Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics

• Published in: IEEE transactions on engineering management Vol. 70; no. 9; pp. 3229 - 3243
• Main Authors: Devarajan, Jinil Persis, Manimuthu, Arunmozhi, Sreedharan, V Raja
• Format: Journal Article
• Language: English
• Published: United States IEEE 01-09-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Black swan event; Corona; COVID-19; COVID-19 (novel corona); Cycle ratio; Cycle time; data analytics; Data models; Decision making; deep learning; Drainage facilities; Emerging Technologies; Engineering Management; extreme learning machine (ELM); Health care; long short-term memory (LSTM); Machine learning; Mathematical analysis; Medical personnel; Medical services; multilayer perceptron; Pandemics; prediction; Predictions; Predictive analytics; Predictive models; Project Management; Resource management; Technology Assessment, Forecasting, Planning and Transfer; time series; Time series analysis; deep learning; data analytics; long short-term memory (LSTM); multilayer perceptron; prediction; time series; COVID-19 (novel corona); extreme learning machine (ELM)
• ISSN: 0018-9391; 1558-0040
• DOI: 10.1109/TEM.2021.3076603

COVID-19 pandemic has questioned the way healthcare operations take place globally as the healthcare professionals face an unprecedented task of controlling and treating the COVID-19 infected patients with a highly straining and draining facility due to the erratic admissions of infected patients. However, COVID-19 is considered as a white swan event. Yet, the impact of the COVID-19 pandemic on healthcare operations is highly uncertain and disruptive making it as a black swan event. Therefore, the study explores the impact of the COVID-19 outbreak on healthcare operations and develops machine learning-based forecasting models using time series data to foresee the progression of COVID-19 and further using predictive analytics to better manage healthcare operations. The prediction error of the proposed model is found to be 0.039 for new cases and 0.006 for active COVID-19 cases with respect to mean absolute percentage error. The proposed simulated model further could generate predictive analytics and yielded future recovery rate, resource management ratios, and average cycle time of a patient tested COVID-19 positive. Further, the study will help healthcare professionals to devise better resilience and decision-making for managing uncertainty and disruption in healthcare operations.