Quality Control Recommendations and Procedures for In-Clinic Laboratories (original) (raw)
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ASVCP guidelines: quality assurance for point-of-care testing in veterinary medicine
Veterinary Clinical Pathology, 2013
Point-of-care testing (POCT) refers to any laboratory testing performed outside the conventional reference laboratory and implies close proximity to patients. Instrumental POCT systems consist of small, handheld or benchtop analyzers. These have potential utility in many veterinary settings, including private clinics, academic veterinary medical centers, the community (eg, remote area veterinary medical teams), and for research applications in academia, government, and industry. Concern about the quality of veterinary in-clinic testing has been expressed in published veterinary literature; however, little guidance focusing on POCT is available. Recognizing this void, the ASVCP formed a subcommittee in 2009 charged with developing quality assurance (QA) guidelines for veterinary POCT. Guidelines were developed through literature review and a consensus process. Major recommendations include (1) taking a formalized approach to POCT within the facility, (2) use of written policies, standard operating procedures, forms, and logs, (3) operator training, including periodic assessment of skills, (4) assessment of instrument analytical performance and use of both statistical quality control and external quality assessment programs, (5) use of properly established or validated reference intervals, (6) and ensuring accurate patient results reporting. Where possible, given instrument analytical performance, use of a validated 1 3s control rule for interpretation of control data is recommended. These guidelines are aimed at veterinarians and veterinary technicians seeking to improve management of POCT in their clinical or research setting, and address QA of small chemistry and hematology instruments. These guidelines are not intended to be all-inclusive; rather, they provide a minimum standard for maintenance of POCT instruments in the veterinary setting.
Veterinary Clinical Pathology, 2019
Quality indicator a Measure Frequency Workload Review sample log and count total number of patient tests performed At least monthly Suboptimal samples Review sample log and count numbers of hemolyzed, lipemic, insufficient volume, and improper ID At least monthly Test system flags Count device alerts and error flags At least weekly & monthly totals Runs/tests rejected Review QC log and count runs rejected due to device flags At least monthly Review QC log and count runs rejected due to control flags At least monthly Imprecision Calculate mean, standard deviation (SD) and coefficient of variation (CV) of measurements on stable controls At least monthly Operator variability Calculate SD of duplicate repeat patient test results b At least monthly Bias from EQA survey Calculate average bias for each survey event At least 3 times per year Turnaround time (TAT) Tabulate TAT measures At least weekly Calculate average TAT At least monthly Customer feedback Count number of complaints At least monthly Summarize causes Summarize corrective actions Operator competency Supervisor observation and review of operator performance At least annually Laboratory management / supervisor review Inspection and review of all QA measures and reports At least quarterly a In human medicine, the International Organization for Standardization (ISO) mandates that "the laboratory shall establish QIs (quality indicators) to monitor and evaluate performance throughout critical aspects of preexamination, examination and postexamination processes" and that "the process of monitoring QIs shall be planned, which includes establishing the objectives, methodology, interpretation, limits, action plan and duration of measurement." 11,14 b Refers to repeat patient testing for statistical quality control; please see sections 4.1 and 4.7 for more information.
Journal of the American Veterinary Medical Association, 2013
A ll clinicians expect that the results obtained from the diagnostic tests they perform on their patients are accurate and precise, so that correct clinical decisions can be made to manage their patients. Obtaining results that are inaccurate or imprecise can lead to incorrect diagnoses, inappropriate courses of action, and, potentially, patient harm. These expectations apply to in-clinic biochemistry analyzer systems, which have proliferated in general veterinary practice over the past decade. However, despite the popularity of these analyzers, quality-assurance programs and QC systems have been largely neglected in general veterinary practice, with most clinicians relying on manufacturers' claims and occasional calibration of equipment to ensure diagnostic test quality. Quality assurance is an implied concept inherent in every consumer' s purchase of a product or service. Many of the initial quality-assurance procedures came from the manufacturing sector and were the result of the need to produce products that were cost effective and did not fail. 1 Quality-assurance systems were then discussed and applied over time in human diagnostic laboratories beginning in the 1950s 2 and subsequently implemented in veterinary laboratories. These programs have changed and evolved along with new procedures, statistical concepts, and evaluations of system Current quality assurance concepts and considerations for quality control of in-clinic biochemistry testing
Veterinary Clinics of North America-small Animal Practice, 2007
TECHNOLOGIC EVOLUTION AND TRENDS Evolution of laboratory diagnostic instrumentation is driven predominantly by human health care diagnostic market needs. Large central diagnostic laboratory instrumentation systems have evolved to become more automated, capable of higher throughput, and highly sophisticated in test menus and information management capability. The managed health care system in the United States drives most diagnostic testing to large centralized facilities. In contrast, the instrumentation market for small laboratories and physician offices outside North America has driven the development of much smaller systems to meet those needs. These small systems have simultaneously found their way into the ''point of care'' veterinary market. Over the past 20 years, dramatic progress in reduction of the size, complexity, and cost of laboratory instrumentation for hematology and clinical chemistry has made migration of this technology to small facilities progressively more feasible. This has been made possible by the advances in microprocessor control, miniaturization of fluidics, and microfabrication of mechanical devices. Likewise, improvement in signal measurement and processing has improved precision, accuracy, and general reliability in many systems. This progressive trend has resulted in the ability to move relatively sophisticated diagnostic capability from the central laboratory to the veterinary facility. Systems that would previously fill a pickup truck have been reduced to compact bench-top analyzers. The cost Dr. Weiser is a shareholder and part-time employee of Heska Corporation. Dr. Vap is a shareholder and intermittent consultant of Abaxis. Dr. Thrall is a part-time employee of Antech Diagnostics.
Quality Documentation Challenges for Veterinary Clinical Pathology Laboratories
Journal of Veterinary Diagnostic Investigation, 2008
An increasing number of veterinary laboratories worldwide have obtained or are seeking certification based on international standards, such as the International Organization for Standardization/ International Electrotechnical Commission 17025. Compliance with any certification standard or quality management system requires quality documentation, an activity that may present several unique challenges in the case of veterinary laboratories. Research specifically addressing quality documentation is conspicuously absent in the veterinary literature. This article provides an overview of the quality system documentation needed to comply with a quality management system with an emphasis on preparing written standard operating procedures specific for veterinary laboratories. In addition, the quality documentation challenges that are unique to veterinary clinical pathology laboratories are critically evaluated against the existing quality standards and discussed with respect to possible solutions and/or recommended courses of action. Documentation challenges include the establishment of quality requirements for veterinary tests, the use or modification of human analytic methods for animal samples, the limited availability of quality control materials satisfactory for veterinary clinical pathology laboratories, the limited availability of veterinary proficiency programs, and the complications in establishing species-specific reference intervals.
Veterinary clinical pathology / American Society for Veterinary Clinical Pathology, 2012
In December 2009, the American Society for Veterinary Clinical Pathology (ASVCP) Quality Assurance and Laboratory Standards committee published the updated and peer-reviewed ASVCP Quality Assurance Guidelines on the Society's website. These guidelines are intended for use by veterinary diagnostic laboratories and veterinary research laboratories that are not covered by the US Food and Drug Administration Good Laboratory Practice standards (Code of Federal Regulations Title 21, Chapter 58). The guidelines have been divided into 3 reports: (1) general analytical factors for veterinary laboratory performance and comparisons; (2) hematology, hemostasis, and crossmatching; and (3) clinical chemistry, cytology, and urinalysis. This particular report is one of 3 reports and provides recommendations for control of preanalytical and analytical factors related to hematology for mammalian and nonmammalian species, hemostasis testing, and crossmatching and is adapted from sections 1.1 and ...
The quality of veterinary in-clinic and reference laboratory biochemical testing
Veterinary Clinical Pathology, 2012
Background: Although evaluation of biochemical analytes in blood is common in veterinary practice, studies assessing the global quality of veterinary in-clinic and reference laboratory testing have not been reported. Objective: The aim of this study was to assess the quality of biochemical testing in veterinary laboratories using results obtained from analyses of 3 levels of assayed quality control materials over 5 days. Methods: Quality was assessed by comparison of calculated total error with quality requirements, determination of sigma metrics, use of a quality goal index to determine factors contributing to poor performance, and agreement between in-clinic and reference laboratory mean results. The suitability of in-clinic and reference laboratory instruments for statistical quality control was determined using adaptations from the computerized program, EZRules3. Results: Reference laboratories were able to achieve desirable quality requirements more frequently than in-clinic laboratories. Across all 3 materials, > 50% of in-clinic analyzers achieved a sigma metric ! 6.0 for measurement of 2 analytes, whereas > 50% of reference laboratory analyzers achieved a sigma metric ! 6.0 for measurement of 6 analytes. Expanded uncertainty of measurement and ± total allowable error resulted in the highest mean percentages of analytes demonstrating agreement between in-clinic and reference laboratories. Owing to marked variation in bias and coefficient of variation between analyzers of the same and different types, the percentages of analytes suitable for statistical quality control varied widely. Conclusion: These findings reflect the current state-of-the-art with regard to in-clinic and reference laboratory analyzer performance and provide a baseline for future evaluations of the quality of veterinary laboratory testing.
Veterinary Clinical Pathology, 2018
Background: Quality control procedures are an important part of the overall quality assurance for production of accurate and reliable hematologic results. Objectives: This study aimed to validate a quality control material-based procedure and assess two patient-based quality control procedures (repeat patient testing [RPT] and average of normals [AoN]) with the ADVIA 120 Hematology System. Methods: Requirements for quality control procedures were obtained with the computerized statistical and quality program, EZRules3. The procedures were evaluated comparing the probability of error detection (Ped), probability of false rejection (Pfr), and sigma metrics. Results: All three of the quality control procedures could be applied with 1-3s control rules, achieving the desired quality requirements. Validation of the quality control materials achieved values for Ped and Pfr of ≥90% and 0%, respectively. Patient-based procedures obtained a ≥85% Ped and a 0% Pfr, except for platelets in the AoN procedure, which achieved a 77% Ped. The RPT achievable total errors were similar to those of the traditional quality control materials and the AoN procedures, except for platelets, which had an achievable total error of 75%. Conclusions: Patient-based procedures are suitable for veterinary laboratories. The RPT approach may benefit laboratories with limited budgets and low hematology caseloads. The AoN procedure may benefit laboratories with higher hematology caseloads.
Chapter Number Quality Control in Clinical Laboratories
2010
1.1 The automated analyzers in clinical laboratories Nowadays, the overwhelming majority of laboratory results in clinical laboratories is being generated by automated analyzers. Modern automated analyzers are highly sophisticated instruments which can produce a tremendous number of laboratory results in a very short time. This is achieved thanks to the integration of technologies from three different scientific fields: analytical chemistry, computer science and robotics. The combination of these technologies substitutes a huge number of glassware equipment and tedious, repetitive laboratory work. As a matter of fact, the laboratory routine work has diminished significantly. Today laboratory personnel’s duties have been shifted from manual work to the maintenance of the equipment, internal and external quality control, instrument calibration and data management of the generated results.