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Machine learning to assist clinical decision-making during the COVID-19 pandemic.

ABSTRACT: Background:The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information. Main body:While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." Throughout the patient care pathway, there are opportunities for ML-supported decisions based on collected vitals, laboratory results, medication orders, and comorbidities. With rapidly growing datasets, there also remain important considerations when developing and validating ML models. Conclusion:This perspective highlights the utility of evidence-based prediction tools in a number of clinical settings, and how similar models can be deployed during the COVID-19 pandemic to guide hospital frontlines and healthcare administrators to make informed decisions about patient care and managing hospital volume.

SUBMITTER: Debnath S

PROVIDER: S-EPMC7347420 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Machine learning to assist clinical decision-making during the COVID-19 pandemic.

Debnath Shubham S Barnaby Douglas P DP Coppa Kevin K Makhnevich Alexander A Kim Eun Ji EJ Chatterjee Saurav S Tóth Viktor V Levy Todd J TJ Paradis Marc D MD Cohen Stuart L SL Hirsch Jamie S JS Zanos Theodoros P TP

Bioelectronic medicine 20200710

<h4>Background</h4>The number of cases from the coronavirus disease 2019 (COVID-19) global pandemic has overwhelmed existing medical facilities and forced clinicians, patients, and families to make pivotal decisions with limited time and information.<h4>Main body</h4>While machine learning (ML) methods have been previously used to augment clinical decisions, there is now a demand for "Emergency ML." Throughout the patient care pathway, there are opportunities for ML-supported decisions based on ...[more]

PMID: 32665967

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Project description:Aims and objectivesThe purpose of this study was to report the psychometric properties, including validity and reliability, of the decision fatigue scale (DFS).BackgroundDecision fatigue may impair nurses' ability to make sound clinical decisions and negatively impact patient care. Given the negative impact of the COVID-19 pandemic on psychological well-being and the workplace environment, decision fatigue may be even more apparent among clinical nurses. Valid assessment of this condition among clinical nurses may inform supportive interventions to mitigate the negative sequelae associated with states of decision fatigue.DesignThis study was a secondary analysis of a parent study using a cross-sectional descriptive design.MethodsA convenience sample of 160 staff nurses was recruited online from across the United States. Participants completed a demographic questionnaire and subjective measures of decision fatigue, nursing practice environment scale and traumatic stress. Exploratory factor analysis (EFA), correlation coefficients and internal consistency reliability coefficients were computed to examine the DFS's validity and reliability within this sample.ResultsThe EFA yielded a single factor, 9-item version of the DFS. The DFS scores were strongly correlated with traumatic stress and moderately correlated with the nursing practice environment, and the scale displayed appropriate internal consistency.ConclusionsThis is the first known study to provide evidence of the DFS's validity and reliability in a sample of registered nurses working during the COVID-19 pandemic. The results of this study provide evidence of a reliable and valid assessment instrument for decision fatigue that can be used to measure the burden of decision-making among registered nurses.Relevance to clinical practiceGiven the relationship between traumatic stress and the nursing work environment, decision fatigue may be a modifiable target for interventions that can enhance the quality of decision-making among clinical nurses.

Dataset Information

Machine learning to assist clinical decision-making during the COVID-19 pandemic.

Publications

Machine learning to assist clinical decision-making during the COVID-19 pandemic.

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