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Extra-analytical quality indicators and laboratory performances
Accepted Manuscript Extra-analytical quality indicators and laboratory performances
Laura Sciacovelli, Ada Aita, Mario Plebani PII: DOI: Reference:
S0009-9120(17)30121-2 doi: 10.1016/j.clinbiochem.2017.03.020 CLB 9510
To appear in:
Clinical Biochemistry
Received date: Revised date: Accepted date:
7 February 2017 23 March 2017 24 March 2017
Please cite this article as: Laura Sciacovelli, Ada Aita, Mario Plebani , Extra-analytical quality indicators and laboratory performances. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Clb(2017), doi: 10.1016/j.clinbiochem.2017.03.020
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Extra-analytical Quality Indicators and Laboratory Performances
Laura Sciacovelli, Ada Aita, Mario Plebani.
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Department of Laboratory Medicine, University Hospital, Padova (Italy).
Corresponding Author: Laura Sciacovelli Department of Laboratory Medicine University Hospital Padova (Italy) [email protected]
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ACCEPTED MANUSCRIPT Abstract In the last few years much progress has been made in raising the awareness of laboratory medicine professionals about the effectiveness of quality indicators (QIs) in monitoring, and improving upon, performances in the extra-analytical phases of the Total Testing Process (TTP). An effective system for management of QIs includes the implementation of an internal assessment system and participation in inter-laboratory comparison. A well-designed internal assessment system allows the identification of critical activities and their systematic monitoring. Active
laboratory with respect to that of other participating laboratories.
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participation in inter-laboratory comparison provides information on the performance level of one
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In order to guarantee the use of appropriate QIs and facilitate their implementation, many
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laboratories have adopted the Model of Quality Indicators (MQI) proposed by Working Group “Laboratory Errors and Patient Safety” (WG-LEPS) of IFCC, since 2008, which is the result of
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international consensus and continuous experimentation, and updating to meet new, constantly emerging needs.
Data from participating laboratories are collected monthly and reports describing the statistical
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results and evaluating laboratory data, utilizing the Six Sigma metric, issued regularly. Although the results demonstrate that the processes need to be improved upon, overall the
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comparison with data collected in 2014 shows a general stability of quality levels and that an improvement has been achieved over time for some activities. The continuous monitoring of QI
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data allows identification all possible improvements, thus highlighting the value of participation in the inter-laboratory program proposed by WG-LEPS. The active participation of numerous laboratories will guarantee an ever more significant State-of-
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safety.
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the-Art, promote the reduction of errors and improve quality of the TTP, thus guaranteeing patient
Key words: quality indicators; extra-analytical phases; laboratory errors; patient safety.
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Introduction Since the extra-analytical phases of the Total Testing Process (TTP) are known to be more errorprone than intra-analytical phase, it has become a priority for laboratory professionals to use adequate procedures that reduce the risk of errors in these phases, as well as a system ensuring continuous improvement, and enhancement of patient safety [1-5]. Laboratory performance in its entirety hinges on quality of pre-analytical activities, which has a
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direct impact on intra-analytical procedures and post-analytical activities. The performance characteristics of intra-analytical procedures, long monitored by internationally recognised tools,
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such as internal quality control (IQC) procedures and External Quality Assessment
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(EQA)/Proficiency Testing (PT), have stimulated continuous improvement [6-8]. In the last few years, awareness of laboratory medicine professionals has been raised concerning the
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effectiveness of quality indicators (QIs) in monitoring and improving performance in the extraanalytical phases [9-11]. Of course, their efficacy as a tool depends on their correct utilization, calling for: adequate identification and definition of each and every indicator; definition of a
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standardized and systematic data collection procedure; integration of QIs in a coherent strategy of quality improvement; use of QI data to identify appropriate improvement actions. A strong
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commitment of laboratory professionals in using this tool is prerequisite to promoting improvement projects since data on quality indicators do not per se improve quality. Only the correct
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interpretation of, and reaction to, data can clarify the nature of errors (root cause analysis) thus leading to corrective and preventive actions.
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The use of QIs covering the entire TTP, required by the International Standard for Accreditation of laboratories ISO 15189:2012, is widely accepted as a process and strategy assisting risk
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identification and management [12-15]. In order to comply with the requirements of International Standard ISO 15189 and to improve the performance of extra-analytical phases, many laboratories have implemented QIs on the basis of their own health care context, purpose and goals, and number and typology of patients [16-18]. However, it is difficult to establish the current error rate because QIs from different laboratories yield incomparable data. The scientific community is now striving to identify the best possible procedure for managing and harmonizing the use of QIs in laboratory medicine [19-21]. In order to guarantee an effective QI management system, implementation of an internal assessment system and participation in inter-laboratory comparison have to be included. A well-designed internal assessment system allows the identification of critical activities and their systematic Pagina 3 di 16
ACCEPTED MANUSCRIPT monitoring, and active participation in inter-laboratory comparison provides information on the performance level of one laboratory compared with that of other participating laboratories.
Internal Assessment System Aim of internal assessment system is to design an operative flow that guarantees appropriate definition and utilization of QIs that successfully keep critical lab activities under control and raise awareness in the laboratory staff concerning the need to adequately manage QIs data. The final
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purpose is to reduce the error rate and improve laboratory performance [22]. The system comprises
a) Definition of a list of QIs that includes the following steps.
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the following.
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- Identification of critical activities included in each phase of TTP.
- Formulation of each indicator, paying attention to correct identification of events to keep
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under observation and undesirable events to be measured. Because most QI data are expressed as percentages, it is important to verify congruity between the data of undesirable events measured (numerator) and events kept under observation (denominator).
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- Composition of the list of QIs considering that a single QI can be monitored by different measurements but, in order to prevent misinterpretation at data analysis, the same
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measurement cannot be used to monitor different QIs. It is also important to ensure that the QIs listed are representative of the extra-analytical phases, which are more error-prone.
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b) Design of a document for each indicator, describing details for the correct understanding and use of QIs. This raises awareness of laboratory staff concerning the importance of QIs and the steps to take for their correct management. The laboratory staff are responsible for describing
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the rationale of a single indicator, the procedure to use for a correct data collection, improving upon defined goals, establishing accountability for each step required (collection, processing
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and analysis of data; implementation of improvement actions), all of which are pivotal for the effectiveness of the system. c) Issuing a Standard Operating Procedure describing all steps, from the identification of QIs to the revision of the entire system, is the key to ensuring that the staff perform all operative steps in a standardized and correct fashion, and guaranteeing them independently by the operator. d) Using a computerized application for recording all undesirable events (e.g. unsuitable samples, data entry errors, reporting delays, etc.) as this overcomes all problems inherent to manual recording, which is also time consuming and requires human resource [23-24]. IT software not only saves time, but also data collection, guaranteeing greater accuracy (all data for collection are actually collected), standardization (results comparable over time), traceable (staff Pagina 4 di 16
ACCEPTED MANUSCRIPT awareness enhanced), and effective (data analysis easier and more timely). For example, the recording of the identification code of a patient sample through the reading of barcode on the sample label allows operators to record all patients and sample details through a single and simple act (Fig. 1). Moreover, the processing and analysis of collected data are simpler and more reliable. e) Using a computerized application to record and visualize the QI data in order to keep their trend under control over time, facilitate data analysis data and identify any actions required.
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In Figure 1 is reported the computerized system used in the Department of Laboratory Medicine of Padova (Italy), in which it is possible to record all undesirable events by all operators in all
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locations of the laboratory. All details of the specific operator and features of samples are recorded.
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In particular:
name, surname, data of birth, identification code related to patient;
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typology of sample, required tests;
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error type;
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possible comments;
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identification code of operator.
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All recorded information can be printed in a list and processed for the statistical elaboration on the
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basis of typology of event. This system has proven effective, saving time and assuring the
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annotation of all events to be recorded.
Participation in Inter-laboratory Comparison In the intra-analytical phase, the monitoring and evaluation of examination procedures in which
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EQA/PT are used to highlight the use of inadequate IQC procedures, diagnostic systems out of control or with unsuitable performances, insufficient training or competence of staff and so forth.
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Likewise, the inter-laboratory comparison for QIs allows assessment of QI management in the internal system of the laboratory. Thanks to participation in an inter-laboratory comparison, the laboratory can verify how its performance level, measured by its internal assessment system, compares with that of other laboratories using the same QIs. Different inter-laboratory comparisons have been implemented worldwide (e.g. Australia, Brazil, China), while in other countries (e.g. UK) only a few surveys have been conducted [25-28]. An inter-laboratory comparison designed by the Working Group “Laboratory Errors and Patient Safety” (WG-LEPS) of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC), used since 2008, provides a Model of Quality Indicators (MQI) and reporting system through a dedicated website (www.ifcc-mqi.com) [29]. Data Pagina 5 di 16
ACCEPTED MANUSCRIPT from participating laboratories are collected monthly and reports describing the statistical results and evaluating laboratory data utilizing the Six Sigma metric issued regularly; the exhaustive information provided enables the laboratory to evaluate the quality level of its processes thanks to a sigma value calculation and to compare its sigma value with the mean sigma value of participants in its Country and of all participants. It is used the short-term sigma that corresponds to defect rate that would be observed if the process were to shift as much as 1.5 sigma (differently by the long-term sigma that corresponds to the defect rate if the process were properly centered). The sigma value
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enables analysis and measurement of the capacity of processes to obviate the production of defects because a six sigma process is virtually a “perfect process” (3.4 defects per million), and three
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sigma is defined the typical quality for processes [30]. Moreover, it is possible to verify the trend
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over time and the frequency distribution of results and sigma values. On the basis of the information provided, the laboratory can identify areas requiring improvement actions, define priorities in
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improvement actions and verify the extent of improvement achieved. However, the limitation of sigma value is that it does not allow assessing what is the extent to which the error rate can be
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tolerated.
In order to guarantee the use of appropriate QIs and facilitate their implementation, many
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laboratories have adopted the MQI proposed by WG-LEPS, that is the result of international consensus and continuous experimentation, and updating to meet new, constantly emerging needs.
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A preliminary MQI was developed in 2008 and tested under real conditions by involved laboratories in order to check the suitability of each QI, and the feasibility of the reporting system. Findings during use the experimental phase, and the list of QIs were discussed in the Consensus
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Conference held in Padova in 2013 (“Harmonization of quality indicators: why, how and when?”), a preliminary consensus being achieved on terminology, rationale, purpose of each and every QI and
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the procedures for data collection. A reviewed and approved MQI issued after the Consensus Conference, has been tested since 2014 through an External Quality Assurance Program (EQAP). Some QI definitions have been modified to facilitate laboratory professionals’ understanding of the role of each indicator. Some QIs have been added and others deleted. A priority score (1, the highest priority) has been assigned to each indicator in order to help laboratories gradually introduce QIs into routine practice, and the reporting system has been simplified to allow homogeneous data collection. A criterion to identify Quality Specifications (QSs) for the assessment of a laboratory’s performance has also been proposed. The reviewed and approved MQI includes 25 quality indicators and 53 measurements: 11 QIs and 28 measurements for the preanalytical phase; 4 and 6 for the intra-analytical phase; 5 and 11 for the post-analytical phase; 3 and Pagina 6 di 16
ACCEPTED MANUSCRIPT 5 for Support processes; 2 and 3 for Outcome Measure [11, 31-33]. The MQI was tested until a further Consensus Conference was held in Padova on 26th October 2016, entitled “Harmonization of quality indicators in Laboratory Medicine: two years later?”. In the last meeting, all findings, arisen in the use of MQI, were discussed in order to identify all possible improvements and achieve a consensus towards harmonization of QIs.
Extra-analytical Performances
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It is widely accepted that the performance of a clinical laboratory is assessed on the basis of degree of imprecision and bias for the intra-analytical phase, and the error rate or number of undesirable
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events measured on using QIs, for the extra-analytical phases [34].
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QI data are representative of extra-analytical performances, and in order to guarantee their reliability, they must come from international laboratories using harmonized QIs that have been
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defined after scientific consensus has been gained. Moreover, they must be managed within an internal assessment system that guarantees: standardized and systematic data collection; effective analysis and evaluation; identification of improvement actions; staff awareness of the importance of
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the correct use of QIs; sound ethical conduct. Only then can data from different laboratories be considered comparable, processed together and, finally, represent the State-of-the-Art linked to the
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error rate of the extra-analytical phases. In this context, QI data collected and processed by WGLEPS can provide a reliable picture of the extra-analytical performances. Overall, data were
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received from 59 laboratories (that consistently participate in the MQI project) in Argentina, Austria, Brazil, Estonia, Germany, Great Britain, India, Italy, Republic of China, Republic of Croatia, Spain, Switzerland, Serbia, Uruguay. Figure 2 reports the mean of sigma values calculated
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on the basis of results collected in 2014 and 2016, for the pre-analytical phase and Figure 3 for the post-analytical phase. Figure 4 reports the turnaround time (TAT), measured from sample receipt to
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the delivery of results, at the 90th percentile related to 4 tests. Although the results demonstrate that the processes need to be improved upon, overall the comparison with data collected in 2014 shows a general stability of quality levels and that an improvement has been achieved over time for some activities. The continuous monitoring of QI data allows identification all possible improvements, thus highlighting the value of participation in the inter-laboratory program proposed by WG-LEPS. Moreover, an evaluation can be made of the utility of sigma values, provided in the report, in estimating the risk probability in risk management procedures, in particular in the analysis of root cause (e.g., Failure Mode and Effect Analysis – FMEA) [13, 15].
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ACCEPTED MANUSCRIPT The achievement of consensus on the list of harmonized QIs is preliminary to the identification of achievable and realistic performance targets. In fact, the Six Sigma metric allows an assessment to be made, based on error frequency, of the level of quality of the processes involved. The definition of the performance specifications for each indicator indicates the extent to which the error rate can be tolerated, given that a goal of “zero defects” is not always achievable. A criterion for defining quality specifications has been proposed, and is currently being tested with a view to evaluating QI data [11, 32].
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In line with the claims of the 1st Strategic conference of the European Federation Clinical Chemistry and Laboratory medicine (EFLM) held in Milan in 2015, the definition of performance
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specifications for extra-analytical phases should follow the same models as that for intra-analytical
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ones: clinical outcome, biological variability, and State-of-the-Art. The State-of-the-Art is the more feasible criterion because no data are available on clinicians’ opinion as a criterion for establishing
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the performance specifications, and the biological variability is not applicable to QI data [34]. The awareness of a need for improvement of extra-analytical quality is great. Performance specifications, established on the basis of State-of-the-Art, have enabled many laboratories to attain
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achievable goals and promote the improvement.
There is consensus about the identification of State-of-the-Art concerning the intra-analytical
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performances from EQA/PT results. Similarly, QI data from international EQAP are representative
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of the State-of-the-Art related to extra-analytical performances.
Conclusion
The use of QIs in Laboratory Medicine is an effective quality assurance tool if managed within a an
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appropriate internal assessment system, and in compliance with harmonization criteria gained through participation in an inter-laboratory comparison, such as that proposed by WG-LEPS [35].
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An international survey demonstrated that laboratory professionals are aware of the need to use QIs in order to comply with ISO 15189 requirements (97%), and most participating laboratories systematically measure QIs for misidentification errors (patients or samples), unsuitable samples, and TAT. Only 52% of laboratories state that they are aware of the project on QIs proposed by WGLEPS despite the fact that it has been widely divulged at International Congresses and in scientific publications (35). The involvement of national Scientific Societies and Accreditation bodies and EQA/PT providers in raising awareness of this project is mandatory. The active participation of numerous laboratories will guarantee an ever more significant State-of-the-Art, promote the reduction of errors and improve quality of the TTP, thus guaranteeing patient safety.
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ACCEPTED MANUSCRIPT Recording of non conformities Intranet website Recording of non conformities
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Traceability of operator with username and password
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Category (i.e. sample, results, request, etc.) Identified event (i.e. haemolysed)
Laboratory area involved
Barcode
Type of event
Lab area
Note
Operator code
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If necessary, a comment can be entered
Sample code identification
Figure 1 – Recording of non conformities by a computerized application used in the Department of Laboratory Medicine of Padova (Italy)
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Pre-analytical phase 6
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Sigma value
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Transcription errors Transcription errors
Wrong Wrongcontainer container
2014
4.67 (4.63 – 4.71)
4.19 (4.15 – 4.23)
4.96 (4.93 – 4.99)
4.25 (4.22 – 4.27)
2016
4.76 (4.71 - 4.81)
4.30 (4.25 – 4.35)
4.98 (4.94 –2016 5.02) 2014
4.24 (4.20 – 4.28)
Unsuitable Unsuitablesamples samples
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Figure 2 – Sigma values (mean and confidence interval) concerning QIs of the preanalytical phase.
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Post-analytical phase 6
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Outside time Reports delivered outside time 2014 2016
Critical results Critical results notified after agreed time
2014
4.81 (4.76 – 4.86)
4.17 (4.03 – 4.32)
2.64 (2.26 – 3.01)
2016
4.99 (4.91 – 5.06)
3.52 (3.28 – 3.75)
2.51 (1.46 – 3.56)
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Figure 3 – Sigma values (mean and confidence interval) of QIs concerning the post-analytical phase .
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TAT of INR TAT of INR
49.1 (47.5 – 50.7)
46.0 (27.3 – 64.7)2014
2016
64.8 (50.7 – 79.0)
42.7 (13.5 – 71.9)
TAT of WBC TAT of WBC
24.5 (15.4 – 32.6)
49.5 (30.3 – 68.7)
18.2 ( 10.4 – 26.2)
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TAT of Troponin TAT of Troponin 2016 52.3 (43.9 – 61.4)
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Figure 4 – Measure of TAT (from receipt of sample to release of result) at 90th percentile (mean of laboratory results and confidence intervals).
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Highlights - Management of Quality Indicators in Laboratory Medicine Quality Indicators results from international laboratories
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State-of-the-Art concerning quality indicators for the extra-analytical phases
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Laura Sciacovelli, Ada Aita, Mario Plebani PII: DOI: Reference:
S0009-9120(17)30121-2 doi: 10.1016/j.clinbiochem.2017.03.020 CLB 9510
To appear in:
Clinical Biochemistry
Received date: Revised date: Accepted date:
7 February 2017 23 March 2017 24 March 2017
Please cite this article as: Laura Sciacovelli, Ada Aita, Mario Plebani , Extra-analytical quality indicators and laboratory performances. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Clb(2017), doi: 10.1016/j.clinbiochem.2017.03.020
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Extra-analytical Quality Indicators and Laboratory Performances
Laura Sciacovelli, Ada Aita, Mario Plebani.
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Department of Laboratory Medicine, University Hospital, Padova (Italy).
Corresponding Author: Laura Sciacovelli Department of Laboratory Medicine University Hospital Padova (Italy) [email protected]
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ACCEPTED MANUSCRIPT Abstract In the last few years much progress has been made in raising the awareness of laboratory medicine professionals about the effectiveness of quality indicators (QIs) in monitoring, and improving upon, performances in the extra-analytical phases of the Total Testing Process (TTP). An effective system for management of QIs includes the implementation of an internal assessment system and participation in inter-laboratory comparison. A well-designed internal assessment system allows the identification of critical activities and their systematic monitoring. Active
laboratory with respect to that of other participating laboratories.
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participation in inter-laboratory comparison provides information on the performance level of one
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In order to guarantee the use of appropriate QIs and facilitate their implementation, many
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laboratories have adopted the Model of Quality Indicators (MQI) proposed by Working Group “Laboratory Errors and Patient Safety” (WG-LEPS) of IFCC, since 2008, which is the result of
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international consensus and continuous experimentation, and updating to meet new, constantly emerging needs.
Data from participating laboratories are collected monthly and reports describing the statistical
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results and evaluating laboratory data, utilizing the Six Sigma metric, issued regularly. Although the results demonstrate that the processes need to be improved upon, overall the
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comparison with data collected in 2014 shows a general stability of quality levels and that an improvement has been achieved over time for some activities. The continuous monitoring of QI
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data allows identification all possible improvements, thus highlighting the value of participation in the inter-laboratory program proposed by WG-LEPS. The active participation of numerous laboratories will guarantee an ever more significant State-of-
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safety.
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the-Art, promote the reduction of errors and improve quality of the TTP, thus guaranteeing patient
Key words: quality indicators; extra-analytical phases; laboratory errors; patient safety.
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Introduction Since the extra-analytical phases of the Total Testing Process (TTP) are known to be more errorprone than intra-analytical phase, it has become a priority for laboratory professionals to use adequate procedures that reduce the risk of errors in these phases, as well as a system ensuring continuous improvement, and enhancement of patient safety [1-5]. Laboratory performance in its entirety hinges on quality of pre-analytical activities, which has a
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direct impact on intra-analytical procedures and post-analytical activities. The performance characteristics of intra-analytical procedures, long monitored by internationally recognised tools,
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such as internal quality control (IQC) procedures and External Quality Assessment
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(EQA)/Proficiency Testing (PT), have stimulated continuous improvement [6-8]. In the last few years, awareness of laboratory medicine professionals has been raised concerning the
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effectiveness of quality indicators (QIs) in monitoring and improving performance in the extraanalytical phases [9-11]. Of course, their efficacy as a tool depends on their correct utilization, calling for: adequate identification and definition of each and every indicator; definition of a
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standardized and systematic data collection procedure; integration of QIs in a coherent strategy of quality improvement; use of QI data to identify appropriate improvement actions. A strong
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commitment of laboratory professionals in using this tool is prerequisite to promoting improvement projects since data on quality indicators do not per se improve quality. Only the correct
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interpretation of, and reaction to, data can clarify the nature of errors (root cause analysis) thus leading to corrective and preventive actions.
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The use of QIs covering the entire TTP, required by the International Standard for Accreditation of laboratories ISO 15189:2012, is widely accepted as a process and strategy assisting risk
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identification and management [12-15]. In order to comply with the requirements of International Standard ISO 15189 and to improve the performance of extra-analytical phases, many laboratories have implemented QIs on the basis of their own health care context, purpose and goals, and number and typology of patients [16-18]. However, it is difficult to establish the current error rate because QIs from different laboratories yield incomparable data. The scientific community is now striving to identify the best possible procedure for managing and harmonizing the use of QIs in laboratory medicine [19-21]. In order to guarantee an effective QI management system, implementation of an internal assessment system and participation in inter-laboratory comparison have to be included. A well-designed internal assessment system allows the identification of critical activities and their systematic Pagina 3 di 16
ACCEPTED MANUSCRIPT monitoring, and active participation in inter-laboratory comparison provides information on the performance level of one laboratory compared with that of other participating laboratories.
Internal Assessment System Aim of internal assessment system is to design an operative flow that guarantees appropriate definition and utilization of QIs that successfully keep critical lab activities under control and raise awareness in the laboratory staff concerning the need to adequately manage QIs data. The final
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purpose is to reduce the error rate and improve laboratory performance [22]. The system comprises
a) Definition of a list of QIs that includes the following steps.
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the following.
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- Identification of critical activities included in each phase of TTP.
- Formulation of each indicator, paying attention to correct identification of events to keep
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under observation and undesirable events to be measured. Because most QI data are expressed as percentages, it is important to verify congruity between the data of undesirable events measured (numerator) and events kept under observation (denominator).
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- Composition of the list of QIs considering that a single QI can be monitored by different measurements but, in order to prevent misinterpretation at data analysis, the same
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measurement cannot be used to monitor different QIs. It is also important to ensure that the QIs listed are representative of the extra-analytical phases, which are more error-prone.
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b) Design of a document for each indicator, describing details for the correct understanding and use of QIs. This raises awareness of laboratory staff concerning the importance of QIs and the steps to take for their correct management. The laboratory staff are responsible for describing
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the rationale of a single indicator, the procedure to use for a correct data collection, improving upon defined goals, establishing accountability for each step required (collection, processing
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and analysis of data; implementation of improvement actions), all of which are pivotal for the effectiveness of the system. c) Issuing a Standard Operating Procedure describing all steps, from the identification of QIs to the revision of the entire system, is the key to ensuring that the staff perform all operative steps in a standardized and correct fashion, and guaranteeing them independently by the operator. d) Using a computerized application for recording all undesirable events (e.g. unsuitable samples, data entry errors, reporting delays, etc.) as this overcomes all problems inherent to manual recording, which is also time consuming and requires human resource [23-24]. IT software not only saves time, but also data collection, guaranteeing greater accuracy (all data for collection are actually collected), standardization (results comparable over time), traceable (staff Pagina 4 di 16
ACCEPTED MANUSCRIPT awareness enhanced), and effective (data analysis easier and more timely). For example, the recording of the identification code of a patient sample through the reading of barcode on the sample label allows operators to record all patients and sample details through a single and simple act (Fig. 1). Moreover, the processing and analysis of collected data are simpler and more reliable. e) Using a computerized application to record and visualize the QI data in order to keep their trend under control over time, facilitate data analysis data and identify any actions required.
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In Figure 1 is reported the computerized system used in the Department of Laboratory Medicine of Padova (Italy), in which it is possible to record all undesirable events by all operators in all
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locations of the laboratory. All details of the specific operator and features of samples are recorded.
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In particular:
name, surname, data of birth, identification code related to patient;
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typology of sample, required tests;
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error type;
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possible comments;
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identification code of operator.
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All recorded information can be printed in a list and processed for the statistical elaboration on the
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basis of typology of event. This system has proven effective, saving time and assuring the
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annotation of all events to be recorded.
Participation in Inter-laboratory Comparison In the intra-analytical phase, the monitoring and evaluation of examination procedures in which
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EQA/PT are used to highlight the use of inadequate IQC procedures, diagnostic systems out of control or with unsuitable performances, insufficient training or competence of staff and so forth.
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Likewise, the inter-laboratory comparison for QIs allows assessment of QI management in the internal system of the laboratory. Thanks to participation in an inter-laboratory comparison, the laboratory can verify how its performance level, measured by its internal assessment system, compares with that of other laboratories using the same QIs. Different inter-laboratory comparisons have been implemented worldwide (e.g. Australia, Brazil, China), while in other countries (e.g. UK) only a few surveys have been conducted [25-28]. An inter-laboratory comparison designed by the Working Group “Laboratory Errors and Patient Safety” (WG-LEPS) of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC), used since 2008, provides a Model of Quality Indicators (MQI) and reporting system through a dedicated website (www.ifcc-mqi.com) [29]. Data Pagina 5 di 16
ACCEPTED MANUSCRIPT from participating laboratories are collected monthly and reports describing the statistical results and evaluating laboratory data utilizing the Six Sigma metric issued regularly; the exhaustive information provided enables the laboratory to evaluate the quality level of its processes thanks to a sigma value calculation and to compare its sigma value with the mean sigma value of participants in its Country and of all participants. It is used the short-term sigma that corresponds to defect rate that would be observed if the process were to shift as much as 1.5 sigma (differently by the long-term sigma that corresponds to the defect rate if the process were properly centered). The sigma value
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enables analysis and measurement of the capacity of processes to obviate the production of defects because a six sigma process is virtually a “perfect process” (3.4 defects per million), and three
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sigma is defined the typical quality for processes [30]. Moreover, it is possible to verify the trend
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over time and the frequency distribution of results and sigma values. On the basis of the information provided, the laboratory can identify areas requiring improvement actions, define priorities in
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improvement actions and verify the extent of improvement achieved. However, the limitation of sigma value is that it does not allow assessing what is the extent to which the error rate can be
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tolerated.
In order to guarantee the use of appropriate QIs and facilitate their implementation, many
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laboratories have adopted the MQI proposed by WG-LEPS, that is the result of international consensus and continuous experimentation, and updating to meet new, constantly emerging needs.
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A preliminary MQI was developed in 2008 and tested under real conditions by involved laboratories in order to check the suitability of each QI, and the feasibility of the reporting system. Findings during use the experimental phase, and the list of QIs were discussed in the Consensus
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Conference held in Padova in 2013 (“Harmonization of quality indicators: why, how and when?”), a preliminary consensus being achieved on terminology, rationale, purpose of each and every QI and
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the procedures for data collection. A reviewed and approved MQI issued after the Consensus Conference, has been tested since 2014 through an External Quality Assurance Program (EQAP). Some QI definitions have been modified to facilitate laboratory professionals’ understanding of the role of each indicator. Some QIs have been added and others deleted. A priority score (1, the highest priority) has been assigned to each indicator in order to help laboratories gradually introduce QIs into routine practice, and the reporting system has been simplified to allow homogeneous data collection. A criterion to identify Quality Specifications (QSs) for the assessment of a laboratory’s performance has also been proposed. The reviewed and approved MQI includes 25 quality indicators and 53 measurements: 11 QIs and 28 measurements for the preanalytical phase; 4 and 6 for the intra-analytical phase; 5 and 11 for the post-analytical phase; 3 and Pagina 6 di 16
ACCEPTED MANUSCRIPT 5 for Support processes; 2 and 3 for Outcome Measure [11, 31-33]. The MQI was tested until a further Consensus Conference was held in Padova on 26th October 2016, entitled “Harmonization of quality indicators in Laboratory Medicine: two years later?”. In the last meeting, all findings, arisen in the use of MQI, were discussed in order to identify all possible improvements and achieve a consensus towards harmonization of QIs.
Extra-analytical Performances
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It is widely accepted that the performance of a clinical laboratory is assessed on the basis of degree of imprecision and bias for the intra-analytical phase, and the error rate or number of undesirable
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events measured on using QIs, for the extra-analytical phases [34].
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QI data are representative of extra-analytical performances, and in order to guarantee their reliability, they must come from international laboratories using harmonized QIs that have been
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defined after scientific consensus has been gained. Moreover, they must be managed within an internal assessment system that guarantees: standardized and systematic data collection; effective analysis and evaluation; identification of improvement actions; staff awareness of the importance of
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the correct use of QIs; sound ethical conduct. Only then can data from different laboratories be considered comparable, processed together and, finally, represent the State-of-the-Art linked to the
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error rate of the extra-analytical phases. In this context, QI data collected and processed by WGLEPS can provide a reliable picture of the extra-analytical performances. Overall, data were
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received from 59 laboratories (that consistently participate in the MQI project) in Argentina, Austria, Brazil, Estonia, Germany, Great Britain, India, Italy, Republic of China, Republic of Croatia, Spain, Switzerland, Serbia, Uruguay. Figure 2 reports the mean of sigma values calculated
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on the basis of results collected in 2014 and 2016, for the pre-analytical phase and Figure 3 for the post-analytical phase. Figure 4 reports the turnaround time (TAT), measured from sample receipt to
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the delivery of results, at the 90th percentile related to 4 tests. Although the results demonstrate that the processes need to be improved upon, overall the comparison with data collected in 2014 shows a general stability of quality levels and that an improvement has been achieved over time for some activities. The continuous monitoring of QI data allows identification all possible improvements, thus highlighting the value of participation in the inter-laboratory program proposed by WG-LEPS. Moreover, an evaluation can be made of the utility of sigma values, provided in the report, in estimating the risk probability in risk management procedures, in particular in the analysis of root cause (e.g., Failure Mode and Effect Analysis – FMEA) [13, 15].
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ACCEPTED MANUSCRIPT The achievement of consensus on the list of harmonized QIs is preliminary to the identification of achievable and realistic performance targets. In fact, the Six Sigma metric allows an assessment to be made, based on error frequency, of the level of quality of the processes involved. The definition of the performance specifications for each indicator indicates the extent to which the error rate can be tolerated, given that a goal of “zero defects” is not always achievable. A criterion for defining quality specifications has been proposed, and is currently being tested with a view to evaluating QI data [11, 32].
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In line with the claims of the 1st Strategic conference of the European Federation Clinical Chemistry and Laboratory medicine (EFLM) held in Milan in 2015, the definition of performance
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specifications for extra-analytical phases should follow the same models as that for intra-analytical
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ones: clinical outcome, biological variability, and State-of-the-Art. The State-of-the-Art is the more feasible criterion because no data are available on clinicians’ opinion as a criterion for establishing
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the performance specifications, and the biological variability is not applicable to QI data [34]. The awareness of a need for improvement of extra-analytical quality is great. Performance specifications, established on the basis of State-of-the-Art, have enabled many laboratories to attain
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achievable goals and promote the improvement.
There is consensus about the identification of State-of-the-Art concerning the intra-analytical
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performances from EQA/PT results. Similarly, QI data from international EQAP are representative
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of the State-of-the-Art related to extra-analytical performances.
Conclusion
The use of QIs in Laboratory Medicine is an effective quality assurance tool if managed within a an
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appropriate internal assessment system, and in compliance with harmonization criteria gained through participation in an inter-laboratory comparison, such as that proposed by WG-LEPS [35].
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An international survey demonstrated that laboratory professionals are aware of the need to use QIs in order to comply with ISO 15189 requirements (97%), and most participating laboratories systematically measure QIs for misidentification errors (patients or samples), unsuitable samples, and TAT. Only 52% of laboratories state that they are aware of the project on QIs proposed by WGLEPS despite the fact that it has been widely divulged at International Congresses and in scientific publications (35). The involvement of national Scientific Societies and Accreditation bodies and EQA/PT providers in raising awareness of this project is mandatory. The active participation of numerous laboratories will guarantee an ever more significant State-of-the-Art, promote the reduction of errors and improve quality of the TTP, thus guaranteeing patient safety.
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ACCEPTED MANUSCRIPT Recording of non conformities Intranet website Recording of non conformities
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Traceability of operator with username and password
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Category (i.e. sample, results, request, etc.) Identified event (i.e. haemolysed)
Laboratory area involved
Barcode
Type of event
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Note
Operator code
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If necessary, a comment can be entered
Sample code identification
Figure 1 – Recording of non conformities by a computerized application used in the Department of Laboratory Medicine of Padova (Italy)
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Pre-analytical phase 6
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Sigma value
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Transcription errors Transcription errors
Wrong Wrongcontainer container
2014
4.67 (4.63 – 4.71)
4.19 (4.15 – 4.23)
4.96 (4.93 – 4.99)
4.25 (4.22 – 4.27)
2016
4.76 (4.71 - 4.81)
4.30 (4.25 – 4.35)
4.98 (4.94 –2016 5.02) 2014
4.24 (4.20 – 4.28)
Unsuitable Unsuitablesamples samples
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Figure 2 – Sigma values (mean and confidence interval) concerning QIs of the preanalytical phase.
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Post-analytical phase 6
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Outside time Reports delivered outside time 2014 2016
Critical results Critical results notified after agreed time
2014
4.81 (4.76 – 4.86)
4.17 (4.03 – 4.32)
2.64 (2.26 – 3.01)
2016
4.99 (4.91 – 5.06)
3.52 (3.28 – 3.75)
2.51 (1.46 – 3.56)
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Figure 3 – Sigma values (mean and confidence interval) of QIs concerning the post-analytical phase .
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Post-analytical phase 70
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0 TAT of Potassium TAT of Potassium
TAT of INR TAT of INR
49.1 (47.5 – 50.7)
46.0 (27.3 – 64.7)2014
2016
64.8 (50.7 – 79.0)
42.7 (13.5 – 71.9)
TAT of WBC TAT of WBC
24.5 (15.4 – 32.6)
49.5 (30.3 – 68.7)
18.2 ( 10.4 – 26.2)
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TAT of Troponin TAT of Troponin 2016 52.3 (43.9 – 61.4)
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Figure 4 – Measure of TAT (from receipt of sample to release of result) at 90th percentile (mean of laboratory results and confidence intervals).
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Highlights - Management of Quality Indicators in Laboratory Medicine Quality Indicators results from international laboratories
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State-of-the-Art concerning quality indicators for the extra-analytical phases
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