Use Of Quality Data Research Articles

Cost Effectiveness Analysis and Transportation: Practices, Problems, and Proposals

Cost effective analysis (CEA) is used frequently in health and other fields in which controlled experiments are possible. The problem is, however, that for many activities that are outsourced in infrastructure finance, we have no comparison groups, and when comparison groups are present, we have no data on quality, presenting any interested researcher or decision maker with some serious dilemmas. In this article we establish some basic guidelines for the collection and use of quality data, and associate these with our own errors in attempting CEA with a lack of an existing quality measure.

Training in data definitions improves quality of intensive care data.

Our aim was to assess the contribution of training in data definitions and data extraction guidelines to improving quality of data for use in intensive care scoring systems such as the Acute Physiology and Chronic Health Evaluation (APACHE) II and Simplified Acute Physiology Score (SAPS) II in the Dutch National Intensive Care Evaluation (NICE) registry. Before and after attending a central training programme, a training group of 31 intensive care physicians from Dutch hospitals who were newly participating in the NICE registry extracted data from three sample patient records. The 5-hour training programme provided participants with guidelines for data extraction and strict data definitions. A control group of 10 intensive care physicians, who were trained according the to train-the-trainer principle at least 6 months before the study, extracted the data twice, without specific training in between. In the training group the mean percentage of accurate data increased significantly after training for all NICE variables (+7%, 95% confidence interval 5%-10%), for APACHE II variables (+6%, 95% confidence interval 4%-9%) and for SAPS II variables (+4%, 95% confidence interval 1%-6%). The percentage data error due to nonadherence to data definitions decreased by 3.5% after training. Deviations from 'gold standard' SAPS II scores and predicted mortalities decreased significantly after training. Data accuracy in the control group did not change between the two data extractions and was equal to post-training data accuracy in the training group. Training in data definitions and data extraction guidelines is an effective way to improve quality of intensive care scoring data.

Patient Safety: Grantmakers Join The Effort To Reduce Medical Errors

Patient Safety: Grantmakers Join The Effort To Reduce Medical Errors

Commentary: Methodological Issues for Quality Management in Long-Term Care

The interest in quality management in long-term care has been rapidly escalating. This movement to assess and improve quality parallels the effort carried out by hospital management in the past 10 years. The methodological concerns of the 2 areas are similar. This essay identifies 10 issues to which quality management leaders should pay attention as they begin to expand the capability of addressing quality in long-term care: client-centered performance versus whole-organization performance; standardization of methods and instruments; reliability; and validity, multimethod thinking, the meaning of data, comparability of data across organizations, cost barriers, feedback mechanisms, management use of quality data, and public control of data.

PPDM: The Business Driven Standard For the Petroleum Industry

Introduction Petroleum companies are not in the business of data management or system development, yet they continue to spend enormous sums of money in the eternal challenge of achieving competitive advantage this way. Competitive advantage, however, is realized through better decisions and measured by company performance. What significant decisions have companies made in the past six months (or have not made); which ones were good or bad; and what information could have precluded the results? How much are decisions being impacted today by a lack of timely quality data and what is that data? Is it a significant challenge to manage one's own data without thinking about competitive and predictive information? Perhaps the KISS (Keep It Simple Stupid) principle is often forgotten in managing fundamental, technical data. Companies should be spending their time and money on the data they don't have. The management of data that companies do own would not be an issue if appropriate standards and diligence were applied. Standards play an important role in assuring consistent data quality for use in analytical and decision making processes. Integrating technical data is extremely time consuming when practiced on a project by project basis. This is even more challenging when data has to be copied between many disparate applications. Data for projects is used, then "lost" after first being meticulously analyzed and "cleansed" by technical professionals. There are numerous instances in the industry of seismic data being purchased several times over by companies who are not aware of their asset. There is a movement to solve this problem through implementation of a large software/data vendor single integrated solution. This solution is pursued by large corporations to solve standardization problems, but ignore the fact that there are "best of breed" products employed by smaller organizations that realize opportunities where undiscovered potential exists. How can one employ the best tools when and where needed? How can one manage such an environment? The competitive nature of our business, and the seemingly endless advances in technology, provide a sobering perspective on the importance of flexibility and responsiveness in a changing environment. We must think of the ideal solution being: any data; any application; instantaneously available; and intuitive and then define what it will take to get there. Standards are key and must be addressed across all elements of: interface, communication, access, and data. All applications will work seamlessly if each of these elements are addressed. The information technology business is undeniably the weakest in setting standards for data. An industry data standard to support petroleum industry professionals does exist. The Public Petroleum Data Model (PPDM) is a cost effective, proven standard solution and has significant implementation throughout the world. PPDM is not difficult to implement or maintain and has many compliant software products available through vendor members that cover all aspects of the petroleum business. PPDM compliant software allows integration of all aspects of the petroleum business including land; geophysics; geology; reserves; facilities; production; stratigraphy; inventory and records management (seismic data/well cores/interpretations/...); contracts; business associates and more.

An empirical analysis of effective TQM implementation in the Hong Kong electronics manufacturing industry

The objective of this research is to develop a conceptual model of total quality management (TQM) implementation and test it empirically. The main research problem of this study is to investigate the effect of adopting TQM principles on quality achievement in manufacturing. The hypotheses specifying relationships between the core parts of the model including quality management infrastructure, core quality management practices, quality performance, and customer satisfaction are tested using survey research method. Data were collected from 50 managers of 25 manufacturing companies in Hong Kong. Correlation analysis on the ratings given by two different respondents from the same company shows that employee relations strongly facilitates the use of quality data and reporting, while training supports the function of product design, supplier quality management, and quality data and reporting. The results also indicate that employee relations and training have direct effects on quality performance which, in turn, relates to customer satisfaction. Practitioners are advised to build a supportive environment with a strong emphasis on human and organizational aspects to promote effective TQM implementation. © 1999 John Wiley & Sons, Inc.

An empirical analysis of effective TQM implementation in the Hong Kong electronics manufacturing industry

The objective of this research is to develop a conceptual model of total quality management (TQM) implementation and test it empirically. The main research problem of this study is to investigate the effect of adopting TQM principles on quality achievement in manufacturing. The hypotheses specifying relationships between the core parts of the model including quality management infrastructure, core quality management practices, quality performance, and customer satisfaction are tested using survey research method. Data were collected from 50 managers of 25 manufacturing companies in Hong Kong. Correlation analysis on the ratings given by two different respondents from the same company shows that employee relations strongly facilitates the use of quality data and reporting, while training supports the function of product design, supplier quality management, and quality data and reporting. The results also indicate that employee relations and training have direct effects on quality performance which, in turn, relates to customer satisfaction. Practitioners are advised to build a supportive environment with a strong emphasis on human and organizational aspects to promote effective TQM implementation. © 1999 John Wiley & Sons, Inc.

Probing the Validity of Normative Data

Data validity is as critical an issue for practice as it is for research. This Research Note is a brief account of an effort to subject family functioning data to a validity analysis by using multiple informants. Results indicate that knowledge about variations in degree of validity by subject areas canenhance the quality of data for use in professional practice.