Using a Markov Chain Model to Analyze the Relationship Between Avoidable Days and Critical Care Capacity

Area of Science:

• Critical Care Medicine
• Health Systems Engineering
• Hospital Operations Research

Background:

• Hospital capacity strain is a major challenge in critical care settings.
• Avoidable days (AD) are commonly used to measure hospital capacity and efficiency.
• Understanding metrics that impact critical care operations is crucial for improving patient flow and resource allocation.

Purpose of the Study:

• To investigate the relationship between avoidable days (AD) and surgical cancellations in a cardiovascular intensive care unit (ICU).
• To determine if reducing AD impacts the rate of surgical cancellations, a key indicator of critical care capacity.
• To analyze the effect of patient discharge probability on AD and surgical cancellations using a simulation model.

Main Methods:

• A Markov chain model was employed to simulate patient flow in a cardiovascular ICU.
• The model incorporated length of stay data from 108 simulated patients reflecting the real population.
• The probability of patient discharge was systematically varied to assess its impact on AD and surgical cancellations.

Main Results:

• The simulation demonstrated that increasing the probability of patient discharge led to a decrease in avoidable days (AD).
• Despite the reduction in AD, the surgical cancellation rate remained unaffected across all simulated discharge probabilities.
• The findings indicate that AD may not be a reliable predictor of surgical scheduling capacity in critical care.

Conclusions:

• There is no discernible relationship between avoidable days (AD) and critical care capacity, as evidenced by the surgical cancellation rate.
• Metrics like AD may not accurately reflect the operational constraints that lead to surgical cancellations in ICUs.
• Further research is needed to identify more relevant metrics for assessing and managing critical care capacity and surgical scheduling.