Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N35/00—Automatic analysis not limited to methods or materials provided for in any single one of groups G01N1/00 - G01N33/00; Handling materials therefor
  • G01N35/00584—Control arrangements for automatic analysers
  • G01N35/00594—Quality control, including calibration or testing of components of the analyser
  • G01N35/00613—Quality control
  • G01N35/00623—Quality control of instruments
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
  • G16H40/20—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
  • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis

Definitions

• Pathology service has benefited in recent years by improvements in the availability of sensitive medical instrumentation for performing medical analysis, an increased number of options for sources of pathology services and system integration through implementation of laboratory networks to permit transferability of patient data.
• the benefits of these advances in pathology service have also provided benefits for clinicians in treating patients, which treatment may sometimes be driven by the results of tests conducted on medical instrumentation.
• Continued improvements in patient care are sought by standardizing laboratory practice among different medical laboratories to permit increased transferability of patient data.
• Patient data transferability encompasses the concepts of precise and consistent results in relation to patient tests obtained from different analytical instruments or laboratories. That is, if patient data is to be transferrable, comparably correct and agreeing results for patient tests should be obtained from different analytical instruments and/or laboratories or pathology service networks .
• Comparing analytical results between instruments and/or laboratories can thus provide a measure of compliance with quality or performance standards to help ensure and improve quality of patient care.
• Techniques to measure and control variability between instruments and/or laboratories have been attempted previously, such as those based on the above-discussed models, but with limited or inconsequential success.
• Analytical variability between instruments and laboratories is a consequence of differences in the conditions that apply when analysis is performed.
• variability is assessed by repeating analysis of a sample, referred to as a QC (Quality Control) or Control sample.
• Statistical analysis of the results quantifies the variation within the data set. Parameters calculated in the statistical analysis are used to compare performance of different instruments.
• the process described forms the basis of the Internal Quality Control (IQC) Program administered by the Laboratory .
• IQC Internal Quality Control
• the IQC process is helpful in comparing the same analytical method but provides no indication as to whether the performance is sufficiently good to report clinically useful results. Result accuracy is assessed by External Quality Assessment (EQA) programs where the result values, reported by different laboratories using the same method, are compared. The procedure is labor intensive and time consuming which increases as the number of instruments increases .
• IQC performance is typically measured using Westgard rules, which represent a measure of quality control for which test results may be accepted or rejected.
• Westgard rules or multi- rules, are typically implemented so that violation of one quality control rule, such as a control measure falling more than two standard deviations outside the mean, triggers the application of other, potentially more stringent, quality control rules. The sum of the rules produces a quality control measure for a given test or instrument to determine if test results are acceptable or not.
• Westgard or multi-rules are typically implemented m software packages that facilitate collection and graphical display of quality control data. These types of software packages typically use the concept of total error (TE) for application to a specific analytical test to determine quality performance.
• TE total error
• Westgard rules are sometimes used to alert an analyzer operator to trends in numerical data where graphical displays are impractical.
• Westgard rules are associated with automated procedures used to accept or reject test results based on agreed upon rules that may be established by professional consensus, for example.
• TE calculation often uses EQA data to ensure precise and consistent results in determining quality control performance.
• Software packages that implement IQC do not typically provide a linkage for EQA input, which is otherwise provided on a manual input basis. Accordingly, calculation of TE for each test on every analyzer in a laboratory or network becomes significantly difficult.
• EQA data is not implemented in conjunction with software packages that provide IQC processes, laboratories typically apply general Westgard rules to all tests on every analyzer in the laboratory, regardless of test performance.
• general Westgard rules may not produce sufficiently specific quality control performance criteria for each test that is performed on each analyzer.
• the benefits that can be obtained through the application of Westgard or multi- rules can be significantly reduced in the absence of EQA data, resulting in an increased false rejection rate for IQC processes, leading to additional or increased costs and delays in producing results .
• EQA programs represent another aspect of determining quality control performance for analytical instruments and laboratories.
• EQA programs typically call for ongoing testing, for example, to establish how an instrument or laboratory may generally deviate from a norm.
• the results of EQA programs are typically used to calculate TE for a given test, instrument or laboratory, for example.
• EQA testing calls for the running of a base analyte for a given test, instrument or laboratory to determine how the results compare with expected results.
• the expected results may be developed based on widespread testing results for the same analyte, or established norms for test results based on general agreement in the scientific and analytical chemistry community.
• EQA programs provide an estimate of method bias, which can be used to determine whether the test, instrument or laboratory is obtaining results that are consistent with previous results, or other instruments or laboratories.
• EQA program results are typically used in conjunction with IQC processes to determine TE for a given test, instrument or laboratory to determine compliance with quality control standards that may be developed by agreement of scientists or analytical chemists. Accordingly, as with the IQC process issues mentioned above, EQA program results can impact overall quality performance for pathology services .
• EQA programs face several challenges with respect to precision, that is, whether the test, instrument or laboratory returns a correct result.
• One difficulty with EQA programs is that different programs use different statistical methods, and are therefore not directly comparable with respect to the results obtained.
• some laboratories in the United Kingdom use an EQA program operated under the National Quality Assurance Advisory Panel (NQAAP) , which is designed for stand alone laboratories, rather than as a comprehensive program.
• NQAAP National Quality Assurance Advisory Panel
• Some EQA programs have implemented quality control standards that have gradually evolved as analytical capabilities and quality have improved. However, as the quality standards are gradually narrowed or limited in their specification, outlying results, which may represent valid data, are judged to be outside of the more narrow quality constraints.
• the quality specifications for the EQA program tends to be influenced by performance variations observed with the largest group representing a single technology.
• the present disclosure provides systems and methods for analyzing and managing quality performance for a comprehensive group of entities performing pathology services .
• the present disclosure provides for centrally collecting raw data obtained from individual tests, medical instrumentation or analyzers, laboratories and/or hospitals.
• the centrally collected raw data is formatted and manipulated in accordance with statistical techniques and performance standards to obtain quality performance data.
• the quality performance data can be shared across medical networks and can be presented in graphical format along with established guidelines for quality performance.
• the systems and methods provide quality performance data that is consistent in accordance with specified levels of precision and/or bias .
• data is centrally collected in accordance with specified times or events on an ongoing basis.
• the centrally collected data is thus kept current to provide a certain level of quality performance measurements on an ongoing basis.
• the process of collecting data can be fully automated, thus reducing the complexity and time associated with the prior production of quality performance measures .
• the centrally collected raw data is formatted as XML for improved transportability.
• the XML formatted data can be provided to various points in a medical network or through an intranet or the Internet to present quality performance data to a variety of end users .
• the systems and methods of the present disclosure provide graphical plots to illustrate quality performance for various medical instruments over a period of time.
• the various plots provided can be manipulated to obtain the formatted or raw data used to create the plots.
• the disclosed systems and methods may also provide a tiered quality performance plot for instrument quality performance that shows, for example, minimal/maximal, desirable and/or optimal quality performance levels .
• QC results are generated by each instrument in a laboratory network.
• Instrument QC data is sent to linked servers
• the servers transfer the data to a central computer system on a scheduled basis.
• the central computer system collates data from the instruments and data from an external quality assessment program.
• the raw data is processed to generate a structured data source which is then used as the basis for generating summary data tables and graphic displays.
• the disclosed systems and methods can provide improved and more uniform quality performance measures across a significant span of tests, instruments, laboratories and/or hospitals to enhance and permit the transferability of patient data.
• the disclosed systems and methods permit improved performance monitoring and feedback for laboratories and hospitals on an aggregate basis to improve consistency and quality performance, all of which tends to improve patient care.
• Fig. 1 is a diagram of a quality management system in accordance with an exemplary embodiment of the present disclosure
• Fig. 2 is a table illustrating quality performance values for listed analytes
• Fig. 3 is a display of an error report for quality performance management in accordance with an exemplary embodiment of the present disclosure
• Fig. 4 is a display of quality data and calculations relevant to determining quality performance measures
• Fig. 5 is a Levey Jennings plot showing the results of analyte quality performance over a number of analyzers
• Fig. 6 is a three-level plot showing precision versus time in accordance with an exemplary embodiment of the present disclosure.
• Fig. 7 is a flowchart illustrating an exemplary embodiment of the present disclosure.
• the present application claims benefit of British Provisional Application No. 1015230.4, filed September 13, 2010, entitled A METHOD AND SYSTEM FOR MANAGING ANALYTICAL QUALITY IN NETWORKED LABORATORIES, the entire contents of which are hereby incorporated herein by reference.
• the present disclosure derives measurements for quality performance based on experiential or evidence-based data collected from analytical instrumentation on an ongoing basis. The data is used to calculate quality performance measures in relation to a given reference population for a reference interval to determine how individual tests, instrumentation, laboratories and/or medical service provider networks agree with target or expected results.
• Software implementing the present disclosure can use the CentraLink Data Management System supplied by Siemens Corporation.
• the quality performance is intended to permit the reporting of a clinically useful result to the physician.
• the determination of the analytical quality is conducted on the basis of scientific studies used to determine the "biological variation" of an analyte within an individual and the wider population.
• One outcome of this research is a performance specification for "in-vitro" diagnostic tests, which details the degree of accuracy and precision that should be attained for an analytical method to correctly assess the clinical status of the patient.
• a review of the origins and practices related to determining quality performance follows.
• Medical laboratory test results are used for a number of purposes, including diagnosis, screening and monitoring. Single point test results are typically compared with population-based reference intervals, or nationally or locally derived values that are typically based on expert decisions. The use of medical laboratory test results that are used for monitoring are often directed to observing patient status as the patient progresses through a medical condition. Patient monitoring thus represents a challenge for maintaining analytical consistency with serial tests being performed over long periods of time with multiple instruments in a network of laboratories and/or hospitals. The presence of analytical variation in precision or bias between instruments tends to negatively impact test utility for diagnostic and monitoring purposes.
• the reference population is preferably homogenous and the criteria for reference subject inclusion is preferably clearly defined.
• the preferred minimum number for a confidence interval of 90% is at least 120 reference subjects and the calculation is preferably performed according to International Federation of Clinical Chemistry (IFCC) recommendations.
• IFCC International Federation of Clinical Chemistry
• the analytical variation during the period of production and application of reference intervals is preferably tightly controlled because a loosely or uncontrolled analytical process may result in a disagreement between the reference interval and the population to which it is applied.
• the reference interval of a test is the product of within- subject-biological-variation (CVi) and the between-subject- biological-variation (CV G ) of the reference population, the pre- analytical and the analytical variability (CV A ) at the time of the reference interval establishment. While attempts are usually made to reduce the pre-analytical errors during the establishment of a reference interval, the analytical bias, the method imprecision and within-subject-biological-variation contribute to the population and method specific reference interval. Therefore, a significant change in the analytical performance of a method can have a marked effect on the validity of a derived reference interval .
• a change in a method bias tends to alter the proportion of the reference population outside each reference limit, resulting in an increase/decrease in the rate of false positive/negative cases.
• An increase in the method imprecision tends to widen the reference interval and increase the overlap between the diseased and healthy populations.
• the effect of changes in the method bias and imprecision on the validity of a shared reference range indicates that varying the two parameters causes the proportion of patients falling outside the reference intervals to be significantly altered.
• maintaining the bias as a proportion of CV B indicates that the integrity of the reference intervals is protected.
• analytical performance of methods used to derive reference intervals should be monitored by laboratory networks.
• analytical performance By proactively monitoring analytical performance, analytical variation within the same laboratory or between laboratories within the same laboratory network can be minimized.
• results of tests with high individuality such as those tests with a low CV I /CV G ratio, for example creatinine tests, can exhibit significant change but still lie within the reference interval.
• tests with low individuality such as those with a high C I /CV G ratio, for example iron tests, can have a change in test result that spans the entire reference interval without significance .
• RCV reference change value
• RCV 2 1/2 (CVi 2 + CV A 2 ) 1 2
• CV X indicates the intra-individual biological variation
• CV A indicates the analytical imprecision
• Z is a constant that relates to probability.
• Movement of patients between network sites means patients samples are being measured on multiple instruments. Each instrument has a degree of imprecision and bias.
• the RCV value, after including the analytical bias ( ⁇ ) as a variance, can be expressed as follows .
• Total analytical error (TE A ) is calculated as method bias plus Z times the method imprecision, as follows.
• B M represents bias to the method mean.
• a value for Z is typically chosen to represent a 95% probability level for a population, which typically sets Z at a value of 1.65.
• the biological variation based total error is derived from a three level model, discussed in greater detail below. The total error biological is calculated in accordance with the following equation.
• TE A 1.65 * (kCVi ) + k' (CVi + CVQ )
• n 150 % TE A
• Table 1 illustrates a TE measurement for six different immunoassay analyzers (Centaur XP) with locations that were distributed over three different sites. Each analyzer was provided with calibration material and reagents using identical batch material. One-hundred and fifty patient samples were measured on each analyzer. Passing and Bablock conversion equations were established for the results of each analyzer. The average percent total error was calculated for each analyzer as shown in Table 1. The size of the total error percentage shows a significant variation among the different analyzers. The significant variation causes a widening of the reference interval from 10-25 pmol/L to 8-26 pmol/L, resulting in a loss of diagnostic performance. If the reference interval is to be maintained, the percent total error should be less than about 6.3%.
• the method bias should be less than 0.125 * (CVi 2 + CV G 2 ) 1 2 and the precision should be less than 0.35 * CVi .
• a predetermined optimal limit for percent total error for these analyzers with these tests is approximately 4.9%, which is less than the above-noted 6.3%, suggesting that the previously determined "optimal" limit is adequate to protect the transferability of the reference interval.
• a quality monitoring program can be defined with acceptable limits for analytical performance using the above discussed concepts.
• the performance characteristics are compared objectively to well documented analytical goals.
• the same set of standards and quality specifications are preferably applicable to all analytical components of a given network.
• a number of models that provide stable specifications have been proposed. While the present disclosure may use any of a number of models, the specifications based on a biological variation endorsed by a conference in Sweden in 1999 sponsored by the International Union of Pure and Applied Chemistry, the World Health Organisation and the IFCC are used for the following examples. It should be apparent that the present disclosure is not limited to this model, and that other models or combination of models may be used with comparable results.
• One of the goals of the selected model is to determine whether global strategies for laboratory quality could be established.
• the conference consensus statement created a hierarchy for the five available quality specification models: clinical requirement; specifications based on biological variation; specifications based on professional recommendations; Quality specifications by EQAS organizers; and published data on the state of the art.
• the next model in the hierarchy is the biological variation based quality specification.
• This model is probably the most widely accepted candidate.
• the biological variation model can, however, be used as a guide and to calculate the maximum bias and precision allowable to protect the validity of the shared reference interval.
• a three level model permits more flexibility by considering what is achievable by the current technology.
• This model is operational and can provide stable quality specifications for a network.
• One major advantage for the biological variation model is that a database of biological variation is available for a large number of tests. Almost 316 tests have been scrutinized and collated by various researchers. In the application of quality performance to a network of medical service providers, this model can have a wide range of application.
• the three level model has been used in quality planning for internal quality control procedures. It is used in the present disclosure to derive acceptable limits for performance in EQA. It can also be used to set limits for satisfactory analytical performance when a new method or instrument is evaluated. This model also has the potential to be used for new equipment tenders .
• the three levels model has an important feature over all other biological variation models in that it permits a scale to be formed that allows the acceptable quality goal to become more stringent with time and with improvement in technology. Allocation of a test to a level of the model need not be permanent, and is preferably considered to be dynamic to permit allocation to change with improvement of analytical performance. Whenever a test achieves a stable level of performance, an upgrade for the specification can be applied.
• the system may be limited in relation to the lack of availability of biological variation for some analytes and the observation that the biological variation for some analytes is higher in disease than in health. However, for the present, the biological variation in health has been chosen because it will result in more stringent analytical goals. Gender-related biological variation is not available for some hormones e.g.
• the IQC control limits in accordance with the present disclosure are based on the biological variation model. Similar standardized practice with similar limits for each analyzer in the network have also been specified. Furthermore, the systems and methods of the present disclosure have effectively introduced real time monitoring for the method precision by rerunning patient samples if the IQC measurement exceeded 2SD.
• the analytical TE value can be used for Westgard rules derivation.
• the software implementing the disclosed systems and methods can calculate and evaluate the analytical total error, which provides several advantages. First, the availability of total error data can permit the derivation of analyte specific Westgard rules where the analytical TE is greater than biologically derived TE in a consistent manner. Second, the software can help identify which component of TE, i.e. bias and imprecision, requires attention. For example, for those assays with inherent bias, the laboratory may achieve acceptable performance by improving the analytical precision.
• the model based on biological variation describes three acceptable levels of performance, optimal, desirable and minimal/maximal. Each level allows different combinations of imprecision and bias and allows different fixed percentages of the population to fall outside the reference limits for level of bias. Data for the biological variation for almost all analytes in mainstream biochemistry is available and can be used to calculate the quality specifications for imprecision bias and total error.
• an exemplary embodiment of the disclosed systems and methods uses a network of three teaching hospitals and associated district general hospitals that serves a population of 1.3 million.
• the core laboratories have identical hardware and assay methods.
• This network is used to develop and maintain IQC limits based on biological variation specifications in accordance with the present disclosure.
• the process provides several components, including a comparison of attained analytical CV A with allowable C B .
• the biological CV is calculated in accordance with the three level model that includes optimal, desirable and minimal/maximal levels. The CV A is then compared across the three level model to determine quality performance.
• Fig. 2 details the performance of 32 analytes reviewed for development of quality performance criteria. Imprecision of 25 general chemistry and 17 immunoassay analytes were monitored by analyzing two levels of commercial quality control (QC) material daily over a period of 3 months. The imprecision for the 32 analytes is evaluated against target imprecision derived from objective criteria based on biological variation. The number of QC data points for each analyte ranged from 600 to 1,200 for general chemistry analytes and from 90 to 140 immunoassay analytes. The entries in Fig. 2 illustrate low IQC data, which is potentially more relevant since better performance is typically harder to achieve at lower IQC levels. The entries presented in bold represent a current level of performance, while the entries with an asterisk represent temporarily out of control assays.
• the target goal for each analyte is selected on the basis of studying the probability at 95% interval that the dispersion around a measurement at the three levels of performance is significant.
• the formula for calculating the analytical goal follows.
• T is the time interval between doses
• T is the half life of the drug.
• the dispersion that encompasses 95% of values is calculated as follows.
• the derivation of the analytical goal also considers the clinical preferences for a test.
• the biological variation model describes three levels of allowable performances.
• the level selected is preferably based on the recommendation of the use of a test in the clinical setting. For example, recommendations may be provided indicating the use of lower goals, such as may be relevant in some tests, e.g. cholesterol.
• the analytical goals are used for precision where the CV B is used as a target value in a monthly IQC performance evaluation process.
• the analytical goals are used for the use of the bias data (bias to our method mean B M ) by evaluation for each analyte obtained from EQA against bias values derived for the three levels model.
• the total analytical error (TE A ) is calculated for every analyte on a monthly basis, using CV A and B M . TE A can then be evaluated against the total error derived from the biological variation criteria.
• IQC and EQA data are collected from all the sites of the medical services network.
• the system permits the application of the same analytical goal for all the laboratories in the network and provides a statistical tool to analyse the analytical data, wherever the test is performed in the network .
• FIG. 1 illustrates an architecture 100 for implementing the disclosed systems and methods.
• a central network 110 includes a database 112 to hold all quality indicators (IQC and EQA) .
• the capacity of database 112 is sufficient to store 10 years data, for example.
• Central network 110 which may be implemented as a single computing device, houses software used to capture the IQC data on a regular basis from analyzers 120-123 in respective core biochemistry laboratories 130-133. This data is then automatically imported into a file accessible to the pathology network's information system 140.
• the file is formatted as an XML file to provide a structured data source.
• the software can present the data from the file in graphical format 144 and tabular format 142, 146, 148.
• IQC tables 142 can give a cumulative summary of the calculation of the monthly mean, SD, number of outliers outside 4 SD and the reagent lot number (which may be entered manually or automatically recorded) for every test/analyzer 120-123 in core biochemistry laboratories 130-133.
• the software also provides access to the IQC raw data tabulated in a spreadsheet, for example. This raw data may be used for further statistical analysis to help in troubleshooting poor performance. Navigation tools provide easy access to all results stored in database 112 and allow performance to be monitored over time. QC data is retrievable by individual instrument, date or control lot number.
• Fig. 1 also illustrates the electronic or automatic input of EQA data 150.
• EQA program data is provided in collaboration with a number of sources, such as, for example, the UK NEQAS Scheme Organisers.
• the collection of EQA data can be done electronically, preferably in a pre-agreed format that is compatible with the central database 112.
• the software compares the method bias against the allowable bias derived from the three levels model, for example the bias to the method mean is calculated.
• the software compares all EQA performance indicators for all laboratories within the network against a modified EQA quality specification in accordance with the present disclosure and against each other to generates a summary report .
• the current EQA limits for EQA performance indictors can be modified to use the method group range as the upper and lower limits for acceptable performance in terms of EQA. This process provides relatively narrow limits to help obtain early detection of poor performance.
• a representative tabular display 300 provides quality performance data that is generated on a monthly basis.
• Display 300 provides a list of tests that have exceeded an assigned permitted limit for TE .
• Display 300 includes buttons 310, 312 that can be actuated to respectively provide a data table display or a graphic display, represented in Figs. 4 and 5-6, respectively. In this way, a user can obtain pertinent quality performance data quickly and easily, while also having the option of reviewing data used to formulate the quality performance measures as well as graphical data for quality performance.
• a tabular data display 400 illustrates data derived from network analyzers, as well as calculated values for quality performance measures.
• display 400 lists a number of different tests 402 that are selectable by a user to review data related to the given test.
• Display 400 can provide raw data 404 collected in a spreadsheet format for further review by the user.
• the calculated values for CV% derived from the standard deviation and mean, as well as CV- B%, which is the biological variation (CV%) from performance specification data can also be provided.
• Display 400 also provides Bias%, which is the percentage difference between mean and method mean for EQA data. Also provided is Bias-B%, which is the allowable Bias% from performance specification data.
• Calculated total error percent TE% is also displayed as derived from CV%t and Bias%.
• Total error bias percent, TE-B% is also provided as the allowable total error percent from performance specification data. Display 400 thus provides a wealth of information related to the experiential or evidentiary results for specific tests on specific instruments, which can be grouped or aggregated based on a number of criteria, such as tests that are common to different instruments, instruments as a group, laboratory network, date or other grouping arrangements.
• a Levey-Jennings plot 500 shows quality performance data for a set of instruments 510-517.
• a Levey-Jennings plot is a quality control graphical presentation of deviation from a mean, and typically shows representative standard deviations to indicate how much the data deviates from the mean.
• Display 500 shows an example of a Levey-Jennings Graph generated by the software of the present disclosure.
• the representative example of display 500 is for a glucose mean measured on eight analyzers 510-517 over a six month period.
• the IQC target mean is 3.5 mmol/L and the analytical goal is determined as the desirable level.
• the biological CV for glucose at the desirable level is 2.9%.
• Each point on the plot of display 500 represents the mean of 200-400 points per month.
• the shaded areas represent 1SD, 2SD and 3SD.
• the single Westgard rule of 1 2S D is used. No patient data is reported for display 500 if the standard deviation is greater than 2SD .
• a graphical plot display 600 is shown as an example of a precision graph generated by the software of the present disclosure.
• the example shows glucose precision over a time interval.
• the different shaded regions represent the optimal (bottom) , desirable (middle) and minimal/maximal (upper) quality performance measures for the glucose test.
• the benefit of display 600 is that instruments with poor performance can be noted easily and quickly, which can lead to rapidly implemented corrective measures.
• Display 600 shows the monthly mean of each test/analyzer 510-517 to give the cumulative monthly mean within a time window.
• the software compares the IQC performance for every test over all sites against the allowable performance and presents this with SD limits derived from allowable CV B . These graphs enable the comparison of IQC performance for each test on all sites at a glance.
• Optimal performance analytical imprecision ⁇ 0.25 CVi and bias ⁇ 0.125 (CVi 2 +CV G 2 ) 1 2 .
• Desirable performance analytical imprecision ⁇ 0.5 CVI and bias ⁇ 0.25 (CVi 2 +CV G 2 ) 1 2 .
• Minimal/Maximal performance analytical imprecision ⁇ 0.75 CVi and bias ⁇ 0.375 (CVi 2 +CV G 2 ) 1 2 .
• the biological variation based total error is derived from the three levels model.
• the analytical error (TE A ) is calculated as bias plus Z times of the method imprecision.
• the Z multiplier is typically chosen at the 95% probability level, being 1.65.
• FIG. 7 a flowchart 700 of an exemplary embodiment of the present disclosure is illustrated.
• the embodiment shown in Fig. 7 is a process for monitoring and managing quality performance in a medical services network, and begins with the collection of IQC and EQA data, as illustrated in block 710.
• IQC data is typically collected on an ongoing basis from instrumentation in the medical services network, such as by the activation of a script that may execute on a time or event driven basis.
• the script can cause data stored m the various instruments to be retrieved and forwarded to a central computer for collection and processing.
• the central computer for example, can generate a structured data source from the data, to improve the formatting and transferability of the information, as illustrated in block 712.
• the data may be structured in XML format to permit the transfer of data or data presentation to HTML endpoints or other structured data presentation mechanisms.
• IQC and EQA data can be used to calculate total error TE for a given test, instrument, laboratory or medical services network. Such calculations may be made based on reference intervals related to populations of significance to the test. The calculations may be used to determine parameters for quality performance measures, such as may be provided by Westgard rules.
• the resulting data, as well as the raw or structured data can be presented to an end user in a particular format, as is illustrated in block 716.
• the presentation format may include tabular data for a given test, instrument, laboratory or network, and may be aggregated based on a time interval, a mean or other types of measures that may have significance for quality performance.
• the software used to implement the disclosed systems and methods can be used to generate reports on a monthly basis. Examples of such reports include:
• Example 1 The effect of imprecision at the three levels on serum sodium at low QC level (110 mmol/L) .
• the current data field represents the variation over a 3 month period across 9 separate analyzers.
• Example2 The effect of imprecision at the three levels on glucose mean at 7 mmol/L.
• Glucose is reported to one decimal point through the whole range and at concentration of 7 mmol/L is used diagnostically .
• the current performance lies within the desirable level, however, the optimal level is the ideal target to achieve for clinical decision making.
• Desirable 29 12.5 7.87-6.13 ( 7.9- 6.1)
• the present disclosure provides a number of advantages, including the ability to reduce the number of EQA reports from about 400 reports each month for all the sites to two per month.
• the IQC data from all sites can also be collated and presented in one report, permitting the possibility of significant efficiencies in monitoring and managing quality performance.
• the default page of the data table (see Fig. 3) is a list of tests that have exceeded the permitted limit for Total Error along with hyperlink buttons that can generate a data table or graphic display for the indicated test.
• Pathology is entering a new era and probably facing a number of challenges that may affect the way the pathology service is delivered.
• Current medical practice is increasingly becoming more evidenced based and ever increasing numbers of clinical strategies are being established for diagnosis monitoring and screening. It is becoming more important to delineate techniques to define quality specifications for the analyses required to support these clinical developments.
• the present disclosure meets the needs for quality performance for the present environment, while incorporating flexibility that permits solutions for future pathology service configurations.
• the present disclosure also relates to a device or an apparatus for performing these operations.
• the apparatus can be specially constructed for the required purpose, or the apparatus can be a general-purpose computer selectively activated or configured by a computer program stored in the computer.
• various general-purpose machines employing one or more processors coupled to one or more computer readable medium, described below, can be used with computer programs written in accordance with the teachings herein, or it may be more convenient to construct a more specialized apparatus to perform the required operations.
• the disclosed system and method can also be embodied as computer readable code on a computer readable medium.
• the computer readable medium is any data storage device that can store data, which can thereafter be read by a computer system. Examples of the computer readable medium include hard drives, read-only memory, random-access memory, CD-ROMs, CD-Rs, CD-RWs, magnetic tapes and other optical and non-optical data storage devices.
• the computer readable medium can also be distributed over a network- coupled computer system so that the computer readable code is stored and executed in a distributed fashion.

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