A predictive model for the post-pandemic delay in elective treatment.

Area of Science:

• Healthcare Management
• Operations Research
• Public Health

Background:

• The COVID-19 pandemic severely disrupted healthcare services globally, leading to cancellations of elective treatments.
• In the UK, this resulted in a 30% decrease in new waiting list entries, creating an anticipated post-pandemic backlog.
• Hospitals face challenges in quantifying this backlog and predicting patient treatment delays.

Purpose of the Study:

• To develop and demonstrate a discrete-event simulation approach for predicting elective waiting list backlogs.
• To quantify potential patient treatment delays based on prioritization policies.
• To provide a tool for post-pandemic healthcare planning and resource allocation.

Main Methods:

• Utilized discrete-event simulation to model elective waiting lists.
• Applied the model to endoscopy waiting list data from Cambridge University Hospitals.
• Analyzed scenarios to assess the impact of prioritization policies and resource investment.

Main Results:

• The simulation predicted that 21% of patients will experience delays exceeding the 18-week National Health Service (NHS) threshold.
• Scenario analysis indicated that increased investment in NHS resources significantly improves waiting list management.
• The model offers a quantifiable method for predicting backlog and delays.

Conclusions:

• The discrete-event simulation model is a valuable tool for hospitals managing post-pandemic treatment backlogs.
• Effective prioritization policies and resource investment are crucial for mitigating patient delays.
• This approach can aid global healthcare systems in planning and optimizing elective treatment pathways.