Cultural factors data collection

The first area focuses on the cultural and organizational factors that will have a major influence on the effectiveness of a human error data collection system and how well the information derived from such a system is translated into successful error reduction strategies. Regardless of how effectively the technical issues are dealt with, the system will not be successful imless there is a culture in the organization which provides support for the data gathering process. No data collection system aimed at identifying human error causes of accidents will be workable without the active cooperation of the workforce. 

A company s culture can make or break even a well-designed data collection system. Essential requirements are minimal use of blame, freedom from fear of reprisals, and feedback which indicates that the information being generated is being used to make changes that will be beneficial to everybody. All three factors are vital for the success of a data collection system and are all, to a certain extent, under the control of management. To illustrate the effect of the absence of such factors, here is an extract from the report into the Challenger space shuttle disaster ... 

Workforce Support for Data Collection and Incident Analysis Systems Few of the incident investigation and data collection systems reviewed provide any guidelines with regard to how these systems are to be introduced into an organization. Section 6.10 addresses this issue primarily from the perspective of incident reporting systems. However, gaining the support and ownership of the workforce is equally important for root cause analysis systems. Unless the culture and climate in a plant is such that personnel can be frank about the errors that may have contributed to an incident, and the factors which influenced these errors, then it is unlikely that the investigation will be very effective. 

In this chapter we will rephrase, summarise and extend the set of practical aspects related to designing and implementing near miss reporting systems. First five general factors will be listed, followed by a more detailed discussion of two of these data collection, and acceptability. Also the overall important factor of training will be briefly outlined, Finally the relationship between an organisation s prevailing view of human error and its safety culture will be discussed. 

Reliability and Safety Data Collection and Analysis Fault Identification and Diagnostics Maintenance Modelling and Optimisation Structural Reliability and Design Codes Software Reliability Consequence Modelling Uncertainty and Sensitivity Analysis Safety Culture Organizational Learning Human Factors... 

First, we will present the results that were apphed to the data collected fiom Hospital M. We calculated a mean annual reporting rate over the three years 2004— 06 as well as a mean score for each safety culture factor (cf Tables 4.2-4.5) for each of 18 work units. A rank-based correlation analysis (Spearman s rho) was applied to these cross-unit data and the analysis of results is shown in Table 4.8. 

In this subsection, we apply another type of safety performance data, i.e., self-reported staff attitudes to error reporting and interaction with the patient, to the test of criterion validity of the safety culture factors. For this purpose, we used the nurse sample of the Japanese data including more than 17,000 questionnaire responses collected from 82 hospitals (Itoh and Andersen, 2010). An example of resrrlts of correlation analysis is shown in Table 4.10 in terms of Spearman s rho, using the mild outcome case in the three vignettes offered - results for the near-miss and severe cases were quite similar to this case. 

CIEF was also used to follow the production of recombinant antithrombin III (r-AT Iff) in cultures of hamster kidney cells.111 r-AT III inhibits serine proteases such as blood factors (IXa, Xa, and XIa) and thrombin. Interference by the media from which the samples were collected posed some difficulties because some of the media components have similar characteristics to those of the compounds of interest. CIEF was used to determine the pis of the separated components after sample purification by HPLC. Three major peaks showed pis of 4.7, 4.75, and 4.85, and three minor peaks had pis of 5.0, 5.1, and 5.3. These data closely resembled the data already published for serum AT III based on conventional IEF. 

This paper describes the development of a tool set for incident investigation that incorporates the use of a suite of tools to assess human error, violations and safety culture as an integral part of the investigation. This approach helps to overcome issues associated with a delay in the investigation of human factors that can occur if such techniques are applied in a stand-alone context. This approach also has the benefit of collecting data on the human aspects of safety as a whole, rather than independently conducting several forms of analysis. This allows the complex relationships between people, the organisation, the environment and the task to be captured. 

The first round of interviews focused on estabUshing a baseUne measure of the focal company s leadership, culture, structure and performance at the time the transformational process began. In addition data was collected on the relationships and the extent of information transfer that existed between the supply chain partners. The second round of interviews was designed to monitor changes in these attributes, processes and relationships over the 12 month period and to probe the factors that promoted or hindered the change process. 

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