<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vandenberghe, H E E</style></author><author><style face="normal" font="default" size="100%">Viviane Van Casteren</style></author><author><style face="normal" font="default" size="100%">Jonckheer, P</style></author><author><style face="normal" font="default" size="100%">Bastiaens, H</style></author><author><style face="normal" font="default" size="100%">Johan Van der Heyden</style></author><author><style face="normal" font="default" size="100%">Lafontaine, M-F</style></author><author><style face="normal" font="default" size="100%">De Clercq, E</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Collecting information on the quality of prescribing in primary care using semi-automatic data extraction from GPs' electronic medical records.</style></title><secondary-title><style face="normal" font="default" size="100%">Int J Med Inform</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Int J Med Inform</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Aged, 80 and over</style></keyword><keyword><style  face="normal" font="default" size="100%">Anti-Inflammatory Agents, Non-Steroidal</style></keyword><keyword><style  face="normal" font="default" size="100%">Belgium</style></keyword><keyword><style  face="normal" font="default" size="100%">Data collection</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug Prescriptions</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Medical Audit</style></keyword><keyword><style  face="normal" font="default" size="100%">Medical Records Systems, Computerized</style></keyword><keyword><style  face="normal" font="default" size="100%">middle aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Osteoarthritis</style></keyword><keyword><style  face="normal" font="default" size="100%">Physicians, Family</style></keyword><keyword><style  face="normal" font="default" size="100%">Practice Patterns, Physicians'</style></keyword><keyword><style  face="normal" font="default" size="100%">Quality of Health Care</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2005 Jun</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">74</style></volume><pages><style face="normal" font="default" size="100%">367-76</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;OBJECTIVES: &lt;/b&gt;To evaluate a semi-automatic data extraction from the electronic medical record (EMR) of general practitioners (GPs) through a comparison with a paper sheets data collection simultaneously used in a primary care research project on the quality of prescribing for osteoarthritis in the elderly.&lt;/p&gt;&lt;p&gt;&lt;b&gt;SUBJECTS: &lt;/b&gt;One hundred and fifty-two GPs using five different EMR-software systems participated with the semi-automatic data extraction from the EMR and 233 GPs collected data with paper registration sheets.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;The proportion of patients with respectively a drug prescription, paracetamol, a non-steroidal anti-inflammatory drug (NSAID) and ibuprofen were compared between the semi-automatic extraction and the paper data collection and among the EMR-software systems.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Using the semi-automatic data extraction, a significantly lower proportion of patients on drugs was obtained compared to the paper data collection (adjusted OR: 0.31; 95% CI 0.25-0.39). However, the proportion of patients on a specific type of drug was comparable. Within the results from the semi-automatic extraction, the results were heterogeneous among the different EMR-software systems.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;The semi-automatic data extraction with multiple EMR-software systems proposed in this study seems suitable for quality of prescribing assessment in primary care. However, it may be less reliable when only a single EMR-software is used.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/15893259?dopt=Abstract</style></custom1></record></records></xml>