Table 2.
Attitudes toward the tool and factors affecting implementation.
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Values | |||||
| Attitudesa, mean (SD) | 1-7b | |||||
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“I would use the clinical decision support toolc.” | 5.6 (1.4) | ||||
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“I find the clinical decision support tool to be useful in my job.” | 5.7 (1.3) | ||||
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“I find the clinical decision support tool to be easy to use.” | 5.8 (1.2) | ||||
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“In general, the clinic would support my use of this clinical decision support tool.” | 5.0 (1.7) | ||||
| Factors affecting implementation (rank order)d | ||||||
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Factor, mean (SD) | |||||
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Whether its use improves health | 2.5 (1.7) | |||
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Accuracy | 2.7 (1.7) | |||
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Usability | 3.7 (1.5) | |||
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Impact on clinic workflows | 3.9 (1.6) | |||
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Cost | 4.2 (1.7) | |||
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Whether its use reduces costs to the health care system | 4.3 (1.6) | |||
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A team of clinicians and staff were tasked with predicting whether the 1000 individuals with diabetes in your practice would have a hemoglobin A1c >9% in the next year. The following year, your practice announced that the team accurately predicted the fate of 800 of these individuals. How many people would the AIe tool need to accurately categorize for you to consider using it? | |||||
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Values, mean (SD); range | 617 (273); 20-900 | |||
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Distribution of responses, n (%) | ||||
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0-200 | 3 (14) | ||
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201-400 | 1 (5) | ||
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401-600 | 6 (27) | ||
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601-800 | 6 (27) | ||
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801-1000 | 6 (27) | ||
a1 indicates strongly disagrees, and 7 indicates strongly agree.
bRange of possible responses.
cn=21.
d1 indicates the most important factor, and 6 indicates the least important factor.
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