An automated tool for a uniform decentralized quality control and data analysis in multicenter studies with health care registry data

ABSTRACTObjectivesThe EU FP7 funded project CEPHOS-LINK investigates hospital re-admissions of patients with a psychiatric disorder in 6 European countries by using linked health care registry data. In addition to the problems of different healthcare, payment and data collection systems, coordinating quality control, data analysis, and statistical modeling of sensitive data with six partners is challenging. For this purpose we have designed a secure online data analysis tool to diminish the time necessary to get results and incremental adaptions of reports as well as decreasing the chance and effects of misunderstandings between national and linguistic boundaries. ApproachA comprehensive study protocol clearly defining variables to be obtained and methods to be applied has been put together. The protocol is based on a thorough investigation of the different healthcare systems and related registries. It became clear that nonetheless misconceptions occur and the incremental improvements consumed vast amounts of available resources. Therefore a system which automatically creates the required reports including all tables, graphics and statistical models including data preparation based on a defined data structure has been developed. The report system is based on the statistical environment R and the document markup language LaTeX, tightly integrated with R's package “knitr”.As this highly flexible solution is not straight forward to apply and implies various technical dependencies, a secure online platform hiding all technical details from the users has been developed. Utilizing state of the art software containers based on Linux and docker, a customized VPN solution, authentication and SSL encryption were put together. The web application itself is developed with R's “shiny” package and allows users to simply upload a dataset in the predefined format, interactively explore the contents, apply filters and generate the customizable, standardized report. Additionally, an offline version of the application is available for all major (desktop) operating systems. ResultsThe new platform advances data analysis and reporting in a situation where several partners are involved in analyzing local datasets, as is the case of the CEPHOS-LINK project. Integrating new features, graphics and research topics can be managed centrally while users can update their results and reports in nearly no time. ConclusionThe additional effort spent on developing a customized platform for quality control, data analysis and reporting has been worth the effort. Benefits include quick detection of implausible results, unifying the layout and graphics often depending on the software utilized and an established common data structure.

Download Full-text

Computerized Preparation of Two-Way Analysis of Variance Control Charts for Clinical Chemistry

Clinical Chemistry ◽

10.1093/clinchem/18.3.250 ◽

1972 ◽

Vol 18 (3) ◽

pp. 250-257 ◽

Cited By ~ 4

Author(s):

J H Riddick ◽

Roger Flora ◽

Quentin L Van Meter

Keyword(s):

Quality Control ◽

Data Analysis ◽

Computer Program ◽

Analysis Of Variance ◽

Control Charts ◽

Clinical Chemistry ◽

Control Data ◽

Laboratory Error ◽

Quality Control Data

Abstract A system of quality-control data analysis by computer is described, in which two-way analysis of variance is used for partitioning sources of laboratory error into day-to-day, within-day, betweenpools and additivity variation. The partition for additivity is described in detail as to its advantages and applications. In addition, control charts based on two-way analysis of variance computations are prepared each month by computer. This computer program is designed to operate with the IBM 1800 or 1130 computers or any computer with a Fortran IV compiler. Examples are presented of use of the control charts and of tables of analysis of variance.

Download Full-text

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

Journal of Pharmaceutical Research International ◽

10.9734/jpri/2021/v33i55b33856 ◽

2021 ◽

pp. 133-137

Author(s):

G. Anuradha ◽

S. Santhini Gopalakrishnan ◽

. Hemalatha

Keyword(s):

Health Care ◽

Quality Control ◽

Health Care System ◽

Six Sigma ◽

Clinical Laboratory ◽

Control Data ◽

Sigma Level ◽

Level 1 ◽

Quality Control Data ◽

Level 2

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.

Download Full-text

Application of the registration template for in-laboratory quality control data analysis in the MS Excel program in the activities of a medical laboratory technician

Medsestra (Nurse) ◽

10.33920/med-05-2102-07 ◽

2021 ◽

pp. 63-73

Author(s):

Svetlana Anatolyevna Plyashkova ◽

Lilia Vladimirovna Motorina ◽

Vlada Vladimirovna Glukhova

Keyword(s):

Quality Control ◽

Data Analysis ◽

Medical Laboratory ◽

Control Data ◽

Laboratory Technician ◽

The Creation ◽

Medical Laboratory Technician ◽

Quality Control Data

The article provides information on the creation and application of a template for registration and analysis of intralaboratory quality control data. The reasons for creating a template are considered; the activities of a medical laboratory technician without using a template and with its application are analyzed. The study confirms that the use of a ready-made template created in a spreadsheet processor greatly facilitates and optimizes the work of a medical laboratory technician.

Download Full-text

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

Journal of Analytical & Pharmaceutical Research ◽

10.15406/japlr.2021.10.00385 ◽

2021 ◽

Vol 10 (5) ◽

pp. 196-210

Author(s):

Ashraf Mina ◽

Shanmugam Banukumar ◽

Santiago Vazquez

Keyword(s):

Health Care ◽

Quality Control ◽

Quality Assurance ◽

Clinical Pathology ◽

Internal Quality ◽

External Quality ◽

Control Data ◽

Iso 15189 ◽

Care Costs ◽

Quality Control Data

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.

Download Full-text