Management of Data and its Quality to ensure its fit for Purpose
Speaker(s) / Author(s): G. Zwane
Rand Water Analytical Services
PO Box 3526, Vereeniging, South Africa
e-mail: [email protected]
Phone: 016 430 8400 Fax: 016 455 2055
Abstract
Data management within a laboratory plays an importance role in optimising processes, and it
encourages innovation. This enables the laboratory to operate in a manner that differentiates itself
from the rest, thus becoming the preferred service provider. The quality of data has a direct link to
customer confidence as well as laboratory integrity. As in most cases it turns to influence customer
decision to “call back” again for service.
Deviations from internal processes expectations/norm(s) are experienced from time to time during
data production. However, the challenge is to bring these non-conformances to a minimum, hence
the use of these quality control tools within Chemistry Labs in Rand Water (result interfacing, batch
actions, production specifications etc.).
Data management is a systematic process of controlling, protecting and monitoring the validity of
data throughout its life cycle within the laboratory. This is achieved by monitoring process quality
from sample receipt to reporting of results. Moreover, it strengthens the laboratory compliance to
clause 5.9 (assuring the quality of test and calibration results) and 5.10 (reporting the results) of ISO
17025. It also provides an audit trail in cases where customers complain about laboratory service or
quality of results.
The biggest benefit of employing data management is the production of defensible analytical results
which ultimately grants the laboratory a higher status in the analytical world.
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1. INTRODUCTION
Data is classified as being fit for the purpose when it conforms to set criteria (Ramly et al.,
2004). The laboratory generates data throughout its processes under strictly controlled
conditions, which are governed by clause 5.9 of ISO 17025 quality management standard for
accredited laboratories. In the laboratory’s case, the quality control system is considered
effective when its purpose is fulfilled (Figure 1). Quality control is the generation of data for the
purpose of assessment and monitoring on the performance of the analytical method, and how
it’s operating under the current laboratory conditions (http://who.int/water.sanitation_health).
Precisely, Rand Water Chemistry Laboratory functions in such a manner that it generates data
and information that will later be used for laboratory analytical process optimization and to
enhance the quality of data produced.
Figure 1: Building an effective laboratory QC system Cooper (2006)
The Chemistry Laboratory uses quality control tools such as Laboratory Information
Management Systems (LIMS) for gathering and storing data, use of production specifications to
monitor non-compliance as well as inter-laboratory studies for the reliability of the results. The
quality control system is applied throughout the laboratory processes from sample receipt to
reporting of results(Hughes, 2006). The laboratory strives for excellence in supporting Rand
Water to fulfill the mandatory requirement in the South African National Water act 36 of 1998
that states “potable water should be treated to acceptable standards that are not detrimental to
human health”.
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2. LABORATORY QUALITY DATA GENERATION AND ITS MANAGEMENT
Data produced creates a perception not only about the laboratory but about the entire
organization; hence the need to put stringent control measures in place. This is achieved by
monitoring and assessing current processes for improvement and ultimately continuous
excellence. Nevertheless, deviations are inevitable during data production in the laboratory
environment, therefore the laboratory’s main challenge is to trace, manage and reduce nonconforming work (Cooper, 2008). The elimination and control of variability is undoubtedly of
significant importance within the laboratory because of the narrow margin (Cooper, 2008)
between accurate data and the data that is fit for the purpose. Fosters (2004) states that a
customer is considered satisfied when high quality results are produced with a certain level of
excellence and brought through on time. In attainment of the success the laboratory needs a well
calculated plan from its sample reception, analysis and reporting, coupled with the appropriate
quality management system that is aligned to its strategy.
2.1 Sample receiving
Samples are collected and received according to the schedule. It is vital to remember that data
management starts with sample collection, since data reported is as good as the sample
collected. The process of collecting samples involves preparation of sample containers that are
clearly identified by Labware-LIMS generated numbers, scanned out prior to sampling and
scanned into the laboratory after sampling. Important to note is that the number allocated to
sampling point by Labware-LIMS is the link between the bottle prepared and the collected
sample.
2.2 Sample analysis
Samples are then organized in lab-batches for analyses to eliminate the risk of untraceable test
work, as well as to enhance process integrity. Sample folders or routes are used for batching
samples, where a sample comprises of different and independently Lab-number linked to a
sampling point. The lab numbers ensure that correct results are linked to the specific sample
analysed as per approved laboratory procedures and finally those results are reported on
Labware-LIMS.
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To protect data integrity, results are automatically exported to Labware-LIMS by means of
instrument interfacing, therefore eliminating data transmission errors. Instrument interfacing is a
technique used to facilitate communication between the instrument software and LabwareLIMS. Data interfaced from the instrument to Labware-LIMS undergoes quality control checks
using batch actions.
Batch actions are configured on Labware-LIMS for the benefit of the laboratory to detect nonconforming work and data prior approval. These involve detecting of samples data deviations
from configured Rand water production and SANS 241 specifications. Batch actions are meant
to safeguard the quality of data produced, including management of auto-replicates (re-analyses)
of data that do not comply with internal QCs and national standards.
2.3 Proficiency Testing Scheme
To ensure the effectiveness of internal processes the laboratory participates in national and
international Proficiency testing schemes as stated by ISO/IEC 17011:2004 “The accreditation
body shall ensure that its accredited laboratories participate in proficiency testing or other
comparison programmes, where available and appropriate, and that corrective actions are
carried out when necessary”.
Laboratory competence is tested through the use of Proficiency Testing schemes. Besides
proving the quality and credibility of results produced by the laboratory in PT participation,
laboratory processes are also tested for efficiencies. Excellent performance in PT schemes
assures the quality of internal laboratory processes, and such interventions give confidence in
decision taken on water quality related issues.
2.4 Internal audits
All control points and control measures are important and should be afforded ongoing attention
through internal audit. Audits are meant to monitor compliance to internal procedures, as well as
identifying areas of concern. This grants the laboratory a chance to develop appropriate control
measures to ensure monitoring and protection of data. The benefit of internal audits is to
promote consistency in laboratory activities, improve process control to ensure that data
produced by the laboratory is accurate and fit for purpose.
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The internal audit provides a mechanism to reduce deviations while minimising customer doubt.
This drives improvement actions on future processes.
3. CONCLUSION
Analytical quality assurance comprises of all steps taken by the laboratory including quality
control to assure those who receive the data that the laboratory is producing valid result.
Clearly, the sample life cycle within Chemistry laboratory processes has been monitored for
quality on every step, to entrust compliance to requirements. Furthermore, the effectiveness of
the quality management system was eventually realised by means of reduced non-conforming
tests and improved accurate results produced by the laboratory (Ramly et al., 2004). This has
been made possible by the deployment of LIMS as part of the quality assurance tool throughout
the sample life cycle which has demonstrated significant value.
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REFERENCES
1. Cooper, G. 2008. Basic Lessons in Laboratory Quality Control
2. Cooper, G. 2006. Building an Effective Laboratory QC System
3. Fosters, S.T. 2004. Managing quality an integral approach
4. ISO 17025 2008. “General requirements for the competence of testing laboratories”
5. Ramly, F.E., YusoF, S.M., Bakar, K.A. 2004. Effectiveness of quality auditing in the
automotive industries (case study)
6. (http://who.int/water.sanitation_health)
7. Hughes, H. 2006. A Practical Guide to Scientific Management for Postdocs and New Faculty
8. South African National Water act 36 of 1998, Pretoria, South Africa.
9. ISO/IEC 17011:2004 Conformity assessment
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