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

Ellen K. Kerns, Vincent S. Staggs, Sarah D. Fouquet, Russell J. McCulloh

Research output: Contribution to journalArticle

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 (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.

Original languageEnglish (US)
Pages (from-to)630-636
Number of pages7
JournalJournal of the American Medical Informatics Association
Volume26
Issue number7
DOIs
StatePublished - Apr 11 2019

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Clinical Decision Support Systems
Confidence Intervals
Equipment and Supplies
Odds Ratio
Fever
Logistic Models
Electronic Health Records
Length of Stay
Thorax
X-Rays

Keywords

  • Clinical decision support
  • Clinical practice guideline
  • Guideline adherence
  • Mobile applications
  • Quality improvement

ASJC Scopus subject areas

  • Health Informatics

Cite this

Estimating the impact of deploying an electronic clinical decision support tool as part of a national practice improvement project. / Kerns, Ellen K.; Staggs, Vincent S.; Fouquet, Sarah D.; McCulloh, Russell J.

In: Journal of the American Medical Informatics Association, Vol. 26, No. 7, 11.04.2019, p. 630-636.

Research output: Contribution to journalArticle

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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 (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.",
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