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 .
Clinical effectiveness of a patient decision aid to improve decision quality and glycaemic control in people with diabetes making treatment choices: a cluster randomised controlled trial (PANDAs) in general practice
Objective To determine the effectiveness of a patient decision aid ( PDA ) to improve decision quality and glycaemic control in people with diabetes making treatment choices using a cluster r and omised controlled trial ( RCT ) . Design A cluster RCT . Setting 49 general practice s in UK r and omised into intervention ( n=25 ) and control ( n=24 ) . Participants General practice s Inclusion criteria : > 4 medical partners ; list size > 7000 ; and a diabetes register with > 1 % of practice population . 191 practice s assessed for eligibility , and 49 practice s r and omised and completed the study . Patients People with type 2 diabetes mellitus ( T2DM ) taking at least two oral glucose-lowering drugs with maximum tolerated dose with a glycosolated haemoglobin ( HbA1c ) greater than 7.4 % ( IFCC HbA1c > 57 mmol/mol ) or advised in the preceeding 6 months to add or consider changing to insulin therapy . Exclusion criteria : currently using insulin therapy ; difficulty reading or underst and ing English ; difficulty in underst and  ing the purpose of the study ; visual or cognitive impairment or mentally ill . A total of 182 assessed for eligibility , 175 r and omised to 95 intervention and 80 controls , and 167 completion and analysis .  Intervention Brief training of clinicians and use of PDA with patients in single consultation . Primary outcomes Decision quality ( Decisional Conflict Scores , knowledge , realistic expectations and autonomy ) and glycaemic control ( glycosolated haemoglobin , HbA1c ) . Secondary outcomes Knowledge and realistic expectations of the risks and benefits of insulin therapy and diabetic complications . Results Intervention group : lower total Decisional Conflict Scores ( 17.4 vs 25.2 , p<0.001 ) ; better knowledge ( 51.6 % vs 28.8 % , p<0.001 ) ; realistic expectations ( risk of ‘ hypo ’ , ‘ weight gain ’ , ‘ complications ’ ; 81.0 % vs 5.2 % , 70.5 % vs 5.3 % , 26.3 % vs 5.0 % respectively , p<0.001 ) ; and were more autonomous in decision-making ( 64.1 % vs 42.9 % , p=0.012 ) . No significant difference in the glycaemic control between the two groups . Conclusions Use of the P AND As decision aid reduces decisional conflict , improves knowledge , promotes realistic expectations and autonomy in people with diabetes making treatment choices in general practice . IS RCT N Trials Register Number 14842077
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 .