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All Journal INDONESIAN JOURNAL OF MEDICAL LABORATORY SCIENCE AND TECHNOLOGY Journal of Indonesian Medical Laboratory and Science (JoIMedLabS) Mukhtabar: Journal of Medical Laboratory Technology Jurnal Kesehatan Siliwangi
Ridwanna, Surya
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Biochemistry, Genetics & Molecular Biology Chemistry Dentistry Environmental Science Health Professions Immunology & microbiology Medicine & Pharmacology Social Sciences

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Penentuan Taksiran Ketidakpastian (Uncertainty) dengan Metode Top-down dan Korelasinya Terhadap Total Error (TE) pada Parameter Hematologi Salvia Maharani, Farah; Ridwanna, Surya; Harianto, Harianto; Rinaldi, Sonny Feisal
Journal of Indonesian Medical Laboratory and Science Vol 5 No 1: April 2024
Publisher : Asosiasi Institusi Pendidikan Tinggi Teknologi Laboratorium Medik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53699/joimedlabs.v5i1.190

Abstract

Background: Uncertainty Measurement (UM) is defined as a parameter associated with a measurement result that characterizes a dispersion of values that can reasonably be associated with a quantity. ISO 15189 clause 5.5.1.4. states that the laboratory shall determine measurement uncertainty for each measurement procedure in the examination phase used to report measured quantity values on patients samples. Objectives: According to Westgard, there is debate about difference between the concepts of Uncertainty and Total Error (TE) so this study was conducted which aims to determine the uncertainty value of hematological parameters and determine the correlation of Uncertainty and TE. Materials and Methods: The material in this research are data of Internal Quality Control (IQC), External Quality Control (EQC), and Uncertainty data from the Sysmex XN-1000 calibrator. Results: Through the results the average uncertainty value of Sysmex XN-1000 2022 for leukocyte with low, medium and high levels, are ±19.05%, ±18.07 %, and ±15.94%, while for erythrocyte are ±4.46%, 4.10%, 4.16%, for hemoglobin are ±5.63, ±5.07, ±5.01, for hematocrit are ±8.99%, ±8.19%, ±8.19%, and for platelet are ±79.23%, ±62.23%, ±58.29%. Conclusions: Based on the uncertainty and TE calculated permonth for each lot number during 2022, the correlation was obtained between the Uncertainty and TE among leukocyte and hematocrit were stated to have a weak correlation, for hemoglobin and platelet it was stated quite correlated and erythrocytes are stated to be strongly correlated.
PENGGUNAAN SIX SIGMA SEBAGAI EVALUASI KONTROL KUALITAS PADA ALAT HEMATOLOGY ANALYZER SYSMEX XN-550 Nidianti, Della; Feisyal Rinaldi, Sonny; Rahmat, Mamat; Ridwanna, Surya
Jurnal Kesehatan Siliwangi Vol. 4 No. 3 (2024): JURNAL KESEHATAN SILIWANGI
Publisher : Politeknik Kesehatan Kemenkes Bandung

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Abstract

Six Sigma uses defects per unit as the unit of measurement. Accordingly, the goal of this study is to ascertain the haematology parameters' sigma value, QC rule, and TEa value at the Pramita Lab Pajajaran Bandung Branch. This type of research is a descriptive analytic. The data used in tis study can be obtained from the results of Internal Quality Control in Pramita Lab, A total of 5 parameters were calculate bu Six Sigma using the Total Error Allowable (TEa) from Biological Variation and CLIA. TEA is mapped based on the area in the graph. Decision making for the selection of TEa is based on the TEa selection algorithm. Based on the results of the study, the precision on the parameters of hemoglobin, hematocrit, leukocytes, thrombocyte and erythrocytes was acceptable. The average of 6 months control value of Sigma the including the acquisition of world class ( > 6 sigma) consisting of parameters leukocytes so that QC rule is 1-3ss N=3, hemoglobin and erythrocytes obtain excellent ( 5 – 6 sigma ) so that QC rule is 2 of 3-2s and R4s N = 3. Hematocrit and thrombocyte obtain good ( 4 – 5 sigma) so that QC rule is 3-1s N = 3. The TEa from Desirable Biological Variation can be used for examination of thrombocyte. Minimal Biological Variation can be used for examination of erythrocytes. Optimal Biological variation can be used for examination of leukocytes. CLIA can be used for examination of Hemoglobin and hematocrit.
Comparison of the sigma metrics using the total error allowable algorithm with variation of bias source Rinaldi, Sonny Feisal; Ibadurrahmah, Anisa Agustia; Ridwanna, Surya; Harianto, Harianto
JURNAL INDONESIA DARI ILMU LABORATORIUM MEDIS DAN TEKNOLOGI Vol 6 No 1 (2024): Advancements in Biomedical Research: Insights from Medical Laboratory and Technol
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33086/ijmlst.v6i1.4930

Abstract

Sigma Metrics, as a quality indicator, have been widely applied in clinical laboratories to assess the performance of analytical methods. Described in the document Clinical and Laboratory Standards Institute (CLSI) EP15- A3, the use of target values can be sourced from certified reference standards, survey materials from the Proficiency Testing (PT)/External Quality Assessment (EQA), materials used in inter-laboratory quality control programs and internal quality control materials with predetermined targets. This research aims to determine whether there is a difference in the sigma metrics between the bias derived from the manufacturer's target value and those from the peer group source in the External Quality Assurance Services (EQAS) program. The research methodology employed is descriptive comparative analysis, utilizing the results of material inspection data for 15 internal quality control parameters of Clinical Chemistry over a span of 2 years at the Pramita Laboratory in Bandung. The calculation of the sigma metrics commences with computing the coefficient of variation (CV), and the appropriate Total Error aalowable (Tea) sources for each parameter are determined beforehand using the TEa algorithm. The research findings indicate a difference between the sigma metrics derived from the manufacturer's target value and those from the EQAS-peer group target value, accounting for 33% or 10 parameters out of the total parameters with 2 levels of inspection are calculated on the sigma scale. However, in 67% or 20 parameters out of the total parameters, no such difference is observed. Bias associated with the target value from the manufacturer and the EQAS peer group shows no significant difference, suggesting that the laboratory can utilize pre-existing target values confidently.
Evaluation of Quality Indicators with Lean Six Sigma In Pre and Post Analytical Laboratories Nurhadi, Muhammad Ihsan; Ridwanna, Surya; Rinaldi, Sonny Feisal; Nurhayati, Betty
Mukhtabar Journal of Medical Laboratory Technology Vol 3 No 1 (2025): Mukhtabar: Journal of Medical Laboratory Technology (April 2024)
Publisher : LPPM STIKes Muhammadiyah Ciamis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52221/mjmlt.v3i1.795

Abstract

Background & Objective: Medical laboratories have an important role of 60-70% in diagnosis, patient monitoring, and prevention and treatment of diseases. Therefore, the laboratory must maintain and ensure the quality of service process. This study aims to determine the sigma value and identify waste in pre and post analytical in the laboratory, as well as determine Improvement proposals in order to reduce errors pre and post analytical laboratory. Method: This research design is descriptive study that Analyzes the process and quality indicators at pre and post analytical with the Lean Six Sigma approach, which is a combination of Lean methods that focus on eliminating waste and Six Sigma that focuses on eliminating defects. This research was conducted as a process Improvement effort with five Six Sigma work steps (DMAIC) and identified eight types of waste (DOWNTIME). Result: The results showed that the sigma value at pre and post analytical was 4.6 and 3.5 Sigma, and total sigma value for pre-post analytical was 4.3 Sigma so that both had not met the minimum target achievement of 5 Sigma (Excellent). There are two quality indicators that require improvement and enhancement including suitability of sample and TAT. The results also show that there are 4 wastes, consisting of 1 Defects, 1 Waiting, and 2 Not Utilizing Employees Knowledge. Conclusion: Proposed Improvements are then given so that all waste identified in this study can be minimized so that the achievement of sigma quality indicators can increase.
Co-Authors Feisyal Rinaldi, Sonny Harianto Harianto Ibadurrahmah, Anisa Agustia Mamat Rahmat Nidianti, Della Nurhadi, Muhammad Ihsan Nurhayati, Betty Rinaldi, Sonny Feisal Salvia Maharani, Farah