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
