Examining the Association Between ICU Operational Conditions and Clinical Decision-Making
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Professional Development and Education, Administration, 2022
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INTRODUCTION: The ICU work environment requires care teams to continually make appropriate, accurate, and timely decisions. This study aimed to identify the association between the operational conditions and the decision-making in a quantitative way. METHODS: This study included temporal data at one medical ICU (MICU) of Mayo Clinic in Rochester, MN, between February 2016 to March 2018. During this period, 4,822 unique patients had been admitted to the facility (a total of 6,240 MICU admissions). We developed a univariate piecewise Poisson regression model where the number of hourly medication orders was regressed on the hourly patient census. Here, the medication order and the patient census are considered as a surrogate of the decision-making and the operational condition, respectively. Then, a generalized F-test was conducted to examine whether the increase rates of multiple pieces were identical. Also, we compared the numbers of medication orders per patient per hour obtained per piece. In addition, conditioned on a high presence of severely ill patients, the regression model with the same independent and dependent variables was constructed to compare different operational conditions. RESULTS: We identified a breakpoint that the increase rate started to be reduced; the number of medication orders per patient per hour was significantly reduced over 56% occupancy (18 patients) of the ICU (average=0.74; standard deviation (SD)=0.56 vs. average=0.65; SD=0.48; p < 0.001). This indicates the amount of decision-making decreased by 13% when at a high presence of patients. That is, it was associated with the delay of decision-making. Furthermore, the delay deteriorated when at a high presence of severely ill patients; the breakpoint shifted to a lower patient census (16 patients), and the quantity decreased by 22% over the breakpoint (average=0.81; SD=0.59 vs. average=0.63; SD=0.47; p < 0.001). CONCLUSIONS: Our statistical models revealed that demanding operational conditions were associated with the delay of decision-making. We propose adaptive clinical resource allocation (e.g., teleICU consultants as backup consultants) to mitigate the delay. Also, this study highlights quantitative comparisons of decision-making in different operational conditions. This allows for effective clinical resource allocation.
Meta Tag
Content Type Presentation
Knowledge Area Professional Development and Education
Knowledge Area Administration
Knowledge Level Advanced
Membership Level Select
Tag Informatics
Tag Intensivist
Tag Innovation
Tag Staffing
Tag Healthcare Delivery
Year 2022
Jae Young Park
ICU operational conditions
clinical decision making
systems engineering approach
cognitive overload


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