Data quality in trauma transfusion studies and the impact of missing data on predicting massive transfusion

Maximizing data quality may be especially difficult in trauma-related clinical research. Strategies are needed to improve data quality and assess the impact of data quality on clinical predictive models. This study had two objectives. The first was to compare missing data between two multi-center trauma transfusion studies: a retrospective study (RS) using medical chart data with minimal data quality review and the PRospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study with standardized quality assurance. The second objective was to assess the impact of missing data on clinical prediction algorithms by evaluating blood transfusion prediction models using PROMMTT data. RS (2005-06) and PROMMTT (2009-10) investigated trauma patients receiving ≥ 1 unit of red blood cells (RBC) from ten Level I trauma centers. Missing data were compared for 33 variables collected in both studies using mixed effects logistic regression (including random intercepts for study site). Massive transfusion (MT) patients received ≥ 10 RBC units within 24h of admission. Correct classification percentages for three MT prediction models were evaluated using complete case analysis and multiple imputation based on the multivariate normal distribution. A sensitivity analysis for missing data was conducted to estimate the upper and lower bounds of correct classification using assumptions about missing data under best and worst case scenarios. Most variables (17/33=52%) had <1% missing data in RS and PROMMTT. Of the remaining variables, 50% demonstrated less missingness in PROMMTT, 25% had less missingness in RS, and 25% were similar between studies. Missing percentages for MT prediction variables in PROMMTT ranged from 2.2% (heart rate) to 45% (respiratory rate). For variables missing >1%, study site was associated with missingness (all p≤0.021). Survival time predicted missingness for 50% of RS and 60% of PROMMTT variables. MT models complete case proportions ranged from 41% to 88%. Complete case analysis and multiple imputation demonstrated similar correct classification results. Sensitivity analysis upper-lower bound ranges for the three MT models were 59-63%, 36-46%, and 46-58%. Prospective collection of ten-fold more variables with data quality assurance reduced overall missing data. Study site and patient survival were associated with missingness, suggesting that data were not missing completely at random, and complete case analysis may lead to biased results. Evaluating clinical prediction model accuracy may be misleading in the presence of missing data, especially with many predictor variables. The proposed sensitivity analysis estimating correct classification under upper (best case scenario)/lower (worst case scenario) bounds may be more informative than multiple imputation, which provided results similar to complete case analysis.^

Data consistency management in an open smart home management platform

In this paper, the authors introduce a novel mechanism for data management in a middleware for smart home control, where a relational database and semantic ontology storage are used at the same time in a Data Warehouse. An annotation system has been designed for instructing the storage format and location, registering new ontology concepts and most importantly, guaranteeing the Data Consistency between the two storage methods. For easing the data persistence process, the Data Access Object (DAO) pattern is applied and optimized to enhance the Data Consistency assurance. Finally, this novel mechanism provides an easy manner for the development of applications and their integration with BATMP. Finally, an application named "Parameter Monitoring Service" is given as an example for assessing the feasibility of the system.

Personal data usage management

Personal data about users (customers) is a key component for enterprises and large organizations. Its correct analysis and processing can produce relevant knowledge to achieve different business goals. For example, the monetisation of this data has become a valuable asset for many companies, such as Google, Facebook or Twitter, that obtain huge profits mainly from targeted advertising.

Open business intelligence: on the importance of data quality awareness in user-friendly data mining

Citizens demand more and more data for making decisions in their daily life. Therefore, mechanisms that allow citizens to understand and analyze linked open data (LOD) in a user-friendly manner are highly required. To this aim, the concept of Open Business Intelligence (OpenBI) is introduced in this position paper. OpenBI facilitates non-expert users to (i) analyze and visualize LOD, thus generating actionable information by means of reporting, OLAP analysis, dashboards or data mining; and to (ii) share the new acquired information as LOD to be reused by anyone. One of the most challenging issues of OpenBI is related to data mining, since non-experts (as citizens) need guidance during preprocessing and application of mining algorithms due to the complexity of the mining process and the low quality of the data sources. This is even worst when dealing with LOD, not only because of the different kind of links among data, but also because of its high dimensionality. As a consequence, in this position paper we advocate that data mining for OpenBI requires data quality-aware mechanisms for guiding non-expert users in obtaining and sharing the most reliable knowledge from the available LOD.

Quality management, environmental management and firm performance in the Spanish hotel industry

Comunicación presentada en el XVI Simposio Internacional de Turismo y Ocio, ESADE, 23 mayo 2007.

Internalization of quality management in service organizations

Purpose – The purpose of this paper is to analyze the internalization of quality management (QM) on the basis of quality certifiable standards – also referred to as meta-standards – in service organizations. More specifically, the paper analyzes the case of the internalization of a quality standard in the Spanish hotel industry. Design/methodology/approach – The paper examines the relationships between the measures of internalization, benefit, QM tools and motivation, using partial least squares in the framework of the structural equation modeling technique. Findings – The results show that the hotels that have internalized the standard to a greater extent are more likely to be driven by internal motivation, develop more QM tools and achieve greater benefits than the hotels with a lower degree of internalization. Originality/value – As previous studies have examined these issues in relation to the internalization of ISO standards, the present study adds to this important stream of research and contributes by advancing the understanding of these issues through the case of a specific standard for the hotel industry.

Quality Management, Environmental Management and Organizational Design in the Hotel Industry: A Qualitative Study

The purpose of this paper is twofold. First, the paper analyzes the relationship between quality management and environmental management and their effects on hotel performance. Second, the article examines the relationship between these two management systems and organizational design. The paper uses an exploratory, qualitative approach based on interviews with managers and experts in the hotel industry. Based on a content analysis of interviews, the results lead to several propositions. Specifically, quality and environmental management influence hotel performance through mediating variables. Moreover, the implementation of quality management facilitates the implementation of environmental management. Furthermore, the implementation of these two management systems is associated with an increase of formalization and decentralization. The paper contributes to the analysis of quality management, environmental management, organizational design and performance in a joint manner, which has not been attempted before in the hotel industry. In addition, it helps extend the findings about these links in manufacturing and service organizations to the hotel industry.

Organizational design, quality management and competitive advantage in hotels

Purpose – The aim of this study is to examine the relationship between practices of quality management (QM) and the characteristics of organizational design, and QM and competitive advantage. Design/methodology/approach – The study uses a partial least squares approach to test these relationships in 350 hotels in Spain. Findings – The findings show that QM influences specialization, formalization and interdepartmental interactions, and that QM practices influence both cost and differentiation competitive advantage. The results also indicate the importance of QM strategic and operational systems as practices that have a key impact on the characteristics of organizational design. Similarly, the QM operational system is key in the relationship between QM and cost competitive advantage. Finally, the QM operational, information and strategic systems positively influence differentiation competitive advantage. Practical implications – When hotels adopt QM practices, there will be significant changes in a number of organizational variables, including specialization, formalization and interdepartmental interactions. This paper provides empirical evidence that QM practices improve both cost and differentiation competitive advantage in the hotel industry. Originality/value – There has been little research on the effects of QM on organizational design in the hotel industry. The contribution of this paper is that analyze the effects of QM on organizational design and competitive advantage, extending knowledge about these issues in a specific sector.

Proceedings of the Second American-Soviet Symposium on the Use of Mathematical Models to Optimize Water Quality Management, Bloomfield Hills, Michigan, USA, August 27-30, 1979 /

Mode of access: Internet.

Air quality management.

Mode of access: Internet.

Water quality management program, supplemental guidance for FY 81

Cover title

Control charts as a tool in data quality improvement.

Mode of access: Internet.

Water quality management five year strategy, fy 81-baseline.

Jan. 1980.

Conservation districts and 208 water quality management ... /

Mode of access: Internet.