A simple heuristic model for injury prevention

The purpose of this paper is to propose a simple heuristic model that provides diagnostic capabilities and prevention insights.

The paper brings together findings from previous research including injury statistics from several industries to illustrate that the model's predicted results can be found in practice. This is a conceptual paper that applies a simple heuristic model to existing data. The model leads to an equation with four parameters: a rate of improvement reflecting prevention, a rate of deterioration reflecting obsolescence and lapsing of procedures and practices, an intrinsic limit reflecting technological capability, and a “viscosity” that adds the impact of management system malfunction to the technological limits and normal delays.

The model says that, on the average, injury rates decrease with time if the rate of rejection is greater than the rate of mortality. If “r”<“m” injury rates increase exponentially with time, and drastic results can follow. When “r”=”m” the model produces a constant rate of failure that will continue until something is done to increase “r” or decrease “m”. A constant rate of failure means that an apparent safety limit has been reached. Unless this corresponds to the technological limit, a constant rate means that some preventable failures are recurring with regularity: they risk being accepted as “hazards of the job”. Stable periods may be normal, but they can lead to complacency.

The heuristic power of the model is evident in that parameters and insights from applying it can help define prevention activities to reduce the rate of injury and, by implication, to lengthen operational periods between consecutive injuries.

The drum model can help managers understand the separate but related effects of technology and management on injury rates. The model can be used to seek prevention possibilities hidden in the aggregate data, and it can help the manager to use period data to identify areas or groups in need of help.