Nicolas Delvaux, Katrien Van Thienen, Annemie Heselmans, Stijn Van de Velde, Dirk Ramaekers, Bert Aertgeerts1,2,3,4. 1. From the Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium (Drs Delvaux, Heselmans, Ramaekers, and Aertgeerts). 2. the Department of Public Health, Vrije University Brussels, Brussels, Belgium (Dr Van Thienen). 3. the GUIDES project, Norwegian Institute of Public Health, Oslo, Norway (Dr Van de Velde). 4. and the Centre for Evidence-Based Medicine (CEBAM), Belgian Branch of the Dutch Cochrane Collaboration, Leuven, Belgium (Drs Ramaekers and Aertgeerts).
Abstract
CONTEXT: - Inappropriate laboratory test ordering has been shown to be as high as 30%. This can have an important impact on quality of care and costs because of downstream consequences such as additional diagnostics, repeat testing, imaging, prescriptions, surgeries, or hospital stays. OBJECTIVE: - To evaluate the effect of computerized clinical decision support systems on appropriateness of laboratory test ordering. DATA SOURCES: - We used MEDLINE, Embase, CINAHL, MEDLINE In-Process and Other Non-Indexed Citations, Clinicaltrials.gov, Cochrane Library, and Inspec through December 2015. Investigators independently screened articles to identify randomized trials that assessed a computerized clinical decision support system aimed at improving laboratory test ordering by providing patient-specific information, delivered in the form of an on-screen management option, reminder, or suggestion through a computerized physician order entry using a rule-based or algorithm-based system relying on an evidence-based knowledge resource. Investigators extracted data from 30 papers about study design, various study characteristics, study setting, various intervention characteristics, involvement of the software developers in the evaluation of the computerized clinical decision support system, outcome types, and various outcome characteristics. CONCLUSIONS: - Because of heterogeneity of systems and settings, pooled estimates of effect could not be made. Data showed that computerized clinical decision support systems had little or no effect on clinical outcomes but some effect on compliance. Computerized clinical decision support systems targeted at laboratory test ordering for multiple conditions appear to be more effective than those targeted at a single condition.
CONTEXT: - Inappropriate laboratory test ordering has been shown to be as high as 30%. This can have an important impact on quality of care and costs because of downstream consequences such as additional diagnostics, repeat testing, imaging, prescriptions, surgeries, or hospital stays. OBJECTIVE: - To evaluate the effect of computerized clinical decision support systems on appropriateness of laboratory test ordering. DATA SOURCES: - We used MEDLINE, Embase, CINAHL, MEDLINE In-Process and Other Non-Indexed Citations, Clinicaltrials.gov, Cochrane Library, and Inspec through December 2015. Investigators independently screened articles to identify randomized trials that assessed a computerized clinical decision support system aimed at improving laboratory test ordering by providing patient-specific information, delivered in the form of an on-screen management option, reminder, or suggestion through a computerized physician order entry using a rule-based or algorithm-based system relying on an evidence-based knowledge resource. Investigators extracted data from 30 papers about study design, various study characteristics, study setting, various intervention characteristics, involvement of the software developers in the evaluation of the computerized clinical decision support system, outcome types, and various outcome characteristics. CONCLUSIONS: - Because of heterogeneity of systems and settings, pooled estimates of effect could not be made. Data showed that computerized clinical decision support systems had little or no effect on clinical outcomes but some effect on compliance. Computerized clinical decision support systems targeted at laboratory test ordering for multiple conditions appear to be more effective than those targeted at a single condition.
Authors: Chase D Hendrickson; Michael F McLemore; Kathryn M Dahir; Shari Just; Zahra Shajani-Yi; Joseph LeGrand; Christoph U Lehmann; Asli Weitkamp Journal: Appl Clin Inform Date: 2020-02-26 Impact factor: 2.342
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Authors: Nicolas Delvaux; An De Sutter; Stijn Van de Velde; Dirk Ramaekers; Steffen Fieuws; Bert Aertgeerts Journal: Implement Sci Date: 2017-12-06 Impact factor: 7.327