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 Clinical implementation of pharmacogenomics via a health system-wide research biobank: the University of Colorado experience

2020 ◽  
Vol 21 (6) ◽  
pp. 375-386 ◽  
Author(s):  
Christina L Aquilante ◽  
David P Kao ◽  
Katy E Trinkley ◽  
Chen-Tan Lin ◽  
Kristy R Crooks ◽  
...  
Keyword(s):  
Decision Support ◽  
Health System ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Clinical Implementation ◽  
Support Tool ◽  
University Of Colorado ◽  
The University ◽  
The Impact

In recent years, the genomics community has witnessed the growth of large research biobanks, which collect DNA samples for research purposes. Depending on how and where the samples are genotyped, biobanks also offer the potential opportunity to return actionable genomic results to the clinical setting. We developed a preemptive clinical pharmacogenomic implementation initiative via a health system-wide research biobank at the University of Colorado. Here, we describe how preemptive return of clinical pharmacogenomic results via a research biobank is feasible, particularly when coupled with strong institutional support to maximize the impact and efficiency of biobank resources, a multidisciplinary implementation team, automated clinical decision support tools, and proactive strategies to engage stakeholders early in the clinical decision support tool development process.

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

scholarly journals Reach, Adoption and Sustainability of a Mobile Clinical Decision Support Tool Paired with a National Quality Improvement Project Targeting Emergency Department Febrile Infant Management

2020 ◽  
Author(s):  
Ellen K Kerns ◽  
Russell McCulloh ◽  
Hongying Dai
Keyword(s):  
Quality Improvement ◽  
Clinical Practice ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Confounding Factors ◽  
Children's Hospitals ◽  
Improvement Project ◽  
Children’S Hospitals ◽  
National Quality

Abstract Background: Electronic clinical decision support (ECDS) tools are often developed within quality improvement (QI) projects to increase adherence with the latest clinical practice guidelines (CPGs).However, scalability and sustainability of ECDS beyond the time and location of their associated project are very limited. Deploying ECDS using a mobile app (mECDS) has shown the potential to be a viable method of overcoming these limitations. However, it is unclear what pattern the spread of uptake and use of such a tool might follow.Methods: In 2016, our team released a freely available mECDS as part of a national multi-site project entitled Reducing Variation in Infant Sepsis Evaluation (REVISE). For this study, we evaluated trends in weekly mECDS usage measures defined as 1) REVISE metric related screen views (MetricHits), 2) unique designated market areas (DMA) where the mECDS was used (Unique DMAs), 3) density of use or MetricHits/Unique DMAs (HitsPerDMA). Linear regressions were performed to examine the app usage trends measures across three time periods (during REVISE, 1-year post, 2-year post). Separate regression analyses were performed among DMAs that contained a REVISE site and those that did not. The number of REVISE sites in the DMA, the number of children’s hospitals in the DMA, DMA population, and season were also evaluated as confounding factors.Results: Strong growth in the number of unique DMAs and MetricHits occurred during the period of 1-year post the REVISE project. The overall usage continued to be relatively stable during the period of 2-years post. HitsPerDMA had stronger growth in DMAs with a REVISE site than those without. MetricHits were higher in DMAs with a larger population, more REVISE sites, and more children’s hospitals. There were also more MetricHits in the summer than in the winter months. Conclusions: Both temporal and spatial increases in mECDS app usage were found as evidence of a contagion method of spread. Other confounding factors may also play a role in app reach, adoption, and sustainability. Further research is needed to determine the factors driving passive diffusion and its impact on clinical practice and patient outcomes.

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