Quality Control Management and Communication Between Radiologists and Technologists

The present study seeks to demonstrate the feasibility of avoiding the preliminary phase, which is mandatory in all conventional approaches for internal quality control (IQC) management. Apart from savings on the resources consumed by the preliminary phase, the alternative approach described here is able to detect any analytic problems during the startup and provide a foundation for subsequent conventional assessment. A new dynamically updated predictive control chart (PCC) is used. Being Bayesian in concept, it utilizes available prior information. The manufacturer's prior quality control target value, the manufacturer's maximum acceptable interassay coefficient of variation value and the interassay standard deviation value defined during method validation in each laboratory, allow online IQC management. An Excel template, downloadable from journal website, allows easy implementation of this alternative approach in any laboratory. In the practical case of prothrombin percentage measurement, PCC gave no false alarms with respect to the 1ks rule (with same 5% false-alarm probability on a single control sample) during an overlap phase between two IQC batches. Moreover, PCCs were as effective as the 1ks rule in detecting increases in both random and systematic error after the minimal preliminary phase required by medical biology guidelines. PCCs can improve efficiency in medical biology laboratories.

The purposes of this research were analysis the components, to develop the quality management for production model and to develop the manual for application of quality management for production in truck body assembly industry. The study was a mixed method of qualitative and quantitative approaches. The research tools for the qualitative study were in-depth interview and focus group whereas the research tools for quantitative study were questionnaires. The sampling were the production manager, product design manager, quality control manager and factory manager. The qualitative analysis used content analysis. These statistics of quantitative used were frequency, mean, percentage, standard deviation and factor analysis. The research finding indicated that 1) the quality management for production model in truck body assembly industry consisted of 3 managerial dimensions, Dimensions: 1) Quality Planning which contains 4 components including product design, product control plan, operation procedure and operation Standard. Dimension: 2) Quality Control which contains 3 components including document and quality control method, measuring instrument control and quality inspector management. Dimension: 3) Quality Improvement which contains 4 components including quality improvement planning, quality improvement operating, quality improvement checking and problem solving. The model of quality management for production in truck body assembly industry was verified in agreement with the experts in the focus group meeting.

Next-generation sequencing of large numbers of individuals presents challenges in data preparation, quality control, and statistical analysis because of the rarity of the variants. The Genetic Analysis Workshop 17 (GAW17) data provide an opportunity to survey existing methods and compare these methods with novel ones. Specifically, the GAW17 Group 2 contributors investigate existing and newly proposed methods and study design strategies to identify rare variants, predict functional variants, and/or examine quality control. We introduce the eight Group 2 papers, summarize their approaches, and discuss their strengths and weaknesses. For these investigations, some groups used only the genotype data, whereas others also used the simulated phenotype data. Although the eight Group 2 contributions covered a wide variety of topics under the general idea of identifying rare variants, they can be grouped into three broad categories according to their common research interests: functionality of variants and quality control issues, family-based analyses, and association analyses of unrelated individuals. The aims of the first subgroup were quite different. These were population structure analyses that used rare variants to predict functionality and examine the accuracy of genotype calls. The aims of the family-based analyses were to select which families should be sequenced and to identify high-risk pedigrees; the aim of the association analyses was to identify variants or genes with regression-based methods. However, power to detect associations was low in all three association studies. Thus this work shows opportunities for incorporating rare variants into the genetic and statistical analyses of common diseases.

One way to improve the accessibility and usefulness of drug literature is to manage document-based information with a digital image management system. However, before such a system is procured, it is vital that the pharmaceutical firm's needs be assessed with care. This presentation first defines a context within which technology applications such as digital imaging should be considered. Next, the usual steps in application development are described, noting that the first key step is needs assessment. The underlying goals of needs assessment are discussed and critical issues to be considered during a needs assessment are enumerated. Finally, a detailed approach to conducting a needs assessment that has been used successfully in pharmaceutical firms is presented. Each step in the process is described, typical findings are discussed, and some pitfalls are highlighted.

The users of soil-cement in the water resources application have broadened over the past years. Still one construction related task has remained essentially the same: quality control. Soil-cement is a highly compacted mixture of soil; usually sand, Portland cement, and water. It has the properties of a low-strength concrete. Without adequate controls on the quality of the materials and construction methods, the performance of the project may not meet expectations. A successful soil-cement project begins with the development of a quality control program with the responsibilities of each party clearly defined. The quality control plan should address submittals which must receive approval from the project engineer; mixture proportioning considerations; minimum requirements for equipment such as the mixing plant, compactors, and curing equipment; types and frequencies of material testing; and acceptance criteria for construction procedures and individual material tests. Quality control practices during construction do hot have to negatively impact the contractor's production. Each member of the project team, from the laborer to the project manager, can be aware and trained in his or her responsibilities to ensure a quality product. The benefits of having everyone on the project buy into and execute their responsibilities will minimize any rework and ensure that the performance criteria will be met or exceeded. This paper will focus on quality control issues where soil-cement is used for bank stabilization grade control structures and slope protection. Issues involving mixing of soil-cement will be directed at central plant pugmills.

Extended abstract of a paper presented at Microscopy and Microanalysis 2006 in Chicago, Illinois, USA, July 30 – August 3, 2005

To deal with the complicated quality tracking and control problem of interenterprise and meet the rapidly changing customer needs in networked manufacturing environment, combined with the similarity of multilayer organization structure and work flow among core enterprise and member enterprises, a multi-agent quality tracking and control model based on fractal for inter-enterprise is proposed. With the aid of fractal concept, the complicated quality tracking and control process of inter-enterprise could be decomposed into multiple quality tracking and control units which are also fractals. Each fractal consists of six function modules: fractal organizer, quality monitor, quality analyzer, quality controller and quality reporter. Using agent technology, each of these modules could autonomously cooperate and negotiate with others while processing its own jobs. By the interaction and negotiation activities of multiagent, quality tracking and control of inter-enterprise could be realized. The dynamic restructuring process (DRP) allows inter-enterprise quality tracking and control to be more efficient and effective by reconfiguring fractals. This research reduces the difficult of inter-enterprise quality tracking and control and lays foundation for the cooperative quality control of manufacturing process.

Molecular testing in non-small cell lung cancer (NSCLC) identifies patients likely to respond to targeted therapeutics. Panel-based testing often employs next-generation sequencing, but challenges include high sample failure rates, quality control issues, high tissue requirements and long turnaround times. Significant proportions of patients do not receive appropriate targeted therapy, with inferior clinical outcomes. Aspyre Lung is a targeted genomic profiling assay for 114 actionable or prognostic genomic variants across 11 genes with a two-day turnaround time of specimen to result. We profiled 198 NSCLC patient tissue samples using Aspyre Lung and a next-generation sequencing- (NGS)-based assay. Cohort A comprised 107 samples that failed to inform due to NGS quality checks, and Cohort B 91 samples that underwent successful NGS testing. Results were compared, and discrepancies resolved by orthogonal methods. For Cohort A (NGS fails), 103 (96 %) passed Aspyre Lung quality control, successfully yielding genomic results, including 48 (47 %) samples with ≥ one variant. In Cohort B (NGS pass), all samples passed Aspyre quality control with 97 % concordance to NGS-based testing. Notably, 80 % EGFR variant-positive samples were stages I and II. Aspyre Lung profiled 96 % of samples where NGS-based methods failed, and uncovered variants in samples successfully tested by NGS and deemed negative. Aspyre Lung detected ALK and EGFR variants from patients with early-stage disease, demonstrating utility as a rapid screening assay prior to neoadjuvant immuno-chemotherapy consideration.

Gene expression array technologies are rapidly emerging for use in various genome-wide studies in biology and medicine (1)(2)(3). Results from the recently completed sequencing of the human genome predict that the number of genes in the human genome is much smaller (∼33 000 genes) than anticipated (4). This implies that the major complexity in biological mechanisms in humans lies in the synergistic effects among various genes. Therefore, to decipher the secrets of our life and, ultimately, to find the cures for many human diseases, biologists must deal with multiple genes and their interactive transcripts simultaneously. High-throughput biotechnologies, including gene chip approaches, will play an important role in these studies (5). However, quality control over thousands of gene expression values and full utilization of the information from these high-throughput data are extremely difficult, and important issues in quality control and bioinformatic approaches have not been resolved (6)(7). A series of careful analyses on the variability of array instrumentation and on the statistical evaluation of gene expression intensities are required for reliable and consistent inference on gene chip data. Specifically, sources of error and their confidence levels on these high-throughput measurements need to be better understood because some can significantly alter our inferences and conclusions (8). The purpose of this report is to dispel three common misconceptions about array experiments. It often is believed that reproducibility of gene expression data is high enough to perform bioinformatic discovery without a replicated chip experiment and that duplicated array data are necessary only for confirmation of such a discovery, which may help publication in high-standard journals. Investigators who have successfully obtained resources for high-cost gene chip experiments must optimize their experimental strategy for their study goals within limited resources. Therefore, for an initial study, single-chip experiments for each …

Large-scale mass-spectrometry-based proteomics experiments are complex and prone to analytical variability, requiring rigorous quality checks across each step in the workflow: sample preparation, chromatography, mass spectrometry, and the bioinformatics stages. This includes quality control (QC) measures that address biological and technical variation. Most QC approaches involve detecting sample outliers and monitoring parameters related to sample preparation and mass spectrometer performance. Evaluating these parameters regularly is essential for reliable downstream analysis and proteomics research. Here, we introduce "QCeltis", a Python package designed to facilitate automated QC analysis across the proteomics workflow, aiding in the identification of technical biases and consistency verification. QCeltis is a versatile tool for detecting QC issues in large-scale data-independent acquisition proteomics experiments by not only identifying sample preparation and acquisition issues but also aiding in differentiating between QC issues vs batch effects. QCeltis is available for command-line use in Windows and Linux environments. We present three case studies showcasing QCeltis's capabilities across different data sets, including depleted plasma, whole blood vs plasma, and dried blood spot samples, emphasizing its potential impact on large-scale proteomics projects. This package can be used to enhance data reliability and enable nuanced downstream analysis and interpretation for proteomics studies.

本研究旨在探讨申办者在医疗器械临床试验中构建文件质量控制体系的必要性，并介绍基于量化评价的全面质量控制体系。该体系以国内相关规范为指导，涵盖研究中心和申办者文件夹的质控，通过评分机制客观评估文件质量，并结合质控员–监查员–研究者反馈环路以及质控员–质控体系负责人–质控体系环路，实现问题发现、整改和持续改进。超过85%的临床试验质控评分呈现出逐次上升的趋势，质控中问题的发生率也呈现出下降趋势。该体系有效提升了临床试验文件质量，为申办者提供了标准化、可操作的质控流程，为提升我国医疗器械临床试验整体管理水平提供了参考。This study aims to investigate the necessity for sponsors to establish a quality control system for clinical trial documentation in medical device clinical trials and to introduce a quantitatively assessed total quality control system. Guided by domestic regulations, this system encompasses the quality control of both investigator and sponsor Trial Master File (TMF). TMF quality can be assessed objectively through a scoring mechanism, which is integrated with a feedback loop involving quality controllers, monitors, and investigators, as well as a loop between quality controllers, quality control management, and the quality control system, to facilitate problem identification, rectification, and continuous improvement. Over 85% of clinical trial quality control scores showed a trend of successive increase, and the incidence of issues identified during quality control also demonstrated a decreasing trend. The system effectively enhances the quality of clinical trial documentation, providing sponsors with a standardized and operational quality control process, and serves as a reference for improving the overall management level of medical device clinical trials in our country.

Thorough quality control (QC) can be time consuming when working with large-scale medical imaging datasets, yet necessary, as poor-quality data can lead to erroneous conclusions or poorly trained machine learning models. Most efforts to reduce data QC time rely on quantitative outlier detection, which cannot capture every instance of algorithm failure. Thus, there is a need to visually inspect every output of data processing pipelines in a scalable manner. We design a QC pipeline that allows for low time cost and effort across a team setting for a large database of diffusion-weighted and structural magnetic resonance images. Our proposed method satisfies the following design criteria: 1.) a consistent way to perform and manage quality control across a team of researchers, 2.) quick visualization of preprocessed data that minimizes the effort and time spent on the QC process without compromising the condition/caliber of the QC, and 3.) a way to aggregate QC results across pipelines and datasets that can be easily shared. In addition to meeting these design criteria, we also provide a comparison experiment of our method to an automated QC method for a T1-weighted dataset of images and an inter-rater variability experiment for several processing pipelines. The experiments show mostly high agreement among raters and slight differences with the automated QC method. While researchers must spend time on robust visual QC of data, there are mechanisms by which the process can be streamlined and efficient.

PurposeThe purpose of this paper is to put forward the view that systems theory, cybernetics, information theory, and dissipative structure theory should be the fundamental principles underlying quality management. These “new sciences” relate to quality management not just in terms of philosophy but also in the requirements of the well‐structured quantitative analysis methods.Design/methodology/approachDiscusses several issues in quality management and control from the views of the new sciences, then proposes the concept of computer‐aided statistical process control (CASPC) to bridge traditional statistical process control and automatic process control, and the use of ARIMA models to achieve CASPC for linear systems.FindingsNotes that there are implications of the new sciences for quality control and presents a discussion of same.Originality/valueIs of value by pointing out that computer‐aided statistical process control for dynamic systems can be the realistic way of leading towards more advanced systematic control approaches.

Incorporating Quality Improvement Projects Into Stroke Care and Research.

Quality control and due diligence in project management: Getting decisions right by taking the outside view