Implementation and evaluation of electronic clinical decision support for compliance with pneumonia and heart failure quality indicators

2009 ◽  
Vol 66 (4) ◽  
pp. 389-397 ◽  
Author(s):  
Kristin Niemi ◽  
Siobhan Geary ◽  
Barbara Quinn ◽  
Mark Larrabee ◽  
Kevin Brown
Keyword(s):  
Heart Failure ◽  
Decision Support ◽  
Quality Indicators ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Electronic Clinical Decision Support

scholarly journals Utilization and effects of mobile electronic clinical decision support on pediatric asthma care quality in the emergency department and inpatient setting

JAMIA Open ◽  
2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Ellen Kerns ◽  
Russell McCulloh ◽  
Sarah Fouquet ◽  
Corrie McDaniel ◽  
Lynda Ken ◽  
...  
Keyword(s):  
Emergency Department ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Pediatric Asthma ◽  
Clinical Decision ◽  
Care Quality ◽  
Asthma Care ◽  
Mixed Effect ◽  
National Quality ◽  
Electronic Clinical Decision Support

Abstract Objective To determine utilization and impacts of a mobile electronic clinical decision support (mECDS) on pediatric asthma care quality in emergency department and inpatient settings. Methods We conducted an observational study of a mECDS tool that was deployed as part of a multi-dimensional, national quality improvement (QI) project focused on pediatric asthma. We quantified mECDS utilization using cumulative screen views over the study period in the city in which each participating site was located. We determined associations between mECDS utilization and pediatric asthma quality metrics using mixed-effect logistic regression models (adjusted for time, site characteristics, site-level QI project engagement, and patient characteristics). Results The tool was offered to clinicians at 75 sites and used on 286 devices; cumulative screen views were 4191. Children’s hospitals and sites with greater QI project engagement had higher cumulative mECDS utilization. Cumulative mECDS utilization was associated with significantly reduced odds of hospital admission (OR: 0.95, 95% CI: 0.92–0.98) and higher odds of caregiver referral to smoking cessation resources (OR: 1.08, 95% CI: 1.01–1.16). Discussion We linked mECDS utilization to clinical outcomes using a national sample and controlling for important confounders (secular trends, patient case mix, and concomitant QI efforts). We found mECDS utilization was associated with improvements in multiple measures of pediatric asthma care quality. Conclusion mECDS has the potential to overcome barriers to dissemination and improve care on a broad scale. Important areas of future work include improving mECDS uptake/utilization, linking clinicians’ mECDS usage to clinical practice, and studying mECDS’s impacts on other common pediatric conditions.

scholarly journals Interrupting providers with clinical decision support to improve care for heart failure

2019 ◽  
Vol 131 ◽  
pp. 103956
Author(s):  
Saul Blecker ◽  
Jonathan S. Austrian ◽  
Leora I. Horwitz ◽  
Gilad Kuperman ◽  
Donna Shelley ◽  
...  
Keyword(s):  
Heart Failure ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision

scholarly journals Estimating the impact of deploying an electronic clinical decision support tool as part of a national practice improvement project

2019 ◽  
Vol 26 (7) ◽  
pp. 630-636 ◽  
Author(s):  
Ellen K Kerns ◽  
Vincent S Staggs ◽  
Sarah D Fouquet ◽  
Russell J McCulloh
Keyword(s):  
Clinical Practice ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Market Area ◽  
Improvement Project ◽  
Support Tool ◽  
Febrile Infant ◽  
The Impact ◽  
Electronic Clinical Decision Support

Abstract 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 (metric-related 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.

scholarly journals Evaluation of an Electronic Clinical Decision Support Tool for Incident Elevated BP in Adolescents

2018 ◽  
Vol 18 (1) ◽  
pp. 43-50 ◽  
Author(s):  
Elyse Olshen Kharbanda ◽  
Stephen E. Asche ◽  
Alan Sinaiko ◽  
James D. Nordin ◽  
Heidi L. Ekstrom ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Decision Support Tool ◽  
Electronic Clinical Decision Support
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