Clinical Research Computing

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.Offers case studies, based on real-life examples where possible, to engage the readers with more complex examplesProvides studies backed by technical details, e.g., schema diagrams, code snippets or algorithms illustrating particular techniques, to give the readers confidence to employ the techniques described in their own settingsOffers didactic content organization and an increasing complexity through the chapters

Autorentext
Dr. Nadkarni has been working in the field of biomedical informatics since 1989, with over 100 peer-reviewed publications in the field. He is the lead developer of TrialDB, an open-source clinical study data management system, which is used at multiple locations nationally and internationally. He is an Associate Editor of the Journal of the American Medical Informatics Association (JAMIA) since 2005, and was elected Fellow of the American College of Medical Informatics (ACMI) in 2002.

Inhalt
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

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