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Secondary Medical Research Databases

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Computer Medical Databases

Part of the book series: Health Informatics ((HI))

Abstract

Secondary medical databases are classified in this book in accordance with their objectives, which usually are to support clinical research, administrative functions, medical education, or public health. Since very large medical databases can collect, integrate, store, and provide data from various sources and can support multiple purposes, they can serve both as a primary databases if the data are initially collected for direct patient care, and can also serve as secondary databases when the data are also used for other purposes (Glichlich 2007). After primary medical databases began to be established in the 1960s, it soon became evident that the secondary collections of information extracted from primary clinical databases could be of great value in supporting clinical research, improving the clinical decision-making process, and improving the quality of health care. As computer storage devices became larger and less costly, a great variety of secondary clinical databases emerged in these six decades.

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References

  • Basmajian D. The center for health services research. Oakland: Kaiser Permanente. Spectrum. 1989;(fall):1–2.

    Google Scholar 

  • Blois MS. Medical records and clinical databases: what is the difference. Proc AMIA Cong. 1982:86–9.

    Google Scholar 

  • Brandt CA, Sun K, Charpentier P, Nadkarni PK. Integration of web-based and PC-based clinical research databases. Methods Inf Med. 2004;43:287–95.

    PubMed  CAS  Google Scholar 

  • Cimino JJ, McNamara TJ, Meridith T, et al. Evaluation of a proposed method for representing drug terminology. Proc AMIA. 1999:47–1.

    Google Scholar 

  • Davies AR. Health care researchers’ needs for computer-based patient records. In: Ball MF, Collen MF, editors. Aspects of the computer-based patient record. New York: Springer; 1992. p. 46–56.

    Google Scholar 

  • Detmer WM, Shortliffe EH. A model of clinical query management that supports integration of biomedical information over the World Wide Web. Proc SCAMC. 1995:898–2.

    Google Scholar 

  • Entine SM. Wisconsin storage and retrieval system: a data management system for a clinical cancer center. Proc SCAMC. 1982:813–7.

    Google Scholar 

  • Fienberg SF, Martin ME, Straf ML, National Research Council, Committee on National Statistics, editors. Sharing research data. Washington, DC: National Academy Press; 1985.

    Google Scholar 

  • Frey R, Girardi S, Wiederhold G. A filing system for medical research. Journees D’Informatique Medicale. 1970: 511–6.

    Google Scholar 

  • Friedlin FJ, McDonald CJ. A software tool for removing patient identification information from clinical documents. JAMIA. 2008;15:601–10.

    PubMed  Google Scholar 

  • Garfolo BT, Keltner L. A computerized disease register. Proc MEDINFO. 1983:909–2.

    Google Scholar 

  • Glichlich RE, Dreyer NA, editors. Registries for evaluating patient outcomes: a user’s guide. AHRQ Pub. # 07-EHC001-1. Rockville: Agency for Healthcare Research and Quality, 2007(Apr):1–233.

    Google Scholar 

  • Gordis L, Gold E. Privacy, confidentiality, and the use of medical records in research. Science. 1980;207:153–6.

    Article  PubMed  CAS  Google Scholar 

  • Herrmann FR, Safran C. Exploring a hospital-wide database: integrating statistical functions with ClinQuery. Proc AMIA. 1992:583–7.

    Google Scholar 

  • Hlatky M. Using databases to evaluate therapy. Stat Med. 1991;10:647–52.

    Article  PubMed  CAS  Google Scholar 

  • Hripcsak G, Cimino JJ, Sengupta S. WebCis: large scale deployment of a Web-based clinical information system. Proc AMIA. 1999:804–8.

    Google Scholar 

  • Kohane IS, van Wingerde FJ, Fackler JC, et al. Sharing electronic medical records across multiple heterogenous and competing insitutions. Proc AMIA. 1996:608–2.

    Google Scholar 

  • Kurreeman F, Liao K, Chibnik L, et al. Genetic basis of autoantibody positive and negative rheumatoid arthritis risk in a multi-ethnic cohort derived from electronic health records. Am J Hum Genet. 2011;88:57–69.

    Article  PubMed  CAS  Google Scholar 

  • Logan JR, Britell S, Delcambre LM, et al. Representing multi-database study schemas for reusability. Proc STB. 2010:21–5.

    Google Scholar 

  • Loukides G, Denny JC, Malin B. The disclosure of diagnosis codes can breach research participant’s privacy. JAMIA. 2010a;17:322–7.

    PubMed  Google Scholar 

  • Loukides G, Gkoulalas-Divanis A, Malin B. Anonymization of electronic records for validating genome-wide associations. Proc Natl Acad Sci USA. 2010b;107:898–903.

    Article  Google Scholar 

  • McGuire AL, Fisher R, Cusenza P, et al. Confidentiality, privacy, and security of genetic and genomic test information in electronic health records: points to consider. Genet Med. 2008;10:495–9.

    Article  PubMed  Google Scholar 

  • Mirel BR, Wright Z, Tenenbaum JD, et al. User requirements for exploring a resource inventory for clinical research. Proc AMIA CRI. 2010:31–5.

    Google Scholar 

  • Nadkarni PM, Brandt CM, Marenco L. WebEAV: automatic meta-driven generation of web interfaces to entity-attribute-value-databases. J Am Med Inform Assoc. 2000;7:343–56.

    Article  PubMed  CAS  Google Scholar 

  • Niland JC, Rouse L. Clinical research needs, Chap 3. In: Lehman HP, Abbott PA, Roderer NK, editors. Aspects of electronic health record systems. New York: Springer; 2006. p. 31–46.

    Google Scholar 

  • Porter D, Safran C. On-line searches of a hospital data base for clinical research and patient care. Proc SCAMC. 1984:277–9.

    Google Scholar 

  • Pryor DB, Califf RM, Harrell FE, et al. Clinical data bases: accomplishments and unrealized potential. Med Care. 1985;23:623–47.

    Article  PubMed  CAS  Google Scholar 

  • Safran C. Using routinely collected data for clinical research. Stat Med. 1991;10:559–64.

    Article  PubMed  CAS  Google Scholar 

  • Safran C, Porter D. New uses of a large clinical database. In: Orthner HF, Blum BI, editors. Implementing health care information systems. New York: Springer; 1989. p. 123–32.

    Chapter  Google Scholar 

  • Safran C, Porter D. New uses of the large clinical data base at the Beth Israel Hospital in Boston. Proc SCAMC. 1986:114–9.

    Google Scholar 

  • Safran C, Chute CG. Exploration and exploitation of clinical databases. Int J Biomed Comput. 1995;39:151–6.

    Article  PubMed  CAS  Google Scholar 

  • Safran C, Rury CD, Lightfoot J, Porter D. ClinQuery: a program for interactive searching of clinical data. Proc SCAMC. 1989:414–8.

    Google Scholar 

  • Safran C, Porter D, Rury CD, et al. ClinQuery: searching a large clinical database. MD Comput. 1990;7:144–53.

    PubMed  CAS  Google Scholar 

  • Schoenberg R, Schoenberg I, Safran C. An object-oriented tool for clinical queries. Proc AMIA. 2000:1130.

    Google Scholar 

  • Shortliffe EH, Barnett GO, Cimino JJ, et al. Collaborative medical informatics research using the Internet and the World Wide Web. Proc AMIA. 1996:125–9.

    Google Scholar 

  • Sittig DF, Kuperman GL, Teich JM. WWW-based interfaces to clinical information systems: the state of the art. Proc AMIA. 1996:694–8.

    Google Scholar 

  • Sweeney L. Replacing personally-identifying information in medical records, the Scrub system. Proc AMIA. 1996:333–7.

    Google Scholar 

  • Sweeney L. Guaranteeing anonymity when sharing medical data, the Datafly system. Proc AMIA. 1997:333–7

    Google Scholar 

  • Sweeney L. k-Anonymity: a model for ting privacy. Int J Uncertainty Fuzziness Knowledge based Syst. 2002;10:557–70.

    Article  Google Scholar 

  • Van Wingerde FJ, Schindler J, Kilbridge P, et al. Using HL7 and the World Wide Web for unifying patient data from remote databases. Proc AMIA. 1996:643–7.

    Google Scholar 

  • Weber GM, Murphy SN, McMurry AJ, et al. The shared health research information network (SHRINE): a prototype federated query tool for clinical data repositories. JAMA. 2009;16:624–30.

    Google Scholar 

  • Wyatt M, Robinson J, Gordon G, et al. DASI – a federated data access and sharing initiative. Proc AMIA STB. 2010:122.

    Google Scholar 

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© 2012 Springer-Verlag London Limited

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Collen, M.F. (2012). Secondary Medical Research Databases. In: Computer Medical Databases. Health Informatics. Springer, London. https://doi.org/10.1007/978-0-85729-962-8_6

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  • DOI: https://doi.org/10.1007/978-0-85729-962-8_6

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