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

Abstract

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.

Figure 1

A representative workflow. Processes are represented as a series of trajectories, where each opportunity for violation is indicated by a fork. A proportion of trajectories are indicated to diverge far enough away from the prescribed work practice to create the risk of an adverse event. Note that the likelihood of each trajectory is not indicated, and this is the focus of the proposed method.

Figure 2

A distribution of trajectories for the misidentification scenario indicates the gradual amelioration of risk for a patient with incorrect details. The boxes indicate the important steps in the policy of the patient transfer process, vertical arrows indicate chronology, and horizontal channels indicate information transfer between the six agents. The shaded trajectories indicate the presence of risk at each point in the process, and percentages indicate the proportion of the completed simulations that are associated with risk along the given trajectory. DOB, date of birth; ID, identification.

Figure 3

A distribution of trajectories for the infection control scenario indicates the increasing risk associated with lack of adequate infection control in the in-patient transfer process. The boxes indicate the important steps in the policy of the patient transfer process, vertical arrows indicate chronology, and horizontal channels indicate information transfer between the six agents. The shaded trajectories indicate the presence of risk at each point in the process, and percentages indicate the proportion of the completed simulations that are associated with risk along the given trajectory. IC, infection control.