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

Area of Science:

• Healthcare Operations Management
• Epidemiological Forecasting
• Predictive Analytics in Public Health

Background:

• The COVID-19 pandemic presented unprecedented challenges to global healthcare systems, straining resources and demanding adaptive operational strategies.
• While initially considered a white swan event, the pandemic's disruptive and uncertain impact on healthcare operations aligns with characteristics of a black swan event.
• Erratic patient admissions and the need for effective treatment protocols highlighted critical vulnerabilities in existing healthcare operational frameworks.

Purpose of the Study:

• To investigate the impact of the COVID-19 outbreak on healthcare operations.
• To develop machine learning-based forecasting models for predicting COVID-19 progression.
• To utilize predictive analytics for enhanced healthcare operations management.

Main Methods:

• Time series data analysis was employed to build machine learning forecasting models.
• Predictive analytics were integrated to simulate future healthcare operational scenarios.
• Model performance was evaluated using mean absolute percentage error (MAPE).

Main Results:

• The proposed model achieved a prediction error of 0.039 for new COVID-19 cases and 0.006 for active cases (MAPE).
• Simulated analytics provided insights into future recovery rates, resource management ratios, and average patient cycle times.
• The models demonstrated accuracy in forecasting key epidemiological and operational metrics.

Conclusions:

• Machine learning and predictive analytics offer robust tools for navigating healthcare disruptions caused by pandemics.
• The developed models can enhance decision-making, improve resilience, and optimize resource allocation in healthcare operations.
• Proactive management strategies informed by accurate forecasting are crucial for mitigating the impact of future health crises.