Pre-analytical quality indicators in laboratory medicine: Performance of laboratories participating in the IFCC working group “Laboratory Errors and Patient Safety” project

2019 ◽  
Vol 497 ◽  
pp. 35-40 ◽  
Author(s):  
Laura Sciacovelli ◽  
Giuseppe Lippi ◽  
Zorica Sumarac ◽  
Isabel Garcia del Pino Castro ◽  
Agnes Ivanov ◽  
...  
Author(s):  
Laura Sciacovelli ◽  
Giuseppe Lippi ◽  
Zorica Sumarac ◽  
Jamie West ◽  
Isabel Garcia del Pino Castro ◽  
...  

AbstractThe knowledge of error rates is essential in all clinical laboratories as it enables them to accurately identify their risk level, and compare it with those of other laboratories in order to evaluate their performance in relation to the State-of-the-Art (i.e. benchmarking) and define priorities for improvement actions. Although no activity is risk free, it is widely accepted that the risk of error is minimized by the use of Quality Indicators (QIs) managed as a part of laboratory improvement strategy and proven to be suitable monitoring and improvement tools. The purpose of QIs is to keep the error risk at a level that minimizes the likelihood of patients. However, identifying a suitable State-of-the-Art is challenging, because it calls for the knowledge of error rates measured in a variety of laboratories throughout world that differ in their organization and management, context, and the population they serve. Moreover, it also depends on the choice of the events to keep under control and the individual procedure for measurement. Although many laboratory professionals believe that the systemic use of QIs in Laboratory Medicine may be effective in decreasing errors occurring throughout the total testing process (TTP), to improve patient safety as well as to satisfy the requirements of International Standard ISO 15189, they find it difficult to maintain standardized and systematic data collection, and to promote continued high level of interest, commitment and dedication in the entire staff. Although many laboratories worldwide express a willingness to participate to the Model of QIs (MQI) project of IFCC Working Group “Laboratory Errors and Patient Safety”, few systematically enter/record their own results and/or use a number of QIs designed to cover all phases of the TTP. Many laboratories justify their inadequate participation in data collection of QIs by claiming that the number of QIs included in the MQI is excessive. However, an analysis of results suggests that QIs need to be split into further measurements. As the International Standard on Laboratory Accreditation and approved guidelines do not specify the appropriate number of QIs to be used in the laboratory, and the MQI project does not compel laboratories to use all the QIs proposed, it appears appropriate to include in the MQI all the indicators of apparent utility in monitoring critical activities. The individual laboratory should also be able to decide how many and which QIs can be adopted. In conclusion, the MQI project is proving to be an important tool that, besides providing the TTP error rate and spreading the importance of the use of QIs in enhancing patient safety, highlights critical aspects compromising the widespread and appropriate use of QIs.


Author(s):  
Laura Sciacovelli ◽  
Mauro Panteghini ◽  
Giuseppe Lippi ◽  
Zorica Sumarac ◽  
Janne Cadamuro ◽  
...  

AbstractThe improving quality of laboratory testing requires a deep understanding of the many vulnerable steps involved in the total examination process (TEP), along with the identification of a hierarchy of risks and challenges that need to be addressed. From this perspective, the Working Group “Laboratory Errors and Patient Safety” (WG-LEPS) of International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) is focusing its activity on implementation of an efficient tool for obtaining meaningful information on the risk of errors developing throughout the TEP, and for establishing reliable information about error frequencies and their distribution. More recently, the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) has created the Task and Finish Group “Performance specifications for the extra-analytical phases” (TFG-PSEP) for defining performance specifications for extra-analytical phases. Both the IFCC and EFLM groups are working to provide laboratories with a system to evaluate their performances and recognize the critical aspects where improvement actions are needed. A Consensus Conference was organized in Padova, Italy, in 2016 in order to bring together all the experts and interested parties to achieve a consensus for effective harmonization of quality indicators (QIs). A general agreement was achieved and the main outcomes have been the release of a new version of model of quality indicators (MQI), the approval of a criterion for establishing performance specifications and the definition of the type of information that should be provided within the report to the clinical laboratories participating to the QIs project.


Diagnosis ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Wilson Shcolnik ◽  
Fernando Berlitz ◽  
Cesar Alex de O. Galoro ◽  
Vinicius Biasoli ◽  
Rafael Lopes ◽  
...  

AbstractObjectivesIn the laboratory medicine segment, benchmarking is the process in which institutions seek to compare with the macro environment (performance comparison and best practices with different laboratories) and improve their results based on quality indicators. The literature has highlighted the vulnerability of the pre-analytical phase in terms of risks and failures and the use of interlaboratory comparison as an opportunity to define a strategic performance benchmark aligned with the laboratory medicine sector, which has been a promising strategy to ensure continuous improvement, identifying within the pre-analytical process the critical activities to guarantee patient safety. In this context, this paper aims to present the three-year experience (2016–2018) of the Benchmarking Program and Laboratory Indicators – in Portuguese, Programa de Benchmarking e Indicadores Laboratoriais (PBIL) – with emphasis on pre-analytical indicators and their comparison against literature references and other programs of benchmarking in the area of laboratory medicine. PBIL is organized by the Brazilian Society of Clinical Pathology/Laboratory Medicine (SBPC/ML) in conjunction with Controllab and coordinated by a Brazilian group with representatives from different countries.MethodsThe data presented in this paper involving the performance results of 180 laboratories with active participation. Results are presented in percentage (%, boxplot graphical in quartiles) and Sigma metric, recognized as the metric that best indicates the magnitude of failures in a process. The Pareto Chart was used to facilitate ordering and to identify the main errors in the pre-analytical phase. The Radar Chart was made available in this work for the purpose of comparing the results obtained in Sigma by the PBIL and IFCC Working Group Laboratory Errors and Patient Safety (WG LEPS).ResultsIn the study period, just over 80% of the pre-analytical failures are related to Blood culture contamination (hospital-based and non-hospital-based laboratories), Recollect and Non-registered exams, with failure rates of 2.70, 1.05 and 0.63%, respectively. The performance of the PBIL program participants was in line with the literature references, and allowed to identify benchmarks in the laboratory medicine market, target of PBIL, with best practices were observed for some indicators.ConclusionsThe results of the program demonstrate the importance of an ongoing program comparative performance-monitoring program for setting more robust goals and consequently reducing laboratory process failures. Even with these promising premises and results, the contextualized analysis of the program indicators, point to a still significant number of failures in our market, with possibilities for improvement in order aiming to ensure more robust and effective processes.


2021 ◽  
Vol 9 (2) ◽  
pp. 64-70
Author(s):  
Arumalla VK ◽  
Chelliah S ◽  
Madhubala V

Background: Pre-analytical errors account for up to 70% of all the errors made in laboratory diagnostics which are mostly not directly under laboratory control. Laboratories across the world have been using different Quality indicators (QIs) for identifying and quantification of pre-analytical errors. Objective of the present study is to identify the different pre-analytical errors with their frequency and to assess the pre-analytical phase performance of emergency laboratory by using harmonized Quality Indicators and six sigma metrics. Methods and material: A prospective observational study was conducted from January 2019 to December 2019 to monitor the inappropriateness of samples and test request forms. We have quantified the performance of pre-analytical phase of our emergency laboratory based on the harmonized QIs proposed by The International Federation of Clinical Chemistry Working Group on Laboratory Errors and Patient Safety (IFCC- WGLEPS) and six sigma metrics. Results: Emergency laboratory received a total of 55431 samples during Jan- 2019 to Dec- 2019. Number of pre-analytical errors were 1089 which accounted for 1.96% of total samples received. Haemolysed samples, clotted samples and samples with insufficient volume were contributed to 37%, 26% and 15% of the total pre-analytical errors respectively. Conclusions: Pre-analytical phase performance of our emergency laboratory complies with the quality specifications laid by the International Federation of Clinical Chemistry Working Group on Laboratory Errors and Patient Safety (IFCC-WGLEPS). Implementation of harmonised QIs assures the comparability of laboratory findings with different laboratories across the world.


2012 ◽  
Vol 31 (3) ◽  
pp. 174-183 ◽  
Author(s):  
Nada Majkić-Singh ◽  
Zorica Šumarac

Quality Indicators of the Pre-Analytical PhaseQuality indicatorsare tools that allow the quantification of quality in each of the segments of health care in comparison with selected criteria. They can be defined as an objective measure used to assess the critical health care segments such as, for instance, patient safety, effectiveness, impartiality, timeliness, efficiency, etc. In laboratory medicine it is possible to develop quality indicators or the measure of feasibility for any stage of the total testing process. The total process or cycle of investigation has traditionally been separated into three phases, the pre-analytical, analytical and post-analytical phase. Some authors also include a »pre-pre« and a »post-post« analytical phase, in a manner that allows to separate them from the activities of sample collection and transportation (pre-analytical phase) and reporting (post-analytical phase). In the year 2008 the IFCC formed within its Education and Management Division (EMD) a task force calledLaboratory Errors and Patient Safety (WG-LEPS)with the aim of promoting the investigation of errors in laboratory data, collecting data and developing a strategy to improve patient safety. This task force came up with the Model of Quality Indicators (MQI) for the total testing process (TTP) including the pre-, intra- and post-analytical phases of work. The pre-analytical phase includes a set of procedures that are difficult to define because they take place at different locations and at different times. Errors that occur at this stage often become obvious later in the analytical and post-analytical phases. For these reasons the identification of quality indicators is necessary in order to avoid potential errors in all the steps of the pre-analytical phase.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Hilal Aksoy ◽  
Abdullah Ozturk ◽  
Dilek Tarhan ◽  
Ibrahim Dolukup ◽  
Duygu Ayhan Baser

Abstract Objectives Our aim in this study is to provide information about the rate of errors in the process of the biochemistry laboratories in the hospitals in Turkey with the “Indicators”. Methods The hospitals calculate their own data according to the indicator cards defined by the Ministry of Health of Turkey and enter into the system once in a year. In this study we examined the quality indicators related to the disruptions in the biochemistry laboratory of hospitals for the year of 2018. Results All indicators except “Non-timely reported result rate in biochemistry laboratory” are found to be significantly higher in university hospitals. This indicator is found to be significantly higher in private hospitals(p:0.030) “Lost sample rate in biochemistry laboratory” is found to be significantly higher in Eastern Anatolia Region (p:0.000) and “Non-timely reported result rate in biochemistry laboratory” is found to be significantly higher in Aegean Region (p:0.008). Conclusions The ratio of non-timely reported result rate is the most seen disruption in biochemistry laboratories. It may be due to lots of reasons; lack of biochemistry equipment, lack of staff, problems in transportation, etc. The management of hospitals and the staff should take measures and regulations about problems.


Author(s):  
Jamie West ◽  
Jennifer Atherton ◽  
Seán J Costelloe ◽  
Ghazaleh Pourmahram ◽  
Adam Stretton ◽  
...  

Preanalytical errors have previously been shown to contribute a significant proportion of errors in laboratory processes and contribute to a number of patient safety risks. Accreditation against ISO 15189:2012 requires that laboratory Quality Management Systems consider the impact of preanalytical processes in areas such as the identification and control of non-conformances, continual improvement, internal audit and quality indicators. Previous studies have shown that there is a wide variation in the definition, repertoire and collection methods for preanalytical quality indicators. The International Federation of Clinical Chemistry Working Group on Laboratory Errors and Patient Safety has defined a number of quality indicators for the preanalytical stage, and the adoption of harmonized definitions will support interlaboratory comparisons and continual improvement. There are a variety of data collection methods, including audit, manual recording processes, incident reporting mechanisms and laboratory information systems. Quality management processes such as benchmarking, statistical process control, Pareto analysis and failure mode and effect analysis can be used to review data and should be incorporated into clinical governance mechanisms. In this paper, The Association for Clinical Biochemistry and Laboratory Medicine PreAnalytical Specialist Interest Group review the various data collection methods available. Our recommendation is the use of the laboratory information management systems as a recording mechanism for preanalytical errors as this provides the easiest and most standardized mechanism of data capture.


Author(s):  
Per Hyltoft Petersen ◽  
Callum G Fraser ◽  
Lone Jørgensen ◽  
Ivan Brandslund ◽  
Marta Stahl ◽  
...  

At a conference on ‘Strategies to Set Global Analytical Quality Specifications in Laboratory Medicine’ in Stockholm 1999, a hierarchy of models to set analytical quality specifications was decided. The consensus agreement from the conference defined the highest level as ‘evaluation of the effect of analytical performance on clinical outcomes in specific clinical settings’ and the second level as ‘data based on components of biological variation’. Here, the many proposals for analytical quality specifications based on biological variation are examined and the outcomes of the different models for maximum allowable combined analytical imprecision and bias are illustrated graphically. The following models were investigated. (1) The Cotlove et al. (1970) model defining analytical imprecision (%CVA) in relation to the within-subject biological variation (%CVw-s) as: %CVA≤ 0·5 × %CVW-S (where %CV is percentage coefficient of variation), (2) The Gowans et al. (1988) concept, which defines a functional relationship between analytical imprecision and bias for the maximum allowable combination of errors for the purpose of sharing common reference intervals. (3) The European Group for the Evaluation of Reagents and Analytical Systems in Laboratory Medicine (EGE Lab) Working Group concept, which combines the Cotlove model with the Gowans concept using the maximal acceptable bias. (4) The External Quality Assessment (EQA) Organizers Working Group concept, which is close to the EGE Lab Working Group concept, but follows the Gowans et al. concept of imprecision up to the limit defined by the model of Cotlove et al. (5) The ‘three-level’ concept classifying analytical quality into three levels: optimum, desirable and minimum. The figures created clearly demonstrated that the results obtained were determined by the basic assumptions made. When %CVW-S is small compared with the population-based coefficient of variation [%CVp = (%CV2W-S +%CV2B-S)1/2], the EGE Lab and EQA Organizers Working Group concepts become similar. Examples of analytical quality specifications based on biological variations are listed and an application on external quality control is illustrated for plasma creatinine.


2019 ◽  
Vol 57 (6) ◽  
pp. 822-831 ◽  
Author(s):  
Rui Zhou ◽  
Yali Wei ◽  
Laura Sciacovelli ◽  
Mario Plebani ◽  
Qingtao Wang

Abstract Background Quality indicators (QIs) are crucial tools in measuring the quality of laboratory services. Based on the general QIs of the Working Group “Laboratory Errors and Patient Safety (WG-LEPS)” of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC), specific QIs have been established in order to monitor and improve the quality of molecular diagnostics, and to assess the detection level of associated disease. Methods A survey was conducted on 46 independent commercial laboratories in China, investigated using questionnaires and on-site inspections. Specific QIs established were mainly based on the specific laboratory work-flow for molecular diagnoses. The specific QI results from three volunteer laboratories were collected and used to validate their effectiveness. Results Of the 46 laboratories participating in the study, 44 (95.7%), conducted molecular diagnostics. Of 13 specific established QIs, six were priority level 1, and seven, priority level 3. At pre-evaluation of data from the three volunteering laboratories, it was found that the newly classified specific QIs had outstanding advantages in error identification and risk reduction. Conclusions Novel specific QIs, a promising tool for monitoring and improving upon the total testing process in molecular diagnostics, can effectively contribute to ensuring patient safety.


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