Modelling the backlog of COVID-19 cases for a surgical group

Area of Science:

• Health Care Management
• Surgical Workflow Optimization
• Pandemic Response Planning

Background:

• The COVID-19 pandemic significantly disrupted healthcare delivery, leading to substantial surgical backlogs.
• Existing healthcare systems face increased demand, with limited guidance for managing post-pandemic recovery.
• Delayed patient care due to backlogs can result in adverse downstream health consequences.

Purpose of the Study:

• To introduce an online tool designed to assist surgical groups in managing patient backlogs.
• To enable surgeons to explore the impact of resource allocation on surgical wait times.
• To provide a method for simulating different resource management strategies to mitigate care delays.

Main Methods:

• Development of an accessible online platform (www.covidbacklog.com).
• Input of basic variables related to surgical group resources and patient demand.
• Utilization of a computer program to generate wait time forecasts for surgical backlogs.

Main Results:

• The tool provides a forecast of patient backlog clearance times.
• It allows for the simulation of various resource allocation scenarios.
• Enables data-driven decision-making for surgical group resource management.

Conclusions:

• The online tool offers a practical solution for surgical groups facing post-pandemic patient backlogs.
• Facilitates proactive planning to optimize resource allocation and minimize patient wait times.
• Aims to improve healthcare system resilience and prevent negative outcomes from delayed surgical care.