dc.contributor.author | Datta, Shoumen | |
dc.date.accessioned | 2010-10-08T16:24:21Z | |
dc.date.available | 2010-10-08T16:24:21Z | |
dc.date.issued | 2010-12-15 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/58972 | |
dc.description | Contents
Introduction
Problem Space
Background Existing EMR
Data and Information
Wireless Monitoring
Molecular Semantics
Auxiliary Space
Potential for Growth
Back to Basics
Conclusion
Acknowledgment
References | en_US |
dc.description.abstract | Patients want answers, not numbers. Evidence-based medicine must have numbers to generate answers. Therefore, analysis of numbers to provide answers is the Holy Grail of healthcare professionals and its future systems. Lack of action due to paralysis from analysis of risk associated with the complexities in healthcare is no longer acceptable in view of spiraling costs. Generating data without improving
the quality of healthcare service and extracting its value for business benefits will not provide the return on investment (ROI). Distributed data and their relationships
are dispersed in multiple network of systems or system of systems (SOS).
The role of data analysis is central. The comatose stage of the Information Age due to data overload and information overdose is predicting its demise unless new ideas emerge as its savior. The imminent death of the information age makes it imperative to better understand the systems age. The single most important system
that deserves our attention in the twenty-first century is the healthcare ecosystem.
The convergence of characteristics such as enterprise, innovation, research,
and entrepreneurship (EIRE), often common in organizations with foresight in
parallel with the vision to drive convergence of biomedical sciences, engineering,
and information communication technologies, may act as the purveyor to advance
healthcare for the progress of civilization. | en_US |
dc.language.iso | en | en_US |
dc.publisher | CRC Press | en_US |
dc.relation.ispartofseries | Nano Sensors;Healthcare | |
dc.rights | Attribution-Noncommercial 3.0 United States | en |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/3.0/us/ | en |
dc.title | Future Healthcare | en_US |
dc.title.alternative | Chapter 8 | en_US |
dc.type | Book chapter | en_US |
dc.contributor.department | Massachusetts Institute of Technology. Auto-ID Laboratory | |