Investigating the cognitive capacity constraints of an ICU care team using a systems engineering approach

Abstract

Background: ICU operational conditions may contribute to cognitive overload and negatively impact on clinical decision making. We aimed to develop a quantitative model to investigate the association between the operational conditions and the quantity of medication orders as a measurable indicator of the multidisciplinary care team's cognitive capacity.

Methods: The temporal data of patients at one medical ICU (MICU) of Mayo Clinic in Rochester, MN between February 2016 to March 2018 was used. This dataset includes a total of 4822 unique patients admitted to the MICU and a total of 6240 MICU admissions. Guided by the Systems Engineering Initiative for Patient Safety model, quantifiable measures attainable from electronic medical records were identified and a conceptual framework of distributed cognition in ICU was developed. Univariate piecewise Poisson regression models were built to investigate the relationship between system-level workload indicators, including patient census and patient characteristics (severity of illness, new admission, and mortality risk) and the quantity of medication orders, as the output of the care team's decision making.

Results: Comparing the coefficients of different line segments obtained from the regression models using a generalized F-test, we identified that, when the ICU was more than 50% occupied (patient census > 18), the number of medication orders per patient per hour was significantly reduced (average = 0.74; standard deviation (SD) = 0.56 vs. average = 0.65; SD = 0.48; p < 0.001). The reduction was more pronounced (average = 0.81; SD = 0.59 vs. average = 0.63; SD = 0.47; p < 0.001), and the breakpoint shifted to a lower patient census (16 patients) when at a higher presence of severely-ill patients requiring invasive mechanical ventilation during their stay, which might be encountered in an ICU treating patients with COVID-19.

Conclusions: Our model suggests that ICU operational factors, such as admission rates and patient severity of illness may impact the critical care team's cognitive function and result in changes in the production of medication orders. The results of this analysis heighten the importance of increasing situational awareness of the care team to detect and react to changing circumstances in the ICU that may contribute to cognitive overload.

Keywords: Cognitive function; Electronic medical records; Organizational decision making; Situational awareness; Systems approach; Workload.

Fig. 1

The conceptual framework of distributed cognition in ICUs. Information and data of patients are fed to the care team for clinical decision making. The distributed cognitive system includes both the care team and artifacts like technology. The output includes clinical interventions such as medication orders. The system workload factors (such as the number of patients and patient characteristics) affect the input (e.g., change in quantity) and subsequently affect the decision-making process (e.g., trigger cognitive overload)

Fig. 2

ICU system dynamics. The first layer describes individual patient stays in ICUs. ICU admission for each patient is represented by one or more box(es) depending on locations (bed sites) where they are taken care of. The width of the boxes reflects patients’ LOS and patients within the first 3 h post-admission are considered as new patients. Their severity of illness evaluated as non-severe (mild) or severe is differentiated by box border colors (orange and red, respectively). Also, filled colors indicate the mortality risk of patients: high (yellow) and low (gray). The second layer describes the dynamics of system workload factors and the output of medication orders in the MICU. Patients’ medication orders are marked as green stars. The system workload factors reflect the care team’s workload and cognitive load at the time point

Fig. 3

The number of medication orders generated during each hour of the first 48 h since ICU admission by IMV usage (orange for patients who had ever used IMV during their stay vs. blue for those who had not): a the percentage of patients who had generated medication orders, and b the hourly average of per patient medication orders for each elapsed hour

Fig. 4

Medication orders per hour against patient census: a overall; b when controlling for the time periods (for the rounding period, samples with low and high census were excluded due to low sample size); c when controlling for the presence of severe patients (high vs. low); and d when controlling for the presence of new patients and high mortality risk patients, respectively. The high presence of severe patients was defined as an ICU operational condition with more than 60% of the present patients having ever used IMV at the moment. The high presence of new patients was defined as an ICU operational condition with more than one new patient at the moment. The high presence of high mortality risk patients was defined as an ICU operational condition with more than 33% of the present patients having higher SOFA scores than the chosen criteria at the moment. An error bar indicates a 95% confidence interval for the average of medication orders given a certain patient census