A Curriculum Development and Quality Assessment Model Based on the Formation of Professional Identity

Educators are increasingly urged to improve quality control mechanisms. Improving quality involves more than simply increasing the variety of options for assessments. It calls for a new model of curriculum design focused on the formation of professional competencies students will need in the workplace along with the integration of theory-based learning activities and assessments supported by instructional technologies. This chapter will present a framework that educators can use to guide the integration of learning activities, assessments, and instructional technologies in a manner that guides students in the development of professional competencies for success in the workplace and also provides a stream of quality control data that can be used to measure both educational and organizational effectiveness.

Evaluation of Quality Control Data of Hormones Using Six Sigma Metrics Tool in Clinical Laboratory

Background: In health care system it is necessary to provide high quality and reliable test results to the patients. Many clinical laboratories are using six sigma as a tool to improve the quality control in health care system. Keeping this in mind, the present study was conducted using the quality control data of hormones under NABL(National Accreditation Board for Testing and Calibration Laboratories) which were assayed in our clinical laboratory. Materials and Methods: In this retrospective study, both the internal and external quality control data of 11 hormones were collected for a period of 6 months from April 2020 to September 2020 and the six sigma analysis was done. Results: Testosterone level 1(6.8), level 2(6.5) and Folate level1(6.9), level 2(6.6) showed sigma level more than 6 and hence excellent performance. The hormones, FT3 level 1(3.7), level 2(4.8), HCG level 2(3.6), TSH level 1(4.8), level 2(4.7) and Vitamin B12 level 1(4.4), level 2(4.5) showed average performance with sigma level between 3.5 and 6. The hormones, FT4 level 1(1.7), level 2(2), HCG level 1(2.2), Prolactin level 1(3), level 2(3.3), FSH level 1(1.9), level 2(2.0), LH level 1(2), level 2(1.9) and Progesterone level 1(3.4), level 2(3.3) showed poor performance with sigma level less than 3.5. Conclusion: Stringent rules need not be applied for hormones with sigma>6. Moreover, control limits can be relaxed to 3S so that false rejections can be minimized. For hormones with sigma< 6, internal QC rules have to be strictly applied and the root cause analysis has to be done. To conclude, six sigma metrics is a powerful quality control tool which helps to improve the performance of the clinical laboratory and hence the efficiency of the health cares system.

Evaluation of Quality Control Data of Clinical Chemistry Parameters using Six Sigma Metrics Tool in Clinical Laboratory

Background: Physicians rely on laboratory results for treating patients. So it is the duty of laboratories to assure quality of the results released. So laboratory performance should be validated to maintain the quality. Six sigma has now gained recent interest in monitoring the laboratory quality.This study was designed to gauge the clinical chemistry parameters based on six sigma metrics. Materials and Methods: In this retrospective study, both the internal and external quality control data of 26 clinical chemistry parameters were collected for a period of 6 months from June 2020 to November 2020 and the six sigma analysis was done at the Central clinical biochemistry laboratory of Chettinad Hospital and research institute. Results: AST, amylase, lipase, triglyceride, HDL, iron, magnesium, creatine kinase showed sigma values more than 6.Uric acid, total protein, ALT, direct bilirubin, GGT,cholesterol, cholesterol, calcium, TIBC and phosphorus shows sigma values between 3.5 to 6. Glucose, BUN, creatinine, albumin, Na, K, Chloride, showed sigma values less than 3.5. Conclusion: Six sigma metrics can help in improving the quality of laboratory performance and also helps to standardisethe actual amount of QC that is required by the laboratory for maintaining quality.

Quality Management in Libraries Case Study: Book Collection Retrieval in Academic Libraries

Quality management in the library is related to quality control of the library's work to meet user expectations and contributes to the continued success of the organization. However, quality control is lacking and not be the main focus in many libraries. This paper aims to explain how quality management improves work results in libraries. The research method used in this paper is qualitative research using a case study method. We conduct research in Library X that already underwent quality control in book processing activities. The data were collected through observation, and interviews for book processing activities and its quality control data during 2018-2020. The data was then analyzed document analysis. The study found that Library X could perform better by improving the quality of book processing and fixing the error just before they put the book on its shelves. Quality control gives a comprehensive evaluation in Library X either for humans, processes, and systems of book processing activities that help Library X conduct its duty to provide their user needs and expectations. Quality control and quality management also help Library X describe the library working atmosphere and can be used for giving motivation to all librarians and staff to give better service and performance for the end-users.

How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow

Spectral flow cytometry is an upcoming technique that allows for extensive multicolor panels, enabling simultaneous investigation of a large number of cellular parameters in a single experiment. To fully explore the resulting high-dimensional single cell datasets, high-dimensional analysis is needed, as opposed to the common practice of manual gating in conventional flow cytometry. However, preparing spectral flow cytometry data for high-dimensional analysis can be challenging, because of several technical aspects. In this article, we will give insight into the pitfalls of handling spectral flow cytometry datasets. Moreover, we will describe a workflow to properly prepare spectral flow cytometry data for high dimensional analysis and tools for integrating new data at later time points. Using healthy control data as example, we will go through the concepts of quality control, data cleaning, transformation, correcting for batch effects, subsampling, clustering and data integration. This methods article provides an R-based pipeline based on previously published packages, that are readily available to use. Application of our workflow will aid spectral flow cytometry users to obtain valid and reproducible results.

A novel practical approach to calculate measurement uncertainty in clinical pathology laboratories using quality control data with the use of biological variation where applicable

Background: Measurement Uncertainty (MU) can assist the interpretation and comparison of the laboratory results against international diagnostic protocols, facilitate a reduction in health care costs and also help protect laboratories against legal challenges. Determination of MU for quantitative testing in clinical pathology laboratories is also a requirement for ISO 15189. Methods: A practical and simple to use statistical model has been designed to make use of data readily available in a clinical laboratory to assess and establish MU for quantitative assays based on internal quality control data to calculate Random Error and external quality assurance scheme results to calculate Systematic Error. The model explained in this article has also been compared and verified against quality specifications based on Biological Variation. Results: Examples that explain and detail MU calculations for the proposed model are given where different components of MU are calculated with tabulated results. Conclusions: The designed model is cost-effective because it utilises readily available data in a clinical pathology laboratory. Data obtained from internal quality control programs and external quality assurance schemes are used to calculate the MU using a practical and convenient approach that will not require resources beyond what is available. Such information can additionally be useful not only in establishing limits for MU to satisfy ISO 15189 but also in selecting and/or improving methods and instruments in use. MU can as well play an important role in reducing health care costs as shown by examples in the article.

Filtering method based on cluster analysis to avoid salinity drifts and recover Argo data in less time

Currently there is a huge amount of freely available hydrographic data, and it is increasingly important to have easy access to it and to be provided with as much information as possible. Argo is a global collection of around 4000 active autonomous hydrographic profilers. Argo data go through two quality processes, real time and delayed mode. This work shows a methodology to filter profiles within a given polygon using the odd–even algorithm; this allows analysis of a study area, regardless of size, shape or location. The aim is to offer two filtering methods and to discard only the real-time quality control data that present salinity drifts. This takes advantage of the largest possible amount of valid data within a given polygon. In the study area selected as an example, it was possible to recover around 80 % in the case of the first filter that uses cluster analysis and 30 % in the case of the second, which discards profilers with salinity drifts, of the total real-time quality control data that are usually discarded by the users due to problems such as salinity drifts. This allows users to use any of the filters or a combination of both to have a greater amount of data within the study area of their interest in a matter of minutes, rather than waiting for the delayed-mode quality control that takes up to 12 months to be completed. This methodology has been tested for its replicability in five selected areas around the world and has obtained good results.

Two approaches for measurement uncertainty estimation: which role for bias? Complete blood count experience

Objectives Measurement uncertainty is described as a magnitude indicates the distribution of the measurement results. AACB Guideline suggests that bias should not be included in the uncertainty calculation contrary to Nordtest Guideline. The aim of the study is to calculate the uncertainty values of certain complete blood count (CBC) parameters and evaluate the contribution of bias. Methods This retrospective study was performed with the quality control data of January–December 2017 of two different CBC autoanalyser models (Beckman Coulter LH780 and DXH800). Measurement uncertainties were calculated according to AACB and Nordtest Guidelines. Imprecision, i.e. measurement uncertainty, varies with concentration. Imprecision values of user manuals, as performance characteristics of autoanalyzers, were used for assessment. Results User manuals imprecision values of different levels of platelets are between 3.3 and 14%. As the concentrations of platelets decrease, imprecision is observed to increase. This is expected to be parallel with measurement uncertainty. Contrary to user manuals, uncertainty values of AACB found to be so close to each other (between 3.41% and 4.80%), regardless of concentration level. However Nordtest guideline is more compatible with user manuals (between 6.97% and 15.35%). Conclusions When evaluated with the performance expectations, bias should be used in measurement uncertainty. Calculation of uncertainty for different concentration level is also important. Amaç Ölçüm belirsizliği, bir büyüklük olarak ölçüm sonuçlarının dağılımını ifade eder. AACB Kılavuzu, Nordtest Kılavuzunun aksine, biasın belirsizlik hesaplamasına dahil edilmesini önermemektedir. Çalışmanın amacı, belirli tam kan sayımı (CBC) parametrelerinin belirsizlik değerlerini hesaplamak ve biasın katkısını değerlendirmektir. Gereç ve Yöntemler Bu retrospektif çalışma, iki farklı CBC otoanalizör modelinin (Beckman Coulter LH780 ve DXH800) Ocak-Aralık 2017 kalite kontrol verileriyle gerçekleştirildi. Ölçüm belirsizlikleri AACB ve Nordtest Kılavuzlarına göre hesaplandı. İmpresizyon, dolayısıyla ölçüm belirsizliği, konsantrasyona göre değişir. Belirsizlik sonuçlarını değerlendirmede otoanalizörlerin kullanım kılavuzlarında yer alan impresizyona dayalı performans özellikleri kullanılmıştır. Bulgular Farklı trombosit düzeylerinin kullanım kılavuzlarındaki impresizyon değerleri %3.3-%14 arasındadır. Trombosit konsantrasyonu düştükçe impresizyon artar. Bunun ölçüm belirsizliği ile paralel olması beklenir. Kullanım kılavuzlarının aksine, AACB’ye göre hesaplanan belirsizlik değerleri, konsantrasyon seviyesinden bağımsız olarak birbirine çok yakın (%3.41–%4.80 arasında) bulundu. Bununla birlikte, Nordtest kılavuzunun sonuçları,otoanalizörlerin kullanım kılavuzları ile daha uyumlu bulundu (%6.97–%15.35 arasında). Sonuç Performans beklentileri ile değerlendirildiğinde, ölçüm belirsizliğinde bias kullanılmalıdır. Farklı konsantrasyon seviyeleri için belirsizliğin ayrı ayrı hesaplanması da önemlidir.