Chapter 10 – Information Technology

Publisher Summary Though related to clinical information technology, clinical research information technology poses its own set of data management challenges that are unique. The problems of using clinical data repositories as a data source for research are examined in this chapter, as are the advantages of using pre-research clinical registries. Clinical data warehousing principles are described, including detail on database design, the organization of metadata, and work processes. Techniques for applying information technology to the vexing problem of study participant recruitment are discussed. A review of the principles of data collection for clinical research covers issues of automation, data validity and the integration of research data collection with clinical care. The discussion also touches briefly on data standards in clinical research, clinical trial management systems, publicly available biomedical literature databases, and emerging approaches to data integration using new Web technologies.

[1]  Blaz Zupan,et al.  Predictive data mining in clinical medicine: Current issues and guidelines , 2008, Int. J. Medical Informatics.

[2]  Donald C. Trost,et al.  Information mining over heterogeneous and high-dimensional time-series data in clinical trials databases , 2006, IEEE Transactions on Information Technology in Biomedicine.

[3]  David F. Lobach,et al.  Medical data mining: knowledge discovery in a clinical data warehouse , 1997, AMIA.

[4]  N L Foster,et al.  Balanced centralized and distributed database design in a clinical research environment. , 2000, Statistics in medicine.

[5]  Isaac S Kohane,et al.  Model Formulation: A Self-scaling, Distributed Information Architecture for Public Health, Research, and Clinical Care , 2007, J. Am. Medical Informatics Assoc..

[6]  Vivian S. Gainer,et al.  A Visual Interface Designed for Novice Users to find Research Patient Cohorts in a Large Biomedical Database , 2003, AMIA.

[7]  C. Sabatti,et al.  The Human Phenome Project , 2003, Nature Genetics.

[8]  Liora Alschuler,et al.  Model Formulation: Implementing Single Source: The STARBRITE Proof-of-Concept Study , 2007, J. Am. Medical Informatics Assoc..

[9]  Lonnie Blevins,et al.  A system for sharing routine surgical pathology specimens across institutions: the Shared Pathology Informatics Network. , 2007, Human pathology.

[10]  D Gibson,et al.  Is double data entry necessary? The CHART trials. CHART Steering Committee. Continuous, Hyperfractionated, Accelerated Radiotherapy. , 1994, Controlled clinical trials.

[11]  Perry L. Miller,et al.  Metadata-driven creation of data marts from an EAV-modeled clinical research database , 2002, Int. J. Medical Informatics.

[12]  Barry Robson,et al.  Data mining and clinical data repositories: Insights from a 667, 000 patient data set , 2006, Comput. Biol. Medicine.

[13]  Isaac S. Kohane,et al.  Data mining by clinicians , 1998, AMIA.

[14]  Perry L. Miller,et al.  Research Paper: Exploring the Degree of Concordance of Coded and Textual Data in Answering Clinical Queries from a Clinical Data Repository , 2000, J. Am. Medical Informatics Assoc..

[15]  Daniel S Greenberg washington National Institutes of Health moves ahead with “PubMed Central” , 1999, The Lancet.

[16]  Barry P. Markovitz Viewpoint: Biomedicine's Electronic Publishing Paradigm Shift: Copyright Policy and PubMed Central , 2000, J. Am. Medical Informatics Assoc..

[17]  Eugene Y Chan,et al.  Advances in sequencing technology. , 2005, Mutation research.

[18]  R. Sandler,et al.  Research recruitment through US central cancer registries: balancing privacy and scientific issues. , 2006, American journal of public health.

[19]  Toby J. Teorey Database modeling & design : the fundamental principles , 1994 .

[20]  Ben Shneiderman,et al.  Designing the User Interface: Strategies for Effective Human-Computer Interaction , 1998 .

[21]  W. H. Inmon,et al.  Building the data warehouse , 1992 .

[22]  Prakash M. Nadkarni,et al.  Data Extraction and Ad Hoc Query of an Entity– Attribute–Value Database , 2000 .

[23]  Narayanaswamy Srinivasan,et al.  Cascade PSI-BLAST web server: a remote homology search tool for relating protein domains , 2006, Nucleic Acids Res..

[24]  Henry C. Chueh,et al.  Optimizing healthcare research data warehouse design through past COSTAR query analysis , 1999, AMIA.

[25]  D. King,et al.  A quantifiable alternative to double data entry. , 2000, Controlled clinical trials.

[26]  R. Hornung,et al.  Effect of a clinical trial alert system on physician participation in trial recruitment. , 2005, Archives of internal medicine.

[27]  Ralph Kimball,et al.  The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling , 1996 .

[28]  Abraham Silberschatz,et al.  Database System Concepts , 1980 .

[29]  Alexa T. McCray,et al.  Application of Technology: Design and Implementation of a National Clinical Trials Registry , 2000, J. Am. Medical Informatics Assoc..

[30]  Gisela Büchele,et al.  Single vs. double data entry. , 2005, Epidemiology.