Quality records collection

Are quality records collected, indexed, accessed, filed, stored, maintained, and dispositioned in accordance with documented procedures ... 

The standard requires the supplier to establish and maintain documented procedures for the collection of quality records. 

The filing provisions you have made should enable your records to be readily retrievable however, you need to maintain your files if the stored information is to be of any use. In practice, records will collect at the place they are created and unless promptly removed to secure files may be mislaid, lost, or inadvertently destroyed. Once complete, quality records should not be changed. If they are subsequently found to be inaccurate, new records should be created. Alterations to records should be prohibited as they bring into doubt the validity of any certification or authentication as no one will know whether the alteration was made before or after the records were authenticated. In the event that alterations are unavoidable due to time or economic reasons, errors should be struck through in order that the original wording can still be read, and the new data added and endorsed by the certifying authority. 

You may only need one procedure that covers all the requirements but this is not always practical. The provisions you make for specific quality records should be included in the procedures for controlling the activity being recorded. For example, provisions for inspection records should be included in the inspection procedures, provisions for design review records should be included in the design review procedure. Within such procedures you should provide the forms (or content requirements for the records), the identification, collection/submission provisions, the indexing and filing provisions. It may be more practical to cover the storage, disposal, and retention provisions in separate procedures because they may not be type-dependent. Where each department retains its own records, these provisions may vary and therefore warrant separate procedures. 

The procedure is established and maintained for identification, collection, indexing, filing, storage, maintenance, and disposition of quality records. 

So far we have assumed that the instrument used to collect the data from the sample is based upon measured data where appropriate. While this is true of many audit instruments, this is not the only way to collect audit data. Interviews with participants (Drury 1990a), interviews and group meetings to locate potential errors (Eox 1992), and archival data such as injury of quality records (Mir 1982) have been used. AU have potential uses with, as remarked earlier, a judicious range of methods often providing the appropriate composite audit system. 

Most high quality spectral collections have been obtained with moderate spectral resolution - 2 cm-1 to 5 cm-1 with a peak-to-peak noise level of less than 1% T. If data are recorded with less resolution then there is a risk of loss of fine detail even for condensed phase spectra. It is this fine detail that helps in the discrimination of chemically or structurally similar compounds. Furthermore, crystalline compounds and also certain liquids, such as indene, can have narrow absorption bands that are resolution sensitive. A high noise level, as indicated in Figure 2d is undesirable but not necessarily unavoidable. Techniques such as signal... 

The principal tool for performance-based quality assessment is the control chart. In a control chart the results from the analysis of quality assessment samples are plotted in the order in which they are collected, providing a continuous record of the statistical state of the analytical system. Quality assessment data collected over time can be summarized by a mean value and a standard deviation. The fundamental assumption behind the use of a control chart is that quality assessment data will show only random variations around the mean value when the analytical system is in statistical control. When an analytical system moves out of statistical control, the quality assessment data is influenced by additional sources of error, increasing the standard deviation or changing the mean value. 

In order to demonstrate the achievement of quality and the effectiveness of the quality system, records will need to be gathered in from the locations where they were produced. This is more than a convenience because you will be unable to analyze all the data efficiently unless you need it in front of you. If you are lucky enough to operate a computer network and all the data is available on the network, data collection is a simple affair. However, many organizations still rely on paper records and therefore you will need a means of enabling such records to be either submitted to the analysis points or collected from source. To facilitate the collection of records you will need to insert submission or collection instructions in the relevant procedures which specify the records. 

It should be noted that the data collection and conversion effort is not trivial, it is company and plant-specific and requires substantial effort and coordination between intracompany groups. No statistical treatment can make up for inaccurate or incomplete raw data. The keys to valid, high-quality data are thoroughness and quality of personnel training comprehensive procedures for data collection, reduction, handling and protection (from raw records to final failure rates) and the ability to audit and trace the origins of finished data. Finally, the system must be structured and the data must be coded so that they can be located within a well-designed failure rate taxonomy. When done properly, valuable and uniquely applicable failure rate data and equipment reliability information can be obtained. 

Documentation of data origin is essential. Each completed data collection form needs to contain a file reference number or code to connect it to the documentation sources. This provides an essential trail to audit data quality, to confirm risk or reliability estimates or to investigate data values that appear questionable. Procedures to control data during handling, processing, recording, and reviewing are also necessary to prevent loss of data and to assure that opportunities are not lost to check the content of a form, by... 

A major complaint of clinical sites is the difficulty and time required for data entry (when they have to enter the data through a keyboard) and the effort required to resolve queries, which are often returned weeks or even months after a patient visit. Machine-read data, whether collected by optical mark read or SmartPen , ensure that data are both entered and validated, with queries returned, in a matter of minutes after they are recorded. Coupled with a quick feedback loop, this system ensures that query rates are typically about one-tenth those for web-based EDC systems and even lower for paper-and-hand entry systems. This system also highlights recurring problems and areas of potential improvement that may impair study timeliness and quality. 

Audits of each phase of the study should include personnel training, preparation of collection forms, application calibration, each sample collection procedure, sample transport, each type of chemical analysis, data recording, data entry, data verification and data storage. Data collection in the field is often tedious if automated logging devices are not in place. To ensure data integrity, the paper and ink used for field studies should be waterproof. Each data collection form should contain appropriate locations for information detailing the time and location of sample collection, sample transport and sample analysis. Data collection forms should be stored in an orderly fashion in a secure location immediately upon return of field teams from the field at the end of each day. It is also important for data quality for studies to collect necessary field data seven days per week when required. In our experience, poor study quality is likely when field sample and data collection do not proceed on weekends. 

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