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Accreditation and Quality Assurance: Journal for Quality, Comparability and Reliability in Chemical Measurement (v.10, #1-2)
The European Conference on quality in the spotlight in medical laboratories, Antwerp Belgium, 18–19 March 2004
by J. C. Libeer; H. M. J. Goldschmidt (pp. 1-2).
Once more the vexing quest for agreed terminology and ... the concepts behind the terms
by Paul De Bièvre (pp. 3-4).
Is it safe to have a laboratory test? by D. Joe Boone (pp. 5-9).
A recent US Institute of Medicine report indicated that up to 98,000 deaths and more than 1 million injuries occur each year in the United States due to medical errors. These include diagnostic errors, such as an “error or delay in diagnosis, failure to employ indicated tests” and the “use of outmoded tests.” Laboratory tests provide up to 80% of the information used by physicians to make important medical decisions, therefore it is important to determine how often laboratory testing mistakes occur, whether they cause patient harm, where they are most likely to occur in the testing process, and how to prevent them from occurring. A review of the literature and a US Quality Institute Conference in 2003 indicates that errors in laboratory medicine occur most often in the pre-analytical and post-analytical steps in the testing process, but most of the quality improvement efforts focus on improving the analytical process. Measures must be developed and employed to reduce the potential for mistakes in laboratory medicine, including better indicators for the quality of laboratory service. Users of laboratory services must be linked with the laboratory’s information system to assist them with decisions about test ordering, patient preparation, and test interpretation. Quality assessment efforts need to be expanded beyond external quality assessment programs to encompass the detection of non-analytical mistakes and improving communication between the users of and providers of laboratory services. The actual number of mistakes in laboratory testing is not fully recognized, because no widespread process is in place to either determine how often mistakes occur or to systematically eliminate sources of error. We also tend to focus on mistakes that result in adverse events, not the near misses that cause no observable harm. The users of laboratory services must become aware of where testing mistakes can occur and actively participate in designing processes to prevent mistakes. Most importantly, healthcare institutions need to adopt a culture of safety, which is implemented at all levels of the organization. This includes establishing closer links between providers of laboratory services and others in the healthcare delivery system. This was the theme of a 2003 Quality Institute Conference aimed at “making the laboratory a key partner in patient safety.” Plans to create a permanent public–private partnership, called the Institute for Quality in Laboratory Medicine, whose mission is to promote improvements in the use of laboratory tests and laboratory services are underway.
Keywords: Medical errors; Laboratory mistakes; Quality Institute; Institute for Quality in Laboratory Medicine; Patient safety; Improving laboratory services
Clinical quality vs analytical performance: what are the right targets and target values? by James O. Westgard (pp. 10-14).
For optimal performance of laboratory tests, testing processes should be designed to provide clinically useful quality and QC procedures should be selected to assure that the necessary clinical quality is achieved in routine production. One important issue is how to define clinical quality. Today’s practice guidelines, quality regulations, and quality standards provide some targets for analytical quality, but they fail to adequately address clinical quality. Target values for precision and accuracy are not the same as clinical quality, though analytical performance certainly contributes to the clinical quality of test results. How can we proceed in our quest to improve quality when there are inconsistencies and inadequacies in the specifications found in practice guidelines, regulations, and standards? Today target values are often being set for the wrong targets. A better approach is possible if we focus on test interpretation guidelines to define clinical quality, then derive specifications for the accuracy and precision that are appropriate for the method, as well the QC rules and numbers of control measurements that are necessary to guarantee the desired quality will be achieved in routine operation of the testing process.
Keywords: Target values; Clinical quality; Analytical performance; Quality control
The impact of metrological traceability on the validity of creatinine measurement as an index of renal function by Birgitte Wuyts; Dirk Bernard; Nele Van Den Noortgate; Johan Van De Walle; Bruno Van Vlem; Rita De Smet; Frank De Geeter; Raymond Vanholder; Jean-Claude Libeer; Joris Delanghe (pp. 15-19).
When the calibration of a routine measurement procedure is traced back to metrological higher order, a significant discrepancy can occur between the analytical conditions of the routine measurement and the analytical conditions that were used in the clinical studies upon which the decision-making criteria are based. This can lead to serious interpretation errors with possible dramatic consequences for patients. The calibration of the creatinine Jaffé method is an excellent example of the importance of medical traceability. The compensated Jaffé method correlated accurately with the reference method and the compensated Jaffé creatinine clearance (CrCl), Cockroft and Gault and MDRD with the 51Cr EDTA clearance. The Schwartz estimate based upon the compensated Jaffé and enzymatic method overestimated, while uncompensated Jaffé slightly underestimated glomerular filtration rate (GFR). The situation in children is complex since serum creatinine concentrations are much lower in infants, rendering tubular secretion relatively more important. Low-molecular weight proteins have been suggested to replace serum creatinine as a marker for GFR. β-trace protein, cystatin C, and β2-microglobulin showed good correlation with GFR. However, care should be taken in patients presenting with some malignant tumors, since significant increases of cystatin C in patients with metastatic melanoma or colorectal cancer has been reported.
Keywords: Renal function; Creatinine; Glomerular filtration rate; Traceability; Reference method
Practical and theoretical implications of target values in clinical chemistry: a QC approach by Viviane Van Hoof; Hugo Cluckers; Vera Verhaeghe; Marleen Truyens; An Hennebel (pp. 20-26).
The automation of test validation procedures in a routine clinical laboratory by integrating artificial intelligence with the lab information system (LIS) is the focus of this study. Test validation consists of three “layers”: (1) acceptance of patient results based on technical criteria (“technical validation”), (2) acceptance of patient results based on internal quality control (QC) results (“QC validation”) and (3) medical validation of the patient protocol. The emphasis here is put on QC validation. General concepts regarding the setting of performance limits for internal QC results are briefly reviewed. As a practical example, we describe how the different layers of test validation were integrated with Molis, a LIS (Sysmex, Barchon, Belgium) used in a routine clinical biochemistry lab. PGP5 software (PGP, Brussels, Belgium) is used for technical validation and is installed on a Linx work-cell (Thermo Clinical Labsystems, Vantaa, Finland). The latter integrates four different Vitros instruments (OCD, Beerse, Belgium). QC validation and medical validation are performed with QC-Today (IL, Zaventem, Belgium) and VALAB (Erim, Toulouse, France), respectively. Both programs are bi-directionally coupled with the LIS. After 2 years of experience with this integrated system, it is clear that automation of validation rules and procedures has enhanced efficiency and overall quality of patient results.
Keywords: Automation; Artificial intelligence; Test validation; Internal QC
Performance evaluation of a G-6-PD assay employing an adapted error grid graph by George J. Reclos; Tijen Tanyalcin (pp. 27-31).
Glucose-6-phosphate dehydrogenase (G-6-PD) deficiency is one of the most common genetic diseases, characterized by neonatal jaundice or hemolysis during adulthood. Neonatal screening for this disease has been established in many countries in the Mediterranean and Middle East region but not yet in Turkey. The performance of a new fully quantitative G-6-PD kit employing hemoglobin normalization was statistically evaluated and the cut-off points and reference values for the Izmir region (Aegean coast of Turkey) were established, including a long term performance evaluation of the method. Statistical acceptance of the bias and variation were also clinically evaluated using new tools, such as the Parkes error grid graph. The kit used is particularly suitable for use with Guthrie cards as well as with whole blood samples. We report here on the results of the evaluation, emphasizing the methodology we used for it.
Keywords: Reference values; Cut-off values; Glucose-6-phosphate dehydrogenase; Receiver operating characteristics; Error grid graph; Standard deviation indexes
GUM and six sigma approaches positioned as deterministic tools in quality target engineering by Frits van Merode; Hanneke Molema; Henk Goldschmidt (pp. 32-36).
In order to improve the quality and logistics of care, health care organizations should deal with uncertainty of demand and supply, inflexibility of the health care organization and its capacity and organizational complexity. What is needed is integrated process management and standardization to improve quality and safety of care, patient logistics and working conditions of staff. This paper tries to fuse (and in that way better understand) the concept of six sigma with the guide to the expression of uncertainty in measurement in relation to the concept of target engineering.
Keywords: Quality models; Critical control points; Error rate
How medical quality is improved: clinical management of patients with acute cardiac ischaemia by Jaap W. Deckers (pp. 37-40).
The clinical management of patients with acute ischaemic syndromes continues to evolve. It is therefore useful to review the most common categories of such patients, with specific emphasis on the contribution of some well-known laboratory assessments. Key questions on diagnosis, prognosis and medical quality are discussed.
Keywords: Clinical management; Medical laboratory test; Medical diagnosis
Quality through the pharmaceutical chain of care by René Grouls; Wim van de Laar; Henk M. J. Goldschmidt; Frits G. van Merode (pp. 41-46).
Many medical professionals are involved in patient care processes. For pharmaceutical care this results in many information transfer moments. To provide optimal care, communication and information the transfer should be conducted in a timely manner, fully transparent, complete and relevant. The TRANSFORM project is directed towards the development of a reference information model of the pharmaceutical care chain with the aim to improve the availability (time, place, completeness) and access of pharmaceutical information regarding patients, thereby resulting in continuity and quality of pharmaceutical care, reduction in medical errors and improvement in patient safety through the design of a safer healthcare system. TRANSFORM leads to improved insight into the processes and data transfer points in the pharmaceutical chain of care. Focussed on laboratory medicine and pharmacy, the implementation of the integration of laboratory test and pharmacy information may result in major improvements in drug therapy monitoring and guidance (i.e. drug impact monitoring). Because of the overwhelming amount of data generated by this integration of drugs, drug effects and laboratory test results, an online decision support system is warranted.
Keywords: Reference model; Chain of care; Pharmaceutical care; Pharmacy; Medication error
Quality in point-of-care testing: what drives the system—personnel, regulatory standards, or instrumentation? by Sharon S. Ehrmeyer; Ronald H. Laessig (pp. 47-51).
Any assessment of the quality of testing at point of healthcare always focuses on three factors: personnel, the effectiveness of regulatory standards governing the processes, and the capabilities of the analytical instrumentation. Even a casual evaluation of the environment surrounding today’s point-of-care testing suggests that a new quality paradigm is emerging. Focusing only on the analytical process, state-of-the-art instruments currently in routine use demonstrate, de facto, that the responsibility for traditional quality attributes—accuracy (traceability), precision, reliability, quality control, data interpretation, etc.—now are fully in the hands of the manufacturer. However, in the US all testing, including point-of-care testing, must continue to meet regulatory quality-assurance mandates including quality-control practices. In January 2003 the US government published the “Final Rule” under the Clinical Laboratory Improvement Amendments that introduced new quality-assessment requirements, including equivalent quality control, for analytical processes. The concept of equivalent quality control applies to instruments employing internal and/or procedural controls, an area currently dominated by point-of-care test systems. While manufacturers, personnel and regulators all continue to drive aspects of the quality of point-of-care testing, with the Final Rule incorporating a total quality management-based approach, the laboratory director is given some novel quality-control options. While the director retains full responsibility for guaranteeing that results of appropriate quality are provided to clinicians, the manufacturer can assume responsibility for daily, routine quality control, when the director approves, using one of three equivalent quality-control options for a test system.
Keywords: Clinical Laboratory Improvement Amendments; Laboratory regulations; Laboratory directors; Quality instruments; Equivalent quality control
Quality targets in dipstick urinalysis by Paul Tighe (pp. 52-54).
Point of care testing (POCT) of urine has been practiced for many centuries. It has come particularly into its own in the second half of the 20th century with the development of tablet- and later dipstick-testing systems. Unfortunately, it has become embedded in clinical practice outside the laboratory, missing the development of the quality culture that has developed inside the laboratory. Analysis of the results of a Urine Quality Assurance Programme demonstrate the value and the need for this quality culture.
Keywords: Urinalysis; Point of care testing; Quality control; Training of personnel
Implementation of an integrated glucose POCT system by Viviane Van Hoof; Hugo Cluckers; Vera Verhaeghe; An Hennebel; Marleen Truyens; Edith Van Loock; Pierre Rombouts; Dirk Maes; Jan Van Elven; Katrien Lesage; Mark Flothmann; Lieve Nijs; Filip Colpaert; Werner Vercammen; Dominique Bolain (pp. 55-59).
In response to a change of the Belgian National Directives whereby hospital laboratories became responsible for all point-of-care testing (POCT) performed within hospital walls a standardized and automated POC glucose-testing system was implemented in our hospital. The system consists of 50 AccuCheck Inform instruments (Roche Diagnostics, Vilvoorde, Belgium), 50 docking stations, a DataCare Server, and connections to the medical laboratory information system (MOLIS, Sysmex, Barchon, Belgium) and to the hospital information system. Implementation involved many parties and extensive preparation and communication. Key issues were bar-coded patient and user identification, training, and responsibilities. One year after the hospital wide implementation of this system the quality of POC glucose testing has significantly increased, thereby improving patient safety. This study describes a stepwise change over involving the medical laboratory and with a focus on hands-on quality.
Keywords: Point-of-care testing; Glucose testing; Hospital-wide implementation scheme; Medical laboratory automation