Article type
Year
Abstract
Background: A computerized decision support system (CDSS) has been defined as a computerized system that uses case-based reasoning to assist clinicians in assessing disease status, in making a diagnosis, in selecting appropriate therapy or in making other clinical decisions. It was reported that CDSS can promote better decisions by filtering information overloads. However, little is known about the ways and to what extent CDSS can support this work.
Objectives:To investigate the ways and the extent to which CDSS can filter information overloads and improve clinical decisions.
Methods: We searched MEDLINE, EMBASE, the Cochrane Library and four main Chinese databases including CBM, VIP, CNKI and Wanfang using the search term “decision support systems, clinical”, “decision-making, computer assisted”, “CDSS”, “expert systems” , “ overload information”, “overload knowledge” etc. from 1977 to March 2015. We used Microsoft Excel 2007 to perform data extraction and analysis.
Results: We included a total of 610 studies in our study; publication dates ranged from 1990 to 2015. In 2011 the number of studies reached a peak with 96 studies accounting for 15.58% of the years’ publication. The CDSS can filter an information overload and improve clinical decision in six major ways: basic or advanced clinical decision support (48.5%), computer-assisted drug therapy (36.7%), computer-assisted diagnosis (4.38%), computer-assisted therapy (3.73), reminder system (2.92%), and disease management (1.30%). The embedding of CDSS into clinical care offers opportunities to reduce inappropriate drug prescriptions (40.2%), drug overdoses (17.5%) and unnecessary testing (6.58%). Commentary reviews and cross-sectional studies took up approximately 46.1% of the included studies, with 30.52% (188 studies) and 15.58% (96 studies) respectively.
Conclusions: CDSS is effective to some degree in filtering information overloads and improving clinical decisions. As with any healthcare innovation, CDSS should be rigorously evaluated before widespread dissemination into clinical practice.
Objectives:To investigate the ways and the extent to which CDSS can filter information overloads and improve clinical decisions.
Methods: We searched MEDLINE, EMBASE, the Cochrane Library and four main Chinese databases including CBM, VIP, CNKI and Wanfang using the search term “decision support systems, clinical”, “decision-making, computer assisted”, “CDSS”, “expert systems” , “ overload information”, “overload knowledge” etc. from 1977 to March 2015. We used Microsoft Excel 2007 to perform data extraction and analysis.
Results: We included a total of 610 studies in our study; publication dates ranged from 1990 to 2015. In 2011 the number of studies reached a peak with 96 studies accounting for 15.58% of the years’ publication. The CDSS can filter an information overload and improve clinical decision in six major ways: basic or advanced clinical decision support (48.5%), computer-assisted drug therapy (36.7%), computer-assisted diagnosis (4.38%), computer-assisted therapy (3.73), reminder system (2.92%), and disease management (1.30%). The embedding of CDSS into clinical care offers opportunities to reduce inappropriate drug prescriptions (40.2%), drug overdoses (17.5%) and unnecessary testing (6.58%). Commentary reviews and cross-sectional studies took up approximately 46.1% of the included studies, with 30.52% (188 studies) and 15.58% (96 studies) respectively.
Conclusions: CDSS is effective to some degree in filtering information overloads and improving clinical decisions. As with any healthcare innovation, CDSS should be rigorously evaluated before widespread dissemination into clinical practice.