There are an increasing number of studies reporting the efficacy of educational strategies to facilitate the development of knowledge and skills underpinning evidence based practice (EBP). To date there is no standardised guideline for describing the teaching, evaluation, context or content of EBP educational strategies. The heterogeneity in the reporting of EBP educational interventions
George Siemens Lecture:
Sponsors of health care price and quality transparency initiatives often identify all consumers as their target audiences, but the true audiences for these programs are much more limited. In 2007, only 11 percent of American adults looked for a new primary care physician, 28 percent needed a new specialist physician
In fee-for-service Medicare, the dispersion of patients’ care among multiple physicians will limit the effectiveness of pay-for-performance initiatives that rely on a single retrospective method of assigning responsibility for patient care.
Findings: PCPs vary in their threshold for referring a patient, which results in both the underuse and the overuse of specialists. Many referrals do not include a transfer of information, either to or from the specialist; and when they do, it often contains insufficient data for medical decision making. Care across the primary-specialty interface
Conclusions. PCPs’ referral decisions are influenced by a complex mix of patient, physician, and health care system structural characteristics. Factors associated with more discretionary referrals may lower PCPs’ thresholds for referring problems that could have been managed in their entirety within primary care settings.
This paper describes our considerations and methods for implementing an open-source centralized research data repository (CRDR) and reports its impact on retrospective outcomes research capacity in the urology department at Columbia University. We performed retrospective pretest and post-test analyses of user acceptance, workflow efficiency, and publication quantity and quality (measured
This plan addresses the five essential components of learning powered by technology: Learning, Assessment, Teaching, Infrastructure, and Productivity.
The report discusses the promise of sophisticated digital learning systems for collecting and analyzing very large amounts of fine-grained data (“big data”) as users interact with the systems. It proposes that this data can be used by developers and researchers to improve these learning systems and strive to discover more
Big data is everywhere—even in education. Researchers and developers of online learning, intelligent tutoring systems, virtual labs, simulations, and learning management systems are exploring ways to better understand and use learning analytics to improve teaching and learning.