Use of quality indicators to compare point-of-care testing errors in a neonatal unit and errors in a STAT central laboratory

2015 ◽  
Vol 53 (2) ◽  
Author(s):  
Miguel Cantero ◽  
Maximino Redondo ◽  
Eva Martín ◽  
Gonzalo Callejón ◽  
María Luisa Hortas
Keyword(s):  
Quality Indicators ◽  
Point Of Care ◽  
Sample Volume ◽  
Error Rates ◽  
Internal Quality ◽  
Central Laboratory ◽  
Point Of Care Testing ◽  
Patient Identification ◽  
Preanalytical Phase ◽  
Analytical Phase

AbstractPoint-of-care testing (POCT), like other laboratory tests, can be affected by errors throughout the total testing process. To evaluate quality error rates, the use of quality indicators (QIs) is recommended; however, little information is available on the quality error rate associated with POCT. The objective of this study was to investigate quality error rates related to POCT and compare them with central laboratory (CL) testing.We studied standardized QIs for POCT in comparison to CL testing. We compared error rates related to requests, collection, and handling of samples and results from external quality assessment program (EQAP) and internal quality control (IQC).The highest difference between POCT and CL testing was observed for QI related to patient identification, 45.3% vs. 0.02% (p<0.001). Regarding specimen collection and handling, the QI related to samples without results was also higher in POCT than in CL testing, 15.8% vs. 3.3% (p<0.001). For the QI related to insufficient sample volume, we obtained 2.9% vs. 0.9% (p=0.27). Unlike QIs for the preanalytical phase, QIs for the analytical phase had better results in POCT than CL testing. We obtained 8.3% vs. 16.6% (p=0.13) for QI related to unacceptable results in EQAP and 0.8% vs. 22.5% (p<0.001) for QI related to unacceptable results in IQC.Our results show that the preanalytical phase remains the main problem in POCT like in CL testing and that monitoring of quality indicators is a very valuable tool in reducing errors in POCT.

scholarly journals Quality Error Rates in Point-of-Care Testing

2011 ◽  
Vol 57 (9) ◽  
pp. 1267-1271 ◽  
Author(s):  
Maurice J O'Kane ◽  
Paul McManus ◽  
Noel McGowan ◽  
PL Mark Lynch
Keyword(s):  
Error Rate ◽  
Drugs Of Abuse ◽  
Point Of Care ◽  
Error Rates ◽  
Adverse Impact ◽  
Central Laboratory ◽  
Point Of Care Testing ◽  
Laboratory Service ◽  
Laboratory Staff ◽  
Hb A1c

BACKGROUND Although a theoretical consideration suggests that point-of-care testing (POCT) might be uniquely vulnerable to error, little information is available on the quality error rate associated with POCT. Such information would help inform risk/benefit analyses when one considers the introduction of POCT. METHODS This study included 1 nonacute and 2 acute hospital sites. The 2 acute sites each had a 24-h central laboratory service. POCT was used for a range of tests, including blood gas/electrolytes, urine pregnancy testing, hemoglobin A1c (Hb A1c), blood glucose, blood ketones, screening for drugs of abuse, and urine dipstick testing. An established Quality Query reporting system was in place to log and investigate all quality errors associated with POCT. We reviewed reports logged over a 14-month period. RESULTS Over the reporting period, 225 Quality Query reports were logged against a total of 407 704 POCT tests. Almost two-thirds of reports were logged by clinical users, and the remainder by laboratory staff. The quality error rate ranged from 0% for blood ketone testing to 0.65% for Hb A1c testing. Two-thirds of quality errors occurred in the analytical phase of the testing process. These errors were all assessed as having no or minimal adverse impact on patient outcomes; however, the potential adverse impact was graded higher. CONCLUSIONS The quality error rate for POCT is variable and may be considerably higher than that reported previously for central laboratory testing.

Technology to improve quality and accountability

2006 ◽  
Vol 44 (6) ◽  
Author(s):  
Jonathan Kay
Keyword(s):  
Communication Technology ◽  
Current Practice ◽  
Point Of Care ◽  
Error Rates ◽  
Internet Communication ◽  
Central Laboratory ◽  
Point Of Care Testing ◽  
Technology Process ◽  
Control Procedures ◽  
Auto Identification

AbstractA body of evidence has been accumulated to demonstrate that current practice is not sufficiently safe for several stages of central laboratory testing. In particular, while analytical and perianalytical steps that take place within the laboratory are subjected to quality control procedures, this is not the case for several pre- and post-analytical steps. The ubiquitous application of auto-identification technology seems to represent a valuable tool for reducing error rates. A series of projects in Oxford has attempted to improve processes which support several areas of laboratory medicine, including point-of-care testing, blood transfusion, delivery and interpretation of reports, and support of decision-making by clinicians. The key tools are auto-identification, Internet communication technology, process re-engineering, and knowledge management.

Haemoglobin A1c: evaluation of a new HbA1c point-of-care analyser Bio-Rad in2it in comparison with the DCA 2000 and central laboratory analysers

2009 ◽  
Vol 46 (5) ◽  
pp. 373-376 ◽  
Author(s):  
Chin-Pin Yeo ◽  
Carol Hui-Chen Tan ◽  
Edward Jacob
Keyword(s):  
Point Of Care ◽  
Outpatient Setting ◽  
Haemoglobin A1c ◽  
Central Laboratory ◽  
Point Of Care Testing ◽  
Laboratory Methods ◽  
Deming Regression ◽  
Difference Test ◽  
Roche Cobas ◽  
Hba1c Levels

Background Point-of-care-testing (POCT) of haemoglobin Alc (HbA1c) is popular due to its fast turnaround of results in the outpatient setting. The aim of this project was to evaluate the performance of a new HbA1c POCT analyser, the Bio-Rad in2it, and compare it with the Siemens DCA 2000, Bio-Rad Variant II and Roche Tina-quant HbA1c Gen 2 assay on the cobas c501. Methods Imprecision of the four methods were compared by computing total imprecision from within-run and between-run data. A total of 80 samples were also compared and analysed by Deming regression and Altman–Bland difference test. Results Study of total imprecision of the in2it at HBA1c levels of 6.0% and 10.4% produced a coefficient of variation (%CV) of 3.8% and 3.7%, respectively. These results were more favourable as compared with the DCA 2000 but did not match the low imprecision of the central laboratory methods, the Bio-Rad Variant II and the Roche cobas c501. Comparison between the in2it and the central laboratory analysers, Bio-Rad variant II and cobas c501, revealed positive bias of 12% and 10%, respectively, supported by corresponding Deming regression equation slopes of +1.18 and +1.14. Comparison between the DCA 2000 and the central laboratory analysers revealed a bias that became increasingly positive with rising HbA1c concentrations with Deming regression analysis also revealing proportional and constant differences. Conclusions The in2it is a suitable POCT analyser for HbA1c but its less than ideal precision performance and differences with the central laboratory analysers must be communicated to and noted by the users.

Point-of-Care Testing Error: Sources and Amplifiers, Taxonomy, Prevention Strategies, and Detection Monitors

2005 ◽  
Vol 129 (10) ◽  
pp. 1262-1267 ◽  
Author(s):  
Frederick A. Meier ◽  
Bruce A. Jones
Keyword(s):  
Direct Observation ◽  
Test Performance ◽  
Point Of Care ◽  
Point Of Care Testing ◽  
Patient Identification ◽  
Test Procedures ◽  
Method Performance ◽  
Therapeutic Implications ◽  
Reasonable Approach ◽  
Structured Observation

Abstract Context.—In a survey performed 4 years ago, testing venues doing only point-of-care testing (POCT) made up 78% of sites for patient testing licensed under federal regulations. Objectives.—To identify sources of POCT error, to present a classification of such errors, to suggest strategies to prevent errors, and to describe monitors that assess and reduce the frequency of errors. Design.—To identify sources of POCT error, large studies of error among US Federal Certificate of Waiver laboratories (CoWs) and practitioner-performed microscopy certificate holders were reviewed. To facilitate investigation and management of POCT error, a taxonomy of such errors (modified from a classification previously published by Gerald Kost) was used to identify 4 steps with error potential in each of the 3 phases (ie, preanalytic, analytic, and postanalytic) of the POCT process. To prevent observed POCT errors, 4 strategies are suggested: direct observation of instrument/method functionality, structured observation of method performance, proficiency testing/use of relevant test scenarios, and autonomation. To assess frequency of errors, a quartet of indices are introduced as detection monitors: order documentation, patient identification, specimen adequacy, and result integrity. Results.—Three sources of POCT error were identified: operator incompetence, nonadherence to test procedures, and use of uncontrolled reagents and equipment. Three other characteristics of many point-of-care tests amplify their risk of error: incoherent regulation, rapid availability of results, and the results' immediate therapeutic implications. Two members of the quartet of detection monitors, order documentation and specimen adequacy, are relatively difficult to measure and are controversial, but the other 2, patient identification and result integrity, are easier to assess and are relatively widely accepted. Conclusions.—Point-of-care testing errors are relatively common, their frequency is amplified by incoherent regulation, and their likelihood of affecting patient care is amplified by the rapid availability of POCT results and the results' immediate therapeutic implications. The modified Kost taxonomy offers a reasonable approach to the identification of POCT errors. Direct observation of test functionality, structured observation of test performance, and testing the competence of POCT operators, as well as autonomation of devices, are strategies to prevent such errors. In this context, we suggest monitoring POCT order documentation, patient identification, specimen integrity, and result reporting to detect errors in this sort of testing.

scholarly journals Acoustofluidic Micromixing Enabled Hybrid Integrated Colorimetric Sensing, for Rapid Point-of-Care Measurement of Salivary Potassium

Biosensors ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 73 ◽  
Author(s):  
Vikram Surendran ◽  
Thomas Chiulli ◽  
Swetha Manoharan ◽  
Stephen Knisley ◽  
Muthukumaran Packirisamy ◽  
...  
Keyword(s):  
Color Change ◽  
Point Of Care ◽  
Sample Volume ◽  
Point Of Care Testing ◽  
Colorimetric Sensing ◽  
In Situ Tests ◽  
Detection Scheme ◽  
Chip Technology ◽  
Whole Saliva ◽  
Microfluidic Biosensor

The integration of microfluidics with advanced biosensor technologies offers tremendous advantages such as smaller sample volume requirement and precise handling of samples and reagents, for developing affordable point-of-care testing methodologies that could be used in hospitals for monitoring patients. However, the success and popularity of point-of-care diagnosis lies with the generation of instantaneous and reliable results through in situ tests conducted in a painless, non-invasive manner. This work presents the development of a simple, hybrid integrated optical microfluidic biosensor for rapid detection of analytes in test samples. The proposed biosensor works on the principle of colorimetric optical absorption, wherein samples mixed with suitable chromogenic substrates induce a color change dependent upon the analyte concentration that could then be detected by the absorbance of light in its path length. This optical detection scheme has been hybrid integrated with an acoustofluidic micromixing unit to enable uniform mixing of fluids within the device. As a proof-of-concept, we have demonstrated the real-time application of our biosensor format for the detection of potassium in whole saliva samples. The results show that our lab-on-a-chip technology could provide a useful strategy in biomedical diagnoses for rapid analyte detection towards clinical point-of-care testing applications.

scholarly journals Impact of point-of-care testing on patients' length of stay in a large emergency department

1996 ◽  
Vol 42 (5) ◽  
pp. 711-717 ◽  
Author(s):  
C A Parvin ◽  
S F Lo ◽  
S M Deuser ◽  
L G Weaver ◽  
L M Lewis ◽  
...  
Keyword(s):  
Emergency Department ◽  
Length Of Stay ◽  
Point Of Care ◽  
Control Period ◽  
Experimental Period ◽  
Central Laboratory ◽  
Point Of Care Testing ◽  
Acceptable Accuracy ◽  
Patient Interview ◽  
Combined Control

Abstract We prospectively investigated whether routine use of a point-of-care testing (POCT) device by nonlaboratory operators in the emergency department (ED) for all patients requiring the available tests could shorten patient length of stay (LOS) in the ED. ED patient LOS, defined as the length of time between triage (initial patient interview) and discharge (released to home or admitted to hospital), was examined during a 5-week experimental period in which ED personnel used a hand-held POCT device to perform Na, K, Cl, glucose (Gluc), and blood urea nitrogen (BUN) testing. Preliminary data demonstrated acceptable accuracy of the hand-held device. Patient LOS distribution during the experimental period was compared with the LOS distribution during a 5-week control period before institution of the POCT device and with a 3-week control period after its use. Among nearly 15 000 ED patient visits during the study period, 4985 patients (2067 during the experimental period and 2918 during the two control periods) had at least one Na, K, Cl, BUN, or Gluc test ordered from the ED. However, no decrease in ED LOS was observed in the tested patients during the experimental period. Median LOS during the experimental period was 209 min vs 201 min for the combined control periods. Stratifying patients by presenting condition (chest pain, trauma, etc.), discharge/admit status, or presence/absence of other central laboratory tests did not reveal a decrease in patient LOS for any patient subgroup during the experimental period. From these observations, we consider it unlikely that routine use of a hand-held POCT device in a large ED such as ours is sufficient by itself to impact ED patient LOS.

scholarly journals Challenges to Glycemic Measurement in the Perioperative and Critically Ill Patient: A Review

2009 ◽  
Vol 3 (6) ◽  
pp. 1270-1281 ◽  
Author(s):  
Andrew D. Pitkin ◽  
Mark J. Rice
Keyword(s):  
Point Of Care ◽  
Turnaround Time ◽  
Sample Volume ◽  
Hospital Environment ◽  
Glucose Measurement ◽  
Critically Ill Patient ◽  
Central Laboratory ◽  
Patients With Diabetes ◽  
Measurement Devices ◽  
Glucose Management

Accurate monitoring of glucose in the perioperative environment has become increasingly important over the last few years. Because of increased cost, turnaround time, and sample volume, the use of central laboratory devices for glucose measurement has been somewhat supplanted by point-of-care (POC) glucose devices. The trade-off in moving to these POC systems has been a reduction in accuracy, especially in the hypoglycemic range. Furthermore, many of these POC devices were originally developed, marketed, and received Food and Drug Administration regulatory clearance as home use devices for patients with diabetes. Without further review, many of these POC glucose measurement devices have found their way into the hospital environment and are used frequently for measurement during intense insulin therapy, where accurate measurements are critical. This review covers the technology behind glucose measurement and the evidence questioning the use of many POC devices for perioperative glucose management.

scholarly journals Reducing patient identification errors related to glucose point-of-care testing

2011 ◽  
Vol 2 (1) ◽  
pp. 22 ◽  
Author(s):  
Liron Pantanowitz ◽  
Gaurav Alreja ◽  
Namrata Setia ◽  
James Nichols
Keyword(s):  
Point Of Care ◽  
Point Of Care Testing ◽  
Patient Identification
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