Query management

A online query management system that allows sites to receive queries within minutes of data submission and resolution to be handled and documented immediately (Fig. 23.3)... 

Query managers include the functionality for management of user queries and are connected to the front-end systems for the end-user. 

Currently informatization of safety quality standardization evaluation work is at low level. Quality evaluation management is characterized by the traditional manual approach. Based on a cloud management platform and mobile terminal can be digitized in management of evaluation content. After input assessment data, the system will automatically generate related evaluation results and statistical information with print function. The system also can be added to the policy query and evaluation method study module at the same time, convenient query management personnel evaluation, improving the efficiency and accuracy of the work. 

Late drawings/technical queries/ tooling delays/errors/shop overload/ out of sequence/overtime/concessions/ shortages/excess work in progress/excess inventory/poor supplier quality/penalty clauses/ lost sales/poor management... 

Nonetheless, these changes in plant names can be difficult to manage. Fortunately, there are a number of databases that provide correlations between historic names and the current names of these plants. We have chosen to use the International Plant Names Index for our analysis [33]. By querying this database we are able to either validate the name of the plant in the historic text or, more frequently, to update the plant name to the current name. 

A different approach is taken by lO-Informatics (www.io-informatics. com), whose Sentient product allows users to manage, analyze, compare, link, and associate data from heterogeneous sources. Instead of creating work-flows, data sources and applications are aggregated upfront in a graphical user interface that allows many kinds of information to be viewed at once, and customized links and queries between them can also be created easily. 

Figure 23.3 The Data Query System is a web-based management tool that sites use to receive and manage queries. See color plate.

Close tracking of data entry means that performance measures can be tracked and managed. Such measures typically include query rates (which can be tracked by site, investigator, quesiton, and any comparators), time to respond to queries, rate of query rejection, and the like, and they provide an opportunity to identify and correct performance issues, whether associated with an individual site or study or a program. This capability alone provides a powerful tool by which sites throughout the world can be closely monitored. 

Web-based data collection and management systems provide a mechanism for remote data entry, where entered data are added to a centralized database once the submit button is pressed. They can be designed to automate the various aspects of clinical trials such as eligibility evaluation, data collection, and tracking specimens. They also serve as a resource site for participating sites to access trial-specific information, facilitate communication, track data queries and their resolutions, and allow administrative management of trials [28, 29]. For these reasons, they play an important role in facilitating the conduct of international clinical trials. 

Before the statistical programmer receives data that are ready for analysis, the clinical data management group cleans the data. This is done through a query process, which is built into the clinical data management system. The clinical data management query process usually looks like this ... 

In order to reduce unnecessary data queries, the statistics group should be consulted early in the clinical database development process to identify variables critical for data analysis. Optimally, the statistical analysis plan would already be written by the time of database development so that the queries could be designed based on the critical variables indicated in the analysis plan. However, at the database development stage, usually only the clinical protocol exists to guide the statistics and clinical data management departments in developing the query or data management plan. 

Most clinical data management systems used for clinical trials today store their data in relational database software such as Oracle or Microsoft SQL Server. A relational database is composed of a set of rectangular data matrices called tables that relate or associate with one another by certain key fields. The language most often used to work with relational databases is structured query language (SQL). The SAS/ACCESS SQL Pass-Through Facility and the SAS/ACCESS LIBNAME engine are the two methods that SAS provides for extracting data from relational databases. 

Notice how the highlighted changes allow for a permanent SAS data set to be created containing only the variables desired and only the records that have all data queries resolved by data management. 

Rapid intestinal transit may result in a false-positive breath test, in particular when hyperosmolar nonabsorbable substrates are used. A false-negative outcome in patients with culture-proven Gram-negative bacilli in the upper gut further query the sensitivity and usefulness of breath tests for clinical practice [10-13]. Positive microbial culture from small intestine is thus advantageous when major alterations of clinical management are considered. 

Laboratory safety data When the CRFs arrive at the data manager s office, questions will arise relating to laboratory safety data. Queries may occur at the investigator site and advice can be requested from the pharmaceutical physician associated with the clinical trial in the sponsor company. 

Sometimes the blind review can throw up data issues that require further evaluation by the data management group with data queries being raised, and these perhaps may result in changes to the database. This sequence of events can cause major headaches and delays in the data analysis and reporting, and so it is important in the planning phase to get the data validation plan correct so that issues are identified and dealt with in an ongoing way. 

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