A simulation framework for mapping risks in clinical processes: the case of in-patient transfers
- 1Centre for Health Informatics, Australian Institute of Health Innovation, University of New South Wales, Sydney, Australia
- 2Centre for Health Systems and Safety Research, Australian Institute of Health Innovation, University of New South Wales, Sydney, Australia
- 3School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
- Correspondence to Dr Adam Dunn, Centre for Health Informatics, University of New South Wales, Sydney, NSW 2052, Australia;
- Received 21 November 2010
- Accepted 24 February 2011
Objective To model how individual violations in routine clinical processes cumulatively contribute to the risk of adverse events in hospital using an agent-based simulation framework.
Design An agent-based simulation was designed to model the cascade of common violations that contribute to the risk of adverse events in routine clinical processes. Clinicians and the information systems that support them were represented as a group of interacting agents using data from direct observations. The model was calibrated using data from 101 patient transfers observed in a hospital and results were validated for one of two scenarios (a misidentification scenario and an infection control scenario). Repeated simulations using the calibrated model were undertaken to create a distribution of possible process outcomes. The likelihood of end-of-chain risk is the main outcome measure, reported for each of the two scenarios.
Results The simulations demonstrate end-of-chain risks of 8% and 24% for the misidentification and infection control scenarios, respectively. Over 95% of the simulations in both scenarios are unique, indicating that the in-patient transfer process diverges from prescribed work practices in a variety of ways.
Conclusions The simulation allowed us to model the risk of adverse events in a clinical process, by generating the variety of possible work subject to violations, a novel prospective risk analysis method. The in-patient transfer process has a high proportion of unique trajectories, implying that risk mitigation may benefit from focusing on reducing complexity rather than augmenting the process with further rule-based protocols.
- Computational modelling
- complex networks
- social network analysis
- computer simulation
- decision support
- prospective risk analysis
- agent-based modelling
- process engineering
- patient safety
Funding This study was supported by an Australian Research Council Linkage grant (LP0775532) and National Health and Medical Research Council (NHMRC) Program Grant 568612.
Competing interests None.
Provenance and peer review Not commissioned; externally peer reviewed.