Workplace road safety risk management: An investigation into Australian practices

2017 ◽  
Vol 98 ◽  
pp. 64-73 ◽  
Author(s):  
Amanda Warmerdam ◽  
Sharon Newnam ◽  
Dianne Sheppard ◽  
Mark Griffin ◽  
Mark Stevenson
Keyword(s):  
Risk Management ◽  
Road Safety ◽  
Safety Risk

Value-based dentistry: Putting the patient first

2016 ◽  
Vol 7 (3) ◽  
pp. 126-129 ◽  
Author(s):  
Sreenivas Koka ◽  
Galya Raz
Keyword(s):  
Risk Management ◽  
Quality Improvement ◽  
Quality Of Care ◽  
Dental Care ◽  
Time Cost ◽  
Safety Risk ◽  
The Cost ◽  
Over Time

What does ‘value’ mean? In the context of dental care, it can be defined as the quality of care received by a patient divided by the cost to the patient of receiving that care. In other words: V =Q/C, where Q equals the quality improvement over time, which most patients view in the context of the outcome, the service provided and safety/risk management, and C equals the financial, biological and time cost to the patient. Here, the need for, and implications of, value-based density for clinicians and patients alike are explored.

Enhancing Safety Risk Management with Quantitative Measures

2017 ◽  
Vol 2603 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Boris Claros ◽  
Carlos Sun ◽  
Praveen Edara
Keyword(s):  
Risk Management ◽  
Management System ◽  
Goodness Of Fit ◽  
Safety Management ◽  
Multinomial Distribution ◽  
Safety Management System ◽  
Safety Risk ◽  
Safety Risks ◽  
Safety Models ◽  
Runway Incursion

At the airfield in hub airports, many activities occur that involve a range of participants, including various-size aircraft, ground vehicles, and workers. The safety management system is FAA's approach for systematically managing aviation safety. A major component of the safety management system is safety risk management (SRM), which entails analysis, assessment, and control of safety risks, including risks on the airfield. Current SRM has few specific safety models to estimate the likelihood or frequency of risks. This paper presents an example for development and incorporation of safety models into SRM. Specifically, it discusses safety models for runway incursion that use the following variables: total and general aviation operations, length of runway by type, number of taxiway intersections, snowfall, precipitation, number of hot spots, and construction activity. Categorization and processing of data were significant because each variable used could take on multiple forms, and some types of data involved review of airfield diagrams. The data used were from 137 U.S. hub airports for 2009 through 2014. For modeling, the negative multinomial distribution was used because it proved suitable for representing overdispersed data such as runway incursion frequency. Performance of the models was assessed through the goodness-of-fit measures of log likelihood, overdispersion, and cumulative residual plots. Models were developed for five severity categories of runway incursions and three types of surface events. The safety modeling approach presented here can serve as a foundation for development of other safety models that can be integrated into SRM to enable quantitative analysis of safety risks.

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