Among CVRM patients , the results tended towards a beneficial effect of CDSS , but only LDL-c target attainment in diabetes patients reached statistical significance .
Prompting , integration into the electronical health record , patient empowerment , and medication support were related to more effective CVRM .
Conclusion We did not find a clear clinical benefit from CDSS in cardiovascular risk factor levels and target attainment .
Some features of CDSS seem more promising than others .
Clinical relevance of CDSS in CVRM might additionally be sought in the improvement of shared decision making and patient empowerment
Background Cardiovascular risk management ( CVRM ) is notoriously difficult because of multi-morbidity and the different phenotypes and severities of cardiovascular disease .
Computerized decision support systems ( CDSS ) enable the clinician to integrate the latest scientific evidence and patient information into tailored strategies .
The effect on cardiovascular risk factor management is yet to be confirmed .
Toward A Human-Centered Hyperlipidemia Management System: The Interaction between Internal and External Information on Relational Data Search
In a distributed information search task , data representation and cognitive distribution jointly affect user search performance in terms of response time and accuracy . Guided by UFuRT ( User , Function , Representation , Task ) , a human-centered framework , we proposed a search model and task taxonomy . The model defines its application in the context of healthcare setting . The taxonomy clarifies the legitimate operations for each type of search task of relational data . We then developed experimental prototypes of hyperlipidemia data displays . Based on the displays , we tested the search tasks performance through two experiments . The experiments are of a within-subject design with a r and om sample of 24 participants . The results support our hypotheses and vali date the prediction of the model and task taxonomy . In this study , representation dimensions , data scales , and search task types are the main factors in determining search efficiency and effectiveness . Specifically , the more external representations provided on the interface the better search task performance of users . The results also suggest the ideal search performance occurs when the question type and its corresponding data scale representation match . The implication s of the study lie in contributing to the effective design of search interface for relational data , especially laboratory results , which could be more effectively design ed in electronic medical records
Among CVRM patients , the results tended towards a beneficial effect of CDSS , but only LDL-c target attainment in diabetes patients reached statistical significance .
Prompting , integration into the electronical health record , patient empowerment , and medication support were related to more effective CVRM .
Conclusion We did not find a clear clinical benefit from CDSS in cardiovascular risk factor levels and target attainment .
Some features of CDSS seem more promising than others .
Clinical relevance of CDSS in CVRM might additionally be sought in the improvement of shared decision making and patient empowerment
Background Cardiovascular risk management ( CVRM ) is notoriously difficult because of multi-morbidity and the different phenotypes and severities of cardiovascular disease .
Computerized decision support systems ( CDSS ) enable the clinician to integrate the latest scientific evidence and patient information into tailored strategies .
The effect on cardiovascular risk factor management is yet to be confirmed .