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Ou YH, Chang YT, Chen DP, Chuang CW, Tsao KC, Wu CH, Kuo AJ, You HL, Huang CG. Benefit analysis of the auto-verification system of intelligent inspection for microorganisms. Front Microbiol 2024; 15:1334897. [PMID: 38562474 PMCID: PMC10982382 DOI: 10.3389/fmicb.2024.1334897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
In recent years, the automatic machine for microbial identification and antibiotic susceptibility tests has been introduced into the microbiology laboratory of our hospital, but there are still many steps that need manual operation. The purpose of this study was to establish an auto-verification system for bacterial naming to improve the turnaround time (TAT) and reduce the burden on clinical laboratory technologists. After the basic interpretation of the gram staining results of microorganisms, the appearance of strain growth, etc., the 9 rules were formulated by the laboratory technologists specialized in microbiology for auto-verification of bacterial naming. The results showed that among 70,044 reports, the average pass rate of auto-verification was 68.2%, and the reason for the failure of auto-verification was further evaluated. It was found that the main causes reason the inconsistency between identification results and strain appearance rationality, the normal flora in the respiratory tract and urine that was identified, the identification limitation of the mass spectrometer, and so on. The average TAT for the preliminary report of bacterial naming was 35.2 h before, which was reduced to 31.9 h after auto-verification. In summary, after auto-verification, the laboratory could replace nearly 2/3 of manual verification and issuance of reports, reducing the daily workload of medical laboratory technologists by about 2 h. Moreover, the TAT on the preliminary identification report was reduced by 3.3 h on average, which could provide treatment evidence for clinicians in advance.
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Affiliation(s)
- Yu-Hsiang Ou
- Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Yung-Ta Chang
- Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Ding-Ping Chen
- Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
- Department of Medical Biotechnology and Laboratory Science, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Graduate Institute of Biomedical Sciences, College of Medicine, Chang, Gung University, Taoyuan,, Taiwan
| | - Chun-Wei Chuang
- Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Kuo-Chien Tsao
- Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Chiu-Hsiang Wu
- Department of Laboratory Medicine, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - An-Jing Kuo
- Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Huey-Ling You
- Departments of Laboratory Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan
| | - Chung-Guei Huang
- Department of Laboratory Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan
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Cai Y, Liu M, Wu Z, Tian C, Qiu S, Li Z, Xu F, Li W, Zheng Y, Xu A, Xie L, Tan X. Diagnostic accuracy of autoverification and guidance system for COVID-19 RT-PCR results. EPMA J 2023; 14:119-129. [PMID: 36540610 PMCID: PMC9755791 DOI: 10.1007/s13167-022-00310-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 12/05/2022] [Indexed: 12/21/2022]
Abstract
BACKGROUND To date, most countries worldwide have declared that the pandemic of COVID-19 is over, while the WHO has not officially ended the COVID-19 pandemic, and China still insists on the personalized dynamic COVID-free policy. Large-scale nucleic acid testing in Chinese communities and the manual interpretation for SARS-CoV-2 nucleic acid detection results pose a huge challenge for labour, quality and turnaround time (TAT) requirements. To solve this specific issue while increase the efficiency and accuracy of interpretation, we created an autoverification and guidance system (AGS) that can automatically interpret and report the COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR) results relaying on computer-based autoverification procedure and then validated its performance in real-world environments. This would be conductive to transmission risk prediction, COVID-19 prevention and control and timely medical treatment for positive patients in the context of the predictive, preventive and personalized medicine (PPPM). METHODS A diagnostic accuracy test was conducted with 380,693 participants from two COVID-19 test sites in China, the Hong Kong Hybribio Medical Laboratory (n = 266,035) and the mobile medical shelter at a Shanghai airport (n = 114,658). These participants underwent SARS-CoV-2 RT-PCR from March 28 to April 10, 2022. All RT-PCR results were interpreted by laboratorians and by using AGS simultaneously. Considering the manual interpretation as gold standard, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were applied to evaluate the diagnostic value of the AGS on the interpretation of RT-PCR results. RESULTS Among the 266,035 samples in Hong Kong, there were 16,356 (6.15%) positive, 231,073 (86.86%) negative, 18,606 (6.99%) indefinite, 231,073 (86.86%, negative) no retest required and 34,962 (13.14%, positive and indefinite) retest required; the 114,658 samples in Shanghai consisted of 76 (0.07%) positive, 109,956 (95.90%) negative, 4626 (4.03%) indefinite, 109,956 (95.90%, negative) no retest required and 4702 (4.10%, positive and indefinite) retest required. Compared to the fashioned manual interpretation, the AGS is a procedure of high accuracy [99.96% (95%CI, 99.95-99.97%) in Hong Kong and 100% (95%CI, 100-100%) in Shanghai] with perfect sensitivity [99.98% (95%CI, 99.97-99.98%) in Hong Kong and 100% (95%CI, 100-100%) in Shanghai], specificity [99.87% (95%CI, 99.82-99.90%) in Hong Kong and 100% (95%CI, 99.92-100%) in Shanghai], PPV [99.98% (95%CI, 99.97-99.99%) in Hong Kong and 100% (95%CI, 99.99-100%) in Shanghai] and NPV [99.85% (95%CI, 99.80-99.88%) in Hong Kong and 100% (95%CI, 99.90-100%) in Shanghai]. The need for manual interpretation of total samples was dramatically reduced from 100% to 13.1% and the interpretation time fell from 53 h to 26 min in Hong Kong; while the manual interpretation of total samples was decreased from 100% to 4.1% and the interpretation time dropped from 20 h to 16 min at Shanghai. CONCLUSIONS The AGS is a procedure of high accuracy and significantly relieves both labour and time from the challenge of large-scale screening of SARS-CoV-2 using RT-PCR. It should be recommended as a powerful screening, diagnostic and predictive system for SARS-CoV-2 to contribute timely the ending of the COVID-19 pandemic following the concept of PPPM.
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Affiliation(s)
- Yingmu Cai
- Joint Laboratory of Shantou University Medical College and Guangdong Hybribio Biotech Ltd, Shantou University Medical College, Shantou, 515041 Guangdong China
- Hybribio Medical Laboratory Group Ltd, Chaozhou, 521000 Guangdong China
- Clinical Research Centre, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041 Guangdong China
| | - Mengyu Liu
- Joint Laboratory of Shantou University Medical College and Guangdong Hybribio Biotech Ltd, Shantou University Medical College, Shantou, 515041 Guangdong China
- Clinical Research Centre, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041 Guangdong China
| | - Zhiyuan Wu
- Beijing Municipal Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, 100069 China
- Centre for Precision Health, Edith Cowan University, Perth, WA 6027 Australia
| | - Cuihong Tian
- Clinical Research Centre, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041 Guangdong China
- Centre for Precision Health, Edith Cowan University, Perth, WA 6027 Australia
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041 Guangdong China
| | - Song Qiu
- Hybribio Medical Laboratory Group Ltd, Chaozhou, 521000 Guangdong China
| | - Zhen Li
- Human Papillomavirus Molecular Diagnostic Engineering Technology Research Centre, Chaozhou, 521000 Guangdong China
| | - Feng Xu
- Human Papillomavirus Molecular Diagnostic Engineering Technology Research Centre, Chaozhou, 521000 Guangdong China
| | - Wei Li
- Joint Laboratory of Shantou University Medical College and Guangdong Hybribio Biotech Ltd, Shantou University Medical College, Shantou, 515041 Guangdong China
- Clinical Research Centre, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041 Guangdong China
| | - Yan Zheng
- Department of Research and Development, Guangdong Research Institute of Genetic Diagnostic and Engineering Technologies for Thalassemia, Chaozhou, 521011 Guangdong China
| | - Aijuan Xu
- Human Papillomavirus Molecular Diagnostic Engineering Technology Research Centre, Chaozhou, 521000 Guangdong China
| | - Longxu Xie
- Hybribio Medical Laboratory Group Ltd, Chaozhou, 521000 Guangdong China
- Human Papillomavirus Molecular Diagnostic Engineering Technology Research Centre, Chaozhou, 521000 Guangdong China
| | - Xuerui Tan
- Clinical Research Centre, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041 Guangdong China
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Zhu J, Wang H, Wang B, Hao X, Cui W, Duan Y, Zhang Y, Ming L, Zhou Y, Ding H, Ou H, Lin W, Lu L, Shang Y, Yang Y, Liang X, Ma J, Sun W, Chen T, Han G, Han M, Yu W, Pan B, Guo W. Combined strategy of knowledge-based rule selection and historical data percentile-based range determination to improve an autoverification system for clinical chemistry test results. J Clin Lab Anal 2022; 36:e24233. [PMID: 35007357 PMCID: PMC8841182 DOI: 10.1002/jcla.24233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/30/2021] [Accepted: 12/18/2021] [Indexed: 11/15/2022] Open
Abstract
Background Current autoverification, which is only knowledge‐based, has low efficiency. Regular historical data analysis may improve autoverification range determination. We attempted to enhance autoverification by selecting autoverification rules by knowledge and ranges from historical data. This new system was compared with the original knowledge‐based system. Methods New types of rules, extreme values, and consistency checks were added and the autoverification workflow was rearranged to construct a framework. Criteria for creating rules for extreme value ranges, limit checks, consistency checks, and delta checks were determined by analyzing historical Zhongshan laboratory data. The new system's effectiveness was evaluated using pooled data from 20 centers. Efficiency improvement was assessed by a multicenter process. Results Effectiveness was evaluated by the true positive rate, true negative rate, and overall consistency rate, as compared to manual verification, which were 77.55%, 78.53%, and 78.3%, respectively for the new system. The original overall consistency rate was 56.2%. The new pass rates, indicating efficiency, were increased by 19%‒51% among hospitals. Further customization using individualized data increased this rate. Conclusions The improved system showed a comparable effectiveness and markedly increased efficiency. This transferable system could be further improved and popularized by utilizing historical data from each hospital.
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Affiliation(s)
- Jing Zhu
- Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Hao Wang
- Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Beili Wang
- Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.,Department of Laboratory Medicine, Xiamen Branch, Zhongshan Hospital, Fudan University, Shanghai, China
| | | | - Wei Cui
- Cancer Hospital Chinese Academy of Medical Sciences, Beijing, China
| | - Yong Duan
- First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yi Zhang
- Qilu Hospital of Shandong University, Jinan, China
| | - Liang Ming
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yingchun Zhou
- The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Haitao Ding
- Inner Mongolia People's Hospital, Huhhot, China
| | - Hongling Ou
- Chinese People's Liberation Army Rocket General Hospital, Beijing, China
| | - Weiwei Lin
- Renji Hospital Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Liu Lu
- Shanghai Dongfang Hospital, Shanghai, China
| | - Yuanjiang Shang
- Tenth Peoples Hospital of Tongji University, Shanghai, China
| | - Yong Yang
- The Second Affiliated Hospital of Soochow University, Suzhou, China
| | | | | | - Wenhua Sun
- Shanghai Songjiang District Central Hospital, Shanghai, China
| | - Te Chen
- The Hospital Group of The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Guang Han
- Guangdong Provincial TCM Hospital, Guangzhou, China
| | - Meng Han
- Tianjin First Central Hospital, Tianjin, China
| | - Weiting Yu
- Tongji Medical College Huazhong University of Science and Technology, Wuhan, China
| | - Baishen Pan
- Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.,Department of Laboratory Medicine, Xiamen Branch, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Wei Guo
- Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China.,Department of Laboratory Medicine, Xiamen Branch, Zhongshan Hospital, Fudan University, Shanghai, China.,Department of Laboratory Medicine, Wusong Branch, Zhongshan Hospital, Fudan University, Shanghai, China
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Data validation techniques used in admission discharge and transfer systems: Necessity of use and effect on data quality. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.101122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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Shi J, Mu RQ, Wang P, Geng WQ, Jiang YJ, Zhao M, Shang H, Zhang ZN. The development of autoverification system of lymphocyte subset assays on the flow cytometry platform. Clin Chem Lab Med 2021; 60:92-100. [PMID: 34533003 DOI: 10.1515/cclm-2021-0736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 09/04/2021] [Indexed: 11/15/2022]
Abstract
OBJECTIVES Peripheral blood lymphocyte subsets are important parameters for monitoring immune status; however, lymphocyte subset detection is time-consuming and error-prone. This study aimed to explore a highly efficient and clinically useful autoverification system for lymphocyte subset assays performed on the flow cytometry platform. METHODS A total of 94,402 lymphocyte subset test results were collected. To establish the limited-range rules, 80,427 results were first used (69,135 T lymphocyte subset tests and 11,292 NK, B, T lymphocyte tests), of which 15,000 T lymphocyte subset tests from human immunodeficiency virus (HIV) infected patients were used to set customized limited-range rules for HIV infected patients. Subsequently, 13,975 results were used for historical data validation and online test validation. RESULTS Three key autoverification rules were established, including limited-range, delta-check, and logical rules. Guidelines for addressing the issues that trigger these rules were summarized. The historical data during the validation phase showed that the total autoverification passing rate of lymphocyte subset assays was 69.65% (6,941/9,966), with a 67.93% (5,268/7,755) passing rate for T lymphocyte subset tests and 75.67% (1,673/2,211) for NK, B, T lymphocyte tests. For online test validation, the total autoverification passing rate was 75.26% (3,017/4,009), with 73.23% (2,191/2,992) for the T lymphocyte subset test and 81.22% (826/1,017) for the NK, B, T lymphocyte test. The turnaround time (TAT) was reduced from 228 to 167 min using the autoverification system. CONCLUSIONS The autoverification system based on the laboratory information system for lymphocyte subset assays reduced TAT and the number of error reports and helped in the identification of abnormal cell populations that may offer clues for clinical interventions.
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Affiliation(s)
- Jue Shi
- NHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, P. R. China.,Key Laboratory of AIDS Immunology, Chinese Academy of Medical Sciences, Shenyang, P. R. China
| | - Run-Qing Mu
- Department of Laboratory Medicine, National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, P. R. China
| | - Pan Wang
- NHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, P. R. China.,Key Laboratory of AIDS Immunology, Chinese Academy of Medical Sciences, Shenyang, P. R. China
| | - Wen-Qing Geng
- NHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, P. R. China.,Key Laboratory of AIDS Immunology, Chinese Academy of Medical Sciences, Shenyang, P. R. China
| | - Yong-Jun Jiang
- NHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, P. R. China.,Key Laboratory of AIDS Immunology, Chinese Academy of Medical Sciences, Shenyang, P. R. China
| | - Min Zhao
- Department of Laboratory Medicine, National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, P. R. China
| | - Hong Shang
- NHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, P. R. China.,Key Laboratory of AIDS Immunology, Chinese Academy of Medical Sciences, Shenyang, P. R. China.,Department of Laboratory Medicine, National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, P. R. China
| | - Zi-Ning Zhang
- NHC Key Laboratory of AIDS Immunology (China Medical University), National Clinical Research Center for Laboratory Medicine, The First Affiliated Hospital of China Medical University, Shenyang, P. R. China.,Key Laboratory of AIDS Immunology, Chinese Academy of Medical Sciences, Shenyang, P. R. China
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Topcu DI, Gulbahar O. A model to establish autoverification in the clinical laboratory. Clin Biochem 2021; 93:90-98. [PMID: 33831387 DOI: 10.1016/j.clinbiochem.2021.03.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 03/23/2021] [Accepted: 03/29/2021] [Indexed: 12/09/2022]
Abstract
OBJECTIVES Autoverification is the process of evaluating and validating laboratory results using predefined computer-based algorithms without human interaction. By using autoverification, all reports are validated according to the standard evaluation criteria with predefined rules, and the number of reports per laboratory specialist is reduced. However, creating and validating these rules are the most demanding steps for setting up an autoverification system. In this study, we aimed to develop a model for helping users establish autoverification rules and evaluate their validity and performance. DESIGN & METHODS The proposed model was established by analyzing white papers, previous study results, and national/international guidelines. An autoverification software (myODS) was developed to create rules according to the model and to evaluate the rules and autoverification rates. The simulation results that were produced by the software were used to demonstrate that the determined framework works as expected. Both autoverification rates and step-based evaluations were performed using actual patient results. Two algorithms defined according to delta check usage (Algorithm A and B) and three review limits were used for the evaluation. RESULTS Six hundred seventeen rules were created according to the proposed model. 1,976 simulation results were created for validation. Our results showed that manual review limits are the most critical step in determining the autoverification rate, and delta check evaluation is especially important for evaluating inpatients. Algorithm B, which includes consecutive delta check evaluation, had higher AV rates. CONCLUSIONS Systemic rule formation is a critical factor for successful AV. Our proposed model can help laboratories establish and evaluate autoverification systems. Rules created according to this model could be used as a starting point for different test groups.
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Affiliation(s)
- Deniz Ilhan Topcu
- Department of Biochemistry, Faculty of Medicine, Başkent University, Ankara, Turkey.
| | - Ozlem Gulbahar
- Department of Medical Biochemistry, Faculty of Medicine, Gazi University, Ankara, Turkey
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Velichko E, Makarov S, Nepomnyashchaya E, Dong G. Molecular Aggregation in Immune System Activation Studied by Dynamic Light Scattering. BIOLOGY 2020; 9:biology9060123. [PMID: 32545635 PMCID: PMC7345249 DOI: 10.3390/biology9060123] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 05/28/2020] [Accepted: 06/07/2020] [Indexed: 01/20/2023]
Abstract
Determination of the concentration and size of the circulating immune complexes in the blood is an essential part of diagnostics of immune diseases. In this work, we suggest using the dynamic light scattering method to determine the sizes of circulating immune complexes in blood serum. By the dynamic light scattering spectrometer, we found that for healthy and sick donors, the size and concentration of circulating immune complexes differed significantly. The dynamics of formation of these complexes were also examined in this work. It was shown that the formation of immune complexes in the blood of healthy donors is faster than the same reactions in the blood serum of donors with diseases. The results can be used in the diagnostics of the immune status and detection of chronic inflammation. We can recommend the dynamic light scattering method for implementation in biomedical diagnostics.
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Affiliation(s)
- Elena Velichko
- Institute of Physics, Nanotechnology and Telecommunications, Peter the Great St.Petersburg Polytechnic University, St. Petersburg 195251, Russia; (S.M.); (E.N.)
- Correspondence: (E.V.); (G.D.)
| | - Sergey Makarov
- Institute of Physics, Nanotechnology and Telecommunications, Peter the Great St.Petersburg Polytechnic University, St. Petersburg 195251, Russia; (S.M.); (E.N.)
| | - Elina Nepomnyashchaya
- Institute of Physics, Nanotechnology and Telecommunications, Peter the Great St.Petersburg Polytechnic University, St. Petersburg 195251, Russia; (S.M.); (E.N.)
| | - Ge Dong
- School of Aerospace Engineering, Tsinghua University, Beijing 100084, China
- Correspondence: (E.V.); (G.D.)
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Autoverification of test results in the core clinical laboratory. Clin Biochem 2019; 73:11-25. [DOI: 10.1016/j.clinbiochem.2019.08.002] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 07/30/2019] [Accepted: 08/02/2019] [Indexed: 02/06/2023]
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Lin X, Cheng B, Cai Y, Jiao X, Yang X, Zhang Q, Wang Y. Establishing and evaluating an auto-verification system of thalassemia gene detection results. Ann Hematol 2019; 98:1835-1844. [PMID: 30953085 DOI: 10.1007/s00277-019-03656-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 03/04/2019] [Indexed: 02/05/2023]
Abstract
The manual verification of gene tests is time-consuming and error prone. In this study, we try to explore a high-efficiency, clinically useful auto-verification system for gene detection of thalassemia. A series of verification elements were rooted in the auto-verification system. Consistency check was applied initially as one of the essential elements in our study. One hundred twenty-four archived cases were used to choose the consistency-check rules' indices from routine blood examination and hemoglobin electrophoresis by the receiver operating characteristic curves. Rule 1 and rule 2 established by the chosen indices were compared by their passing rate, consistency with manual validation, and error rate. Finally, 748 cases were used for verifying the system's feasibility by evaluating the passing rate, turn-around time (TAT), and error rate. The rule 2 had a higher passing rate (67.7% vs. 50.8%) and consistency (0.623 vs. 0.364) than the rule 1 with an error rate of zero. In a "live" valuation, the auto-verification system can reduce the TAT and error rate of verification by 51.5% and 0.13%, respectively, with a high passing rate of 82.8%. The auto-verification system for gene detection of thalassemia in this study can shorten the validation time, reduce errors, and enhance efficiency.
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Affiliation(s)
- Xiaozhe Lin
- The Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Shantou University, Shantou, China
| | - Bizhen Cheng
- The Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Shantou University, Shantou, China
| | - Yingmu Cai
- The Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Shantou University, Shantou, China
| | - Xiaoyang Jiao
- Cell Biology and Genetics Department of Shantou University Medical College, Shantou, China
| | - Xinran Yang
- The Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Shantou University, Shantou, China
| | - Qiaoxin Zhang
- The Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Shantou University, Shantou, China.
| | - Yongni Wang
- The Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Shantou University, Shantou, China
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Wang Z, Peng C, Kang H, Fan X, Mu R, Zhou L, He M, Qu B. Design and evaluation of a LIS-based autoverification system for coagulation assays in a core clinical laboratory. BMC Med Inform Decis Mak 2019; 19:123. [PMID: 31269951 PMCID: PMC6609390 DOI: 10.1186/s12911-019-0848-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Accepted: 06/24/2019] [Indexed: 12/13/2022] Open
Abstract
Background The autoverification system for coagulation consists of a series of rules that allow normal data to be released without manual verification. With new advances in medical informatics, the laboratory information system (LIS) has growing potential for the autoverification, allowing rapid and accurate verification of clinical laboratory tests. The purpose of the study is to develop and evaluate a LIS-based autoverification system for validation and efficiency. Methods Autoverification decision rules, including quality control, analytical error flag, critical value, limited range check, delta check and logical check, as well as patient’s historical information, were integrated into the LIS. Autoverification limited range was constructed based on 5 and 95% percentiles. The four most commonly used coagulation assays, prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and fibrinogen (FBG), were followed by the autoverification protocols. The validation was assessed by the autoverification passing rate, the true-positive cases, the true-negative cases, the false-positive cases, the false-negative cases, the sensitivity and the specificity; the efficiency was evaluated in the turnaround time (TAT). Results A total of 157,079 historical test results of coagulation profiles from January 2016 to December 2016 were collected to determine the distribution intervals. The autoverification passing rate was 77.11% (29,165/37,821) based on historical patient data. In the initial test of the autoverification version in June 2017, the overall autoverification passing rate for the whole sample was 78.75% (11,257/14,295), with 892 true-positive cases, 11,257 true-negative cases, 2146 false-positive cases, no false-negative cases, sensitivity of 100% and specificity of 83.99%. After formal implementation of the autoverification system for 6 months, 83,699 samples were assessed. The average overall autoverification passing rate for the whole sample was 78.86% and the 95% confidence interval (CI) of the passing rate was [78.25, 79.59%]. TAT was reduced from 126 min to 101 min, which was statistically significant (P < 0.001, Mann-Whitney U test). Conclusions The autoverification system for coagulation assays based on LIS can halt the samples with abnormal values for manual verification, guarantee medical safety, minimize the requirements for manual work, shorten TAT and raise working efficiency.
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Affiliation(s)
- Zhongqing Wang
- Department of Health Statistics, School of Public Health, China Medical University, 77 Puhe Road, Shenyang North New Area, Shenyang, 110122, China.,Department of Information Center, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Cheng Peng
- Key Lens Laboratory of Liaoning Province, Department of Ophthalmology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Hui Kang
- Department of Clinical Laboratory, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Xia Fan
- Department of Clinical Laboratory, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Runqing Mu
- Department of Clinical Laboratory, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Liping Zhou
- Department of Clinical Laboratory, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Miao He
- Department of Information Center, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Bo Qu
- Department of Health Statistics, School of Public Health, China Medical University, 77 Puhe Road, Shenyang North New Area, Shenyang, 110122, China.
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Wu J, Pan M, Ouyang H, Yang Z, Zhang Q, Cai Y. Establishing and Evaluating Autoverification Rules with Intelligent Guidelines for Arterial Blood Gas Analysis in a Clinical Laboratory. SLAS Technol 2018; 23:631-640. [PMID: 29787327 DOI: 10.1177/2472630318775311] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Arterial blood gas (ABG) analysis is important for acutely ill patients and should be performed by qualified laboratorians. The existing manual verifications are tedious, time-consuming, and prone to send wrong reports. Autoverification uses computer-based rules to verify clinical laboratory test results without manual review. To date, no data are available on the use of autoverification for ABG analysis. All autoverification rules were established according to AUTO10-A. Additionally, the rules were established using retrospective patient data, and then validated by actual clinical samples in a "live" environment before go-live. The average autoverification passing rate was 75.5%. The turnaround time (TAT) was reduced by 33.3% (27 min vs 18 min). Moreover, the error rate fell to 0.05% after implementation. Statistical analysis resulted in a kappa statistic of 0.92 ( p < 0.01), indicating close agreement between autoverification and senior technician verification, and the chi-square value was 22.4 ( p < 0.01), indicating that the autoverification error rate was lower than the manual verification error rate. Results showed that implementing autoverification rules with intelligent guidelines for ABG analysis of patients with critical illnesses could decrease the number of samples requiring manual verification, reduce TAT, and eliminate errors, allowing laboratorians to concentrate more time on abnormal samples, patient care, and collaboration with physicians.
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Affiliation(s)
- Jie Wu
- 1 Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Meichen Pan
- 1 Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Huizhen Ouyang
- 1 Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Zhili Yang
- 1 Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Qiaoxin Zhang
- 1 Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
| | - Yingmu Cai
- 1 Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China
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Li J, Cheng B, Ouyang H, Xiao T, Hu J, Cai Y. Designing and evaluating autoverification rules for thyroid function profiles and sex hormone tests. Ann Clin Biochem 2018; 55:254-263. [PMID: 28490181 DOI: 10.1177/0004563217712291] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Purpose Following the analytical phase, the current practice of many hospital laboratories involves the manual verification of all test results followed by the production of the report. However, manual verification is a time-consuming and tedious process. In this paper, we provide a detailed description of how to design autoverification rules for thyroid function test profiles and sex hormones. Materials and methods We used DM2 (Data manager 2) to construct the algorithm and build the database for autoverification of thyroid function test profiles and sex hormones, with reference to Boolean logic, Auto 10-A and CLSI'88. The rules consist of checking quality control, instrument error flags, critical values, the analytical measurement range (AMR), the limit range, consistency check and delta check. Firstly, we established the rules in the DM2, collected clinical specimens for validation, then tested the rules in a 'live' environment. Results Agreement was achieved between manual verification by two senior laboratory personnel and verification using the autoverification rules in 99.78% of the cases. The total autoverification rate for all tests was 77.06%. Following implementation of the rules, the laboratory turnaround time (TAT) was reduced by 54.55% and staffing numbers fell from three to two whole time equivalents (WTE). Statistical analysis resulted in a kappa statistic of 0.99 ( P < 0.001). Moreover, after implementing the autoverification rules, the error rate fell to 0.04%, indicating that errors were almost completely eliminated. Conclusion Implementing autoverification rules can reduce TAT, minimize the number of samples that require manual verification and allow for a reduction in staffing numbers. It also allows laboratory staff to devote more time and effort to the handling of problematic test results and contributing to improved patient care.
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Affiliation(s)
- Jiancheng Li
- 1 Department of Clinical Laboratory, Central Hospital of Hengyang, Hunan, People's Republic of China
| | - Bizhen Cheng
- 2 Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Guangdong, People's Republic of China
| | - Huizhen Ouyang
- 2 Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Guangdong, People's Republic of China
| | - Tongtong Xiao
- 2 Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Guangdong, People's Republic of China
| | - Jing Hu
- 2 Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Guangdong, People's Republic of China
| | - Yingmu Cai
- 2 Department of Clinical Laboratory, The First Affiliated Hospital of Shantou University Medical College, Guangdong, People's Republic of China
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Riley PW, Gallea B, Valcour A. Development and Implementation of a Coagulation Factor Testing Method Utilizing Autoverification in a High-volume Clinical Reference Laboratory Environment. J Pathol Inform 2017; 8:25. [PMID: 28706751 PMCID: PMC5497483 DOI: 10.4103/jpi.jpi_95_16] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2016] [Accepted: 03/28/2017] [Indexed: 11/18/2022] Open
Abstract
Background: Testing coagulation factor activities requires that multiple dilutions be assayed and analyzed to produce a single result. The slope of the line created by plotting measured factor concentration against sample dilution is evaluated to discern the presence of inhibitors giving rise to nonparallelism. Moreover, samples producing results on initial dilution falling outside the analytic measurement range of the assay must be tested at additional dilutions to produce reportable results. Methods: The complexity of this process has motivated a large clinical reference laboratory to develop advanced computer algorithms with automated reflex testing rules to complete coagulation factor analysis. A method was developed for autoverification of coagulation factor activity using expert rules developed with on an off the shelf commercially available data manager system integrated into an automated coagulation platform. Results: Here, we present an approach allowing for the autoverification and reporting of factor activity results with greatly diminished technologist effort. Conclusions: To the best of our knowledge, this is the first report of its kind providing a detailed procedure for implementation of autoverification expert rules as applied to coagulation factor activity testing. Advantages of this system include ease of training for new operators, minimization of technologist time spent, reduction of staff fatigue, minimization of unnecessary reflex tests, optimization of turnaround time, and assurance of the consistency of the testing and reporting process.
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Affiliation(s)
| | | | - Andre Valcour
- Center for Esoteric Testing, Laboratory Corporation of America Holdings, Burlington, NC, USA
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