Measures of User experience in a Streptococcal pharyngitis and Pneumonia Clinical Decision Support Tools

D. Mann; M. Knaus; L. McCullagh; A. Sofianou; L. Rosen; T. McGinn; J. Kannry

doi:10.4338/ACI-2014-04-RA-0043

Applied Clinical Informatics, Table of Contents

Appl Clin Inform 2014; 05(03): 824-835
DOI: 10.4338/ACI-2014-04-RA-0043

Research Article

Schattauer GmbH

Measures of User experience in a Streptococcal pharyngitis and Pneumonia Clinical Decision Support Tools

Authors

D. Mann

¹Department of Medicine, Section of Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, MA, USA
M. Knaus

²Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, NY, USA
L. McCullagh

²Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, NY, USA
A. Sofianou

³Department of Medicine, Division of General Internal Medicine, Mount Sinai School of Medicine, New York, NY, USA
L. Rosen

²Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, NY, USA
T. McGinn

²Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, NY, USA
J. Kannry

³Department of Medicine, Division of General Internal Medicine, Mount Sinai School of Medicine, New York, NY, USA

Abstract

Summary

Objective: To understand clinician adoption of CDS tools as this may provide important insights for the implementation and dissemination of future CDS tools.

Materials and Methods: Clinicians (n=168) at a large academic center were randomized into intervention and control arms to assess the impact of strep and pneumonia CDS tools. Intervention arm data were analyzed to examine provider adoption and clinical workflow. Electronic health record data were collected on trigger location, the use of each component and whether an antibiotic, other medication or test was ordered. Frequencies were tabulated and regression analyses were used to determine the association of tool component use and physician orders.

Results: The CDS tool was triggered 586 times over the study period. Diagnosis was the most frequent workflow trigger of the CDS tool (57%) as compared to chief complaint (30%) and diagnosis/antibiotic combinations (13%). Conversely, chief complaint was associated with the highest rate (83%) of triggers leading to an initiation of the CDS tool (opening the risk prediction calculator). Similar patterns were noted for initiation of the CDS bundled ordered set and completion of the entire CDS tool pathway. Completion of risk prediction and bundled order set components were associated with lower rates of antibiotic prescribing (OR 0.5; CI 0.2-1.2 and OR 0.5; CI 0.3-0.9, respectively).

Discussion: Different CDS trigger points in the clinician user workflow lead to substantial variation in downstream use of the CDS tool components. These variations were important as they were associated with significant differences in antibiotic ordering.

Conclusions: These results highlight the importance of workflow integration and flexibility for CDS success.

Citation: Mann D, Knaus M, McCullagh L, Sofianou A, Rosen L, McGinn T, Kannry J. Measures of user experience in a streptococcal pharyngitis and pneumonia clinical decision support tools. Appl Clin Inf 2014; 5: 824–835

http://dx.doi.org/10.4338/ACI-2014-04-RA-0043

Keywords

Usability - workflow - clinical decision support - electronic health records - clinical prediction rules - evidence-based medicine

Full Text

References

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