Automation in clinical laboratories can help reduce errors, decrease turnaround times, and improve productivity. It is important to fully understand current laboratory processes and identify bottlenecks before implementing automation solutions. A successful automation project requires optimizing existing workflows, clear objectives, evaluation of alternatives, and consideration of all costs. Future laboratory automation may include more integrated systems, robotics, and artificial intelligence to further improve efficiency and quality of testing.
Use of laboratory instruments and specimen processing equipment to perform clinical laboratory assays with only minimal involvement of technologist .
Automation in clinical laboratory is a process by which analytical instruments perform many tests with the least involvement of an analyst.
The International Union of Pure and Applied Chemistry (IUPAC) define automation as "The replacement of human manipulative effort and facilities in the performance of a given process by mechanical and instrumental devices that are regulated by feedback of information so that an apparatus is self-monitoring or self adjusting”.
Quality in clinical laboratory is a continuous journey of improving processes through team work, innovative solutions, regulatory compliance with final objective to meet the evolving needs of clinicians & patients.
Introduction of Automation of the Analytical Process
Unit Operations
Specimen identification
Specimen preparation
Specimen delivery
Specimen loading and aspiration
Specimen processing
Sample induction and internal transport
Reagent handling and storage
Chemical reaction phase
Measurement approaches
Signal processing, data handling and process control
Applications of automation in clinical lab
This document discusses quality control, quality assurance, and quality assessment in medical laboratories. It defines key terms like quality control, quality assurance, and quality assessment. Quality control refers to analytical measurements used to assess data quality, while quality assurance is an overall management plan to ensure data integrity. Quality assessment determines the quality of results generated by evaluating internal and external quality programs. The document outlines quality assurance and quality control processes like standard operating procedures, equipment and reagent validation, personnel competency, and documentation. It also discusses error types, control chart interpretation, and Westgard rules for evaluating quality control results.
The document discusses various pre-analytical and post-analytical errors that can occur in clinical laboratories. It notes that errors commonly occur in specimen receiving, sampling, transport, and results reporting. Some common errors include entering the wrong patient data, incomplete patient information, misidentifying tests, collecting samples from patients with the wrong test orders, and not fulfilling all requested investigations. The document also discusses various biological and environmental factors that can influence laboratory test results, such as patient posture, exercise, underlying medical conditions, drug use, and diet. Proper specimen collection and handling is important to avoid pre-analytical errors.
This is a powerpoint of automation in clinical chemistry. This comprises the definition of automation, steps of the analytical process, and detail about the continuous flow analyzer.Thus, this will be helpful for the students of medical laboratory, biochemistry students and teachers.
Automation in clinical biochemistry provides several benefits such as reducing workload, increasing throughput, improving accuracy, and eliminating human error. There are various steps in automated analysis including sample collection, identification, delivery, preparation, and analysis. Automation uses laboratory instruments and equipment to perform assays with minimal human involvement. Common types of automated analyzers are continuous flow analyzers, discrete analyzers, batch analyzers, and random access analyzers. Automation allows for processing of larger sample volumes and multiple tests per sample.
This document discusses laboratory errors, their causes, types, and impacts. It describes that errors can occur in the pre-analytical, analytical, and post-analytical phases of testing and provide examples of errors in each phase. Errors are categorized as either determinate (systemic) errors, which are reproducible and can be identified and corrected, or indeterminate (random) errors, which are caused by uncontrollable variables and cannot be eliminated. The key goals are improving precision by reducing indeterminate errors and improving accuracy by reducing determinate errors.
Use of laboratory instruments and specimen processing equipment to perform clinical laboratory assays with only minimal involvement of technologist .
Automation in clinical laboratory is a process by which analytical instruments perform many tests with the least involvement of an analyst.
The International Union of Pure and Applied Chemistry (IUPAC) define automation as "The replacement of human manipulative effort and facilities in the performance of a given process by mechanical and instrumental devices that are regulated by feedback of information so that an apparatus is self-monitoring or self adjusting”.
Quality in clinical laboratory is a continuous journey of improving processes through team work, innovative solutions, regulatory compliance with final objective to meet the evolving needs of clinicians & patients.
Introduction of Automation of the Analytical Process
Unit Operations
Specimen identification
Specimen preparation
Specimen delivery
Specimen loading and aspiration
Specimen processing
Sample induction and internal transport
Reagent handling and storage
Chemical reaction phase
Measurement approaches
Signal processing, data handling and process control
Applications of automation in clinical lab
This document discusses quality control, quality assurance, and quality assessment in medical laboratories. It defines key terms like quality control, quality assurance, and quality assessment. Quality control refers to analytical measurements used to assess data quality, while quality assurance is an overall management plan to ensure data integrity. Quality assessment determines the quality of results generated by evaluating internal and external quality programs. The document outlines quality assurance and quality control processes like standard operating procedures, equipment and reagent validation, personnel competency, and documentation. It also discusses error types, control chart interpretation, and Westgard rules for evaluating quality control results.
The document discusses various pre-analytical and post-analytical errors that can occur in clinical laboratories. It notes that errors commonly occur in specimen receiving, sampling, transport, and results reporting. Some common errors include entering the wrong patient data, incomplete patient information, misidentifying tests, collecting samples from patients with the wrong test orders, and not fulfilling all requested investigations. The document also discusses various biological and environmental factors that can influence laboratory test results, such as patient posture, exercise, underlying medical conditions, drug use, and diet. Proper specimen collection and handling is important to avoid pre-analytical errors.
This is a powerpoint of automation in clinical chemistry. This comprises the definition of automation, steps of the analytical process, and detail about the continuous flow analyzer.Thus, this will be helpful for the students of medical laboratory, biochemistry students and teachers.
Automation in clinical biochemistry provides several benefits such as reducing workload, increasing throughput, improving accuracy, and eliminating human error. There are various steps in automated analysis including sample collection, identification, delivery, preparation, and analysis. Automation uses laboratory instruments and equipment to perform assays with minimal human involvement. Common types of automated analyzers are continuous flow analyzers, discrete analyzers, batch analyzers, and random access analyzers. Automation allows for processing of larger sample volumes and multiple tests per sample.
This document discusses laboratory errors, their causes, types, and impacts. It describes that errors can occur in the pre-analytical, analytical, and post-analytical phases of testing and provide examples of errors in each phase. Errors are categorized as either determinate (systemic) errors, which are reproducible and can be identified and corrected, or indeterminate (random) errors, which are caused by uncontrollable variables and cannot be eliminated. The key goals are improving precision by reducing indeterminate errors and improving accuracy by reducing determinate errors.
This document discusses laboratory errors and quality control in clinical testing. It describes three types of errors - pre-analytical, analytical, and post-analytical. Pre-analytical errors can occur before the sample reaches the lab due to improper patient preparation, collection, storage, or transport. Analytical errors occur during testing and can be due to issues with samples, equipment, reagents, or operator technique. Post-analytical errors involve improper result reporting. The document emphasizes the importance of quality control, calibration, and statistical analysis to monitor performance and identify errors. Quality control charts can reveal random errors or systematic shifts and trends.
This document discusses laboratory errors in medical practice. It notes that 0.1-3% of laboratory tests have errors, with most occurring in the pre-analytic and post-analytic phases rather than the analytic phase. Common pre-analytic errors include inappropriate test requests, order entry mistakes, misidentification of patients, and improper sample collection, transport, or storage. Analytic errors are less than 10% of total errors. The document also provides examples of how biological and behavioral factors can influence test results, and discusses clinical performance characteristics of medical tests.
This document provides an overview of quality control in clinical biochemistry laboratories. It discusses that quality control aims to ensure test results are correct by minimizing errors. Errors can occur in the pre-analytical, analytical, and post-analytical phases. The pre-analytical phase, involving sample collection and handling, accounts for most errors. Laboratories use internal quality control methods like calibration, controls, and Levey-Jennings charts daily, as well as external quality assurance programs, to monitor performance and identify errors. Maintaining quality control is important for generating accurate, reliable test results.
Quality assurance in the department of clinical biochemistryDipesh Tamrakar
This document discusses quality assurance and control in clinical laboratories. It explains that quality control aims to ensure test results are correct by monitoring performance through tools like internal quality control and external quality assessment. The document outlines the pre-analytical, analytical and post-analytical phases of testing and discusses specific quality control procedures used at each stage like storage of controls, monitoring control data, and troubleshooting out of control errors. Westgard rules for determining if quality control is in or out of control are also explained.
Automation in clinical laboratories aims to minimize manual processes and variability. It can automate pre-analytic, analytic and post-analytic phases through sample identification, delivery, processing, loading, analysis and result reporting. Analyzers like continuous flow, centrifugal, discrete and random access systems automate chemical reactions and measurements. Total laboratory automation integrates many automated analyzers and modular systems to further streamline testing. While automation reduces costs, errors and improves efficiency over time, it also increases initial costs and risks downtime and disruption to laboratory workflows.
In the continuous quality journey, Controlling laboratory Errors is an integral part & focusing on analytical, post-analytical process is the first step. Developing a reporting culture followed by thorough analysis and implementation of appropriate corrective, preventive actions is required.
This document discusses quality assurance in hematology laboratories. It defines key terms like accuracy, precision, and components of quality assurance like pre-analytical, analytical, and post-analytical stages. It describes the importance of proper specimen collection and handling in the pre-analytical stage. The analytical stage involves internal and external quality control. Specific controls for hematology analyzers like Latron beads and 6C & retics controls are discussed. The importance of result verification, critical value notification, and collaboration in the post-analytical stage is highlighted. Calibration, proficiency testing, and the role of risk assessment in ensuring patient safety are also summarized.
Quality control, quality assurance, and quality assessment are important concepts for ensuring accuracy and reliability in medical laboratory testing. Quality control refers to internal processes like running controls to verify test accuracy during each run. Quality assurance encompasses the overall program to deliver correct results. Quality assessment challenges these programs through external proficiency testing. Proper documentation, trained personnel, validated methods and equipment, and monitoring control rules are key to achieving the goals of quality control, quality assurance and providing quality medical laboratory testing.
The document discusses preanalytical errors that can occur in medical laboratories. It identifies that the preanalytical phase, which includes specimen collection, transport, and processing, is where the majority of errors take place. Proper procedures and techniques are important for collecting and handling specimens to avoid errors that can compromise patient diagnosis and treatment. The document outlines various steps in the preanalytical process and potential sources of errors at each step."
Analytical and post-analytical errors can occur in clinical chemistry laboratories. Analytical errors include issues like test systems not being calibrated properly, controls being out of range but results still reported, improper measurements or reagents, and instrument maintenance issues. This can lead to inaccuracies, imprecisions, insensitivities, and linearity problems. Post-analytical errors involve things like transcription mistakes in reporting results, reports going to the wrong location, illegible reports, or reports not being sent at all. Laboratories should develop systematic workflows, continuously monitor for errors, and strengthen defenses to minimize vulnerabilities and their impacts, which can include inadequate patient care, misdiagnosis, harm, or even death.
This document discusses quality control in laboratories. It defines key terms like quality assurance, quality assessment, total quality management, and continuous quality improvement. It describes factors that can affect quality like pre-analytical, analytical, and post-analytical variables. The importance of standard operating procedures, proficiency testing, and documenting quality control procedures is emphasized. Maintaining accurate and precise results through internal quality control using control charts and Westgard rules is also outlined.
Automation in biochemistry refers to using instruments to perform biochemical tests with minimal human involvement. Automated systems can perform many steps like sample handling, reagent addition, reaction incubation, and measurement that were previously done manually. The main types of automated analyzers are continuous flow analyzers, discrete autoanalyzers, and random access analyzers. Continuous flow analyzers pass samples and reagents sequentially through a single analytical pathway. Discrete autoanalyzers separate each sample and reagent in individual containers, allowing multiple tests per sample. Random access analyzers perform tests on batches of samples, selecting tests for each sample. Automated systems provide benefits like higher throughput, reduced variability, and less manual labor, but also have high initial costs.
This document discusses various types of interferences that can occur in immunoassays and affect the accuracy of results. It describes how heterophilic antibodies, human anti-animal antibodies, autoanalytes, and other substances can cause interference through cross-reactivity, hook effects, or by altering antibody binding. Common analytes affected include hormones, tumor markers, cardiac markers, and drugs. The document also outlines methods for detecting interference, such as using alternative assays, sample blocking and dilution, and looking for inconsistencies between clinical findings and results.
QUALITY
Conformance to the requirements of users or customers satisfaction of their needs and expectations.
Total Quality Management
A management approach that focuses on processes and their improvement.
Urine analysis is an integral part of a clinical laboratory. automation techniques in urine biochemistry, their priniciplas and microscopy along with their advantages and disadvantages are outlined.
This is a series of notes on clinical pathology, useful for postgraduate students and practising pathologists. It covers all internal and external quality control techniques. The topics are presented point wise for easy reproduction.
As information technology continues to open new pathways in clinical diagnostics and broaden how we measure and define success, lab directors must consider how IT can complement their investment in automation. In fact, automation and IT systems can no longer be regarded as separate laboratory solutions, but rather must be viewed as a single entity that exists to maximize input and output across the laboratory continuum. With automation serving to centralize testing and tube throughput and IT to aggregate and display testing data quickly and accurately, the benefits of the whole clearly become more important than the sum of its parts.
That is why it is paramount to research the availability and capabilities of an IT system that can enhance an automation solution. When automation and IT functionality combine, the closed-system loop provides seamless, total process management at a magnitude far greater than can be achieved by an automation solution that lacks adequate IT support.
Clinical laboratories that use AI have both possibilities and obstacles. It is crucial to create rules that guarantee fairness, security, and dependability for AI systems. Guidelines for regulators and parties involved in creating medical products based on artificial intelligence have previously been released by numerous international organizations.
This document discusses laboratory errors and quality control in clinical testing. It describes three types of errors - pre-analytical, analytical, and post-analytical. Pre-analytical errors can occur before the sample reaches the lab due to improper patient preparation, collection, storage, or transport. Analytical errors occur during testing and can be due to issues with samples, equipment, reagents, or operator technique. Post-analytical errors involve improper result reporting. The document emphasizes the importance of quality control, calibration, and statistical analysis to monitor performance and identify errors. Quality control charts can reveal random errors or systematic shifts and trends.
This document discusses laboratory errors in medical practice. It notes that 0.1-3% of laboratory tests have errors, with most occurring in the pre-analytic and post-analytic phases rather than the analytic phase. Common pre-analytic errors include inappropriate test requests, order entry mistakes, misidentification of patients, and improper sample collection, transport, or storage. Analytic errors are less than 10% of total errors. The document also provides examples of how biological and behavioral factors can influence test results, and discusses clinical performance characteristics of medical tests.
This document provides an overview of quality control in clinical biochemistry laboratories. It discusses that quality control aims to ensure test results are correct by minimizing errors. Errors can occur in the pre-analytical, analytical, and post-analytical phases. The pre-analytical phase, involving sample collection and handling, accounts for most errors. Laboratories use internal quality control methods like calibration, controls, and Levey-Jennings charts daily, as well as external quality assurance programs, to monitor performance and identify errors. Maintaining quality control is important for generating accurate, reliable test results.
Quality assurance in the department of clinical biochemistryDipesh Tamrakar
This document discusses quality assurance and control in clinical laboratories. It explains that quality control aims to ensure test results are correct by monitoring performance through tools like internal quality control and external quality assessment. The document outlines the pre-analytical, analytical and post-analytical phases of testing and discusses specific quality control procedures used at each stage like storage of controls, monitoring control data, and troubleshooting out of control errors. Westgard rules for determining if quality control is in or out of control are also explained.
Automation in clinical laboratories aims to minimize manual processes and variability. It can automate pre-analytic, analytic and post-analytic phases through sample identification, delivery, processing, loading, analysis and result reporting. Analyzers like continuous flow, centrifugal, discrete and random access systems automate chemical reactions and measurements. Total laboratory automation integrates many automated analyzers and modular systems to further streamline testing. While automation reduces costs, errors and improves efficiency over time, it also increases initial costs and risks downtime and disruption to laboratory workflows.
In the continuous quality journey, Controlling laboratory Errors is an integral part & focusing on analytical, post-analytical process is the first step. Developing a reporting culture followed by thorough analysis and implementation of appropriate corrective, preventive actions is required.
This document discusses quality assurance in hematology laboratories. It defines key terms like accuracy, precision, and components of quality assurance like pre-analytical, analytical, and post-analytical stages. It describes the importance of proper specimen collection and handling in the pre-analytical stage. The analytical stage involves internal and external quality control. Specific controls for hematology analyzers like Latron beads and 6C & retics controls are discussed. The importance of result verification, critical value notification, and collaboration in the post-analytical stage is highlighted. Calibration, proficiency testing, and the role of risk assessment in ensuring patient safety are also summarized.
Quality control, quality assurance, and quality assessment are important concepts for ensuring accuracy and reliability in medical laboratory testing. Quality control refers to internal processes like running controls to verify test accuracy during each run. Quality assurance encompasses the overall program to deliver correct results. Quality assessment challenges these programs through external proficiency testing. Proper documentation, trained personnel, validated methods and equipment, and monitoring control rules are key to achieving the goals of quality control, quality assurance and providing quality medical laboratory testing.
The document discusses preanalytical errors that can occur in medical laboratories. It identifies that the preanalytical phase, which includes specimen collection, transport, and processing, is where the majority of errors take place. Proper procedures and techniques are important for collecting and handling specimens to avoid errors that can compromise patient diagnosis and treatment. The document outlines various steps in the preanalytical process and potential sources of errors at each step."
Analytical and post-analytical errors can occur in clinical chemistry laboratories. Analytical errors include issues like test systems not being calibrated properly, controls being out of range but results still reported, improper measurements or reagents, and instrument maintenance issues. This can lead to inaccuracies, imprecisions, insensitivities, and linearity problems. Post-analytical errors involve things like transcription mistakes in reporting results, reports going to the wrong location, illegible reports, or reports not being sent at all. Laboratories should develop systematic workflows, continuously monitor for errors, and strengthen defenses to minimize vulnerabilities and their impacts, which can include inadequate patient care, misdiagnosis, harm, or even death.
This document discusses quality control in laboratories. It defines key terms like quality assurance, quality assessment, total quality management, and continuous quality improvement. It describes factors that can affect quality like pre-analytical, analytical, and post-analytical variables. The importance of standard operating procedures, proficiency testing, and documenting quality control procedures is emphasized. Maintaining accurate and precise results through internal quality control using control charts and Westgard rules is also outlined.
Automation in biochemistry refers to using instruments to perform biochemical tests with minimal human involvement. Automated systems can perform many steps like sample handling, reagent addition, reaction incubation, and measurement that were previously done manually. The main types of automated analyzers are continuous flow analyzers, discrete autoanalyzers, and random access analyzers. Continuous flow analyzers pass samples and reagents sequentially through a single analytical pathway. Discrete autoanalyzers separate each sample and reagent in individual containers, allowing multiple tests per sample. Random access analyzers perform tests on batches of samples, selecting tests for each sample. Automated systems provide benefits like higher throughput, reduced variability, and less manual labor, but also have high initial costs.
This document discusses various types of interferences that can occur in immunoassays and affect the accuracy of results. It describes how heterophilic antibodies, human anti-animal antibodies, autoanalytes, and other substances can cause interference through cross-reactivity, hook effects, or by altering antibody binding. Common analytes affected include hormones, tumor markers, cardiac markers, and drugs. The document also outlines methods for detecting interference, such as using alternative assays, sample blocking and dilution, and looking for inconsistencies between clinical findings and results.
QUALITY
Conformance to the requirements of users or customers satisfaction of their needs and expectations.
Total Quality Management
A management approach that focuses on processes and their improvement.
Urine analysis is an integral part of a clinical laboratory. automation techniques in urine biochemistry, their priniciplas and microscopy along with their advantages and disadvantages are outlined.
This is a series of notes on clinical pathology, useful for postgraduate students and practising pathologists. It covers all internal and external quality control techniques. The topics are presented point wise for easy reproduction.
As information technology continues to open new pathways in clinical diagnostics and broaden how we measure and define success, lab directors must consider how IT can complement their investment in automation. In fact, automation and IT systems can no longer be regarded as separate laboratory solutions, but rather must be viewed as a single entity that exists to maximize input and output across the laboratory continuum. With automation serving to centralize testing and tube throughput and IT to aggregate and display testing data quickly and accurately, the benefits of the whole clearly become more important than the sum of its parts.
That is why it is paramount to research the availability and capabilities of an IT system that can enhance an automation solution. When automation and IT functionality combine, the closed-system loop provides seamless, total process management at a magnitude far greater than can be achieved by an automation solution that lacks adequate IT support.
Clinical laboratories that use AI have both possibilities and obstacles. It is crucial to create rules that guarantee fairness, security, and dependability for AI systems. Guidelines for regulators and parties involved in creating medical products based on artificial intelligence have previously been released by numerous international organizations.
This document discusses process improvement techniques like Lean Six Sigma. It describes how Six Sigma aims to reduce defects to 3.4 per million opportunities. The DMAIC methodology is outlined as a problem-solving approach to define, measure, analyze, improve, and control processes. Common causes of medical errors are reviewed. Case studies demonstrate applying Lean tools like value stream mapping to identify waste in laboratory sample processing and reduce turnaround times. The document promotes applying quality improvement strategies to enhance efficiency, compliance and reduce costs in clinical laboratories.
Inpeco is a global leader in total laboratory automation, providing solutions like ProTube for sample collection centers and FlexLab for clinical laboratories. Their solutions combine open system automation with full sample traceability from collection to results. This enables accurate and validated test results, expedited delivery of results, and increased productivity while freeing up staff for more valuable tasks. Inpeco's vision is to advance the diagnostic testing process through continued innovation.
A Productive Method for Improving Test EffectivenessShradha Singh
This document proposes a new approach for test suite selection and prioritization that aims to improve test effectiveness. The approach has three components: 1) A predictive component that prioritizes test cases based on their historical failure rates, with tests that failed more often run more frequently. 2) A coverage component that selects test cases based on code coverage data to target parts of the code affected by changes. 3) A decision component that prioritizes important test cases using an algorithm. The approach is intended to select a subset of test cases from large test suites to reduce validation time and improve resource utilization while still effectively testing software. Experiments showed the approach decreased validation cycles and reduced time to market.
Gale Technologies - A Leading Innovative Software Solutions Provider Explains...Galetech
This document outlines a 7-step process for automating a network test lab using physical-layer switches. The steps include: 1) Assessing automation needs; 2) Deciding what to automate; 3) Choosing infrastructure like switches; 4) Choosing a lab management solution; 5) Planning the architecture; 6) Setting the administrative framework; and 7) Integrating with existing automation. The goal is to enable dynamic test beds, shorten test cycles, and improve testing through remote access and parallel tests. Physical-layer switches allow automated reconfiguration to quickly set up and run multiple tests.
Presentation carried out in Rome the 26th January, 2011 during HEALTHINF-BIOSTEC 2011 about CONTINUOUS CLINICAL PATHWAYS EVALUATION BY USING AUTOMATIC LEARNING ALGORITHMS
Authors: Carlos Fernández-Llatas, Teresa Meneu, Jose Miguel Benedí and Vicente Traver
This document summarizes the features and benefits of Attune Middleware for integrating medical device data. The middleware can [1] automate lab workflows to eliminate manual errors, improve turnaround time, and focus on critical cases; [2] integrate devices and third party systems to allow centralized data access and device control; and [3] put an end to data inconsistencies and human error to ensure accurate and efficient results.
A Laboratory Information Management System (LIMS) is software that allows you to effectively manage samples and associated data. By using a LIMS, your lab can automate workflows, integrate instruments, and manage samples and associated information.
Lab automation provides faster and more productive processing of samples which allows for more accurate and consistent test results turnaround times. It uses barcoded specimens and carriers to move samples and report results. Around 60-70% of medical decisions are based on lab test results, so automation helps address increasing testing volumes and demands for faster results by reducing contamination risks and manual work while improving quality and precision. The automation also promotes appropriate test selection to help treatment by giving online report access across Bangladesh.
This document discusses using Lean Six Sigma to analyze and optimize workflows in Laboratory Information Management Systems (LIMS). It provides an overview of LIMS and examples of sequential and state machine workflows. The goals of applying a Lean Six Sigma framework to LIMS workflow analysis are to improve complex workflows, learn relevant tools, and ensure consistent, accurate results. Case studies examine assessing a microbiology system and redesigning LIMS workflows. Future developments may include greater mobile integration and external monitoring of LIMS data.
This document discusses quality assurance in mycobacteriology laboratories. It describes the three main components of a quality assurance system as quality control, external quality assessment, and quality improvement. Quality control procedures should address pre-analytical, analytical, and post-analytical phases of testing. Monitoring performance indicators such as contamination rates, turnaround times, and proficiency testing scores helps to evaluate laboratory performance and identify areas for improvement.
The document discusses automation in clinical chemistry laboratories. It describes how automation can increase efficiency by performing many manual tasks automatically. The analytic process is divided into preanalytic, analytic, and postanalytic phases, with the analytic phase being the most automated. Automation occurs through continuous flow analyzers, centrifugal analyzers, and discrete analyzers. Continuous flow analyzers use tubing to flow reagents and samples, while discrete analyzers keep each sample and reaction separate to minimize carryover. Point-of-care testing also utilizes automation by performing tests outside the main laboratory, such as in physician offices and ICUs.
Internal Quality Control Lecture MD General 2014 Course, Clin Path Ain Shams ...Adel Elazab Elged
The document discusses internal quality control in clinical laboratories. It defines key terms like quality control, quality assurance, and quality management. It explains the importance of internal quality control in ensuring accurate and reliable test results. Quality control involves running control samples alongside patient samples and using statistical tools like control charts and Westgard rules to monitor the analytical process and ensure it is in control. Factors that could indicate the process is out of control are also summarized.
Analytical Method & Technology Transfer Ispe GuideCrown Cork & Seal
This document provides guidance on effectively transferring analytical methods between laboratories. It recommends a standardized process for transferring technology with minimum documentation. This includes establishing acceptance criteria before transfer, executing training at the receiving unit, and issuing a transfer report confirming the receiving unit's qualification. The guidance outlines experimental designs and acceptance criteria for transferring various analytical methods like assay, content uniformity, impurities testing, and dissolution testing. Acceptance criteria are generally based on statistical analyses showing equivalence between laboratory results.
The lab management module in a hospital is a vital component of a comprehensive healthcare management system, often considered the best healthcare management system. This system, sometimes referred to as a smart hospital management system, is an integrated software solution designed to streamline and optimize various aspects of healthcare administration and patient care. The lab management module plays a crucial role in this ecosystem.
Maintaining Quality Control in Lab Operations - Strategy and Impact.pptxMocDoc
Learn how to implement effective quality control strategies in laboratory operations and understand the impact it can have on your organization's success. Read on to discover the best practices and techniques for maintaining high-quality standards in your lab operations.
This document discusses key concepts in quality assurance and quality control for analytical chemistry measurements. It defines terms like accuracy, precision, systematic and random errors. It also describes common quality control measures used to ensure quality of analytical data, including calibration standards, blanks, recovery studies, precision and accuracy studies, and method detection limits. Key quality control samples are discussed like matrix spikes, laboratory control samples, and surrogates.
The document discusses the application of statistical tools to enhance productivity and quality control in industries. It explains key concepts like process control, process capability indices, acceptance sampling plans, and their use in quality management. Statistical process control techniques like control charts are used to monitor processes and make data-driven decisions about product and process quality. Acceptance sampling balances protecting consumers from defects and encouraging quality production.
Lonza Moda Solution Overview 2010 Final 18 Aug2010rich_kelley
The document describes Lonza's MODA solution for automating quality control processes. Key features of the MODA solution include making the quality control workflow entirely paperless, utilizing mobile devices and RFID technology to automate data capture, and providing on-demand reporting and analytics. The MODA solution is estimated to provide tangible benefits like cost reduction, improved productivity, and enhanced compliance through error reduction and faster data access. A demonstration of the MODA solution's mobile data collection capabilities and paperless quality control workflow is also included.
This document provides guidelines for point of care testing (POCT). It defines POCT as any test performed near the patient to enable immediate treatment decisions. The main laboratory is responsible for all POCT and must ensure results match central lab outputs. POCT devices must have internal quality controls, standard operating procedures, and staff training. Quality control practices depend on the technology but manufacturers' claims on precision are important due to challenges running controls. Participation in proficiency testing is required to monitor performance.
This document discusses quality control procedures for clinical biochemistry testing. It recommends:
- Using Grade I/II water for reconstituting reference materials and reagents.
- Performing hormone stimulation tests under medical supervision in a hospital lab.
- Running a minimum of one control level once daily if less than 25 patient samples are tested, and two control levels once daily if between 25-75 patient samples are tested. For more than 75 patient samples, two control levels should be run twice daily.
- For blood gas measurements, running one control every eight hours and one with each patient sample unless the instrument automatically calibrates at predefined intervals.
Here a detailed gap analysis is presented for the NABL 112 document for issue number 3 and issue number 4. This presentation contain general clauses except the specific requirements for individual area in clinical laboratory
Quality assurance scheme for basic composite medical laboratoriesjdyjdo
This document discusses NABL's new Quality Assurance Scheme (QAS) for basic composite medical laboratories at the entry level. The scheme aims to recognize quality practices in small laboratories that perform basic testing, as getting full ISO 15189 accreditation is not feasible for most small labs due to costs, infrastructure requirements, and stringent protocols. The QAS criteria are based on requirements in the Clinical Establishments Rules of 2012. Laboratories that comply with the QAS can switch to ISO 15189 accreditation later if desired. Benefits of the QAS include a certificate of compliance and recognition of quality efforts for small laboratories.
Preventive health checkups are aimed at identifying potential health issues before symptoms arise. They include assessments of medical history, lifestyle habits, vital signs, and physical examinations. Common lab tests screen for conditions like diabetes and cancer. The appropriate checks and frequency vary by age, gender, family history, and other individual factors. Guidelines recommend checks like blood pressure and cancer screenings from ages 40-64. The takeaway message is that prevention is better than cure, and selective health check packages tailored to each person are most effective.
The document discusses point-of-care testing for diabetes mellitus. It outlines various tests that can be performed at the point-of-care, including glucose and ketone levels from blood and urine, glycated hemoglobin (HbA1c), and microalbumin levels. The advantages of point-of-care testing are highlighted such as rapid results, portability, and improved patient care. Quality control procedures and guidelines for devices are also summarized. Recent technological advances for continuous glucose monitoring through contact lenses or watches are mentioned.
The paradigm shift – central lab to point of carejdyjdo
This document discusses the journey of laboratory testing from bedside testing to centralized laboratories to point-of-care testing. It provides a brief history of early diagnostic tests and outlines the benefits of point-of-care testing such as simpler sample collection and faster turnaround time. It discusses quality requirements for point-of-care testing devices and the roles of the manufacturer, laboratory, and point-of-care coordinator in ensuring quality. It also addresses challenges of point-of-care testing and the importance of oversight committees.
Infertility is defined as the inability to conceive after one year of unprotected intercourse if the woman is under 35 years old, or after 6 months if the woman is over 35. Approximately 15% of reproductive couples struggle with infertility. Both male and female factors can contribute to infertility, with common causes including problems with ovulation, fallopian tubes, uterine or cervical factors, as well as hormonal imbalances, autoimmune disorders, and genetic conditions. A full medical workup evaluates both partners and may include tests to assess hormone levels, egg reserve, sperm count, thyroid function, and more. While the causes of infertility can be complex, many cases can be treated successfully with lifestyle changes, medication, surgery, or assisted
Letter to MREC - application to conduct studyAzreen Aj
Application to conduct study on research title 'Awareness and knowledge of oral cancer and precancer among dental outpatient in Klinik Pergigian Merlimau, Melaka'
Let's Talk About It: Breast Cancer (What is Mindset and Does it Really Matter?)bkling
Your mindset is the way you make sense of the world around you. This lens influences the way you think, the way you feel, and how you might behave in certain situations. Let's talk about mindset myths that can get us into trouble and ways to cultivate a mindset to support your cancer survivorship in authentic ways. Let’s Talk About It!
R3 Stem Cell Therapy: A New Hope for Women with Ovarian FailureR3 Stem Cell
Discover the groundbreaking advancements in stem cell therapy by R3 Stem Cell, offering new hope for women with ovarian failure. This innovative treatment aims to restore ovarian function, improve fertility, and enhance overall well-being, revolutionizing reproductive health for women worldwide.
Comprehensive Rainy Season Advisory: Safety and Preparedness Tips.pdfDr Rachana Gujar
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Full automation
1.
2. Full Automation
Dr. Jayesh Warade
MBBS MD PGDHHCM DCRM PGDip (Endo)
Consultant Biochemistry and Molecular Biology
Quality Manager
Laboratory Services
Meenakshi Mission Hospital and Research Center, Madurai
3. Automation
It is automation of manual processes and
involves automated or robotic equipment.
Automation is the use of control
systems and information technologies to
reduce the need for human work in the
production of goods and services
4. Laboratory Automation
Laboratory automation is the use of
instrument and specimen processing
equipment to perform clinical assay
with only minimal involvement the
technologist
6. Operations in Laboratory
Divided into three phases
preanalytic,
analytic, and
postanalytic
The analytic phase is the most automated, and more
research and development efforts are focusing on
increasing automation of the preanalytic and
postanalytic processes.
7. Inidividual Steps in Process
Specimen acquistion
specimen identification
specimen delivery to laboratory
specimen preparation
specimen loading and aspiration
on analyzer specimen delivery
reagent handling and storage
reagent delivery
chemical reaction phase
measurement approches
signal processing, data handling and process control
16. Laboratory Infromation System
Computerized information management
system designed for laboratories
Manages lab data from sample log-in to
reporting
Interfaces with analytical instruments
Sorts and organizes data into various
report formats
Stores data for future reference and use
19. Autoverification
What is Autoverification? (CAP)
“Autoverification is the process by which patient results
are generated from interfaced instruments and sent to
the LIS, where they are compared against laboratory-
defined acceptance parameters.
If the results fall within these defined parameters, the
results are automatically released to patient reporting
formats without any additional laboratory staff
intervention.
Any data that fall outside the defined parameters is
reviewed by laboratory staff prior to reporting.”
20. Why Autoverification?
↑ Consistency:
Autoverification removes technologist’s “subjectivity” and improves
consistencyof reporting (regardless of the number and skill set of the
technologists in the lab)
↑Quality:
Autoverification reduces errors/mistakes, and improves quality
↓TAT:
Autoverification reduces amount of labor required for validation of
results
About 60-80% of results could be automatically verified, while 20-40%
require further attention.
Holy Trinity of Lab Testing
1.Increase Patient Safety
2.Decrease TAT
3.Cost-Savings
21. Guideline is Available for
Autoverification?
CLSI AUTO 10-A; Volume 26 Number 4
Autoverification of Clinical Laboratory
Test Results Guideline
23. Integrated Automation
Speciment Input Area
Bar code reading Sations
Transport System
High Level sorting or routing device
Automated Centrifuge
Level Detections and evalution of speciment adequacy
Decapping Station
Recapping station
Aliquoter
Interface to Automated Analyzer
Sorter
Take Out Station
Storage and retrival system
25. Middleware
Middleware is connectivity
software that provides a
mechanism for processes to
interact with other
processes running on
multiple networked
machines.
Middleware Application
Programming Interfaces
provide a more functional
set of capabilities than the
OS and network services
provide on their own.
26. Middleware
Sample tracking & routing of samples to different
instruments/work stations.
Differentiation of STAT & Routine samples to be handled efficiently
for fast TAT
Monitoring TAT for STAT & Routine samples
Auto-validation of results based on configurable set of criteria (QC &
Calibration pass, Delta check, Moving average, etc)
Monitoring QC performance of all configured instruments & alert to
user on ‘outliers’
Monitoring ‘instrument maintenance protocols’, error & event logs on
linked instruments
Inventory management on all instruments linked to the middleware
Instant access to patient test orders and results
Instrument reports (events, calibration, reagent status)
27. Systematic Approach to
Automation
Evaluation of needs (move current state to desired state)
Logistics and handling issues
Facilities and space consideration
Temperature considerations
Mapping workflow, timing workflow
Finding bottlenecks and time wasters
Identify possible solutions to meet needs
Evaluation of alternatives
Progress measures
Cost justification
29. WHY AUTOMATION?
Reduce human error
Safety
decrease laboratory costs
improve turnaround time
increase productivity
Run more tests
Test in fewer sites
Operate with fewer instruments.
Retain lower operating costs.
Employ relatively less skilled labor.
Use more automation in a paperless environment
30. Specimen Volumes and Workload
What is laboratory’s specimen volume?
Chart specimen count by hour of day and day
of week
What percentage are centrifuged?
What percentage are aliquotted?
What percentage of specimens are shared
between two lab sections?
What percentage of specimens are
refrigerated or frozen?
31. Handling Considerations
How and where do specimens arrive?Courier
vehicles, tube system, dumb waiter, window,
phlebotomists, patient walk-ins, nurse delivery? Are
these near each other or in separate areas?
Patient registration -is it required, is it before or
after processing, where is it located, who does it -
lab personnel or hospital personnel?
Patient identification: is there a wrist band bar code
system linked to the LIS?
How do phlebotomists verify patient ID?
32. Handling Considerations
Do nurses or patient care assistants (i.e., employees not
under lab control) draw or collect specimens?
For tests ordered on the floors, do LIS labels print on the
floors or in the lab?
Where are tubes centrifuged? Specimen Processing or
Chemistry?
Pour-offs and aliquotting –what is the workload?
Sorting -how much sorting of specimens occurs -in
Specimen Processing and in lab sections?
Transport -delivery by Specimen Processing or pick-up by
labs? What are the distances covered?
33. Handling Considerations
How, where, and for how long are
archived specimens stored?
Centralized or decentralized?
Manual system or using bar codes ?
What is the percentage of repeat testing?
What is the percentage of additional
testing requested to be added to archived
specimens?
34. Facilities and Space
If there is the opportunity to design a new facility, great.
Whether yes or no, here are several worthwhile ideas:
Arrange the facilities in a manner that follows the flow of the
specimens.
Position highest volume testing (Chemistry, Hematology,
etc.) closest to Specimen Receiving and lowest volume
testing furthest away.
Avoid having all lab traffic go through a key area such as
Specimen Receiving.
Position client service and exception handling activities in or
close to Specimen Receiving.
35. Workflow Mapping
Material flows (specimens)
Process flows
Data flow diagram-done at different layers
of detail
Workload map-can be used in simulation
studies
39. Steps For Tracking
Registration
Consultation
Time of prescription by
physician
Billing time
Time of collection
Time of transport to
collection
Receiving at reception and
barcode
Transportation to
segregation
Segregation and transfer to
department
Department reception
Transfer to centrifuge
Centrifuge
Transfer to instrument
Waiting and processing
Updating
Approval
40. Non-Track Automation Possibilities
Wristband bar code systems for
phlebotomy
Document management systems
Autoverification, middleware, and QC
software
PC or LIS-based specimen storage and
retrieval
42. Identifying Possible Solutions to
Meet Needs
Use quality and turn-around time measures, workflow, and
timing studies to find bottlenecks and potential areas for re-
engineering.
Re-engineering of processes should precede introduction of
automation.
Not all solutions need to involve automation
Several seemingly small, low-cost re-engineering projects
sometimes have more impact on laboratory performance
than an expensive automation project.
“Automating a poor process still leaves one with a poor
process.”
43. Re-Engineer Processes
Use continuous quality improvement (CQI) tools such as
Lean and Six Sigma to foster process improvements
Standardize processing procedures to “best practice” solutions
with fewest “hand-offs.”
Reduce or eliminate non-value added handling and sorting.
Eliminate “running around” to find shared specimens.
Redesign workstations so that individuals process orders
from start to finish.
Maximize the number of specimens at test run start times.
44. Evaluation of Alternatives
Define and rank objectives (needs to be filled).
Identify alternative solutions, some of which may not
involve automated equipment.
Match the key features of alternative solutions to the most
important needs of your lab that are solved by those
solutions.
Emphasis in any solution that is selected should be on
process control and process improvement.
A solution with several small steps sometimes is better than
a major implementation of automation.
45. Progress Measures
Median turn-around time
95th percentile turn-around time
Stat turn-around time
Lost specimens
Mislabeled specimens
Billed units per FTE
Rate of hiring of technical employees
46. Objectives To be Included...
To compare and contrast the TAT pre-
and post-LAS
To compare and contrast laboratory errors
pre- and post-LAS
To gauge the level of staff satisfaction and
their feedback post-LAS
To document the advantages and
limitations of the LAS and the continuous
improvement process for the first 6
months.
49. Reasons Why Automation Projects
are Not Successful
Incomplete understanding of current environment...processes, costs,
customer expectations
Loss in flexibility due to fixed processes and limited throughput
Unrealistic expectations of system...cost reduction, throughput, return
on investment
Unplanned and poorly developed ‘workarounds’ required to interface
automation with manual processes
Unclear expectations of system functionality
Overbuilt and unnecessarily complicated system design
Inadequate technical support
Credible and realistic impact analysis never conducted
Hidden costs...labor, supplies, maintenance
Failure to optimize current processes prior to automation→never
automate a poor process!
61. Future Concept Lab Automation
Biorepository-sized automated storage
systems, inexpensive radiofrequency
identification (RFID), Drone - based
sample dispatching, development of
new positions such as
"Robotechnologist", Multilayer
Perception Neural Network
68. Automation Lessons and Take Home
Messages
Know your laboratory’s business!
Map workflow to find bottlenecks
Determine your primary and secondary objectives
Use your workflow map and objectives to authenticate
vendor proposals
Focus on process improvement
Re-engineering processes may have just as much impact
on operations as automation
Maximize use of information technology
Consider alternatives
Justify all costs
Take your time