Download e-book for iPad: A Foundation for Evidence-Driven Practice: A Rapid Learning by Institute of Medicine, Board on Health Care Services,

By Institute of Medicine, Board on Health Care Services, National Cancer Policy Forum, Margie Patlak, Sharon Murphy

ISBN-10: 0309151260

ISBN-13: 9780309151269

The IOM's nationwide melanoma coverage discussion board held a workshop October 5-6, 2009, to envision the right way to practice the idea that of a 'rapid studying future health approach' to the matter of melanoma. This rfile summarizes the workshop.

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Additional info for A Foundation for Evidence-Driven Practice: A Rapid Learning System for Cancer Care: Workshop Summary

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To create caBIG, researchers developed standard rules, a unified architecture, and common language to more easily share information. The caBIG is an open-access, open-development, and open-source federated network. In other words, caBIG is open to all; the planning, testing, validation, and deployment of caBIG tools and infrastructure are open to the entire cancer community. In addition, the underlying software code for the caBIG infrastructure is 26 A FOUNDATION FOR EVIDENCE-DRIVEN PRACTICE available for use and modification, and resources can be controlled locally or integrated across multiple sites.

Most of us do not even know what the background survival rate is for a 70-year-old woman,” she said, let alone the survival statistics of a 70-year-old breast cancer patient with a number of different prognostic variables. Dr. Abernethy stressed the importance of linking decision support tools and models directly into the information technology system. “What good is a model that we have to type [patients’ data] into—that barrier in itself is going to inhibit use. ” she said. She added that there should be a BASIC ELEMENTS AND BUILDING BLOCKS OF A RLHS FOR CANCER 35 process for vetting models, as well as algorithms that enable IT systems to match the most appropriate model to the cancer patient in question.

It also important that the architecture of a computer grid be flexible to accommodate changes in biomedicine so that “data can be aggregated and interpreted correctly now and reinterpreted correctly as knowledge changes,” said Dr. Stead. He suggested separating the data from the system such that the raw signal data are recorded and then later tagged with the current interpretation. In that way, “as our knowledge changes, we can rerun our interpretations of it and re-annotate it as we move forward into the future,” Dr.

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A Foundation for Evidence-Driven Practice: A Rapid Learning System for Cancer Care: Workshop Summary by Institute of Medicine, Board on Health Care Services, National Cancer Policy Forum, Margie Patlak, Sharon Murphy


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