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Jan S Krouwer

Publications and source records attributed to Jan S Krouwer.

9 recordsLinked to original sources
PubMed2006

Recommendation to treat continuous variable errors like attribute errors.

Clinical laboratory errors can be considered as either belonging to attribute or continuous variables. Attribute errors are usually considered to be pre- or post-analytical errors, whereas continuous variable errors are analytical. Goals for each error type are different. Error goals for continuous variables are often specified as limits that contain 95% of the results, whereas attribute error goals are specified as allowed error rates for serious events. This leads to a discrepancy, because for a million results, there can be up to 50,000 medically unacceptable analytical errors, but allowable pre- and post-analytical error rates are much lower than 5%. Steps to remedy this are to classify analytical error rates into severity categories, exemplified by existing glucose error grids. The results in each error grid zone are then counted, as has been recommended by the Food and Drug Administration (FDA). This in effect transforms the continuous variable errors into attribute errors. This is an improvement over current practices for analytical errors, whereby the use of uncertainty intervals is recommended that include only 95% of the results (i.e., leaves out the worst 5%), and it is precisely this 5% of results that are likely to be in the most severe zones of an error grid.

Health Planning Guidelines↗
PubMed2004

An improved failure mode effects analysis for hospitals.

OBJECTIVE: To review the Failure Mode Effects Analysis (FMEA) process recommended by the Joint Commission on Accreditation of Health Organizations and to review alternatives. This reliability engineering tool may be unfamiliar to hospital personnel. DATA SOURCES: Joint Commission on Accreditation of Health Organizations recommendations, Mil-Std-1629A, and other articles about FMEA were used. STUDY SELECTION: The articles were selected by a literature search that included Web site-accessible material. DATA EXTRACTION: All articles found were used. DATA SYNTHESIS: The results are based on the articles cited and the author's experience in conducting FMEAs in the medical diagnostics industry. CONCLUSIONS: Fault trees and a list of quality system essentials are recommended additions to the FMEA process to help identify failure mode effects and causes. Neglecting mitigations for failure modes that have never occurred is a possible danger when too much emphasis is placed on improving risk priority numbers. A modified Pareto, not based on the risk priority number, is recommended when there are qualitatively different failure mode effects with different severities. Performing a FMEA that both meets accreditation requirements and reduces the risk of medical errors is an attainable goal, but it may require a different focus.

Clinical Laboratory Techniques↗
PubMed2003

Critique of the Guide to the expression of uncertainty in measurement method of estimating and reporting uncertainty in diagnostic assays.

BACKGROUND: The Guide to the Expression of Uncertainty in Measurement (GUM) provides instructions for constructing uncertainty intervals for a measurement. This method is usually reserved for reference materials, but GUM has been recently proposed as a way to express uncertainty for commercial diagnostic assays. METHODS: Using the official GUM standard and published applications of GUM to commercial diagnostic assays, I undertook an analysis to evaluate whether applying GUM to commercial diagnostic assays is warranted. RESULTS: Certain important assays, such as troponin I, would not be candidates for GUM because troponin I is not a well-defined physical quantity. Unlike definitive methods, in which efforts are taken to detect and eliminate all systematic error sources, commercial assays often trade off features such as ease of use and cost with accuracy and allow systematic errors to be present as long as the overall accuracy meets the medical need goal. Laboratories are hindered in preparing GUM models because the knowledge required to specify some systematic errors is often available only to manufacturers. Some non-GUM methods to estimate uncertainty rely on observed data, which include both known and unknown sources of error. The occurrence of large, unknown errors for assays in routine use (e.g., outliers) is not unusual because diagnostic assays must be chemically specific in the presence of thousands of potentially interfering substances. There is no provision in GUM to deal with unexplained outliers, which may lead to uncertainty intervals that are not wide enough. Some clinicians assume that diagnostic assay results have little uncertainty. This situation may be made worse by including an uncertainty interval, which implies certification. CONCLUSIONS: Evaluations for accuracy (total analytical error) based on describing the distribution of result differences between commercial assays and reference methods indicate that some assays have a few results with large differences (e.g., outliers). This leads to a wide accuracy interval (total analytical error limits). It is unlikely that GUM would be able to predict these wide intervals, especially because there is little or no provision for outlier treatment in GUM. Presenting too narrow GUM uncertainty intervals to clinicians would be misleading. The modeling used by practitioners of the GUM method is potentially useful in improving quality, but commercial diagnostic assays are not ready for GUM uncertainty statements.

Clinical Laboratory Techniques↗
PubMed2002

Setting performance goals and evaluating total analytical error for diagnostic assays.

BACKGROUND: Total analytical error has been a useful metric both to assess laboratory assay quality and to set goals. It is often estimated by combining imprecision (SD) and average bias in the equation: total analytical error = bias + 1.65 x imprecision. This indirect estimation model (referred to as the simple combination model) leads to different estimates of total analytical error than that of a direct estimation method (referred to as the distribution-of-differences method) or of simulation. METHODS: A review of the literature was undertaken to reconcile the different estimation approaches. RESULTS: The simple combination model can underestimate total analytical error by neglecting random interference bias and by not properly treating other error sources such as linear drift and outliers. A simulation method to estimate total analytical error is outlined, based on the estimation and combination of total analytical error source distributions. Goals for each total analytical error source can be established by allocation of the total analytical error goal. Typically, the allocation is cost-based and uses the probability of combinations of error sources. The distribution-of-differences method, simple combination model, and simulation method to evaluate total analytical error are compared. Outlier results can profoundly influence quality, but their rates are seldom reported. CONCLUSIONS: Total analytical error should be estimated either directly by the distribution-of-differences method or by simulation. A systems engineering approach that uses allocation of the total analytical error goal into error source goals provides a cost-effective approach to meeting total analytical error. Because outliers can cause serious laboratory error, the inclusion of outlier rate estimates from large studies (e.g., those conducted by manufacturers) would be helpful in assessing assay quality.

Clinical Laboratory Techniques↗