Pre-Analytical variables: Sample Collection (Part 1)

Errors during collection, processing and transport of biological specimens are common (Ref: Tietz Textbook of Clinical Chemistry and Molecular Diagnostics, 4th Ed).

• Common samples collected are
• Whole blood
• Serum
• Plasma
• Saliva
• Pleural, pericardial, ascitic fluid
• Various types of solid tissues
• Spinal, Synovial, amniotic fluid.

 Blood Collection

• Sources – Artery, veins, capillaries
• Venous blood – Venipuncture
• Arterial blood puncture – Arterial blood gas analysis
• Capillaries – Skin puncture, in young children and for point-of-care testing

Quality control in Biochemistry laboratory

Quality Assurance in the biochemistry laboratory is intended to ensure the reliability of the laboratory tests. The objective of quality assurance is to achieve reliable test results by 

• Accuracy
• Precision

Accuracy

This refers to the closeness of the estimated value to that considered to be true. Accuracy can, as a rule, be checked only by the use of reference materials which have been assayed by reference methods.

Precision

This refers to the responsibility of the result, but a test can be precise without being accurate. Precision can be controlled by replicate tests and by repeated tests on previously measured specimens. And the test result or value which we get should be closer to the previous one.

Inaccuracy and/or imprecision occur as a result of using unreliable standards or reagents ,incorrect instrument calibration, or poor technique.eg consistently faulty dilution or the use of a method that gives a reaction that is incomplete or not specific for the test.

First of all, Edward Demming gave the idea about quality control. According to him,

Improved quality=increased productivity at lower cost.

This can be done by

1. Eliminating rework
2. Save time
3. Save labour
4. Save material e.g. reagent, specimen etc.
5. Patient care.

Quality Assurance Programme Includes

• Internal quality control (IQC)
• External quality assessment (EQC)
• Proficiency surveillance
• Standardization

PROFICIENCY TESTING AND LABORATORY ACCREDITATION

Proficiency testing (PT) programs are type of an external quality assessment in which simulated patient specimens made from a common pool are analyzed by the laboratories enrolled in the program. The results are then evaluated to determine the quality of each laboratory’s performance. Government and licensing agencies increasingly use PT as a method for accrediting laboratories thereby giving them official authorization to operate. The performance characteristics of PT programs for detecting bias and imprecision are evaluated using methods similar to those as assessing internal QC. 

A common evaluation criterion is comparison of PT test results with those of peer groups, considering all values that exceed 2SD to be unacceptable. To be totally successful a laboratory should produce correct results on 4 out of 5 specimens for each of the analytes and have an overall score of at least 80% for 3 consecutive challenges. If a laboratory has 2 or more incorrect results for any analyte or has an overall score <80% on tow of 3 consecutive surveys, it is suspended. 

For PT the sample are made by agencies which has target value established by definitive or reference methods. If definitive method is not available a comparative method may be used. If the method group is <20 participants, target value means the overall mean after outliers removal. Or in absence of definitive method, then peer group mean values or groups of methods that agree with the definitive method can be used. This target value is termed the definitive method corrected target value (DMCTV). 

EXTERNAL QUALITY ASSESSMENT (EQA) AND PROFICIENCY TESTING (PT)

All the procedures described earlier have focused on monitoring a single laboratory. These are called internal quality control. In this EQA there is comparison of performance of different laboratories called external quality assessment. The internal QC is necessary for daily monitoring the precision and accuracy of the analytical method, and EQA being important for maintaining the long-term accuracy of the analytical methods.

External Quality Assessment:

These are sponsored by professional societies and manufacturers of control materials. The basic operation of these programs involves having all the participating laboratories analyze the same lot of control material, usually daily as part of internal QC activities. The results are tabulated monthly and sent to the sponsoring group for the data analysis. Summary reports are prepared by the program sponsor and distributed to all participating laboratories. 

These programs are useful only for monthly reviews and period problem solving activities. But due to development in information system real-time external QC is possible. The mean of all results or the mean of results from peer laboratories (those performing the test with similar methods) is taken as the target value and is used for comparison with the individual laboratory’s result.

Statistical significance of any difference between an individual laboratory’s observed result and the group mean can be tested by t-test. When the difference is significant, the laboratory is alerted that its results are biased compared with the results of other laboratories. Another approach is to divide the difference by the overall standard deviation of the group, and then to express the difference in terms of the number of SDs

SDI = (Laboratory result – group mean)/Group SD

 

Where SDI is SD interval or index and Group s is the SD for group of subsets. Differences greater than 2 or 3 indicate that a laboratory is not in agreement with the rest of the laboratories in the program. The SDI is a statistical calculation that is used for peer group comparisons. Participation in interlaboratory QC comparison programs provides peer group QC data based on the same methodology and the same instrument for the same lot number of control material. The SDI can be used to assess the validity of individual laboratory results as compared to a peer group QC result or proficiency sample by calculating the probability of the result obtained. It relates the bias, or difference from the true result, as compared with the SD expected. This calculation is used to assess PT results and other aspects of QA.

Fig. EQAS Cycle  (Source: Tietz clinical chemistry, 4th edition)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Additional information about the nature of systematic error is obtained when there are two different control materials analyzed by each laboratory. For example the laboratory observed mean for material A is plotted on y-axis versus its observed mean for material B on the x-axis; these graphs are called Youden plots. Ideally the point for a laboratory should fall at the center of the plot. Points falling away from the center but on the 45⁰ line suggest a proportional analytical error. Points falling away from the center but not on the 45⁰ line suggest either an error that is constant for both materials or an error that occurs either in just one material.

The inner square of the plot (yellow) represents one standard deviation (1SD). The next larger square (green) represents 2SD, and the outer square (blue) represents 3SD. A horizontal median line is drawn parallel to the X-axis and a second median line is drawn parallel to the Y-axis. The intersection of the two median lines is called the Manhattan Median. One or two 45-degree lines are drawn through the Manhattan Median. The results of at least two different levels of controls (e.g. Level 1/Level 2 or Normal/Abnormal) are then plotted on the chart as X-axis versus Y-axis.

Cumulative sum (Cusum) control chart:

Cumulative sum (Cusum) control chart:

1. Analyze the control material on at least 20 different days, and calculate the mean and SD of those results.
2. Label y-axis cusum. Draw a horizontal line at the midpoint of the y-axis to represent a cusum of zero. Set the range of values above and below to be about 10 times the standard deviation. Label x-axis in terms of time, using day, run number, control observation number or whatever is appropriate.
3.  Introduce control specimens into each analytical run and record the value obtained.
4. Calculate the difference between the value and the expected mean. Obtain the cusum by adding this difference to the cumulative sum of the previous differences. Plot the cusum on the control chart and inspect the plot.I
5. Interpret the charted data by evaluating the slope of the cusum line. A steep slope suggests that a systematic error is present and that the run is out of control.

(Source: Tietz textbook of clinical chemistry, 4th edition)

In figure 19-16, when control values scatter on both sides of the mean, giving both positive and negative differences, the cusum will alternate in sign, and plotted values will wander back and forth across zero line on the control chart. When control values fall mostly on one side of the mean so that most of the differences have same sign, the cusum value increases in magnitude and plotted values will move away form zero line of the control chart. 

The approach to judge the control status in cusum is based on slope of the cusum line. In industry, this is done by constructive templates having a V-shaped section removed from a rectangular sheet of clear plastic. This V-shaped cutout establishes the angle that is the control limit and gives the technique its name of V-mask cusum. The apex of V-mask is positioned on the control chart at a specified distance in front of recently plotted cusum. If all the plotted values are contained within the angle of the V-mask, the method is in control. If any of the plotted values fall outside the angle of V-mask, the method is out of control. 

Sometimes this process is aided by use of special graph paper having an underlying pattern of 45⁰ angles (  <  <  <  ) across the chart. When using this special graph paper, the convention has been to scale the graph so that a change of 2s on the y-axis is the same distance between two points on the x-axis. The 45⁰ angle then represents the slope expected when the observed mean is approximately 2s from the expected mean. 

SELECTION OF QC PROCEDURE

SELECTION OF QC PROCEDURE

Levy-Jennings Control Chart

Control charts were first introduced into the clinical chemistry laboratory by Levy and Jennings in 1950. Here single control values are plotted directly. To use a Levey-Jennings control chart, follow these steps:

1.      Analyze control sample at least 20 different days. Calculate the mean and SD for those results.

2.   Construct a control chart either manually on graph paper or using computer. In the y-axis put control values along with mean ± 4SD which covers about 95% or 99.7% of the measurements. Draw horizontal lines for the mean and upper and lower control limits. Set the control limits as the mean ± 3SD when the number of control observations, n is 2 or greater. When n is 1, control limits may be set as mean ± 2SD. Label x axis in terms of time, using day, run number, control observations number.

3.     Introduce control specimen into each analytical run, record the values and plot each value on the control chart.

4.      When the control value fall within the control limits (±3s), interpret the run as being in control and report the patient results. When a single control value exceeds the control limits, stop the method; do not report patient results. Inspect method to determine the cause for the errors. Resolve the problem, then repeat the entire run specimen and control samples.

In practice 1_2s rule has been used for rejection of test when n = 1. Whenever control value exceeds 2s limit repetitive measurement of control and patient sample should be done. When a second or repeated control value is outside 2s control limit, there must be true rejection and problem solving procedure should be started.

Westgard Multirule chart

The Multirule procedure developed by Westgard and associates uses multiple control rules for interpreting control data. The procedures require a chart having lines for control limits drawn at the mean ± 1s, 2s and 3s. This chart drawing is similar to levey-Jennings chart.

      

      The following control rules are used. 

  

Rule	Description
12s	One control observation exceeding the mean ± 2s Warning sign, control data should be tested by other control rules, do not report the result
13s	One control observation exceeding the mean ± 3s Rejection, due to random error
22s	Two consecutive control data exceeding  same mean  plus 2s or mean minus 2s Rejection, systematic error
R4s	One observation exceeding mean plus 2s and another exceeding mean minus 2s Rejection, random error. Note: this rule applies only within a run not between runs.
41s	Four consecutive observations exceeding mean plus 1s or mean minus 1s Rejection, systematic error
10x	Ten consecutive control data falling on one side of mean (above or below) Rejection, systematic error

CONTROL OF ANALYTICAL VARIABLES

CONTROL OF ANALYTICAL VARIABLES

Analytical error contributes to 20-30% of total error. Many analytical variables must be controlled carefully to assure accurate measurements by analytical methods. Reliable analytical methods are obtained by a careful selection, evaluation, implementation, maintenance and control. Certain variables like water quality, calibration of analytical balances, calibration of volumetric glassware and pipettes, stability of electrical power and temperature of heating baths, refrigerators, freezers, and centrifuges should be monitored as they will affect analytical methods. The control of analytical variables includes,

Choice of analytical methodology – 

Before implementing the method, the overall performance of the method should be checked in the given setting. Those methods whose performance is satisfactory and that is cost effective must be used. Also the reagent stability for long time is desirable.

Reference material and calibration – 

The quality of calibrators and the calibration procedures used are major factors in determining the reliability of the analytical values. The highest quality methods, the definitive methods are to be used to validate reference method and primary reference material. The highest quality reference materials, namely primary reference materials should be used in the development and validation of reference method, calibration of definitive and reference methods, and the production of secondary reference materials. Reference methods should be used to validate field methods. Secondary reference material produced from reference method should be used to provide working calibrators for field methods and to assign values to control materials. Control materials are used only to monitor field methods. Method validation or external quality assurance of field method should be done by reference method and method validation of reference method should be done by definitive method.

Reference material (RM): A material or substance whose property are sufficiently homogenous and well established to be used for calibration of apparatus, assessment of method, or assigning values to materials.

Certified reference material (CRM): A Reference Material, accompanied by a certificate, whose property is certified by a procedure

Calibration material and/or calibrator: A material or device of known characteristics (e.g. concentration, activity, intensity, and reactivity) used to calibrate, graduate, or adjust a measurement procedure or to compare the response obtained with the response of a test specimen and/ or sample.

Control material: A device, solution, or preparation, or pooled collected from human or animal specimen or artificially derived material, intended for use in quality control process.

Documentation of analytical protocols: 

Reproducibility of the method should be maintained by maintaining written protocol or method and procedure manuals. These includes procedure name, clinical significance, principle of method, specimen requirement (volume, patient preparation, interference, minimum requirement for acceptance, criteria of rejection, etc), reagents and equipment’s (including standards along with suppliers, instruction for preparation), procedure (process of performing test, QC procedures, calculations), reference values (may be stratified, should include nature of population studies), comments (variable affecting test like pH temperature, effect of drugs, hazards, safety precautions, etc.), references (to literatures).

Monitoring technical competence: 

Proper training of laboratory personnel to achieve uniformity in technique is important. Personnel should be training for new method.

Inventory control of materials - 

Procedures is necessary to inventory materials and initiate orders when supplies are low. When materials are stable and changes in lot numbers cause problems, large stocks should be maintained. Adequate inventory should be maintained to allow time for additional shipment and testing of additional supplies. Along with the inventory management, the quality of materials purchases should be monitored when they are received like expiry date, adequate volume, control materials, manufactured date, storage condition, etc.

Control of analytical quality using stable control material and control charts

The performance of analytical methods is routinely monitored by analyzing specimens whose concentrations are known followed by comparing the observed values with the known values. The known values are usually represented by an interval of acceptable values, or upper and lower limits for control. When the observed values fall within the control limits, the analyst is assured that the analytical method is functioning properly. When the observed values fall outside the control limits, the analyst should be alerted to the possibility of problems in the analytical determination.

Control material: 

Specimens analyzed for QC purpose are called control materials. They must be stable, available in aliquots or vials. There should be little vial to vial variation so that differences between repeated measurements are attributed to the analytical method alone. The control material should preferably have the same matrix as the test specimens of interest (e.g., a protein matrix may be best when serum is the test material to be analyzed). 

Materials from human sources are preferred but due to some risk of hepatitis infection bovine materials offer a certain advantage in safety are readily available. The concentration of analyte should be in normal and abnormal ranges, corresponding to concentrations that are critical in the medical interpretation of the test results. These control material are supplied as liquid or lyophilized materials they are reconstituted by adding water or diluent solution. Assayed control materials come with a list of values for the concentrations that are expected for that material. Control material for internal quality control can be made by pooling serum samples, screen for presence of infective disease, adjusted to pH 7.1 by concentrated sulphuric acid, aliquot and freeze. Each day 1 vial is taken brought to room temperature and tested.

CONTROL OF PRE-ANALYTICAL VARIABLES

CONTROL OF PRE-ANALYTICAL VARIABLES

Most of the error (>50%) which leads to variation and bias in the result are due to Preanalytical errors and identification Preanalytical components and controlling them to maintain the quality report includes,

System analysis:

The laboratory process starts from the time a physician request for a test to the time of final interpretation of the test result and this whole step constitutes the system. System analysis can identify error prone steps which should be paid most attention. Various error during the process are listed below

a.     Test ordering – the potential errors are inappropriate test, handwriting not legible, wrong patient identification, etc.

b.   Specimen collection – Incorrect tube or container, incorrect patient identification, inadequate volume, invalid specimen (e.g., hemolyzed or too dilute), collected at wrong time, improper transport conditions.

c.    Analytical measurement – Instrument not calibrated correctly, specimen mix up, incorrect volume of specimen, interfering substances present, and instrument precision problem.

d.      Test reporting – wrong patient identification, report not posted in chart, report not legible, report delayed, and transcriptional error.

e.    Test interpretation – interfering substances not recognized, specificity of test not understood, precision limitations not recognized, analytical sensitivity not appropriate, previous value not available for comparison, report not legible.

Types of Preanalytical variables

Monitoring the Preanalytical variables requires the coordinated effort of many individuals and hospital departments, each of which must recognize the importance of these components in quality of service. Variable to consider include the following:

Test usage and practice guidelines – 

The need of test and its cost effectiveness should be identified. There must be guidelines that indicate which tests are needed according to the availability and frequency of requests. There must be careful monitoring of test requests and their appropriateness so that laboratory can make adjustment according to the test requested.

Patient identification –

Identification of patients and specimen is a major concern for laboratories. Most of the errors occur due to improper identification of patient. One method for checking identification is to compare identifiers such as patient’s name, hospital number. The identification on the specimen label should also correspond with the identification on the requisition form. The integration of bar code technology into the analytical systems has significantly reduced identification problems.

Turnaround time – 

Delayed and lost test requisitions, specimens and reports have been major problems for laboratories. An essential feature for monitoring the cause of delay is the recording of the actual time of specimen collection, recipient in the laboratory and reporting test results. Listing of delayed specimens also provide a powerful mechanism for detecting lost specimens or reports. System analysis to identify the areas causing delays and disruption in service can help to address the problem. Laboratory should have record of patient tests so that if the report is lost then it can be retrieved anytime in need.

Laboratory logs– 

Once the serum tube arrives in the laboratory, various logging and monitoring systems are necessary. One should check that patient name and identification number and the test requested on the form match the information on the label of the specimen tube. The specimen should be inspected to confirm adequacy of volume and freedom from problems such as lipaemia or hemolysis. The specimens are then stored appropriately and identification information and arrival time are recorded in master log. After analysis, the results are recorded on the worksheet, and if both the assay and the individual test results pass the QC criteria, the test results are transferred to the result forms for reporting. Transcription error should also be checked and recorded.

Transcription errors – 

Transcriptional errors are more if manual system is there for entry of data. Computerization can reduce these errors.

Patient preparation – 

Proper patient preparation is essential to reduce error. Controllable variables that can affect the result should always be aimed to control. For each and every analyte laboratory should have proper procedures and guidelines for patient preparation. Before collecting samples these procedures should be given to patient either orally or in written form. E.g. fasting specimen, collecting timed urine, collection of blood for catecholamines, etc.

Specimen collection – 

There should be proper guidelines for specimen collection for various analytes. For example prolonged tourniquet application causes local anoxia to cells and this cause small solutes like potassium to leak from cells, protein concentrates in that area which give erroneous results. Blood collected from an arm into which an intravenous infusion is running can be diluted or contaminated. 

Hemolysis occurring during and after collection alters the concentration of analyte. Improper containers and incorrect preservatives greatly affect test results. One way to control this is to have a specially trained laboratory team assigned to specimen collection. Errors detected by limit checks, delta checks (difference between consecutive results on individual patients), or other algorithms should be recorded. Adequacy of specimen during collection should also be considered which will eliminate resampling.  

Specimen transport – 

There should be proper guidelines for specimens transport other wise there may be delay in getting the specimen and analytes alters. Also during transportation specimen may go to wrong location, especially if it is not well labeled. So there must be proper mechanism and person assigned for specimen transport in time. Patients should not be allowed to carry themselves the specimens. In controlling specimen transport, the essential feature is the authority to reject specimen that arrive in the laboratory in an obviously unsatisfactory condition (such as hemolyzed, thawed specimen that should have remained frozen, etc.).

Specimen separation and aliquoting – these includes

·     Centrifuge performance – the centrifuges should be in good condition and this is checked by monitoring speed, timer and temperature

·   

 Container monitoring – collection tubes, pipettes, stoppers, and aliquot tubes are sources of calcium and trace metal contamination.  Cork stoppers should not be used on specimens intended for calcium determinations because false elevations may occur. Plastic containers can adsorb trace amounts and should not be used for substances in low concentration, such as parathyroid hormone.

·    

 Personnel monitoring – The personnel who process the laboratory specimens should be carefully trained and supervised. A procedure manual should be available in processing desk. A performance of processing personnel should be checked. An important part of checking performance is throughput time (number of jobs per given time, or output per given input in given time), which can be calculated if one records the specimen arrival time and time when processing is completed. In optimizing the efficiency of a specimen processing laboratory, there is trade off between the time it takes to record and check parameters and the error rates or inconsistencies of the function. 

TOTAL QUALITY MANAGEMENT (TQM) OF THE CLINICAL LABORATORY

TOTAL QUALITY MANAGEMENT (TQM) OF THE CLINICAL LABORATORY

(Taken from Tietz Textbook of Clinical Chemistry, 4th Edition)

A QLP includes analytical process, general policies, practices, and procedures that are carried out in laboratory and which are required for proper functioning of laboratory. How is laboratory functioning?

QC emphasizes statistical control procedures and non statistical check like linearity checks, reagent and standard checks, temperature monitors, etc. Quality controls represents those techniques and procedures that monitor performance parameters, it helps to monitor sources or error, estimates, the magnitude of errors, and alert laboratory personnel when there are indications that quality has deteriorated, under varying operating conditions. QC consists of tools required to maintain quality. How is the reliability and accuracy of the work and report maintained?

QA (also called proficiency testing) is concerned with broader measure and monitors of laboratory performance, such as turnaround time, specimen identification, patient identification, and test utility. QA encompasses the whole system. QA is done by identification of problem through QI and elimination of problem through QP. How to assure the lab procedure are reliable and accurate?

QI provides a problem solving process for identifying the root cause of a problem and identifying a remedy for the problem. What are the problems faced and how it is dealt?

QP is necessary to standardize the remedy, establish measures for monitoring performance, ensure that the performance is within quality requirement and document new QLP. How problems are dealt?

The new process is then implemented through QLP, measured and monitored through QC and QA, improved through QI, and replanned through QP.

The five-Q framework defines how quality can be managed using PDCA cycle (plan, do, check and act). QP provides the planning step, QLP establishes standard processes for doing things, QC and QA provide measures for checking how well things are done, and QI provides a mechanism for acting on those measures. It provides mechanism or process to be followed to attain the quality requirement.

ELEMENTS OF QUALITY ASSURANCE PROGRAM

Quality assurance program consists of broad spectrum of practices, plans and procedures that will assure (to be sure and confident) that the quality will be maintained. There are several essential elements of quality assurance program.

Commitment:

Dedication to quality service must be the first priority. A true commitment is required by laboratory directors, managers, and supervisors as well as laboratory personnel if the efforts are to be successful.

Facilities and resources:

Laboratory should have administrative support, adequate space, equipment, materials, supplies, staffing, budgeting resources, etc. These facilities and resources should be encouraging to all the persons involved in the service.

Technical competence:

Highly skilled personnel are essential for high quality service. The educational background and experience of all personnel are important. In service training programs helps to develop required skills and competence in running quality laboratory service.

Technical procedures

High quality technical procedures are necessary to provide quality laboratory services. There are 4 groups of technical procedures to be maintained in high quality for quality service.

a.      Control of Preanalytical conditions or variables like test request, patient preparation, patient identification, specimen collection and transportation, specimen processing, preparation of work lists and logs, maintenance of records, labeling of specimen, etc.

b.      The control of analytical variables, like analytical methodology, standardization and calibration procedures, documentation of analytical procedures, monitoring of equipment etc.

c.       Monitoring of analytical quality by using statistical methods and control charts

d.      Control post analytical variables like report delivery, transcription, proper dispatch procedure, etc.

Problem solving mechanism:

There must be a mechanism to identify the problem and implement the solution by making necessary adjustment. These includes in service training programs from QC specialist, frequent use of quality control programs, involvement in External quality assessment program and proficiency testing programs. Administration must heighten the interest of laboratory workers, there must be smooth supply of resources, incentives for good working, providing techniqual skills to handle instrumental problems, etc.

Five Phases of Six Sigma process

Lean and six sigma process consists of five phages described by DMAIC (Define, Measure, Analyze, Improve, and Control).

Define: 

This process involves determining who uses the service or products. E.g. physicians, what are the user’s requirements and expectations? E.g. emergency department will require fast turnaround time, what are the project boundaries? This might involve establishing who might be involved in this project and what are the areas covered. E.g. phlebotomy to draw the specimen. By the end of define phase, both the project team and management have validated the project plans and policies; this means that the team will have defined the overall purpose and potential impact of project, the scope of the project, its resources (who is in the team and how much money is available to implement changes), and expectations-what will be delivered and when.

Measure: 

The performance of process in laboratory can be measured by

Collecting data – in lab, this might involve determining how long it takes to report the results, what is the turnaround time, etc.

Determine defects – For e.g. this might include determining how often specimens are collected in wrong tube, how much is the delay in receiving specimen, random and systematic errors, etc.

Satisfaction of user – this might involve determining if the needs of physicians or other users are being met.

In this phase the teams measures, and assesses the baseline process. E.g. if the goal was to improve the time it takes phlebotomist to get specimens from hospital inpatient population to the laboratory then the measure phase would consist measuring the time it is taking and assessing which step is defective. The measure phase allows the team to measure the severity of the problem. This also measures the bias and errors in laboratory results and methods.

Analyze: 

Examine the data collected during measure phase, regarding error in the process during the measure phase. In this phase the team verifies the root cause of problem using different QC programs and data analysis. The details of the process whether they are running according to the guideline or given criteria are also analyzed and verified.

Improve: 

Improve the process by creative solutions to fix problems and prevent future ones from occurring. At the end of this phase, a team must demonstrate that a process change has been implemented that address the error found in the analyze phase and solves the problem evaluated in the measure phase.

Control: 

This may include continuous monitoring of the new plans. This phase ensures that the quality is maintained by QC mechanisms. For this the QC charts can be used, specimen collection and delivery should be monitored so that there is no delay in delivery, collection of sample according to the guideline should be monitored, etc. Proper functioning of instrument should be assured.

LABORATORY ERROR AND THE LEAN SIX SIGMA PROCESS

LABORATORY ERROR AND THE LEAN SIX SIGMA PROCESS

Originally lean and six-sigma were separate ideas designed to achieve two related metrics: time and error. Lean was designed to eliminate non-value-adding steps and six sigma aimed to reduce variation in process. The Lean Six Sigma projects comprise the Lean's waste elimination projects and the Six Sigma projects based on the quality characteristics of a process. Lean is a process adopted to eliminate waste from a process, first practiced and then formalized into the Toyota Production System.

The objective of lean was to reduce time; the objective of six-sigma was to reduce error. Today both are combined to form lean and six-sigma. Lean six sigma measures the amount of non-value adding steps in a process to reduce variation and improve performance of a process, as part of its core metrics. A process sigma represents the capability of a process to meet (or exceed) the process requirements. It reflects the number of defects (errors) per million opportunities (DPMO). The sigma refers to the number of SDs from the mean a process can be before it is outside the acceptable limits. E.g. if sodium has six sigma performance, then the mean could shift by six SDs and till meet the laboratory requirements. A 6 sigma process has narrow process SD and produces only 3 errors for every million tests. A 3 sigma process has much wider SD and produces about 26,674 errors per million tests.

There are various ways to calculate the sigma of a process. In order to calculate the sigma, defects must be clearly defined. The most straight forward method uses the process yield-the percentage of times that a process is defect free. Another simple method is to calculate the DPMO. Both of these methods then require finding the process sigma on the process sigma chart.

 
Six sigma metrics can be plotted graphically. This chart incorporates many of the measures like total allowable error for given analyte or given process, systematic error and imprecision. Systemic error and imprecision are derived from the COM experiments and AE is by the specification given by CLIA regulations. 

Six-sigma is an evolution in quality management which is widely implemented in business and industry in new millennium. Six sigma metrics are being adopted as universal measure to maintain quality in process. The principles of six-sigma go back to Motorola’s approach to TQM in the early 1990s. This means that variation upto 6 sigmas or 6 standard deviations should fit within the tolerance limits for the process; hence the name six sigma. For this development Motorola won the Malcolm Baldridge Quality award in 1988.

 

Six sigma provides a framework for evaluating process performance and process improvement to reduce variation. The goal for process performance is illustrated in figure below which shows the quality requirements for that measurement or process.

Any process can be evaluated by determining how many sigmas fit within the tolerance limits. There are two methods for assessing process performance in terms of sigma metric. One approach is to measure outcome by inspection. The other approach is to measure variation and predict process performance.

Conversion to sigma metric is done by using standard table available. In health care organization a defect rate of 0.033% (333 DPM) is considered excellent, where error rates from 1% to 5% are often considered acceptable. A 5.0% (50000 DPM) error rate corresponds to 3.15 sigma performance and 1.0% error rate corresponds to 3.85 sigma. Six sigma shows that the goal should be error rate of 0.1% (4.6 sigma) to 0.01% or 100 DPM (5.2 sigma) and ultimately 0.001% (5.8 sigma).

The application of sigma metrics for assessing analytical performance uses the variable obtained during method validation studies, like accuracy, precision, PPP, NPP, sensitivity, specificity parameters and that available from internal and external quality control processes.

For the particular method for given analyte, the allowable error, method bias, method CV, can be obtained from external quality assessment programs or regulatory requirements (like US Clinical Laboratory Improvement Amendment [CLIA] criteria for acceptable performance in proficiency testing). Process variation and bias can be estimated from method validation experiments, peer comparison data, proficiency testing results and routine QC data.

In the laboratory sigma performance of the method can be determined from imprecision: SD or CV and inaccuracy (bias) observed for a method and quality requirement (allowable total error, TE_a) for the test. [Sigma = (TE_a – bias)/SD]. Sigma metric from 6.0 to 3.0 represents the range from best case to worst case respectively. Methods with sigma performance less than 3 are not considered acceptable for production.