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Clinical Research Computing
A Practitioner's Handbook
Using a case study approach to prepare readers for the challenges of providing informatics and computing support for clinical research, this practical handbook covers the full range of relevant perspectives—technological, scientific, organizational, and managerial—for fully exploiting the opportunities created by clinical research computing
Prakash Nadkarni (Author)
9780128031308, Elsevier Science
Paperback, published 27 April 2016
240 pages
23.4 x 19 x 1.6 cm, 0.48 kg
Clinical Research Computing: A Practitioner’s Handbook deals with the nuts-and-bolts of providing informatics and computing support for clinical research. The subjects that the practitioner must be aware of are not only technological and scientific, but also organizational and managerial. Therefore, the author offers case studies based on real life experiences in order to prepare the readers for the challenges they may face during their experiences either supporting clinical research or supporting electronic record systems. Clinical research computing is the application of computational methods to the broad field of clinical research. With the advent of modern digital computing, and the powerful data collection, storage, and analysis that is possible with it, it becomes more relevant to understand the technical details in order to fully seize its opportunities.
1. Foreword 2. An Introduction to Clinical Research Concepts 3. Clinical Research Processes: Technological and Non-technological considerations 4. Core Informatics Technologies 5. Software for Patient Care vs. Software for Research Support: Similarities and Differences 6. Software for Research Data Capture, Storage: Using Clinical Research Information Systems 7. Data Security and Privacy Issues 8. Mobile Technologies 9. Using Electronic Health Record Technology to Support Research: Institutional and Personal Health Records 10. Data Resources: Clinical Repositories, Warehouses and Registries, Standards in Clinical Research 11. Big Data Analytics and Data Mining 12. Conclusions
Subject Areas: Life sciences: general issues [PSA], Medical bioinformatics [MBF]
