A data analysis system for quality control in steel works.
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For the purpose o f expansion of the data analysis activities, a system named IDEA (Information DEsign Aid) has been developed to apply for quality control at Mizushima Works of Kawasaki Steel Corporation (KSC). The system consists of the three sub-systems: analysis support, database structure, and operation support.The system is intended to be highly efficient and useful by providing a large amount of data storage in its items and volumes collected in the production lines, a sophisticated software easily accessible for a number of personnel at the terminals, and activities for supporting the computer utilization in routine and provisional jobs of the terminal users. The system has been found useful in reducing the workload for data collection and analysis and improving the analysis accuracy; this contributes to the efficiency not only in analysis work but also in various staff work.Keywords:
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Recent technological developments have greatly simplified data collection, recording, and transfer in studies relying on direct observation or survey methodology, reducing both cost of data collection and the time necessary to prepare data for analysis. The purpose of this communication is to describe how tablet PC computers can be used in data collection, thereby eliminating intermediate data collection steps and chances for error, and thus reducing overall cost. The data collection procedure used to illustrate this technology is a direct observation study of the factors associated with the delivery of preventive services by dentists and hygienists in their daily practice.
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Several methods of collecting workload data for use in PharmaTrend, a computerized pharmacy management information system, are described. Although the number of indicators produced by PharmaTrend is limited by the input of the user, the integrity of the indicators that are produced is not compromised; this allows the user to implement PharmaTrend on a limited scale, such as for workload only. Workload data that can be used in PharmaTrend are collected routinely by most pharmacies. Many activities are already recorded in logs and other noncomputerized sources. The aid of members of the pharmacy department and other departments may be enlisted in collecting these data. Self-reporting through time sheets and cards can also be used to obtain data on the distribution of work hours. Recording is simplified if employees are told to record only exceptions to their usual job responsibilities. The PharmaTrend manual contains detailed definitions of the workload categories, but simpler and more practical definitions may have to be used. A computer spreadsheet program should probably be used for compiling data if they are collected from several sources; this will facilitate entry into PharmaTrend. Simple methods are available for capturing workload data for input into the PharmaTrend program.
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Data collection is a crucial step in any research design or program. In order to be analysed, this collected data needs to be entered into aspreadsheet or statistical software. Transcribing paper based data is time consuming and often associated with errors. Such errors may be due toan inability to read the data-collector’s handwriting,human mistakes during data entry etc. A system wherein data automatically gets transcribed and uploaded in a database during data collection would be of immense use in this situation. A possible solution for this is mobile phone based data collection, a type of electronic data capture method wherein the processes of data collection and data entry are merged1. Initially electronic data collection was done by hand-helddevices such as Personal Digital Assistants (PDAs). However with the entry of the newer and more sophisticated smartphones in the market,there is a growing possibility of extendingthe success achieved on PDAs to a phone-based platform2. Withthe advent of newer technology software solutions this process can even be done on a standard entry level mobile phone. This paper discusses the use and advantages of using mobile phones for data collection and also provides information about resources for mobile based data collection.
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Large cross-sectional household surveys are common for measuring indicators of neglected tropical disease control programs. As an alternative to standard paper-based data collection, we utilized novel paperless technology to collect data electronically from over 12,000 households in Ethiopia.We conducted a needs assessment to design an Android-based electronic data collection and management system. We then evaluated the system by reporting results of a pilot trial and from comparisons of two, large-scale surveys; one with traditional paper questionnaires and the other with tablet computers, including accuracy, person-time days, and costs incurred.The electronic data collection system met core functions in household surveys and overcame constraints identified in the needs assessment. Pilot data recorders took 264 (standard deviation (SD) 152 sec) and 260 sec (SD 122 sec) per person registered to complete household surveys using paper and tablets, respectively (P = 0.77). Data recorders felt a lack of connection with the interviewee during the first days using electronic devices, but preferred to collect data electronically in future surveys. Electronic data collection saved time by giving results immediately, obviating the need for double data entry and cross-correcting. The proportion of identified data entry errors in disease classification did not differ between the two data collection methods. Geographic coordinates collected using the tablets were more accurate than coordinates transcribed on a paper form. Costs of the equipment required for electronic data collection was approximately the same cost incurred for data entry of questionnaires, whereas repeated use of the electronic equipment may increase cost savings.Conducting a needs assessment and pilot testing allowed the design to specifically match the functionality required for surveys. Electronic data collection using an Android-based technology was suitable for a large-scale health survey, saved time, provided more accurate geo-coordinates, and was preferred by recorders over standard paper-based questionnaires.
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The aim of this study was to compare the workload between tasks undertaken manually and those with the assistance of mechanical devices. The results indicate that the workload during the tasks assisted by mechanisation was usually very low or relatively low. Statistical analysis showed the impact of the way of the work performance on the workload. However, the results also indicate that the workload is not evenly distributed: the effort relating to the working tasks is larger in relation to the work of women than men. The need for mechanisation of the tasks performed by women is higher than that for the mechanisation of tasks performed by men. The type of work performance (manual, or technically assisted) did not clearly show significant differences in the workload. This surprising result indicates that the technical devices used on many farms are still far from perfect. It is concluded that assisting work with mechanical devices reduces the workload of farmers, but the way of work performance (manual or mechanical) was not statistically significant.
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Introduction: The Alberta Cancer Board Palliative Care Research Initiative (ACBPCRI) encourages province-wide collaboration on palliative care research projects. Because of geographic differences in information system infrastructure, it is necessary to evaluate and adopt a data collection tool that will span the variability in system hardware and software. We assessed TELEform® (Cardiff Sofware Inc., Vista CA), an optical recognition-based technology that scans data collection paper forms and exports data to a computer database. We examined work place suitability, data quality, and effective resource utilization (time and cost) during the data collection tool evaluation. Methods: Two hospices and two hospitals from the cities of Edmonton and Calgary participated in the revised Edmonton Staging System (rESS) project that used TELEform as its data collection tool. The evaluation was conducted over a period of 7 months. Data source such as e-mail and summary notes collected primarily through meetings and discussions with management, caregivers, researchers, and clerical staff was used to assess work practice and resource utilization. Descriptive statistics was employed to examine data quality and resource utilization. Results: One hundred seventy eight patients were recruited during the 7-month trial. The costs and time involved in staff training, logistic support, and equipment startup were found to be reasonable. Data error and missing data were 0.4% and 0.6%, respectively. We initially encountered several problems with TELEform. The optical recognition tool could not easily pick up handwritten data. Furthermore, it was unforgiving in the sense that an error was not correctable by an eraser on the paper form. Data collectors found TELEform usage to be easy and simple because it retained the familiarity of paper-based recording. Conclusion: It is important to develop an information infrastructure to support research project data collection for different health settings across health regions. The TELEform based on optical recognition was able to respond to the need for current information processing. We believe that TELEform is a useful tool in terms of work practice, data quality, and resource utilization.
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Abstract Data collection, entry, validation, and management are salient time‐ and resource‐consuming dimensions of all research projects. This is especially true for multisite studies, which pose unique, additional challenges because of their research design requirements. To save time and reduce the number of manual data entry errors, automated processing systems are becoming more widely used. Our research team chose Tele form ™ for data entry and collection for the Smoking Cessation or Reduction in Pregnancy Trial study. This article presents our experience with this new technology.
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Driver mental workload is an often studied concept, however less attention is given to the question of transitions in driver workload. Fourteen adult drivers completed a simulated driving task following a navigation system which would fail at certain intervals. Subjective measures of driver workload were taken and demonstrate that the recovery from a driving stressor is asymmetrical and time-delayed. Drivers' subjective ratings of workload remained high after the stressors were removed. Findings and implications are discussed.
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Entering data on case report forms and subsequently digitizing them in electronic media is the traditional way to maintain a record keeping system in field studies. Direct data entry using an electronic device avoids this two-step process. It is gaining in popularity and has replaced the paper-based data entry system in many studies. We report our experiences with paper- and PDA-based data collection during a fever surveillance study in Pemba Island, Zanzibar, Tanzania. Data were collected on a 14-page case report paper form in the first period of the study. The case report paper forms were then replaced with handheld computers (personal digital assistants or PDAs). The PDAs were used for screening and clinical data collection, including a rapid assessment of patient eligibility, real time errors, and inconsistency checking. A comparison of paper-based data collection with PDA data collection showed that direct data entry via PDA was faster and 25% cheaper. Data was more accurate (7% versus 1% erroneous data) and omission did not occur with electronic data collection. Delayed data turnaround times and late error detections in the paper-based system which made error corrections difficult were avoided using electronic data collection. Electronic data collection offers direct data entry at the initial point of contact. It has numerous advantages and has the potential to replace paper-based data collection in the field. The availability of information and communication technologies for direct data transfer has the potential to improve the conduct of public health research in resource-poor settings.
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The nationally-recognized Susquehanna
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