This article is part of the network’s archive of useful research information. We welcome new content which can be done by submitting an article for review or take part in discussions in an open topic or submit a blog post to take your discussions online.
Clinical Research is a branch of medical science which focuses on testing the efficacy, safety and effectiveness of new drugs, medical devices and biologicals (such as vaccines) prior to approval for public use. The approval of new treatments which are safe for human consumption is premised on the generation of reliable and accurate laboratory data. Such data is generated both at the pre-clinical and clinical phases of drug development. In many emerging markets, the quality of the data generated from laboratories has been questioned. Most of the time, the generation of poor quality laboratory data is caused either by non-existence of quality systems or poor knowledge, understanding, implementation and monitoring of laboratory quality management systems by workers involved in the process. The understanding of the entire quality management system in laboratory operations is therefore critical for the generation of reliable laboratory data. When quality data is generated to support the application of new medications, it results in the development and approval of effective treatments which have a direct effect on reducing the impact of various disease burdens. The focus of this article is to explain in detail the various components of the laboratory quality management system and to help clinical research professionals to generate quality laboratory data by implementing such system.
The main focus of any laboratory test is to produce accurate, reliable and effective results and data which are fit for purpose. Laboratory analyses are costly and time consuming. The available resources in every economy are limited and this means that all the money invested into the laboratory should be well utilised to generate reliable and accurate results. The importance of good quality laboratory services cannot be overemphasized because the practice of evidence-based medicine and the generation of good clinical research data rely solely on the generation of quality laboratory data (1). Reduction in the recall of approved drugs is only possible when appropriate, precise, accurate and unbiased laboratory data is generated. In the medical setting, laboratory data is useful for accurate diagnosis, treatment, monitoring and prognosis of diseases. It is also very important in the provision of epidemiological evidence at local, national and global levels. Additionally, laboratory data is important for regulatory purposes, for the formulation of government policies and to support the approval of new treatments.
Rapid strides have been made recently in creating awareness about quality as well as in articulating various components of a quality system. The lead in this field has been taken by the International Organization for Standardization (ISO). This has resulted in the development of various ISO standard documents which are helpful in the actualization and implementation of quality systems that will result in customer satisfaction. Examples of this are the ISO 9000 series, ISO 17025 for calibration and general laboratories and the recently approved ISO 15189 for clinical laboratories.
Quality is a daily, on-going challenge contributing to technological and scientific development. Generation of good quality laboratory results is premised on the proper understanding of the basic concepts of quality management systems. It has been documented that most errors emanating from laboratories are often caused by poor systems and not humans. This implies that once the system is fully documented, all users of the laboratory should adhere to the documented procedure, policies or guidelines, thereby helping to generate quality and useful laboratory data.
What is Quality Management System (QMS)?
This implies the total (complete, unreserved) implementation of the overall quality system such as Quality Policy, structures, procedures, processes, quality control, quality assurance and quality improvement, which will enable an organization to achieve its desired quality goals. Laboratory Quality Management System therefore means the application of the principles of QMS in the laboratory setting. Laboratory Quality Management Systems, when applied in the day to day operational activities of a laboratory, shall result in the generation of reliable data and acceptable services which will ultimately result in customer satisfaction. For any organisation to achieve its desired quality goals, quality management system must be put in place. Successful implementation of the principles embedded in quality systems implies that all stakeholders concerned should work in synergy to achieve the set goals, the end result being a quality product.
The full implementation of quality management systems does not only aid a laboratory to get accreditation from both local and international organisations, but also helps the laboratory to:
- Generate accurate, precise, reproducible and timely laboratory data
- Improve customer satisfaction
- Improve compliance with regulatory requirements
- Improve Management- Employee working relationship
- Reduce the generation of fallacious results
- Improve training and retraining of employees
- Improve the comparability of laboratory data between laboratories
- Establish the credibility of laboratory with users of the laboratory
Definition of terms associated with QMS
- Quality Policy: A written commitment by the top management put in place with the purpose of actualizing the quality objectives of the organization.
- Structures: In the laboratory context, it means setting up organisational structures, responsibilities, procedures, processes and resources necessary to achieve the quality objectives.
- Quality Control: This is related to operational techniques and activities which occur mainly in the laboratory and are used to monitor analytical performance in relation to accuracy and precision.
- Quality Assessment: All the planned and systematic activities comprising the entire system, preanalytical, analytical and post analytical factors, which have a direct effect on the quality of data generated by a given laboratory.
- Procedure: A specified way of carrying out an activity in the laboratory. When it is documented, it is referred to as the Standard Operating Procedure (SOP). A documented procedure usually contains: what shall be done and by whom, when, where and how it should be performed; what materials, equipment and how it should be controlled and recorded.
- Processes: This entails following a documented procedure to achieve the required results.
- Quality Improvement: This implies the outcome of QC and QA. When deviations occur, the cause is investigated and corrective measures are put in place to improve results.
Implementation of quality in all laboratories irrespective of their location requires a systematic and logical approach. Implementation of quality systems requires commitment from the top management of the laboratory not only for motivation and support, but also for allocation of appropriate resources. It is impossible to implement quality systems in the laboratory if the management does not agree in principle. It is often perceived that the cost of putting up quality systems is high. However, it is worthy to note that the cost of quality (i.e the negative consequences of poor implementation of quality systems) is higher than the actual cost (operational cost) incurred to put up quality systems in a given laboratory. The most essential requirement needed to actualize the setting up of quality systems is a commitment by top management, made in written format which then forms the catalyst that will drive the entire process. .
Essential requirements for quality system
It is the responsibility of the Management to formulate the quality policy of any laboratory. This is a written document highlighting the quality objectives of an organisation. Implementing quality systems in the laboratory is the responsibility of all laboratory staff. After the quality policy is formulated and announced, it is the responsibility of all laboratory staff to ensure that they adhere to the policy statement. A policy statement without willing staff results in an ineffective statement. The involvement of relevant personnel is one of the key successes in the implementation of quality systems.
Most organisations fail because the required structures to make them succeed are not put in place. Structures refer to the operational guidelines of any organisation. In the context of laboratory management, it is the comprehensive, well written management document which should describe in detail the management structure of the laboratory. Putting up the necessary structure(s) in place is a core management responsibility. A chaotic, unstructured and unplanned laboratory in a nutshell lacks a quality system. Such labs may also find it difficult generating an acceptable laboratory data.
For adequate planning and structuring of any laboratory, there is a need to put in place a detailed organogram of the laboratory structure. Such organogram must completely be devoid of bias. Detailed organisational structure which should clearly define communication channels and a clear reporting structure among the laboratory personnel should be well highlighted. Within that structure, each member of staff should be able to locate his or her own job description, responsibilities and his/her relationship with other members of staff who could either be subordinates or superiors.
Management documents should specify the role of each member of staff in any laboratory and should clearly define who is responsible for each role and activity in the laboratory. The documents should also identify the records that should be kept for routine operations, such as equipment calibration and maintenance (equipment log), sample handling, reporting of results, handling of inconsistent results, resolution of conflicts and complains, disposing of treated samples, training and retraining of staff, thus ensuring that a logical and coherent system of record keeping is adopted. Such documentation should be brought together as a single quality manual which shall act as a reference text for the entire laboratory. A few laboratory audits have shown that not many have a well-documented quality manual. A well-documented quality manual is a key to actualising quality management objectives of any laboratory.
The reliability of any laboratory measurement and indirectly a functional quality system is assured only when there is an effective monitoring system. For every run of analytical results, this monitoring system must indicate if the results are reliable and should also indicate when an error occurs. One of the means of assuring the reliability of laboratory data is through quality control.
Quality Control is operational techniques and activities (mainly within the laboratory) that are used to fulfill requirements for quality. The terms ‘internal quality control’ (IQC) and ‘external quality assessment’ (EQA) are commonly used. The former refers to activities conducted within a laboratory to monitor performance and the later refers to activities leading to comparison with other reference laboratories or consensus results amongst several laboratories. Quality control monitors analytical performance in relation to accuracy (closeness of measured values to the target value) and precision (closeness of repeated measured values to one another).
Quality control can be internal (within the laboratory, IQC) or external (outside the laboratory, EQA). The former measures the performance of a given laboratory using previously analysed samples. This implies that IQC assesses whether the performance of the laboratory is sufficiently similar to its own previous performance. External Quality Assessment compares the data generated in a given laboratory with that generated by a reference laboratory. This measures precision and accuracy using reproducibility (obtaining the same laboratory data under different conditions). When laboratory data show inconsistencies, one of the following errors could be responsible. These are:
- Random error - which is present in all laboratory measurements and are unavoidable. They are usually constant and therefore the effect in laboratory analysis may not be very obvious.
- Systematic error – These are errors arising from calibrations and wrong entering of data during calibration and other laboratory operations. For example, if the value of a calibrator for glucose assay is 100mg/dl and the laboratory staff wrongly enters 120mg/dl when calibrating for a glucose assay in a given auto analyzer, such error results in generating wrong laboratory data.
- Gross errors – These errors arise as a result of data falsification and are, obviously, also avoidable. For example, if a potassium analyzer generates a potassium result of 2.1mmol/l and a member of the laboratory staff considers it too low and records 3.8mmol/l as her/his perceived acceptable result. Falsification of data is a serious violation in any clinical research process and it should be avoided.
The use of quality control in the laboratory ensures that these errors are properly monitored for, checked and prevented. It is therefore an integral part of sound quality management system.
A major component of a quality management system is quality assurance (QA). Quality assurance encompasses all the planned activities within the pre analytical, analytical and post analytical processes which, if implemented within the quality system and demonstrated as needed, would result in generating quality data. Quality assurance has become a key issue in all aspects of laboratory operations. In larger laboratories, QA managers will require the appointment of a QA officer who should constantly liaise with management, manage data archives, conduct regular audits, review QA systems and report any QA anomalies to the management. The officer is responsible for the regular inspection of all aspects of the system to ensure staff compliance. He also documents reports on such inspections and audits to management and makes recommendations to improve on the observed deficiencies. In practice, this will involve regularly checking facilities and procedures as they are performed, conducting regular audits (by tracing an analytical sample back through the system from report to sample reception) and ensuring that all appropriate records have been kept.
A closer look at the activities in the quality assurance components will go a long way in appreciating its relevance and effect in quality systems.
Pre-analytical issues: These are events which are performed both outside and inside the laboratory prior to the analytical stage. It usually includes selection of the right sample, sampling, transportation of the sample to the laboratory, sorting, registration, centrifugation and preparation. It contributes to about 65 % of the samples turn-around- time (TAT).
Analytical stage: This comprises activities within the laboratory. It includes sample analysis and documentation of each of the processes in a given procedural manual. It accounts for about 15% of the TAT of the sample
Post analytical stage: This comprises activities after the sample has been processed in the laboratory. It includes reporting the result, dispatching and telephoning. It contributes to about 20% of the TAT.
As discussed earlier a good quality document should be available on handling these pre analytical, analytical and post analytical issues with a view to reducing sample turn- around- time and to improving quality control. Appropriate standard operating procedures should be documented for each of the aforementioned processes in the pre analytical and other stages of the laboratory analysis.
Factors Affecting Quality Assurance: Several human and systematic factors contribute adversely to the laboratory’s ability to achieve quality assurance. Some of the factors are detailed below:
- Creating professional disharmony and salary dichotomy
- Poor motivated and ill-encouraged staff
- Poor remuneration of staff
- Employment process based on bias leading to recruiting poorly trained professionals
- Poorly trained professional
- Lack of re-training of the entire workforce
- Wrong selection of staff
- Poor commitment and dedication to duty
- Lack of adequate knowledge about laboratory operations
- Poor concentration
- Poor illuminated laboratory
- Crowded environment
- High Humidity
- High Lab temperature
- Poorly cleaned glassware
- Use of wrong solution for washing laboratory tools
- The quality of water used for washing
- Calibration errors
- Wrong purchase of instruments with wrong specifications
- Faulty equipment
- Poorly calibrated balances, spectrophotometer and automatic pipettes
- Poorly prepared reagents
- Poorly Stored reagents, controls and chemical
- Poorly validated reagents and instruments prior to use.
Quality Improvement (QI)
Achieving quality laboratory data is not a one size fits all practice. It requires putting systems in place which will ensure a continuous quality improvement. Continuous quality improvement does not only focus on creating a corporate quality culture, but more on the process of quality improvement. Continuous Quality Improvement (CQI) allows individuals involved in the day-to-day operations to change and improve processes and work flows as they deem fit. CQI implementation attempts to develop a quality system that is never satisfied; it strives for constant innovation to improve work processes and systems by reducing time-consuming, low value-added activities. The time and resources saved can now be devoted to planning and coordination other laboratory processes and procedures.
Quality improvement is driven by the following acronym; FOCUS-PDCA, which means:
- F- Find
- O- Organize
- C- Clarify
- U- Understand variations
- S- Select process to improve.
When variation has been identified and decision is taken to improve, the laboratory moves into the process improvement plan:
- Plan—create a time line, including all resources, activities, dates, and personnel training
- Do—implement the plan and collect data
- Check—analyse the results of the plan and check if what was planned has been achieved.
- Act—act on what was learned and determine the next steps
The FOCUS-PDCA acronym is an easy system for management to communicate to laboratory teams and it helps them stay organised and on track with the end result in mind. The system has proven to be very successful for the implementation of quality systems and for continuous quality improvement by the laboratory staff.
The Six Sigma and Lean Principles
Six sigma was developed at Motorola in the 1980s as a method to measure and improve high-volume production processes. Its overall goal was to measure and eliminate waste by attempting to achieve near perfect or perfect results. The term six sigma refers to a statistical measure with not more than 3.4 defects per million productions (2).
Six sigma is a statistically oriented approach to process improvement that uses a variety of tools, including statistical process control (SPC), total quality management (TQM), and design of experiments (DOE). It can also be coordinated with other major initiatives and systems, such as new product development and materials requirement planning (MRP). The following are steps involved in the six sigma process:
- Break down business process flow into individual steps
- Define what defects are present
- Measure the number of defects
- Probe for the root cause
- Implement changes to improve
- Take a long-term view of goals
The Lean principle is a process which focuses on the complete elimination of waste. Simply stated, Lean is a philosophy and a proven long-term approach that aligns everything in the business to deliver increasing customer value. It is about orienting people and systems to deliver a continuous stream of value to the customer, and eliminate waste and deficiencies in the process. Lean is an everyday practice applied at all levels to consistently maintain and improve performance. Lean therefore is a process which:
- Eliminates waste
- Looks to remove repetition of function
- Looks to standardize actions
- Is an on-going process
A well applied Lean principle achieves the following:
- Eliminate possibility of error
- Replace processes with more reliable ones
- Facilitate to make work easier to perform
- Detect errors when it occurs
The principles of Six Sigma and Lean were used by production companies such as General Electric and Ford, but currently these principles are being applied in the laboratory as a component of total quality management system with a view to achieving continuous quality improvement.
In any laboratory operation, including clinical research, any procedure which is not fully documented is the same as one that was not performed. In order to achieve quality clinical research data which meets the expectations of the quality monitors, auditors and regulatory agencies, the adherence to the principles of Laboratory Quality Management Systems (LQMS) is essential. Top management of every organization should show the required commitment to accomplish this. Staff should be motivated and constantly trained to achieve the set quality goals. The basic requirement in implementing the principles of LQMS is the full documentation of all the activities in the laboratory. The cost of poor quality is devastating and clinical research organisations, pharmaceutical companies, sponsors and regulatory agencies should therefore ensure that professionals handling clinical research operations, especially among the emerging markets, adhere strictly to the principles of LQMS. This, with no doubt, will be helpful in generating reliable, precise, accurate and acceptable data which will support the development of new drugs and consequently new, more efficient and effective treatments for public consumption.
- Onyeaghala Augustine (2009) Principles of Total Quality Management for Biomedical and Analytical Scientists, First Edition, Change Publishers, Lagos, PP 52.
- American Society for Quality. www.asq.org,