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 .
A prospective controlled trial of computerized decision support for lipid management in primary care.
OBJECTIVES This study aim ed to assess the uptake and effect in primary care of a computerized decision support system ( DSS ) for the management of  hyperlipidaemia .  METHOD A prospect i ve controlled trial was conducted in 25 practice s covering a population of 150,000 in the city of Birmingham . The Primed system , a specialist developed , rule based DSS for general practice , was introduced prospect ively after a 3-month baseline data collection . The main outcome measures were nine months ' data on prescribing of lipid lowering agents ; use of laboratory tests ; and referrals to secondary care for the investigation of hyperlipidaemia . RESULTS System use was lower than expected . A shift was observed towards requests for appropriate follow-up of previously abnormal lipid results and a greater emphasis on full lipid profiles , in line with the DSS guidelines . Referrals showed a 55 % decrease on those expected ( NS ) . The prescribing evaluation revealed a large variation between practice s , but no significant alteration following system use . Views of users favoured decision support as a concept , but criticised technical problems with the system . CONCLUSIONS Greater integration of DSS software and practice based data h and ling systems is needed . The mode of data capture , and hence both the content and form of knowledge representation , in DSS must take greater account of the primary care consultation process if such systems are to be of use to practitioners
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 .