Performance evaluation of a coagulation laboratory using Sigma metrics

2018 ◽  
Vol 31 (6) ◽  
pp. 600-608
Author(s):  
Muhammad Shariq Shaikh ◽  
Sidra Asad Ali ◽  
Anila Rashid ◽  
Farheen Karim ◽  
Bushra Moiz
Keyword(s):  
Test Results ◽  
Statistical Tool ◽  
Content Type ◽  
Sigma Level ◽  
Clinical Laboratories ◽  
Laboratory Test Results ◽  
World Class ◽  
Low Performance ◽  
Monitoring Performance ◽  
Analytical Errors

Purpose Two-thirds of medical decisions are based on laboratory test results. Therefore, laboratories should practice strict quality control (QC) measures. Traditional QC processes may not accurately reflect the magnitude of errors in clinical laboratories. Six Sigma is a statistical tool which provides opportunity to assess performance at the highest level of excellence. The purpose of this paper is to evaluate performance of the coagulation laboratory utilizing Sigma metrics as the highest level of quality. Design/methodology/approach Quality indicators of the coagulation laboratory from January 1, 2009, to December 31, 2015, were evaluated. These QIs were categorized into pre-analytical, analytical and post-analytical. Relative frequencies of errors were calculated and converted to Sigma scale to determine the extent of control over each process. The Sigma level of 4 was considered optimal performance. Findings During the study period, a total of 474,655 specimens were received and 890,535 analyses were performed. These include 831,760 (93.4 percent) routine and 58,775 (6.6 percent) special tests. Stat reporting was requested for 166,921 (18.7 percent). Of 7,535,146 total opportunities (sum of the total opportunities for all indicators), a total of 4,005 errors were detected. There were 2,350 (58.7 percent) pre-analytical, 11 (0.3 percent) analytical and 1,644 (41 percent) post-analytical errors. Average Sigma value obtained was 4.8 with 12 (80 percent) indicators achieving a Sigma value of 4. Three (20 percent) low-performance indicators were: unacceptable proficiency testing (3.8), failure to inform critical results (3.6) and delays in stat reporting (3.9). Practical implications This study shows that a small number of errors can decrease Sigma value to below acceptability limits. If clinical laboratories start using Sigma metrics for monitoring their performance, they can identify gaps in their performance more readily and hence can improve their performance and patient safety. Social implications This study provides an opportunity for the laboratorians to choose and set world-class goals while assessing their performance. Originality/value To the best of the authors’ knowledge and belief, this study is the first of its kind that has utilized Sigma metrics as a QC tool for monitoring performance of a coagulation laboratory.

scholarly journals Verification of reference intervals in routine clinical laboratories: practical challenges and recommendations

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Yesim Ozarda ◽  
Victoria Higgins ◽  
Khosrow Adeli
Keyword(s):  
Clinical Laboratory ◽  
Local Population ◽  
Age Groups ◽  
External Source ◽  
Reference Intervals ◽  
Laboratory Method ◽  
Test Results ◽  
Multicenter Studies ◽  
Clinical Laboratories ◽  
Laboratory Test Results

Abstract Reference intervals (RIs) are fundamental tools used by healthcare and laboratory professionals to interpret patient laboratory test results, ideally enabling differentiation of healthy and unhealthy individuals. Under optimal conditions, a laboratory should perform its own RI study to establish RIs specific for its method and local population. However, the process of developing RIs is often beyond the capabilities of an individual laboratory due to the complex, expensive and time-consuming process to develop them. Therefore, a laboratory can alternatively verify RIs established by an external source. Common RIs can be established by large, multicenter studies and can subsequently be received by local laboratories using various verification procedures. The standard approach to verify RIs recommended by the Clinical Laboratory Standards Institute (CLSI) EP28-A3c guideline for routine clinical laboratories is to collect and analyze a minimum of 20 samples from healthy subjects from the local population. Alternatively, “data mining” techniques using large amounts of patient test results can be used to verify RIs, considering both the laboratory method and local population. Although procedures for verifying RIs in the literature and guidelines are clear in theory, gaps remain for the implementation of these procedures in routine clinical laboratories. Pediatric and geriatric age-groups also continue to pose additional challenges in respect of acquiring and verifying RIs. In this article, we review the current guidelines/approaches and challenges to RI verification and provide a practical guide for routine implementation in clinical laboratories.

scholarly journals LabRS: A Rosetta stone for retrospective standardization of clinical laboratory test results

2017 ◽  
Vol 25 (2) ◽  
pp. 121-126 ◽  
Author(s):  
Ronald George Hauser ◽  
Douglas B Quine ◽  
Alex Ryder
Keyword(s):  
Laboratory Tests ◽  
Standardized Test ◽  
Clinical Laboratory ◽  
Secondary Data ◽  
The United States ◽  
Test Results ◽  
Original Result ◽  
Clinical Laboratories ◽  
Laboratory Test Results ◽  
Standardization Process

Abstract Objective Clinical laboratories in the United States do not have an explicit result standard to report the 7 billion laboratory tests results they produce each year. The absence of standardized test results creates inefficiencies and ambiguities for secondary data users. We developed and tested a tool to standardize the results of laboratory tests in a large, multicenter clinical data warehouse. Methods Laboratory records, each of which consisted of a laboratory result and a test identifier, from 27 diverse facilities were captured from 2000 through 2015. Each record underwent a standardization process to convert the original result into a format amenable to secondary data analysis. The standardization process included the correction of typos, normalization of categorical results, separation of inequalities from numbers, and conversion of numbers represented by words (eg, “million”) to numerals. Quality control included expert review. Results We obtained 1.266 × 109 laboratory records and standardized 1.252 × 109 records (98.9%). Of the unique unstandardized records (78.887 × 103), most appeared <5 times (96%, eg, typos), did not have a test identifier (47%), or belonged to an esoteric test with <100 results (2%). Overall, these 3 reasons accounted for nearly all unstandardized results (98%). Conclusion Current results suggest that the tool is both scalable and generalizable among diverse clinical laboratories. Based on observed trends, the tool will require ongoing maintenance to stay current with new tests and result formats. Future work to develop and implement an explicit standard for test results would reduce the need to retrospectively standardize test results.

Application of Failure Modes and Effects Analysis (FMEA) During the Pre-analytical Phase in a Greek Biochemistry Laboratory

2019 ◽  
Vol 8 (3) ◽  
pp. 22-35 ◽  
Author(s):  
Marilena Stamouli ◽  
Antonia Mourtzikou ◽  
Petros L Karkalousos ◽  
Zoe Athanasiadou ◽  
Evaggelia Marasidi ◽  
...  
Keyword(s):  
Failure Modes ◽  
Patient Treatment ◽  
Test Results ◽  
Clinical Laboratories ◽  
Bibliographic Data ◽  
Potential Failure ◽  
Analytical Phase ◽  
Fmea Analysis ◽  
Analytical Errors ◽  
Made In

It is well known that the results from clinical laboratories support diagnosis, prognosis and patient treatment. Thus, test results must be relevant, accurate and reliable for patient care. Despite all the automation, errors that are classified as pre-analytical, analytical and post-analytical, are still present. International bibliographic data estimates that approximately 62.0% of the errors made in clinical laboratories are due to errors during the pre-analytical stage. The effect of the pre-analytical errors on the laboratory results has consequences that in many cases can lead to reduction of laboratory quality. In this study, the authors run a failure modes and effects analysis (FMEA) to analyze potential failure risks within the pre-analytical phase, in order to classify them according to severity and likelihood, based on the experience. In the present article, the authors performed an FMEA analysis of the pre-analytical phase of the testing process of a biochemistry laboratory.

Effect of Ibuprofen on Serum Laboratory Test Results

1983 ◽  
Vol 40 (6) ◽  
pp. 1025-1034
Author(s):  
Carol L. Colvin ◽  
Raymond J. Townsend ◽  
William R. Gillespie ◽  
Kenneth S. Albert
Keyword(s):  
Laboratory Test ◽  
Test Results ◽  
Laboratory Test Results

Quality benchmarking of smartphone laboratory medicine applications: comparison of laboratory medicine specialists’ and non-laboratory medicine professionals’ evaluation

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Snežana Jovičić ◽  
Joanna Siodmiak ◽  
Marta Duque Alcorta ◽  
Maximillian Kittel ◽  
Wytze Oosterhuis ◽  
...  
Keyword(s):  
Information Quality ◽  
Rating Scale ◽  
Laboratory Medicine ◽  
Test Results ◽  
Content Quality ◽  
Laboratory Test Results ◽  
System Usability Scale ◽  
Mobile Health Applications ◽  
Fit For Purpose ◽  
Health Applications

AbstractObjectivesThere are many mobile health applications (apps) now available and some that use in some way laboratory medicine data. Among them, patient-oriented are of the lowest content quality. The aim of this study was to compare the opinions of non-laboratory medicine professionals (NLMP) with those of laboratory medicine specialists (LMS) and define the benchmarks for quality assessment of laboratory medicine apps.MethodsTwenty-five volunteers from six European countries evaluated 16 selected patient-oriented apps. Participants were 20–60 years old, 44% were females, with different educational degrees, and no professional involvement in laboratory medicine. Each participant completed a questionnaire based on the Mobile Application Rating Scale (MARS) and the System Usability Scale, as previously used for rating the app quality by LMS. The responses from the two groups were compared using the Mann-Whitney U test and Spearman correlation.ResultsThe median total score of NLMP app evaluation was 2.73 out of 5 (IQR 0.95) compared to 3.78 (IQR 1.05) by the LMS. All scores were statistically significantly lower in the NLMP group (p<0.05), except for the item Information quality (p=0.1631). The suggested benchmarks for a useful appear: increasing awareness of the importance and delivering an understanding of persons’ own laboratory test results; understandable terminology; easy to use; appropriate graphic design, and trustworthy information.ConclusionsNLMP’ evaluation confirmed the low utility of currently available laboratory medicine apps. A reliable app should contain trustworthy and understandable information. The appearance of an app should be fit for purpose and easy to use.

scholarly journals COVID-19 and pregnancy – where are we now? A review

2020 ◽  
Vol 48 (5) ◽  
pp. 428-434 ◽  
Author(s):  
Aleksandra Rajewska ◽  
Wioletta Mikołajek-Bedner ◽  
Joanna Lebdowicz-Knul ◽  
Małgorzata Sokołowska ◽  
Sebastian Kwiatkowski ◽  
...  
Keyword(s):  
Case Fatality ◽  
Fetal Distress ◽  
Delivery Mode ◽  
Test Results ◽  
Rt Pcr ◽  
Third Trimester ◽  
The Third ◽  
Laboratory Test Results ◽  
Protective Antibodies ◽  
Polymerase Chain

AbstractThe new acute respiratory disease severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is highly contagious. It has caused many deaths, despite a relatively low general case fatality rate (CFR). The most common early manifestations of infection are fever, cough, fatigue and myalgia. The diagnosis is based on the exposure history, clinical manifestation, laboratory test results, chest computed tomography (CT) findings and a positive reverse transcription-polymerase chain reaction (RT-PCR) result for coronavirus disease 2019 (COVID-19). The effect of SARS-CoV-2 on pregnancy is not already clear. There is no evidence that pregnant women are more susceptible than the general population. In the third trimester, COVID-19 can cause premature rupture of membranes, premature labour and fetal distress. There are no data on complications of SARS-CoV-2 infection before the third trimester. COVID-19 infection is an indication for delivery if necessary to improve maternal oxygenation. Decision on delivery mode should be individualised. Vertical transmission of coronavirus from the pregnant woman to the fetus has not been proven. As the virus is absent in breast milk, the experts encourage breastfeeding for neonatal acquisition of protective antibodies.

scholarly journals Atypical anti-glomerular basement membrane disease with anti-GBM antibody negativity and ANCA positivity: a case report

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Na Guo ◽  
Qinghua Yin ◽  
Song Lei ◽  
Yanjun He ◽  
Ping Fu
Keyword(s):  
Basement Membrane ◽  
Glomerular Basement Membrane ◽  
Rare Case ◽  
Test Results ◽  
Serum Samples ◽  
Tissue Samples ◽  
Case Presentation ◽  
Severe Renal Insufficiency ◽  
Laboratory Test Results ◽  
Organ Specific

Abstract Background Anti-glomerular basement membrane (anti-GBM) disease is an organ-specific autoimmune disease that involves the lung and kidneys and leads to rapid glomerulonephritis progression, with or without diffuse alveolar hemorrhage, and even respiratory failure. Classic cases of anti-GBM disease are diagnosed based on the presence of the anti-GBM antibody in serum samples and kidney or lung biopsy tissue samples. However, atypical cases of anti-GBM disease are also seen in clinical practice. Case presentation We herein report the rare case of a patient with atypical anti-GBM disease whose serum was negative for the anti-GBM antibody but positive for the myeloperoxidase (MPO) anti-neutrophil cytoplasmic antibody (p-ANCA) and another atypical ANCA. Laboratory test results showed severe renal insufficiency with a creatinine level of 385 μmol/L. Renal biopsy specimen analysis revealed 100% glomeruli with crescents; immunofluorescence showed immunoglobulin G (IgG) linearly deposited alongside the GBM. Finally, the patient was discharged successfully after treatment with plasmapheresis, methylprednisolone and prednisone. Conclusion This patient, whose serum was negative for the anti-GBM antibody but positive for p-ANCA and another atypical ANCA, had a rare case of anti-GBM disease. Insights from this unusual case might help physicians diagnose rare forms of glomerulonephritis and treat affected patients in a timely manner.

Requiem for the STAT Test: Automation and Point of Care Testing

2019 ◽  
Author(s):  
Gurmukh Singh ◽  
Natasha M Savage ◽  
Brandy Gunsolus ◽  
Kellie A Foss
Keyword(s):  
Point Of Care ◽  
Turnaround Time ◽  
Test Automation ◽  
Test Results ◽  
Point Of Care Testing ◽  
Tube System ◽  
Front End ◽  
Laboratory Test Results ◽  
Batch Testing ◽  
Pneumatic Tube System

Abstract Objective Quick turnaround of laboratory test results is needed for medical and administrative reasons. Historically, laboratory tests have been requested as routine or STAT. With a few exceptions, a total turnaround time of 90 minutes has been the usually acceptable turnaround time for STAT tests. Methods We implemented front-end automation and autoverification and eliminated batch testing for routine tests. We instituted on-site intraoperative testing for selected analytes and employed point of care (POC) testing judiciously. The pneumatic tube system for specimen transport was expanded. Results The in-laboratory turnaround time was reduced to 45 minutes for more than 90% of tests that could reasonably be ordered STAT. With rare exceptions, the laboratory no longer differentiates between routine and STAT testing. Having a single queue for all tests has improved the efficiency of the laboratory. Conclusion It has been recognized in manufacturing that batch processing and having multiple queues for products are inefficient. The same principles were applied to laboratory testing, which resulted in improvement in operational efficiency and elimination of STAT tests. We propose that the target for in-laboratory turnaround time for STAT tests, if not all tests, be 45 minutes or less for more than 90% of specimens.

Review of short-text classification

2019 ◽  
Vol 15 (2) ◽  
pp. 155-182 ◽  
Author(s):  
Issa Alsmadi ◽  
Keng Hoon Gan
Keyword(s):  
Social Networks ◽  
Text Classification ◽  
Development Trend ◽  
Practical Reasons ◽  
Significant Implication ◽  
Classification Problems ◽  
Content Type ◽  
Short Text ◽  
Promising Area ◽  
Low Performance

PurposeRapid developments in social networks and their usage in everyday life have caused an explosion in the amount of short electronic documents. Thus, the need to classify this type of document based on their content has a significant implication in many applications. The need to classify these documents in relevant classes according to their text contents should be interested in many practical reasons. Short-text classification is an essential step in many applications, such as spam filtering, sentiment analysis, Twitter personalization, customer review and many other applications related to social networks. Reviews on short text and its application are limited. Thus, this paper aims to discuss the characteristics of short text, its challenges and difficulties in classification. The paper attempt to introduce all stages in principle classification, the technique used in each stage and the possible development trend in each stage.Design/methodology/approachThe paper as a review of the main aspect of short-text classification. The paper is structured based on the classification task stage.FindingsThis paper discusses related issues and approaches to these problems. Further research could be conducted to address the challenges in short texts and avoid poor accuracy in classification. Problems in low performance can be solved by using optimized solutions, such as genetic algorithms that are powerful in enhancing the quality of selected features. Soft computing solution has a fuzzy logic that makes short-text problems a promising area of research.Originality/valueUsing a powerful short-text classification method significantly affects many applications in terms of efficiency enhancement. Current solutions still have low performance, implying the need for improvement. This paper discusses related issues and approaches to these problems.

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