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Sathe LM, Khan NN, Williams JM, Saul R, Jajieh K, Sartippour MR, Young R, Xie J, Marquette DM, Duncan T, Eskin E, Arboleda VA. 3D Printing as an Effective Quality Assurance Implementation in Massive-Scale SARS-CoV-2 Testing at a SwabSeq Next-Generation Sequencing Laboratory. Lab Med 2023; 54:512-518. [PMID: 36810591 DOI: 10.1093/labmed/lmac161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
Massive-scale SARS-CoV-2 testing using the SwabSeq diagnostic platform came with quality assurance challenges due to the novelty and scale of sequencing-based testing. The SwabSeq platform relies on accurate mapping between specimen identifiers and molecular barcodes to match a result back to a patient specimen. To identify and mitigate mapping errors, we instituted quality control using placement of negative controls within a rack of patient samples. We designed 2-dimensional paper templates to fit over a 96-position rack of specimens with holes to show the control tube placements. We designed and 3-dimensionally printed plastic templates that fit onto 4 racks of patient specimens and provide accurate indications of the correct control tube placements. The final plastic templates dramatically reduced plate mapping errors from 22.55% in January 2021 to less than 1% after implementation and training in January 2021. We show how 3D printing can be a cost-effective quality assurance tool to mitigate human error in the clinical laboratory.
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Affiliation(s)
- Laila M Sathe
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
- UCLA SwabSeq COVID19 Laboratory, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
| | - Nishrat N Khan
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
- UCLA SwabSeq COVID19 Laboratory, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
| | - Jazmine M Williams
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
- UCLA SwabSeq COVID19 Laboratory, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
| | - Rosita Saul
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
- UCLA SwabSeq COVID19 Laboratory, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
| | - Kane Jajieh
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
- UCLA SwabSeq COVID19 Laboratory, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
| | - Maryam R Sartippour
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
- UCLA SwabSeq COVID19 Laboratory, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
| | - Rachel Young
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
- UCLA SwabSeq COVID19 Laboratory, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
| | - Joanna Xie
- UCLA SwabSeq COVID19 Laboratory, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
| | - Dawn M Marquette
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
- UCLA SwabSeq COVID19 Laboratory, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
| | - Tiffany Duncan
- UCLA SwabSeq COVID19 Laboratory, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
| | - Eleazar Eskin
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
- UCLA SwabSeq COVID19 Laboratory, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
| | - Valerie A Arboleda
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
- UCLA SwabSeq COVID19 Laboratory, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
- Department of Pathology and Lab Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, US
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Thakur V, Akerele OA, Randell E. Lean and Six Sigma as continuous quality improvement frameworks in the clinical diagnostic laboratory. Crit Rev Clin Lab Sci 2023; 60:63-81. [PMID: 35978530 DOI: 10.1080/10408363.2022.2106544] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Processes to enhance customer-related services in healthcare organizations are complex and it can be difficult to achieve efficient patient-focused services. Laboratories make an integral part of the healthcare service industry where healthcare providers deal with critical patient results. Errors in these processes may cost a human life, create a negative impact on an organization's reputation, cause revenue loss, and open doors for expensive lawsuits. To overcome these complexities, healthcare organizations must implement an approach that helps healthcare service providers to reduce waste, variation, and work imbalance in the service processes. Lean and Six Sigma are used as continuous process improvement frameworks in laboratory medicine. Six Sigma uses an approach that involves problem-solving, continuous improvement and quantitative statistical process control. Six Sigma is a technique based on the DMAIC process (Define, Measure, Analyze, Improve, and Control) to improve quality performance. Application of DMAIC in a healthcare organization provides guidance on how to handle quality that is directed toward patient satisfaction in a healthcare service industry. The Lean process is a technique for process management in which waste reduction is the primary purpose; this is accomplished by implementing waste mitigation practices and methodologies for quality improvement. Overall, this article outlines the frameworks for continuous quality and process improvement in healthcare organizations, with a focus on the impacts of Lean and Six Sigma on the performance and quality service delivery system in clinical laboratories. It also examines the role of utilization management and challenges that impact the implementation of Lean and Six Sigma in clinical laboratories.
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Affiliation(s)
- Vinita Thakur
- Department of Laboratory Medicine, Health Sciences Center, Eastern Health Authority, St. John's, Canada.,Faculty of Medicine, Memorial University of Newfoundland, St. John's, Canada
| | - Olatunji Anthony Akerele
- Department of Laboratory Medicine, Health Sciences Center, Eastern Health Authority, St. John's, Canada
| | - Edward Randell
- Department of Laboratory Medicine, Health Sciences Center, Eastern Health Authority, St. John's, Canada.,Faculty of Medicine, Memorial University of Newfoundland, St. John's, Canada
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