TY - JOUR
T1 - The effect of computerized decision support systems on cardiovascular risk factors
T2 - A systematic review and meta-analysis
AU - Groenhof, T. Katrien J.
AU - Asselbergs, Folkert W.
AU - Groenwold, Rolf H. H.
AU - Grobbee, Diederick E.
AU - Visseren, Frank L. J.
AU - Bots, Michiel L.
AU - Asselbergs, Folkert W.
AU - Nathoe, H. M.
AU - de Borst, G. J.
AU - Bots, Michiel L.
AU - Geerlings, M. I.
AU - Emmelot, M. H.
AU - de Jong, P. A.
AU - Leiner, T.
AU - Lely, A. T.
AU - van der Kaaij, N. P.
AU - Kappelle, L. J.
AU - Ruigrok, Y. M.
AU - Verhaar, M. C.
AU - Visseren, Frank L. J.
AU - Westerink, J.
N1 - Funding Information:
The Utrecht Cardiovascular Cohort is partly supported by ZonMw (grant number: 80–84800–98-34001). Folkert W. Asselbergs is supported by UCL Hospitals NIHR Biomedical Research Centre.
Publisher Copyright:
© 2019 The Author(s).
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2019/6/10
Y1 - 2019/6/10
N2 - 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.
METHODS: We performed a systematic review and meta-analysis evaluating the effects of CDSS on CVRM, defined as the change in absolute values and attainment of treatment goals of systolic blood pressure (SBP), low density lipoprotein cholesterol (LDL-c) and HbA1c. Also, CDSS characteristics related to more effective CVRM were identified. Eligible articles were methodologically appraised using the Cochrane risk of bias tool. We calculated mean differences, relative risks, and if appropriate (I2 < 70%), pooled the results using a random-effects model.
RESULTS: Of the 14,335 studies identified, 22 were included. Four studies reported on SBP, 3 on LDL-c, 10 on CVRM in patients with type II diabetes and 5 on guideline adherence. The CDSSs varied considerably in technical performance and content. Heterogeneity of results was such that quantitative pooling was often not appropriate. 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. However, the variability in CDSS characteristics and heterogeneity of the results - emphasizing the immaturity of this research area - limit stronger conclusions. Clinical relevance of CDSS in CVRM might additionally be sought in the improvement of shared decision making and patient empowerment.
AB - 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.
METHODS: We performed a systematic review and meta-analysis evaluating the effects of CDSS on CVRM, defined as the change in absolute values and attainment of treatment goals of systolic blood pressure (SBP), low density lipoprotein cholesterol (LDL-c) and HbA1c. Also, CDSS characteristics related to more effective CVRM were identified. Eligible articles were methodologically appraised using the Cochrane risk of bias tool. We calculated mean differences, relative risks, and if appropriate (I2 < 70%), pooled the results using a random-effects model.
RESULTS: Of the 14,335 studies identified, 22 were included. Four studies reported on SBP, 3 on LDL-c, 10 on CVRM in patients with type II diabetes and 5 on guideline adherence. The CDSSs varied considerably in technical performance and content. Heterogeneity of results was such that quantitative pooling was often not appropriate. 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. However, the variability in CDSS characteristics and heterogeneity of the results - emphasizing the immaturity of this research area - limit stronger conclusions. Clinical relevance of CDSS in CVRM might additionally be sought in the improvement of shared decision making and patient empowerment.
KW - CDSS
KW - Computerized decision support
KW - Cardiovascular risk management
UR - http://www.scopus.com/inward/record.url?scp=85067093024&partnerID=8YFLogxK
U2 - 10.1186/s12911-019-0824-x
DO - 10.1186/s12911-019-0824-x
M3 - Review article
C2 - 31182084
SN - 1472-6947
VL - 19
SP - 108
JO - BMC Medical Informatics and Decision Making
JF - BMC Medical Informatics and Decision Making
IS - 1
M1 - 108
ER -