Data extraction from a semi-structured electronic medical record system for outpatients: a model to facilitate the access and use of data for quality control and research.
The use of clinical data from electronic medical records (EMRs) for clinical research and for evaluation of quality of care requires an extraction process. Many efforts have failed because the extracted data seemed to be unstructured, incomplete and ridden by errors. We have developed and tested a c...
| Publicado en: | Health Informatics Journal Vol. 15; no. 4; pp. 305 - 320 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Dec2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105278048&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105278048 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14604582 EJK jtl: Health Informatics Journal issn: 14604582 maglogo: Y pubinfo: dt: Dec2009 vid: 15 iid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 105278048 46737788 2010520070 10.1177/1460458209345889 NLM20007655 105278048 ppf: 305 ppct: 15 formats: tig: atl: Data extraction from a semi-structured electronic medical record system for outpatients: a model to facilitate the access and use of data for quality control and research. aug: au: Kristianson KJ Ljunggren H Gustafsson LL affil: Division of Clinical Pharmacology Department of Laboratory Medicine Karolinska Institute Karolinska University Hospital SE-141 86 Huddinge, Sweden; Krister@Kristianson.com sug: subj: Clinical Information Systems Utilization Clinical Research Data Management Data Analysis Database Management Software Evaluation Research Funding Source In Vitro Studies Management Information Systems Quality Assurance Quality Control (Technology) ab: The use of clinical data from electronic medical records (EMRs) for clinical research and for evaluation of quality of care requires an extraction process. Many efforts have failed because the extracted data seemed to be unstructured, incomplete and ridden by errors. We have developed and tested a concept of extracting semi-structured EMRs (Journal III®, Profdoc®) data from 776 diabetes patients in a general practice clinic over a 5 year period. We used standard database management techniques commonly applied in clinical research in the pharmaceutical industry to clean up the data and make the data available for statistical analysis. The key problem was difficulties locating the data, as no standard way to enter the data in the EMR system was reinforced. Furthermore, no built-in edit checks to facilitate data entry were available. Laboratory, drug information and diagnostic data could be used directly while other data such as vital signs required much work to locate and become useful. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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