The Experts below are selected from a list of 89223 Experts worldwide ranked by ideXlab platform
D K Srivastava - One of the best experts on this subject based on the ideXlab platform.
-
exponentiated weibull family for analyzing bathtub Failure Rate Data
IEEE Transactions on Reliability, 1993Co-Authors: G S Mudholka, D K SrivastavaAbstract:A simple generalization of the Weibull distribution is presented. The distribution is well suited for modeling bathtub Failure Rate lifetime Data and for testing goodness-of-fit of the Weibull and negative exponential models as subhypotheses. >
D Low - One of the best experts on this subject based on the ideXlab platform.
-
th d brd 03 experience with error reporting and tracking Database tool for process improvement in radiation oncology
Medical Physics, 2009Co-Authors: S Mutic, P Parikh, S Oddiraju, S Brame, El I Naqa, D LowAbstract:Purpose: To present long‐term results of systematic near‐miss and actual error reporting and analysis system based on a web‐based tool and effects of formal process improvement structure on error Rates and safety culture in radiation therapy (RT). Materials and Methods: An internally developed web‐based system was used to report, track, and analyze errors and near‐misses in a large RT department for almost two years. The system was designed as an efficient and effective process for collecting, storing, and analyzing the Failure Rate Data in individual RT facilities. The aim of the system was to support process improvement in patient care and safety. The reporting tool was designed so individual events could be reported in as little as two minutes. Events were categorized based on functional area, type, and severity of Failure. The events were processed and analyzed by a formal process improvement group which used the Data and statistics collected through the web‐based tool for guidance in reengineering clinical processes. The results for the first nineteen months of clinical use of the system are presented. Results: The collected Data and the process improvement structure resulted in measureable safety and error Rate improvements in several clinical areas. The collected Data was also very effective in identifying ineffective measures and efforts which did not produce improvements in clinical processes. The overall process demonstRated that it was possible to establish and maintain a high functioning safety culture in radiation oncology. The error reporting compliance, though voluntary, was very high and consistent from the inception of the process through the date of this report. Conclusions: Near‐miss and actual error collection process in RT can result in quantifiable safety and error Rate improvements and more importantly it can result in a sustainable safety culture.
-
th d aud a 09 an error reporting and tracking Database tool for process improvement in radiation oncology
Medical Physics, 2008Co-Authors: S Mutic, P Parikh, Eric E Klein, Robert E Drzymala, J M Michalski, D LowAbstract:Patient and employee safety is a critical concern in radiation therapy (RT). Current QA practices and operation processes in RT are typically developed based on rather prescriptive task group reports and regulatory agency requirements. Typically, these programs are not developed with the goal of process optimization and safety but are brute‐force efforts to prevent catastrophic errors. Other industries have been developing processes to improve quality and safety of their operations and products since the 1940s. These processes have become quite sophisticated and the result is that numerous industries have much better performance records than healthcare and RT. Recently the National Academy of Engineering and the Institute of Medicine recommended the systematic application of systems engineering approaches for reforming our health care delivery system. The AAPM subsequently formed a task group charged with developing a structured systematic QA program approach for RT based on industrial principles and practices. Optimization of RT processes and implementation of industrial techniques requires the acquisition of Data regarding performance statistics and Failure or error Rates of individual departments. Most facilities do not have the infrastructure to effectively collect and analyze such Data. We have developed an efficient and effective process for collecting, storing, and analyzing the Failure Rate Data in individual RT facilities that will support process improvement in patient care and safety. The process is based on a web‐based tool for reporting events. The tool is designed so individual events can be reported in as little as two minutes. Events are categorized based on function area, type, and severity of Failure. All events are systematically processed using web‐based tools and stored for future analysis and evaluation of Failure methods and process improvement and prioritization of efforts in an individual RT facility. This work is supported in part by the National Patient Safety Foundation grant.
Alan D Hutson - One of the best experts on this subject based on the ideXlab platform.
-
transformation of the bathtub Failure Rate Data in reliability for using weibull model analysis
Statistical Methodology, 2009Co-Authors: Govind S Mudholkar, Kobby O Asubonteng, Alan D HutsonAbstract:Abstract All statistical methods involve basic model assumptions, which if violated render results of the analysis dubious. A solution to such a contingency is to seek an appropriate model or to modify the customary model by introducing additional parameters. Both of these approaches are in general cumbersome and demand uncommon expertise. An alternative is to transform the Data to achieve compatibility with a well understood and convenient customary model with readily available software. The well-known example is the Box–Cox Data transformation developed in order to make the normal theory linear model usable even when the assumptions of normality and homoscedasticity are not met. In reliability analysis the model appropriateness is determined by the nature of the hazard function. The well-known Weibull distribution is the most commonly employed model for this purpose. However, this model, which allows only a small spectrum of monotone hazard Rates, is especially inappropriate if the Data indicate bathtub-shaped hazard Rates. In this paper, a new model based on the use of Data transformation is presented for modeling bathtub-shaped hazard Rates. Parameter estimation methods are studied for this new (transformation) approach. Examples and results of comparisons between the new model and other bathtub-shaped models are shown to illustRate the applicability of this new model.
G S Mudholka - One of the best experts on this subject based on the ideXlab platform.
-
exponentiated weibull family for analyzing bathtub Failure Rate Data
IEEE Transactions on Reliability, 1993Co-Authors: G S Mudholka, D K SrivastavaAbstract:A simple generalization of the Weibull distribution is presented. The distribution is well suited for modeling bathtub Failure Rate lifetime Data and for testing goodness-of-fit of the Weibull and negative exponential models as subhypotheses. >
S Mutic - One of the best experts on this subject based on the ideXlab platform.
-
th d brd 03 experience with error reporting and tracking Database tool for process improvement in radiation oncology
Medical Physics, 2009Co-Authors: S Mutic, P Parikh, S Oddiraju, S Brame, El I Naqa, D LowAbstract:Purpose: To present long‐term results of systematic near‐miss and actual error reporting and analysis system based on a web‐based tool and effects of formal process improvement structure on error Rates and safety culture in radiation therapy (RT). Materials and Methods: An internally developed web‐based system was used to report, track, and analyze errors and near‐misses in a large RT department for almost two years. The system was designed as an efficient and effective process for collecting, storing, and analyzing the Failure Rate Data in individual RT facilities. The aim of the system was to support process improvement in patient care and safety. The reporting tool was designed so individual events could be reported in as little as two minutes. Events were categorized based on functional area, type, and severity of Failure. The events were processed and analyzed by a formal process improvement group which used the Data and statistics collected through the web‐based tool for guidance in reengineering clinical processes. The results for the first nineteen months of clinical use of the system are presented. Results: The collected Data and the process improvement structure resulted in measureable safety and error Rate improvements in several clinical areas. The collected Data was also very effective in identifying ineffective measures and efforts which did not produce improvements in clinical processes. The overall process demonstRated that it was possible to establish and maintain a high functioning safety culture in radiation oncology. The error reporting compliance, though voluntary, was very high and consistent from the inception of the process through the date of this report. Conclusions: Near‐miss and actual error collection process in RT can result in quantifiable safety and error Rate improvements and more importantly it can result in a sustainable safety culture.
-
th d aud a 09 an error reporting and tracking Database tool for process improvement in radiation oncology
Medical Physics, 2008Co-Authors: S Mutic, P Parikh, Eric E Klein, Robert E Drzymala, J M Michalski, D LowAbstract:Patient and employee safety is a critical concern in radiation therapy (RT). Current QA practices and operation processes in RT are typically developed based on rather prescriptive task group reports and regulatory agency requirements. Typically, these programs are not developed with the goal of process optimization and safety but are brute‐force efforts to prevent catastrophic errors. Other industries have been developing processes to improve quality and safety of their operations and products since the 1940s. These processes have become quite sophisticated and the result is that numerous industries have much better performance records than healthcare and RT. Recently the National Academy of Engineering and the Institute of Medicine recommended the systematic application of systems engineering approaches for reforming our health care delivery system. The AAPM subsequently formed a task group charged with developing a structured systematic QA program approach for RT based on industrial principles and practices. Optimization of RT processes and implementation of industrial techniques requires the acquisition of Data regarding performance statistics and Failure or error Rates of individual departments. Most facilities do not have the infrastructure to effectively collect and analyze such Data. We have developed an efficient and effective process for collecting, storing, and analyzing the Failure Rate Data in individual RT facilities that will support process improvement in patient care and safety. The process is based on a web‐based tool for reporting events. The tool is designed so individual events can be reported in as little as two minutes. Events are categorized based on function area, type, and severity of Failure. All events are systematically processed using web‐based tools and stored for future analysis and evaluation of Failure methods and process improvement and prioritization of efforts in an individual RT facility. This work is supported in part by the National Patient Safety Foundation grant.
