BACKGROUND: A decade of cardiovascular disease (CVD) risk-based guidelines, education programmes and widespread availability of paper-based risk prediction charts have not significantly influenced targeting of CVD risk management in New Zealand primary care practice. A web-based decision support system (PREDICT-CVD), integrated with primary care electronic medical record software was developed as one strategy to address this problem. METHODS: A before-after audit of 3564 electronic patient records assessed the impact of electronic decision support on documentation of CVD risk and CVD risk factors. Participants were patients meeting national guideline criteria for CVD risk assessment, registered with 84/107 (78.5%) general practitioners (GPs) in one large primary care organization who used electronic patient medical records, and had PREDICT-CVD installed. The GPs received group education sessions, practice IT support and a small risk assessment payment. Four weeks of practice visit records were audited from 1 month after installation of PREDICT-CVD, and during the same 4-week period 12 months earlier. RESULTS: Less than 3% of eligible patients had a documented CVD risk before PREDICT-CVD installation. This increased four-fold (RR=4.0; 95% confidence interval 2.4-6.5) after installation and documentation of all relevant CVD risk factors also increased significantly. CONCLUSION: Documentation of CVD risk in primary care patient records in New Zealand is negligible, despite being recommended as a prerequisite for targeted treatment for over 10 years, suggesting that previous strategies were ineffective. We demonstrate that integrated electronic decision support can quadruple CVD risk assessment in just one cycle of patient visits.
BACKGROUND: A decade of cardiovascular disease (CVD) risk-based guidelines, education programmes and widespread availability of paper-based risk prediction charts have not significantly influenced targeting of CVD risk management in New Zealand primary care practice. A web-based decision support system (PREDICT-CVD), integrated with primary care electronic medical record software was developed as one strategy to address this problem. METHODS: A before-after audit of 3564 electronic patient records assessed the impact of electronic decision support on documentation of CVD risk and CVD risk factors. Participants were patients meeting national guideline criteria for CVD risk assessment, registered with 84/107 (78.5%) general practitioners (GPs) in one large primary care organization who used electronic patient medical records, and had PREDICT-CVD installed. The GPs received group education sessions, practice IT support and a small risk assessment payment. Four weeks of practice visit records were audited from 1 month after installation of PREDICT-CVD, and during the same 4-week period 12 months earlier. RESULTS: Less than 3% of eligible patients had a documented CVD risk before PREDICT-CVD installation. This increased four-fold (RR=4.0; 95% confidence interval 2.4-6.5) after installation and documentation of all relevant CVD risk factors also increased significantly. CONCLUSION: Documentation of CVD risk in primary care patient records in New Zealand is negligible, despite being recommended as a prerequisite for targeted treatment for over 10 years, suggesting that previous strategies were ineffective. We demonstrate that integrated electronic decision support can quadruple CVD risk assessment in just one cycle of patient visits.
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