The febrile infant conundrum

2020 ◽  
Author(s):  
Team DFTB
Keyword(s):  
Febrile Infant

AAP Updates Policy on First UTI in Febrile Infant

2011 ◽  
Vol 45 (9) ◽  
pp. 1-9
Author(s):  
SHARON WORCESTER
Keyword(s):  
Febrile Infant

scholarly journals Febrile Infant

2020 ◽  
Vol 39 (6) ◽  
pp. e81-e82 ◽  
Author(s):  
Emily Robbins ◽  
Zara Ilahi ◽  
Philip Roth
Keyword(s):  
Febrile Infant

The Febrile Infant

2019 ◽  
Vol 5 (4) ◽  
pp. 431-447
Author(s):  
Pedro Rino ◽  
Eugenia Hernández
Keyword(s):  
Febrile Infant

Adherence to Guidelines for Managing the Well-Appearing Febrile Infant

2010 ◽  
Vol 26 (12) ◽  
pp. 875-880 ◽  
Author(s):  
William Paul Meehan ◽  
Eric Fleegler ◽  
Richard G. Bachur
Keyword(s):  
Adherence To Guidelines ◽  
Febrile Infant

Case 3: Unexpected Diagnosis in a Febrile Infant

2019 ◽  
Vol 40 (10) ◽  
pp. 535-537
Author(s):  
Jacob Kolker ◽  
Kathryn Halyko ◽  
Cody Tigges
Keyword(s):  
Febrile Infant

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.

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