Nadkarni Metadata-driven Software Systems in Biomedicine

Designing Systems that can adapt to Changing Knowledge
1. Auflage 2011
ISBN: 978-0-85729-510-1
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 411 Seiten

Reihe: Health Informatics

While the use of database technology is ubiquitous throughout IT (and health IT in particular), it is not generally appreciated that, as a database increases in scope, certain designs are far superior to others. In biomedical domains, new knowledge is being generated continually, and the databases that must support areas such as clinical care and research must also be able to evolve while requiring minimal or no logical / physical redesign. Appropriately designed metadata, and software designed to utilize it effectively, can provide significant insulation against change. Many of the larger EMR or clinical research database vendors have realized this, but their designs are proprietary and not described in the literature. Consequently, numerous misconceptions abound among individuals who have not had to work with large-scale biomedical systems, and graduates of a health or bioinformatics program may find that they need to unlearn what they were taught in database and software design classes in order to work productively with such systems. A working knowledge of such systems is also important for individuals who are not primarily software developers, such as health informaticians, medical information officers and data analysts. This book is, in a sense, intended to prepare all of the above individuals for the real world.

Autoren/Hrsg.

Nadkarni, Prakash M.

Inhaltsverzeichnis

1;Metadata-driven Software Systems in Biomedicine;4
2;Copyright Page;5
3;Preface;6
4;Acknowledgments;10
5;Contents;12
6;1: What Is Metadata?;22
7;2: Data Types in the Medical Record;38
8;3: Metadata and the Use of XML;53
9;4: Representing Structured Clinical Data;74
10;5: Building the User Interface for Structured Clinical Data Capture;93
11;6: Medical Decision Support Systems: General Considerations;127
12;7: Challenges in Electronic Decision Support Implementation;141
13;8: Infrastructure for Complex Clinical Decision Support: Rule Engines;161
14;9: Decision Support Infrastructure: Workflows and Service-Oriented Architectures;174
15;10: Complex Decision Support in Practice;193
16;11: Extending the Entity-Attribute-Value Model;209
17;12: Descriptive Metadata: An Introduction to Terminologies;242
18;13: Descriptive Metadata: Implementing Large-Scale Biomedical Ontologies;264
19;14: Clinical Study Data Management Systems;288
20;15: Data Retrieval for Heterogeneous Data Models;308
21;16: Metadata for Data Warehousing;374
22;17: Biomedical Metadata Standards;388
23;Index;402