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 .
Electronic Medical Record-Assisted Design of a Cluster-Randomized Trial to Improve Diabetes Care and Outcomes
Background Electronic medical records ( EMRs ) have the potential to facilitate the design of large cluster-r and omized trials ( CRTs ) . Objective To describe the design of a CRT of clinical decision support to improve diabetes care and outcomes . Methods In the Diabetes Improvement Group-Intervention Trial ( DIG-IT ) , we identified and balanced preassignment characteristics of 12,675 diabetic patients cared for by 147 physicians in 24 practice s of 2 systems using the same vendor ’s EMR . EMR-facilitated disease management was system A ’s experimental intervention ; system B interventions involved patient empowerment , with or without disease management . For our sample , we : ( 1 ) identified characteristics associated with response to interventions or outcomes ; ( 2 ) summarized feasible partitions of 10 system A practice s ( 2 groups ) and 14 system B practice s ( 3 groups ) using intra-cluster correlation coefficients ( ICCs ) and st and ardized differences ; ( 3 ) selected ( blinded ) partitions to effectively balance the characteristics ; and ( 4 ) r and omly assigned groups of practice s to interventions . Results In System A , 4,306 patients , were assigned to 2 groups of practice s ; 8,369 patients in system B were assigned to 3 groups of practice s. Nearly all baseline outcome variables and covariates were well-balanced , including several not included in the initial design . DIG-IT ’s balance was superior to alternative partitions based on volume , geography or demographics alone . Conclusions EMRs facilitated rigorous CRT design by identifying large numbers of patients with diabetes and enabling fair comparisons through preassignment balancing of practice sites . Our methods can be replicated in other setting s and for other conditions , enhancing the power of other translational investigations
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 .