Data collection and reporting system

American National Standard Nuclear Plant Reliability Data Collection and Reporting System. 

Various LIMS configurations may be implemented ranging from a dedicated multifunction laboratory data collection and reporting system to an integrated company wide system. GC, HPLC and other types of instruments generate the raw analytical data that is subsequently processed by the LIMS so direct communications between instruments, equipment and the LIMS computer is necessary. This is achieved via a network or an RS232 interface for bi-directional data transfer. Thus, control and instrument variables, as required for a specific analysis, for example, wavelength, flow-rate, solvent... 

Stage 4 - Identify the critical elements of each risk control system (ie those actions or processes that must function correctly to deliver the outcomes) and set leading indicators Stage 5 - Establish the data collection and reporting system... 

WESS is a web-based safety mishap data collection and reporting system developed for the Navy and Marine Corps WESS, and the disconnected system WESS-DS in Microsoft Access format, provide a real-time data entry and retrieval system with 20 years of data in a consolidated database. 

This requirement is similar to that in clause 4.14.3 under Preventive action since the data collected for preventive action serves a similar purpose. In one case an analysis of company-level data serves to identify overall trends and predict potential failures that will affect achievement of the goals. In the preventive action case, the data serves to identify local and overall trends and predict potential failures that will affect achievement of specified requirements for the product, process, and quality system. It would be sensible to develop a data collection and analysis system that serves all levels in the organization, with criteria at each level for reporting data upwards as necessary. You should not treat this requirement separately from that for preventive action since the same data should be used. However, the explanation given in clause 4.1.5 of Operational performance does include some factors that may not be addressed in your preventive action procedures. 

Purchasing needs to understand these varying needs, and will have to put a data/information collection and reporting system in place that provides the different stakeholders with tailored insights to support their respective management decisions. 

Systems audits in this late phase in clinical trials aim at assessing related procedures to ensure that capable procedures exist for managing and cleaning clinical trial data, for conducting statistical analyses and for preparing the final study report which represents properly the data collected and reported in the clinical trial. Such systems audits are performed across functional boundaries. Such systems audit can be combined with a database audit and/or an audit of the final study report. 

Bioanalytical Method The bioanalytical method should be described with sufficient details to facilitate a clear understanding of sample processing, conditions of instrumental analysis, and the computerized systems used for data collection and reporting. The calibration range, regression type, and weighting factor (if applicable) should be reported. The bioanalytical method protocol/SOP should be included as a report appendix. 

Once you have a system that captures, accurately, over 30% of your real electrical contacts (i.e., near misses, incidents and accidents) you may be able to develop a system for fixing the causes. Preventive safety, not data collection and report writing, must be the reason for a safety reporting system. 

The pilot study of the Mexico City Prospective Lead Study, reported here, was designed primarily to train staff and to improve data collection and analysis systems for the more extensive prospective study to follow. To a... 

The amoimt of time available for the recording of data in incident reporting systems is limited, and hence the information collected is usually confined to short descriptions of the event, its actual and potential consequences and... 

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. 

Products in Group 3 seem to us to represent the future of practical batch process control. In such systems, modern workstations perform the single-user functions (e.g control system design, set-up, and maintenance operator interface data collection historical reporting) for which they were designed, while powerful multitasking controllers perform actual control. As computer hardware and software standards continue to evolve toward distributed networks of processors optimized for specific kinds of tasks, such systems will, we feel, proliferate rapidly. 

Integrated systems using multiple technologies in the areas of communications, data collection, and management have been developed that allow patients to perform and report critical tests at home (Jones et al. [4]). 

We need to transition from quasi-computerized methods, in which the different elements of the analytical process are treated as discrete, paper report tasks, to a comprehensive informatics approach, in which the entire data collection and analysis is considered as a single reusable, extensible, auditable, and reproducible system. Informatics can be defined as the science of storing, manipulating, analyzing, and visualizing information using computer systems. [3]... 

It is interesting to trace the development of instrument automation over the relatively brief period of the past ten to fifteen years. Early in this period, a truly automated instrument was a rare and expensive item built around a costly dedicated minicomputer. Automated data collection and analysis from any instrument which was not automated at the factory was usually accomplished by digitizing the data and storing it on a transportable media such as paper tape. These data were then delivered and fed to a timeshare system of some sort on which the data reduction program ran and which printed a report and sometimes a plot of the data. Often a considerable time delay occured between the generation and the analysis of the data. The scientist was at the mercy of the computer elite who could implement his data logger and provide the necessary computer resources to analyze his data. The process was expensive, both in time and in money. 

Analytical procedure is a systems problem and the samphng, pretreatment, measurement, data collection and reduction, and final reporting all have to be considered in a fiilly automatic approach. Computerization is often considered to he synonymous with automation but, although microprocessor technology is certainly changing the face of automatic instrumentation and influences both the control aspects and the data reduction, computerization is only a part of automation. Computers should simply be considered as tools of the trade within the area of automation. 

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. 

Finally, the Shotscope system contains a reporting mechanism that allows all the data collected and entered into the system to be communicated across a manufacturing enterprise, so that informed decisions can be made. Users can generate production, scrap, downtime, efficiency, and job summary reports, any of which also can be used as documentation that accompanies part shipments. 

Most hospitals collect internal medication error data through a voluntary reporting mechanism. This system is used as the backbone of error reporting because it requires minimal resources for data collection and is supported by organizational risk management programs. Voluntary reporting is presumed to underreport total errors. It is widely believed that... 

A performance improvement effort will require time and resources for data collection and analysis. If the metrics data collection effort is too large for available resources, it is likely that specific data will not be captured and that could impact the validity of the system performance evaluations. Any data collection effort needs appropriate resources to execute the designed plan effectively. Sometimes insufficient or incomplete data can be worse than having no data if the reported data leads to false conclusions. 

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