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

• Published in: BMC anesthesiology 2022-01, Vol.22 (1), p.10-10, Article 10
• Main Authors: Park, Jaeyoung, Zhong, Xiang, Dong, Yue, Barwise, Amelia, Pickering, Brian W
• Format: Article
• Language: English
• Subjects: Aged; Census; Censuses; Clinical decision making; Cognition; Cognitive ability; Cognitive function; Cognitive load; COVID-19; COVID-19 - therapy; Critical care; Decision making; Decision Making, Organizational; Electronic medical records; Failure; Female; Humans; Intensive care; Intensive Care Units; Male; Mechanical ventilation; Middle Aged; Mortality; Nurse practitioners; Organizational decision making; Pandemics; Patient Care Team; Patient Safety; Patients; Regression analysis; SARS-CoV-2; Situational awareness; Systems approach; Workforce planning; Workload; Workloads
• ISSN: 1471-2253; 1471-2253
• DOI: 10.1186/s12871-021-01548-7

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. 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. 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