Healthcare Operations and Black Swan Event for COVID-19 Pandemic: A Predictive Analytics
Ieee Transactions on Engineering Management
; : 15, 2021.
Article
in English
| Web of Science | ID: covidwho-1583761
ABSTRACT
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.
Medical, services; COVID-19; Time, series, analysis; Predictive, models; Pandemics; Data, models; Corona; COVID-19, (novel, corona); data, analytics; deep, learning; extreme, learning, machine, (ELM); long, short-term, memory; (LSTM); multilayer, perceptron; prediction; time, series; extreme, learning-machine; time-series; big, data; networks; models; technologies; simulation; diagnosis; yield; lstm; Business, &, Economics; Engineering
Full text:
Available
Collection:
Databases of international organizations
Database:
Web of Science
Type of study:
Prognostic study
Language:
English
Journal:
Ieee Transactions on Engineering Management
Year:
2021
Document Type:
Article
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