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A Framework for Capturing Clinical Data Sets from Computerized Sources

Clement J. McDonald, MD; J. Marc Overhage, MD, PhD; Paul Dexter, MD; Blaine Y. Takesue, MD; and Diane M. Dwyer, MD
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From the Regenstrief Institute for Health Care and Indiana University Medical Center, Indianapolis, Indiana; and the Maryland Department of Health and Mental Hygiene, Baltimore, Maryland. Note: This article is one of a series of articles comprising an Annals of Internal Medicine supplement entitled “Measuring Quality, Outcomes, and Cost of Care Using Large Databases: The Sixth Regenstrief Conference.” To see a complete list of the articles included in this supplement, please view its Table of Contents. Grant Support: In part by grant HS 07719-03 from the Agency for Health Care Policy and Research, contracts NO1-LM-4-3410 and NO1-LM-6-3456 from the National Library of Medicine, and grant 92196-H from the John A. Hartford Foundation of New York. Requests for Reprints: Clement J. McDonald, MD, Department of Medicine, Regenstrief Institute for Health Care, Indiana University Medical Center, 5th floor RHC, 1001 West 10th Street, Indianapolis, IN 46202. Current Author Addresses: Drs. McDonald, Overhage, Dexter, and Takesue: Department of Medicine, Regenstrief Institute for Health Care, Indiana University School of Medicine, 1001 West 10th Street, Indianapolis, IN 46202. Dr. Dwyer: Epidemiology and Disease Control Program, Maryland Department of Health and Mental Hygiene, 201 West Preston Street, Room 325, Baltimore, MD 21201.

Copyright ©2004 by the American College of Physicians

Ann Intern Med. 1997;127(8_Part_2):675-682. doi:10.7326/0003-4819-127-8_Part_2-199710151-00049
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The pressure to improve health care and provide better care at a lower cost has generated the need for efficient capture of clinical data. Many data sets are now being defined to analyze health care. Historically, review and research organizations have simply determined what data they wanted to collect, developed forms, and then gathered the information through chart review without regard to what is already available institutionally in computerized databases. Today, much electronic patient information is available in operational data systems (for example, laboratory systems, pharmacy systems, and surgical scheduling systems) and is accessible by agencies and organizations through standards for messages, codes, and encrypted electronic mail. Such agencies and organizations should define the elements of their data sets in terms of standardized operational data, and data producers should fully adopt these code and message standards. The Health Plan Employer Data and Information Set and the Council of State and Territorial Epidemiologists in collaboration with the Centers for Disease Control and Prevention and the Association of State and Territorial Public Health Laboratory Directors provide examples of how this can be done.


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Figure 1.
Portions of two related spreadsheets that were jointly developed by the Centers for Disease Control and Prevention, the Council of State and Territorial Epidemiologists, and the Association of State and Territorial Public Health Laboratory Directors.

LOINC = Logical Observations Identifier Names and Codes; SNOMED = Systematized Nomenclature of Medicine.

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