scholarly journals Estimation of change limits (deltacheck) in clinical laboratory

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Maria-José Castro-Castro ◽  
Lourdes Sánchez-Navarro
Keyword(s):  
Clinical Laboratory ◽  
Data Distribution ◽  
Control Procedure ◽  
Standard Laboratory ◽  
Laboratory Errors ◽  
Plausibility Control ◽  
Relative Differences

Abstract Objectives Change limits, more commonly called delta check, are those in which a change in a patient’s measured result in relation to their corresponding preceding measurement is suspected of being erroneous and should be considered as a doubtful result. The aim of this study was to provide change limits for some biochemical and haematological quantities to detect doubtful measured results and to assess its effectiveness to detect erroneous results for their application in and the standardization of the plausibility control. Methods Change limits have been estimated for 13 biochemical and 6 haematological quantities. For each quantity, relative differences (D), expressed as a percentage between the two consecutive measured results from the same patient (from scheduled laboratory requests), were calculated. From these differences (D), the p5 and p95 percentiles of the data distribution were calculated. To assess the effectiveness of the change limits to detect laboratory errors, 43 erroneous laboratory reports, containing different biochemical and haematological quantities, were obtained from the standard laboratory plausibility control procedure. Results From the 43 erroneous laboratory reports, 31 (72%) were due to endovenous administration errors and 12 (28%) were due to mislabeling errors. All erroneous laboratory reports were detected when the change limits of the quantities were combined and applied together. Conclusions The best combination of quantities, which detect all the erroneous reports in the same specimen were: potassium, albumin, creatinine, glucose and haemoglobin.

Extra-analytical quality indicators – where to now?

2017 ◽  
Vol 0 (0) ◽  
Author(s):  
Ada Aita ◽  
Laura Sciacovelli ◽  
Mario Plebani
Keyword(s):  
Quality Indicators ◽  
Clinical Laboratory ◽  
State Of The Art ◽  
Clinical Chemistry ◽  
Large Body ◽  
The State ◽  
Laboratory Errors ◽  
Management Procedure ◽  
Performance Specifications ◽  
Improvement Programs

AbstractA large body of evidence collected in recent years demonstrates the vulnerability of the extra-analytical phases of the total testing process (TTP) and the need to promote quality and harmonization in each and every step of the testing cycle. Quality indicators (QIs), which play a key role in documenting and improving quality in TTP, are essential requirements for clinical laboratory accreditation. In the last few years, wide consensus has been achieved on the need to adopt universal QIs and common terminology and to harmonize the management procedure concerning their use by adopting a common metric and reporting system. This, in turn, has led to the definition of performance specifications for extra-analytical phases based on the state of the art as indicated by data collected on QIs, particularly by clinical laboratories attending the Model of Quality Indicators program launched by the Working Group “Laboratory Errors and Patient Safety” of the International Federation of Clinical Chemistry and Laboratory Medicine. Harmonization plays a fundamental role defining not only the list of QIs to use but also performance specifications based on the state of the art, thus providing a valuable interlaboratory benchmark and tools for continuous improvement programs.

scholarly journals Point-of-care testing in the cardiovascular operating theatre

2006 ◽  
Vol 44 (9) ◽  
Author(s):  
Urs E. Nydegger ◽  
Erich Gygax ◽  
Thierry Carrel
Keyword(s):  
Healthcare Professionals ◽  
Cardiovascular Surgery ◽  
Clinical Laboratory ◽  
Point Of Care ◽  
Operating Table ◽  
Standard Laboratory ◽  
Rapid Progress ◽  
Point Of Care Testing ◽  
Laboratory Procedures ◽  
Major Equipment

AbstractPoint-of-care testing (POCT) remains under scrutiny by healthcare professionals because of its ill-tried, young history. POCT methods are being developed by a few major equipment companies based on rapid progress in informatics and nanotechnology. Issues as POCT quality control, comparability with standard laboratory procedures, standardisation, traceability and round robin testing are being left to hospitals. As a result, the clinical and operational benefits of POCT were first evident for patients on the operating table. For the management of cardiovascular surgery patients, POCT technology is an indispensable aid. Improvement of the technology has meant that clinical laboratory pathologists now recognise the need for POCT beyond their high-throughput areas.Clin Chem Lab Med 2006;44:1060–5.

scholarly journals Pre-Analytical Workstations as a Tool for Reducing Laboratory Errors

2010 ◽  
Vol 29 (4) ◽  
pp. 315-324 ◽  
Author(s):  
Giorgio Rin
Keyword(s):  
Quality Improvement ◽  
Clinical Laboratory ◽  
Error Reduction ◽  
Laboratory Errors ◽  
Continuous Quality ◽  
Complex Process ◽  
Preanalytical Phase ◽  
Analytical Phase ◽  
Total Testing Process

Pre-Analytical Workstations as a Tool for Reducing Laboratory ErrorsReducing errors and improving quality are an integral part of Laboratory Medicine. Laboratory testing, a highly complex process commonly called the total testing process (TTP), is usually subdivided into three traditional (pre-, intra-, and post-) analytical phases. A series of papers published from 1989 drew the attention of laboratory professionals to the pre-analytical phase, which currently appears to be more vulnerable to errors than the other phases. Consequently, the preanalytical phase should be the main target for further quality improvement. Therefore, identifying the critical steps in the pre-analytical phase is a prerequisite for continuous quality improvement, further error reduction and thus for improving patient safety. Use of automated systems where feasible, and use of error reduction/improved quality as a factor when selecting instrumentation are the main tools we have to insure high quality and minimize errors in the pre-analytical phase. The reasons for automation of the pre-analytical phase have become so compelling that it is no longer simply a competitive advantage for laboratories, but rather a competitive necessity. These systems can impact on the clinical/laboratory interface and affect the efficiency, effectiveness and quality of care.

Corrections of Clinical Chemistry Test Results in a Laboratory Information System

2004 ◽  
Vol 128 (8) ◽  
pp. 890-892
Author(s):  
Sihe Wang ◽  
Virginia Ho
Keyword(s):  
Information System ◽  
Patient Safety ◽  
Error Detection ◽  
Clinical Laboratory ◽  
Improve Patient Safety ◽  
Clinical Chemistry ◽  
Laboratory Information System ◽  
Test Volume ◽  
Laboratory Errors ◽  
Made In

Abstract Context.—The recently released reports by the Institute of Medicine, To Err Is Human and Patient Safety, have received national attention because of their focus on the problem of medical errors. Although a small number of studies have reported on errors in general clinical laboratories, there are, to our knowledge, no reported studies that focus on errors in pediatric clinical laboratory testing. Objective.—To characterize the errors that have caused corrections to have to be made in pediatric clinical chemistry results in the laboratory information system, Misys. To provide initial data on the errors detected in pediatric clinical chemistry laboratories in order to improve patient safety in pediatric health care. Design.—All clinical chemistry staff members were informed of the study and were requested to report in writing when a correction was made in the laboratory information system, Misys. Errors were detected either by the clinicians (the results did not fit the patients' clinical conditions) or by the laboratory technologists (the results were double-checked, and the worksheets were carefully examined twice a day). No incident that was discovered before or during the final validation was included. On each Monday of the study, we generated a report from Misys that listed all of the corrections made during the previous week. We then categorized the corrections according to the types and stages of the incidents that led to the corrections. Results.—A total of 187 incidents were detected during the 10-month study, representing a 0.26% error detection rate per requisition. The distribution of the detected incidents included 31 (17%) preanalytic incidents, 46 (25%) analytic incidents, and 110 (59%) postanalytic incidents. The errors related to noninterfaced tests accounted for 50% of the total incidents and for 37% of the affected tests and orderable panels, while the noninterfaced tests and panels accounted for 17% of the total test volume in our laboratory. Conclusion.—This pilot study provided the rate and categories of errors detected in a pediatric clinical chemistry laboratory based on the corrections of results in the laboratory information system. A direct interface of the instruments to the laboratory information system showed that it had favorable effects on reducing laboratory errors.

scholarly journals Determination of errors that compromise the quality of laboratory service in a tertiary hospital

2017 ◽  
Vol 8 (1) ◽  
pp. 64-70
Author(s):  
Kenneth Kipruto Kimengech ◽  
Stanley Kinge Waithaka ◽  
Jackson Onyuka ◽  
Christine Sekadde Kigondu
Keyword(s):  
Laboratory Testing ◽  
Clinical Laboratory ◽  
Clinical Chemistry ◽  
Common Knowledge ◽  
Tertiary Hospital ◽  
Chemistry Laboratory ◽  
Laboratory Service ◽  
Laboratory Errors ◽  
Analytical Errors

Background: Clinical Laboratory testing is a highly complex process that entails numerous procedures. Although it has been known that laboratory testing services are safe, it is increasingly becoming a common knowledge that they are not that safe. Studies have indicated that there are a number of errors that occur due to laboratory testing processes. These errors may not be realized easily during the testing process, but they make significant impact on the results given.Aims and Objective: To determine the levels of pre-analytical, analytical, and post analytical errors found in the analysis of Clinical Laboratory specimen at Kenyatta National Hospital.Materials and Methods: A prospective and descriptive study was carried out at Clinical Chemistry Laboratory, Department of Laboratory Medicine, Kenyatta National Hospital. A total of 346 request forms, specimens/samples and dispatched results were scrutinized and errors documented as per the different variables in the different phases, over a period of three months and the findings were analyzed.Results: Results of the study showed that Preanalytical errors were most common with a frequency of 148(42.8%), followed by analytical errors 114 (32.9%) and post analytical errors 84 (24.3%), respectively.Conclusions: The study concludes that pre-analytical, analytical, and post analytical errors are errors that compromise the quality of laboratory service delivery, which impacts on the patient management and diagnosis. Clinical laboratory errors can be minimized if due diligence and professionalism is adhered in the laboratory.Asian Journal of Medical Sciences Vol.8(1) 2017 64-70

Evaluation of a New Radioimmunoassay Method for Carcinoembryonic Antigen in Plasma, with Use of Zirconyl Phosphate Gel

1972 ◽  
Vol 18 (9) ◽  
pp. 918-922 ◽  
Author(s):  
Tsann Ming Chu ◽  
Gustavo Reynoso
Keyword(s):  
Quality Control ◽  
Carcinoembryonic Antigen ◽  
Clinical Laboratory ◽  
Statistical Quality Control ◽  
Control Procedure ◽  
Statistical Quality ◽  
Quality Control Procedure ◽  
Highly Sensitive ◽  
Radioimmunoassay Method

Abstract A radioimmunoassay of carcinoembryonic antigen in plasma is described and evaluated. The assay can be easily performed and implemented in a clinical laboratory. Assessed by Rodbard’s statistical quality-control procedure, the assay is shown to be highly sensitive, precise, and reproducible.

scholarly journals Development and Application of Computerized Risk Registry and Management Tool Based on FMEA and FRACAS for Total Testing Process

Medicina ◽  
2021 ◽  
Vol 57 (5) ◽  
pp. 477
Author(s):  
Jeonghyun Chang ◽  
Soo Jin Yoo ◽  
Sollip Kim
Keyword(s):  
Risk Management ◽  
Failure Mode ◽  
Clinical Laboratory ◽  
Flow Diagram ◽  
Management Tool ◽  
Laboratory Errors ◽  
Testing Sequence ◽  
Fmea Method ◽  
Total Testing Process ◽  
One Year

Background and Objectives: Risk management is considered an integral part of laboratory medicine to assure laboratory quality and patient safety. However, the concept of risk management is philosophical, so actually performing risk management in a clinical laboratory can be challenging. Therefore, we would like to develop a sustainable, practical system for continuous total laboratory risk management. Materials and Methods: This study was composed of two phases: the development phase in 2019 and the application phase in 2020. A concept flow diagram for the computerized risk registry and management tool (RRMT) was designed using the failure mode and effects analysis (FMEA) and the failure reporting, analysis, and corrective action system (FRACAS) methods. The failure stage was divided into six according to the testing sequence. We applied laboratory errors to this system over one year in 2020. The risk priority number (RPN) score was calculated by multiplying the severity of the failure mode, frequency (or probability) of occurrence, and detection difficulty. Results: 103 cases were reported to RRMT during one year. Among them, 32 cases (31.1%) were summarized using the FMEA method, and the remaining 71 cases (68.9%) were evaluated using the FRACAS method. There was no failure in the patient registration phase. Chemistry units accounted for the highest proportion of failure with 18 cases (17.5%), while urine test units accounted for the lowest portion of failure with two cases (1.9%). Conclusion: We developed and applied a practical computerized risk-management tool based on FMEA and FRACAS methods for the entire testing process. RRMT was useful to detect, evaluate, and report failures. This system might be a great example of a risk management system optimized for clinical laboratories.

scholarly journals Prevention of extra-analytical phase errors by non-analytical automation in clinical laboratory

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ebubekir Bakan ◽  
Nuri Bakan
Keyword(s):  
Clinical Laboratory ◽  
Phase Error ◽  
Diagnostic Errors ◽  
Substantial Part ◽  
Laboratory Diagnostics ◽  
Total Quality ◽  
Error Sources ◽  
Laboratory Errors ◽  
Analytical Phase

Abstract During previous decades, significant improvements in laboratory errors have become a substantial part of reducing preventable diagnostic errors. In clinical laboratory practice, the errors in the testing process are primarily associated with extra-analytical phase error sources, influencing the test result quality profoundly. Thus, the management of these critical error sources makes their effects preventable thanks to automation and computer sciences. The implementation of non-analytical automated systems requires a risk management strategy based on laboratory’s workflow and bottlenecks. Then, the improvements can be measured and evaluated by the usage of quality indicators (QI). Consequently, the total quality of laboratory diagnostics and higher patient safety is closely dependent on this type of automation. This review will help laboratory professionals, managers, and directors improve the total testing processes (TTP). The automation technologies have added a serious impact on the proficiency of laboratory medicine. Several instrumentations have now partially or entirely automated many manual tasks to improve standardization, organization, efficiency, and TTP quality. The implementation of non-analytical automation has made them manageable. As a result, non-analytical automation within and outside the clinical laboratory will necessarily lessen the error sources’ effect on the total test process, enhancing the quality of the test results.

scholarly journals Implementing Self Sustained Quality Control Procedures in a Clinical Laboratory

2013 ◽  
Vol 52 (189) ◽  
pp. 233-237 ◽  
Author(s):  
Roshan Khatri ◽  
Sanjay KC ◽  
Prabodh Shrestha ◽  
J N Sinha
Keyword(s):  
Quality Control ◽  
Clinical Laboratory ◽  
Reliability And Validity ◽  
Disease Diagnosis ◽  
Mitigation Measures ◽  
Internal Quality ◽  
Control Procedure ◽  
Random Errors ◽  
Control Rules ◽  
Control Serum

Introduction: Quality control is an essential component in every clinical laboratory which maintains the excellence of laboratory standards, supplementing to proper disease diagnosis, patient care and resulting in overall strengthening of health care system. Numerous quality control schemes are available, with combinations of procedures, most of which are tedious, time consuming and can be “too technical” whereas commercially available quality control materials can be expensive especially for laboratories in developing nations like Nepal. Here, we present a procedure performed at our centre with self prepared control serum and use of simple statistical tools for quality assurance. Methods: The pooled serum was prepared as per guidelines for preparation of stabilized liquid quality control serum from human sera. Internal Quality Assessment was performed on this sample, on a daily basis which included measurement of 12 routine biochemical parameters. The results were plotted on Levey-Jennings charts and analysed with quality control rules, for a period of one month. Results: The mean levels of biochemical analytes in self prepared control serum were within normal physiological range. This serum was evaluated every day along with patients’ samples. The results obtained were plotted on control charts and analysed using common quality control rules to identify possible systematic and random errors. Immediate mitigation measures were taken and the dispatch of erroneous reports was avoided. Conclusions: In this study we try to highlight on a simple internal quality control procedure which can be performed by laboratories, with minimum technology, expenditure, and expertise and improve reliability and validity of the test reports. Keywords: Levey-Jennings charts; pooled sera; quality control; Westgard Rule.

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