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

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...

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Autores principales: Park, Jaeyoung, Zhong, Xiang, Dong, Yue, Barwise, Amelia, Pickering, Brian W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8724599/
https://www.ncbi.nlm.nih.gov/pubmed/34983402
http://dx.doi.org/10.1186/s12871-021-01548-7
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author Park, Jaeyoung
Zhong, Xiang
Dong, Yue
Barwise, Amelia
Pickering, Brian W.
author_facet Park, Jaeyoung
Zhong, Xiang
Dong, Yue
Barwise, Amelia
Pickering, Brian W.
author_sort Park, Jaeyoung
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12871-021-01548-7.
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spelling pubmed-87245992022-01-04 Investigating the cognitive capacity constraints of an ICU care team using a systems engineering approach Park, Jaeyoung Zhong, Xiang Dong, Yue Barwise, Amelia Pickering, Brian W. BMC Anesthesiol Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12871-021-01548-7. BioMed Central 2022-01-04 /pmc/articles/PMC8724599/ /pubmed/34983402 http://dx.doi.org/10.1186/s12871-021-01548-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Park, Jaeyoung
Zhong, Xiang
Dong, Yue
Barwise, Amelia
Pickering, Brian W.
Investigating the cognitive capacity constraints of an ICU care team using a systems engineering approach
title Investigating the cognitive capacity constraints of an ICU care team using a systems engineering approach
title_full Investigating the cognitive capacity constraints of an ICU care team using a systems engineering approach
title_fullStr Investigating the cognitive capacity constraints of an ICU care team using a systems engineering approach
title_full_unstemmed Investigating the cognitive capacity constraints of an ICU care team using a systems engineering approach
title_short Investigating the cognitive capacity constraints of an ICU care team using a systems engineering approach
title_sort investigating the cognitive capacity constraints of an icu care team using a systems engineering approach
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8724599/
https://www.ncbi.nlm.nih.gov/pubmed/34983402
http://dx.doi.org/10.1186/s12871-021-01548-7
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