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. 2011 Sep-Oct;9(5):398-405.
doi: 10.1370/afm.1286.

A diabetes dashboard and physician efficiency and accuracy in accessing data needed for high-quality diabetes care

Richelle J Koopman  1 , Karl M Kochendorfer, Joi L Moore, David R Mehr, Douglas S Wakefield, Borchuluun Yadamsuren, Jared S Coberly, Robin L Kruse, Bonnie J Wakefield, Jeffery L Belden
Affiliations

A diabetes dashboard and physician efficiency and accuracy in accessing data needed for high-quality diabetes care

Richelle J Koopman et al. Ann Fam Med. 2011 Sep-Oct.

Abstract

Purpose: We compared use of a new diabetes dashboard screen with use of a conventional approach of viewing multiple electronic health record (EHR) screens to find data needed for ambulatory diabetes care.

Methods: We performed a usability study, including a quantitative time study and qualitative analysis of information-seeking behaviors. While being recorded with Morae Recorder software and "think-aloud" interview methods, 10 primary care physicians first searched their EHR for 10 diabetes data elements using a conventional approach for a simulated patient, and then using a new diabetes dashboard for another. We measured time, number of mouse clicks, and accuracy. Two coders analyzed think-aloud and interview data using grounded theory methodology.

Results: The mean time needed to find all data elements was 5.5 minutes using the conventional approach vs 1.3 minutes using the diabetes dashboard (P <.001). Physicians correctly identified 94% of the data requested using the conventional method, vs 100% with the dashboard (P <.01). The mean number of mouse clicks was 60 for conventional searching vs 3 clicks with the diabetes dashboard (P <.001). A common theme was that in everyday practice, if physicians had to spend too much time searching for data, they would either continue without it or order a test again.

Conclusions: Using a patient-specific diabetes dashboard improves both the efficiency and accuracy of acquiring data needed for high-quality diabetes care. Usability analysis tools can provide important insights into the value of optimizing physician use of health information technologies.

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Figures

Figure 1
Figure 1
Diabetes dashboard screen.
Figure 2
Figure 2
Actual time on task for Patient A (conventional electronic health record search) and Patient B (dashboard search).
Figure 3
Figure 3
Total number of mouse clicks for Patient A (conventional electronic health record search) and Patient B (dashboard search).
Figure 4
Figure 4
Number of mouse clicks needed to find each data element. Aspirin = daily use of aspirin; BP = value of last blood pressure; Eye = date of last eye examination; Foot = date of last foot examination; Hb = hemoglobin A1c level; LDL = low-density lipoprotein cholesterol level; Smoke = smoking status; Urine = value of last urine microalbumin-creatinine ratio.
See this image and copyright information in PMC

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