Literature DB >> 9382379

A framework for capturing clinical data sets from computerized sources.

C J McDonald1, J M Overhage, P Dexter, B Y Takesue, D M Dwyer.   

Abstract

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.

Entities:  

Mesh:

Year:  1997        PMID: 9382379     DOI: 10.7326/0003-4819-127-8_part_2-199710151-00049

Source DB:  PubMed          Journal:  Ann Intern Med        ISSN: 0003-4819            Impact factor:   25.391


  16 in total

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3.  Auditing consistency and usefulness of LOINC use among three large institutions - using version spaces for grouping LOINC codes.

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4.  A comparison of the completeness and timeliness of automated electronic laboratory reporting and spontaneous reporting of notifiable conditions.

Authors:  J Marc Overhage; Shaun Grannis; Clement J McDonald
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5.  Vital signs in intensive care: automatic acquisition and consolidation into electronic patient records.

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Authors:  W D Bidgood; B Bray; N Brown; A R Mori; K A Spackman; A Golichowski; R H Jones; L Korman; B Dove; L Hildebrand; M Berg
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Review 7.  Electronic medical records (EMRs), epidemiology, and epistemology: reflections on EMRs and future pediatric clinical research.

Authors:  Richard C Wasserman
Journal:  Acad Pediatr       Date:  2011-05-31       Impact factor: 3.107

8.  A Weighty Problem: Identification, Characteristics and Risk Factors for Errors in EMR Data.

Authors:  Saveli I Goldberg; Maria Shubina; Andrzej Niemierko; Alexander Turchin
Journal:  AMIA Annu Symp Proc       Date:  2010-11-13

9.  Audit and feedback intervention: An examination of differences in chiropractic record-keeping compliance.

Authors:  Nicole M Homb; Shayan Sheybani; Dustin Derby; Kurt Wood
Journal:  J Chiropr Educ       Date:  2014-05-07

10.  Public health surveillance and information technology.

Authors:  R W Pinner
Journal:  Emerg Infect Dis       Date:  1998 Jul-Sep       Impact factor: 6.883

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