample of patients in the Boston Hospital referral region.
Background: There is substantial variation in the cost and intensity of care delivered by US hospitals. We assessed how the structure of patient-sharing networks of physicians affiliated with hospitals might contribute to this variation.
BACKGROUND: Specialty referral patterns can affect health care costs as well as clinical outcomes. For a given clinical problem, referring physicians usually have a choice of several physicians to whom they can refer. Once the decision to refer is made, the choice of individual physician may have important downstream effects. OBJECTIVE: To examine the reasons why