TY - JOUR
T1 - Estimating the impact of deploying an electronic clinical decision support tool as part of a national practice improvement project
AU - Kerns, Ellen K.
AU - Staggs, Vincent S.
AU - Fouquet, Sarah D.
AU - McCulloh, Russell J.
N1 - Funding Information:
REVISE was sponsored by the AAP’s VIP Network and deemed exempt by the AAP institutional review board. Teams at each partici-
Funding Information:
This project was funded in part by a Blue Outcomes Research Award from the Kansas City Area Life Sciences Institute. Dr McCulloh and Ms. Kerns receive support from the Office of the Director of the National Institutes of Health under award UG1OD024953.
PY - 2019/4/11
Y1 - 2019/4/11
N2 - Objective: Estimate the impact on clinical practice of using a mobile device-based electronic clinical decision support (mECDS) tool within a national standardization project. Materials and Methods: An mECDS tool (app) was released as part of a change package to provide febrile infant management guidance to clinicians. App usage was analyzed using 2 measures: metric hits per case (metricrelated screen view count divided by site-reported febrile infant cases in each designated market area [DMA] monthly) and cumulative prior metric hits per site (DMA metric hits summed from study month 1 until the month preceding the index, divided by sites in the DMA). For each metric, a mixed logistic regression model was fit to model site performance as a function of app usage. Results: An increase of 200 cumulative prior metric hits per site was associated with increased odds of adherence to 3 metrics: Appropriate admission (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.06-1.18), appropriate length of stay (OR, 1.20; 95% CI, 1.12-1.28), and inappropriate chest x-ray (OR, 0.82; 95% CI, 0.75-0.91). Ten additional metric hits per case were also associated: OR were 1.18 (95% CI, 1.02-1.36), 1.36 (95% CI, 1.14-1.62), and 0.74 (95% CI, 0.62-0.89). Discussion: mECDS tools are increasingly being implemented, but their impact on clinical practice is poorly described. To our knowledge, although ecologic in nature, this report is the first to link clinical practice to mECDS use on a national scale and outside of an electronic health record. Conclusions: mECDS use was associated with changes in adherence to targeted metrics. Future studies should seek to link mECDS usage more directly to clinical practice and assess other site-level factors.
AB - Objective: Estimate the impact on clinical practice of using a mobile device-based electronic clinical decision support (mECDS) tool within a national standardization project. Materials and Methods: An mECDS tool (app) was released as part of a change package to provide febrile infant management guidance to clinicians. App usage was analyzed using 2 measures: metric hits per case (metricrelated screen view count divided by site-reported febrile infant cases in each designated market area [DMA] monthly) and cumulative prior metric hits per site (DMA metric hits summed from study month 1 until the month preceding the index, divided by sites in the DMA). For each metric, a mixed logistic regression model was fit to model site performance as a function of app usage. Results: An increase of 200 cumulative prior metric hits per site was associated with increased odds of adherence to 3 metrics: Appropriate admission (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.06-1.18), appropriate length of stay (OR, 1.20; 95% CI, 1.12-1.28), and inappropriate chest x-ray (OR, 0.82; 95% CI, 0.75-0.91). Ten additional metric hits per case were also associated: OR were 1.18 (95% CI, 1.02-1.36), 1.36 (95% CI, 1.14-1.62), and 0.74 (95% CI, 0.62-0.89). Discussion: mECDS tools are increasingly being implemented, but their impact on clinical practice is poorly described. To our knowledge, although ecologic in nature, this report is the first to link clinical practice to mECDS use on a national scale and outside of an electronic health record. Conclusions: mECDS use was associated with changes in adherence to targeted metrics. Future studies should seek to link mECDS usage more directly to clinical practice and assess other site-level factors.
KW - Clinical decision support
KW - Clinical practice guideline
KW - Guideline adherence
KW - Mobile applications
KW - Quality improvement
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U2 - 10.1093/jamia/ocz011
DO - 10.1093/jamia/ocz011
M3 - Article
C2 - 30925592
AN - SCOPUS:85068196443
VL - 26
SP - 630
EP - 636
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
SN - 1067-5027
IS - 7
ER -