A simulation framework for mapping risks in clinical processes: the case of in-patient transfers

Area of Science:

• Healthcare systems engineering
• Patient safety research
• Computational modeling in medicine

Background:

• Routine clinical processes are susceptible to individual violations.
• Cumulative effect of these violations can increase the risk of adverse events.
• Understanding this risk cascade is crucial for improving patient safety.

Purpose of the Study:

• To develop an agent-based simulation framework to model cumulative risks from process violations.
• To quantify the risk of adverse events in hospital clinical processes.
• To identify novel methods for prospective risk analysis.

Main Methods:

• Agent-based simulation modeling of clinicians and information systems.
• Calibration and validation using observed patient transfer data.
• Repeated simulations to generate outcome distributions and risk likelihoods.

Main Results:

• Simulations indicated end-of-chain risks of 8% (misidentification) and 24% (infection control).
• Over 95% of simulated patient transfer processes showed unique trajectories.
• This demonstrates significant divergence from prescribed work practices.

Conclusions:

• Agent-based simulation provides a novel prospective risk analysis method for clinical processes.
• The high variability in patient transfer suggests complexity reduction is key for risk mitigation.
• Augmenting processes with more rules may be less effective than simplifying them.