Estimating Worker Risk Levels Using Accident/Incident Data

Kenoyer, Judson L. ; Stenner, Robert D. ; Andrews, William B. ; Scherpelz, Robert I. ; Aaberg, Rosanne L.

Pacific Northwest National Laboratory (U.S.)

关键词: Forecasting;    Us Doe;    Mathematical Models;    29 Energy Planning, Policy And Economy;    Occupational Safety;   

DOI  :  10.2172/966333
RP-ID  :  PNNL-13338
RP-ID  :  AC05-76RL01830
RP-ID  :  966333

美国|英语

来源: UNT Digital Library

【 摘 要 】

The purpose of the work described in this report was to identify methods that are currently being used in the Department of Energy (DOE) complex to identify and control hazards/risks in the workplace, evaluate them in terms of their effectiveness in reducing risk to the workers, and to develop a preliminary method that could be used to predict the relative risks to workers performing proposed tasks using some of the current methodology. This report describes some of the performance indicators (i.e., safety metrics) that are currently being used to track relative levels of workplace safety in the DOE complex, how these fit into an Integrated Safety Management (ISM) system, some strengths and weaknesses of using a statistically based set of indicators, and methods to evaluate them. Also discussed are methods used to reduce risk to the workers and some of the techniques that appear to be working in the process of establishing a condition of continuous improvement. The results of these methods will be used in future work involved with the determination of modifying factors for a more complex model. The preliminary method to predict the relative risk level to workers during an extended future time period is based on a currently used performance indicator that uses several factors tracked in the CAIRS. The relative risks for workers in a sample (but real) facility on the Hanford site are estimated for a time period of twenty years and are based on workforce predictions. This is the first step in developing a more complex model that will incorporate other modifying factors related to the workers, work environment and status of the ISM system to adjust the preliminary prediction.