Data flow in clinical laboratories: could metadata and peridata bridge the gap to new AI-based applications?

In the last decades, clinical laboratories have significantly advanced their technological capabilities, through the use of interconnected systems and advanced software. Laboratory Information Systems (LIS), introduced in the 1970s, have transformed into sophisticated information technology (IT) components that integrate with various digital tools, enhancing data retrieval and exchange. However, the current capabilities of LIS are not sufficient to rapidly save the extensive data, generated during the total testing process (TTP), beyond just test results. This opinion paper discusses qualitative types of TTP data, proposing how to divide laboratory-generated information into two categories, namely metadata and peridata. Being both metadata and peridata information derived from the testing process, it is proposed that the first is useful to describe the characteristics of data, while the second is for interpretation of test results. Together with standardizing preanalytical coding, the subdivision of laboratory-generated information into metadata or peridata might enhance ML studies, also by facilitating the adherence of laboratory-derived data to the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles. Finally, integrating metadata and peridata into LIS can improve data usability, support clinical utility, and advance AI model development in healthcare, emphasizing the need for standardized data management practices.

[SFBC Working Group on sources of errors in laboratory medicine: objectives and key areas of work]. / Groupe de travail SFBC Sur les sources d'erreurs en biologie clinique: Objectifs et axes de travail.

Laboratory medicine plays a crucial role in patient care, contributing to approximately 70 % of clinical decisions. In collaboration with clinicians, laboratory medicine specialists perform analyses that are useful for diagnosis, screening and prevention. Laboratories are known for their efficiency, which is reached through a rigorous quality system. However, errors can occur, especially given the complexity of the total testing process. These errors may lead to severe consequences, such as incorrect diagnoses or delays in treatment. Errors can occur at every stage of the total testing process, those related to the pre-analytical phase being the most prevalent. To reduce medical errors related to laboratory processes, it is essential to provide training for medical and paramedical staff, optimize production automation, and leverage technological advancements. These considerations have led to the creation of a French Working Group on Sources of Errors in Laboratory Medicine, under the aegis of the French lean society of clinical chemistry and laboratory medicine (Société Française de Biologie Clinique - SFBC). The objectives of this working group are to produce an educational handbook on sources of errors in laboratory medicine, provide training for clinical chemists, and conducting applied research projects to better understand the mechanisms behind specific errors. Ultimately, the aim is to minimize errors and enhance the quality of laboratory tests.

Direct-to-consumer testing as consumer initiated testing: compromises to the testing process and opportunities for quality improvement.

Direct-to-consumer testing (DTCT) refers to commercial laboratory tests initiated by laypersons without the involvement of healthcare professionals. As this market grows in size and variety of products, a clear definition of DTCT to ground the conceptualization of their harms and benefits is needed. We describe how three different modalities of DTCT (home self-testing, self-sampled tests, and direct access tests) present caveats to the traditional testing process ('brain-to-brain loop'), and how this might differ between medical vs. non-medical laboratories. We make recommendations for ways to improve quality and reduce errors with respect to DTCT. The potential benefits and harms of DTCT will invariably depend on the context and situation of individual consumers and the types of tests involved. Importantly, implications for both consumers and the healthcare system should be considered, such as the effects on improving health outcomes and reducing unnecessary testing and use of clinical resources. 'Consumer initiation' must be a central defining characteristic of DTCT, to clearly demarcate the key drawbacks as well as opportunities of this type of testing from a laboratory specialists' perspective. The concept of 'consumer initiated testing' should also help define DTCT regulation, and provide a locus of efforts to support consumers as the main decision-makers in the purchasing and conducting of these tests in the absence of clinician gatekeeping.

The surveys on quality indicators for the total testing process in clinical laboratories of Fujian Province in China from 2018 to 2023.

OBJECTIVES: This study investigates the application of 15 Quality Indicators (QIs) in clinical laboratories in Fujian Province, China, from 2018 to 2023. It identifies the main causes of laboratory errors and explores issues in the application of QIs, providing a reference for establishing provincial state-of-the-art and operational quality specifications (QSs). METHODS: All clinical laboratories in Fujian Province were organized to submit general information and original QIs data through the online External Quality Assessment (EQA) system of the National Clinical Laboratory Center (NCCL) for a survey of 15 QIs. Data from 2018 to 2023 were downloaded for statistical analysis, and the current QSs for the 15 QIs in Fujian Province were compared and analyzed with those published by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Working Group on Laboratory Errors and Patient Safety (WG-LEPS). RESULTS: QIs data from 542 clinical laboratories were collected. The survey on data sources showed that the number of laboratories recording QIs data using Laboratory Information Systems (LIS) increased annually, but the growth was modest and the proportion was less than 50 %. Among the laboratories using LIS to record QIs data, 133 continuously participated in this survey for six years, reporting different QIs. Over the six years, all reported QIs showed significant improvement or at least remained stable. The best median Sigma (σ) metrics were for the percentage of critical values notification and timely critical values notification, reaching 6σ, followed by the percentage of incorrect laboratory reports, with σ metrics ranging from 4.9σ to 5.1σ. In contrast, the percentage of tests covered by internal quality control (IQC) (1.5σ-1.7σ) and inter-laboratory comparison (0.1σ) remained consistently low. Compared to the QSs published by IFCC WG-LEPS, the QSs for the 15 QIs in Fujian Province in 2023 were stricter or roughly equivalent, except for the percentage of incorrect laboratory reports (Fujian Province: 0-0.221, IFCC WG-LEPS: 0-0.03). CONCLUSIONS: 1. The application of QIs has significantly improved the quality of testing in clinical laboratories in Fujian Province, but the percentage of tests covered by IQC and inter-laboratory comparison remain low; 2. Effective application of QIs requires the establishment of comprehensive LIS, unified calculation standards, and other supporting measures.

Blood self-sampling devices: innovation, interpretation and implementation in total lab automation.

The introduction of the vacuum tube in 1949 revolutionized blood collection, significantly improving sample quality and patient comfort. Over the past 75 years, laboratory diagnostics have evolved drastically, from manual to automated processes, reducing required test volumes by over 1,000 times. Despite these advancements, venous blood collection presents logistical challenges, including centralized scheduling and a large volume of biological waste due to the imbalance between the needed blood volume (often very little) and the collected volume (often in excess). The COVID-19 pandemic further emphasized the need for decentralized healthcare solutions and patient empowerment. Capillary blood collection, widely used in point-of-care testing, offers a promising alternative, particularly for patients facing frequently, or difficulties with, venous sampling. The Leiden University Medical Center in the Netherlands experienced a 15 % reduction in volume of laboratory tests during and after the pandemic, attributed to patient preference for local blood collection and testing. To address these challenges, self-sampling devices are emerging, empowering patients and streamlining sample logistics. However, challenges such as cost, transportation regulations, and sample volume adequacy persists. Robust devices tailored for total lab automation and sustainable practices are crucial for widespread adoption. Despite hurdles, the integration of self-sampling into diagnostic processes is inevitable, heralding a shift towards patient-centered, proactive healthcare. Practical recommendations include robust device design, ease of use, affordability, sustainability, sufficient quality and acceptability by seamless integration into laboratory workflows. Although obstacles remain, self-sampling represents the future of laboratory diagnostics, offering convenience, cost-effectiveness, interoperability and patient empowerment.

Evaluation of Laboratory Performance in Consideration with Pre analytical and Post analytical Quality Indicators.

Implementation of Quality indicators (QIs) plays an imperative role in improving the total testing process, as it provides a quantitative basis for evaluating the laboratory performance. Besides monitoring of analytical quality specifications, several lines of experimental and clinical evidence have alluded a pivotal role of extra-analytical phases in improving the quality of laboratory services and therefore a relevance of pre- and post-analytical steps have been speculated on the overall quality in the total testing process and consequently on clinical decision-making. This was a retrospective study designed to evaluate and review different extra-analytical quality indicators in NABL accredited clinical biochemistry laboratory at BJ Medical College and Civil Hospital, Ahmedabad, Gujarat in an endeavour to ameliorate the performance of the laboratory. All Clinical Chemistry Laboratory test requests with their respective samples from January 2018 to December 2021 were included in the study. A total of 1,439,011samples were processed, and were evaluated for seven QIs [(% of number of suitable samples not received; QI-8), (% of number of samples received in inappropriate container; QI-9), (% of number of samples hemolysed; QI-10), (% of number of samples with inadequate sample volume; QI 12) (% of number of samples received mismatched; QI 15), (% of number of samples reported after turnaround time; QI 21) and (% of number of samples with critical values informed; QI 22)] based on defined criteria of Quality Specification given by International Federation of Clinical Chemistry. Total number of preanalytical errors was 53,669 (3.72%). Among the preanalytical errors, inadequate sample volume (2.37% of total samples; 63.49% of total pre-analytical errors) was the most common anomaly followed by Not received samples (24.18%) hemolysis (8.26%) mismatched (3.91%) and 0.14% samples were received in Inappropriate container; manifesting that the error frequency was unacceptable for QI 21 and QI 8, acceptable for QI 10, minimally acceptable for QI 15 and optimum for QI QI 9. Furthermore, there was year-wise progressive decline in error rate of inadequate sample volume, hemolysed sample received and mismatched samples. Total number of post analytical errors were 19,002 (1.32%). TAT outlier and critical values communicated were the two QIs evaluated for this phase and results of both QI were within acceptable limits. Quality indicators serve as a tool to monitor process performance and consequently derived error rates warrant active intervention to improve the laboratory services and patient health care. Dissemination of certified documents, regular staff training and evaluation needs to be conducted.

Approaching sustainability in Laboratory Medicine.

INTRODUCTION: Clinical laboratories and the total testing process are major consumers of energy, water, and hazardous chemicals, and produce significant amounts of biomedical waste. Since the processes in the clinical laboratory and the total testing process go hand in hand it mandates a holistic, and comprehensive approach towards sustainability. CONTENT: This review article identifies the various sources and activities in Laboratory Medicine that challenge sustainability and also discusses the various approaches that can be implemented to achieve sustainability in laboratory operations to reduce the negative impact on the environment. SUMMARY: The article highlights how the integration of technological advancements, efficient resource management, staff training and sensitization, protocol development towards sustainability, and other environmental considerations contributes significantly to a sustainable healthcare ecosystem. OUTLOOK: Variables and resources that negatively impact the environment must be identified and addressed comprehensively to attain a long-lasting level of carbon neutrality.

Machine learning-based clinical decision support using laboratory data.

Artificial intelligence (AI) and machine learning (ML) are becoming vital in laboratory medicine and the broader context of healthcare. In this review article, we summarized the development of ML models and how they contribute to clinical laboratory workflow and improve patient outcomes. The process of ML model development involves data collection, data cleansing, feature engineering, model development, and optimization. These models, once finalized, are subjected to thorough performance assessments and validations. Recently, due to the complexity inherent in model development, automated ML tools were also introduced to streamline the process, enabling non-experts to create models. Clinical Decision Support Systems (CDSS) use ML techniques on large datasets to aid healthcare professionals in test result interpretation. They are revolutionizing laboratory medicine, enabling labs to work more efficiently with less human supervision across pre-analytical, analytical, and post-analytical phases. Despite contributions of the ML tools at all analytical phases, their integration presents challenges like potential model uncertainties, black-box algorithms, and deskilling of professionals. Additionally, acquiring diverse datasets is hard, and models' complexity can limit clinical use. In conclusion, ML-based CDSS in healthcare can greatly enhance clinical decision-making. However, successful adoption demands collaboration among professionals and stakeholders, utilizing hybrid intelligence, external validation, and performance assessments.

Sigma metric analysis of quality indicators across the testing process as an effective tool for the evaluation of laboratory performance.

Background: Laboratories across the world are successfully using quality indicators (QIs) to monitor their performance. We aimed to analyze the effectiveness of using the peer group comparison and statistical tools such as sigma metrics for periodic evaluation of QIs and identify potential errors in the preanalytical, analytical, and postanalytical phases. Methods: We evaluated the monthly QIs for 1 year. A total of 11 QIs were evaluated across the three phases of the total testing process, using percentage variance, and sigma metric analysis. Results: Our study observed that based on sigma metric analysis, the performance was good for all the QIs except for the number of samples with the inappropriate specimen hemolyzed samples, clotted samples, and turnaround time (Sigma value < 3). The percentage variance of QIs in all the phases was plotted in a Pareto chart, which helped us in identifying turnaround time and internal quality control performance are the key areas that contribute to almost 80% of the errors among all the QIs. Conclusion: Laboratory performance evaluation using QIs and sigma metric analysis helped us in identifying and prioritizing the corrective actions in the key areas of the total testing process.

The total testing process harmonization: the case study of SARS-CoV-2 serological tests.

The total testing process harmonization is central to laboratory medicine, leading to the laboratory test's effectiveness. In this opinion paper the five phases of the TTP are analyzed, describing, and summarizing the critical issues that emerged in each phase of the TTP with the SARS-CoV-2 serological tests that have affected their effectiveness. Testing and screening the population was essential for defining seropositivity and, thus, driving public health policies in the management of the COVID-19 pandemic. However, the many differences in terminology, the unit of measurement, reference ranges and parameters for interpreting results make analytical results difficult to compare, leading to the general confusion that affects or completely precludes the comparability of data. Starting from these considerations related to SARS-CoV-2 serological tests, through interdisciplinary work, the authors have highlighted the most critical points and formulated proposals to make total testing process harmonization effective, positively impacting the diagnostic effectiveness of laboratory tests.

The Clinical Laboratory Is an Integral Component to Health Care Delivery : An Expanded Representation of the Total Testing Process.

OBJECTIVES: Developing an expanded representation of the total testing process that includes contemporary elements of laboratory practice can be useful to understanding and optimizing testing workflows across clinical laboratory and patient care settings. METHODS: Published literature and meeting reports were used by the coauthors to inform the development of the expanded representation of the total testing process and relevant examples describing its uses. RESULTS: A visual representation of the total testing process was developed and contextualized to patient care scenarios using a number of examples covering the detection of blood culture contamination, use of next-generation sequencing, and pharmacogenetic testing. CONCLUSIONS: The expanded representation of the total testing process can serve as a model and framework to document and improve the use of clinical testing within the broader context of health care delivery. This representation recognizes increased engagement among clinical laboratory professionals with patients and other health care providers as essential to making informed decisions. The increasing use of data is highlighted as important to ensuring quality, appropriate test utilization, and sustaining an efficient workflow across clinical laboratory and patient care settings. Maintaining a properly resourced and competent workforce is also featured as an essential component to the testing process.

The European Register of Specialists in Clinical Chemistry and Laboratory Medicine: code of conduct, version 3 - 2023.

Whilst version 2 focussed on the professional conduct expected of a Specialist in Laboratory Medicine, version 3 builds on the responsibilities for ethical conduct from point of planning to point of care. Particular responsibilities that are outlined include: - The need for evidence when planning a new service, providing assurance that a new test does not do harm - Maintaining respect for patient confidentiality, their religious/ethnic beliefs, the need for informed consent to test, agreement on retrospective use of samples as part of governance envelopes in the pre-analytical phase - Ensuring respect for patient autonomy in the response to untoward results generated in the analytical phase - Supporting the safety of patients in the post-analytical phase through knowledge-based interpretation and presentation of results - The duty of candour to disclose and respond to error across the total testing process - Leading initiatives to harmonise and standardise pre-analytical, analytical and post-analytical phases to ensure more consistent clinical decision making with utilisation of demand management to ensure more equitable access to scarce resources - Working with emerging healthcare providers beyond the laboratory to ensure consistent application of high standards of clinical care In identifying opportunities for wider contributions to resolving ethical challenges across healthcare the need is also highlighted for more external quality assurance schemes and ethics-based quality indicators that span the total testing process.

The harmonization issue in laboratory medicine: the commitment of CCLM.

The analytical quality of the clinical laboratory results has shown a significant improvement over the past decades, thanks to the joint efforts of different stakeholders, while the comparability among the results produced by different laboratories and methods still presents some critical issues. During these years, Clinical Chemistry and Laboratory Medicine (CCLM) published several papers on the harmonization issue over all steps in the Total Testing Process, training an important number of laboratory professionals in evaluating and monitoring all the criticisms inherent to the pre-analytical, as well as analytical and post analytical phases: from the consensus statement on the most informative testing in emergency setting, to the prevention and detection of hemolysis or to patients identification and tube labeling procedures, as far as to different approaches to harmonize hormones measurements or to describe new reference methods or to harmonize the laboratory report. During these years the commitment of the journal, devoted to the harmonization processes has allowed to improve the awareness on the topic and to provide specific instruments to monitor the rate of errors and to improve patients safety.

'Penelope test': a practical instrument for checking appropriateness of laboratory tests.

In medical laboratories, the appropriateness challenge directly revolves around the laboratory test and its proper selection, data analysis, and result reporting. However, laboratories have also a role in the appropriate management of those phases of total testing process (TTP) that traditionally are not under their direct control. So that, the laboratory obligation to act along the entire TTP is now widely accepted in order to achieve better care management. Because of the large number of variables involved in the overall TTP structure, it is difficult to monitor appropriateness in real time. However, it is possible to retrospectively reconstruct the body of the clinical process involved in the management of a specific laboratory test to track key passages that may be defective or incomplete in terms of appropriateness. Here we proposed an appropriateness check-list scheme along the TTP chain to be potentially applied to any laboratory test. This scheme consists of a series of questions that healthcare professionals should answer to achieve laboratory test appropriateness. In the system, even a single lacking answer may compromise the integrity of all appropriateness evaluation process as the inability to answer may involve a significant deviation from the optimal trajectory, which compromise the test appropriateness and the quality of subsequent steps. Using two examples of the check-list application, we showed that the proposed instrument may offer an objective help to avoid inappropriate use of laboratory tests in an integrated way involving both laboratory professionals and user clinicians.

Error evaluation in the laboratory testing process and laboratory information systems.

Background: The laboratory testing process consist of five analysis phases featuring the total testing process framework. Activities in laboratory process, including those of testing are error-prone and affect the use of laboratory information systems. This study seeks to identify error factors related to system use and the first and last phases of the laboratory testing process using a proposed framework known as total testing process-laboratory information systems. Methods: We conducted a qualitative case study evaluation in two private hospitals and a medical laboratory. We collected data using interviews, observations, and document analysis methods involving physicians, nurses, an information technology officer, and the laboratory staff. We employed the proposed framework and Lean problem solving tools namely Value Stream Mapping and A3 for data analysis. Results: Errors in laboratory information systems and the laboratory testing process were attributed to failure to fulfill user requirements, poor cooperation between the information technology unit and laboratory, inconsistency of software design in system integration, errors during inter-system data transmission, and lack of motivation in system use. The error factors are related to system development elements, namely, latent failures that considerably affected the information quality and system use. Errors in system development were also attributed to poor service quality. Conclusions: Complex laboratory testing process and laboratory information systems require rigorous evaluation in minimizing errors and ensuring patient safety. The proposed framework and Lean approach are applicable for evaluating the laboratory testing process and laboratory information systems in a rigorous, comprehensive, and structured manner.

Mapping the Evidence on Rapid Diagnosis of Bloodstream Infections: A Scoping Review.

BACKGROUND: Laboratory and other healthcare professionals participate in developing clinical practice guidelines through systematic review of the evidence. A significant challenge is the identification of areas for analytic focus when the evidence consists of several categories of interventions and outcomes that span both laboratory and clinical processes. The challenge increases when these interventions present as sets of combined interventions. A scoping review may provide a transparent and defensible analytic route forward for systematic reviews challenged in this manner. CONTENT: A scoping review was carried out to characterize the evidence on rapid identification of bloodstream infections. Fifty-five studies previously identified by the supported systematic review were charted in duplicate. Charted records were analyzed using descriptive content analysis and evidence mapping with a 5-step process. SUMMARY: The 5-step analysis culminated in the characterization of 9 different intervention chain configurations that will facilitate the comparison of complex intervention practices across studies. Furthermore, our evidence map indicates that the current evidence base is strongly centered on 3 specific clinical outcomes, and it links these outcomes to the most represented intervention chain configurations. The scoping review effort generated a route forward for the supported systematic review and meta-analysis.

The rise in preanalytical errors during COVID-19 pandemic.

INTRODUCTION: The COVID-19 pandemic has posed several challenges to clinical laboratories across the globe. Amidst the outbreak, errors occurring in the preanalytical phase of sample collection, transport and processing, can further lead to undesirable clinical consequences. Thus, this study was designed with the following objectives: (i) to determine and compare the blood specimen rejection rate of a clinical laboratory and (ii) to characterise and compare the types of preanalytical errors between the pre-pandemic and the pandemic phases. MATERIALS AND METHODS: This retrospective study was carried out in a trauma-care hospital, presently converted to COVID-19 care centre. Data was collected from (i) pre-pandemic phase: 1st October 2019 to 23rd March 2020 and (ii) pandemic phase: 24th March to 31st October 2020. Blood specimen rejection rate was calculated as the proportion of blood collection tubes with preanalytical errors out of the total number received, expressed as percentage. RESULTS: Total of 107,716 blood specimens were screened of which 43,396 (40.3%) were received during the pandemic. The blood specimen rejection rate during the pandemic was significantly higher than the pre-pandemic phase (3.0% versus 1.1%; P < 0.001). Clotted samples were the commonest source of preanalytical errors in both phases. There was a significant increase in the improperly labelled samples (P < 0.001) and samples with insufficient volume (P < 0.001), whereas, a significant decline in samples with inadequate sample-anticoagulant ratio and haemolysed samples (P < 0.001). CONCLUSION: In the ongoing pandemic, preanalytical errors and resultant blood specimen rejection rate in the clinical laboratory have significantly increased due to changed logistics. The study highlights the need for corrective steps at various levels to reduce preanalytical errors in order to optimise patient care and resource utilisation.

The response of total testing process in clinical laboratory medicine to COVID-19pandemic.

INTRODUCTION: Following a pandemic, laboratory medicine is vulnerable to laboratory errors due to the stressful and high workloads. We aimed to examine how laboratory errors may arise from factors, e.g., flexible working order, staff displacement, changes in the number of tests, and samples will reflect on the total test process (TTP) during the pandemic period. MATERIALS AND METHODS: In 12 months, 6 months before and during the pandemic, laboratory errors were assessed via quality indicators (QIs) related to TTP phases. QIs were grouped as pre-, intra- and postanalytical. The results of QIs were expressed in defect percentages and sigma, evaluated with 3 levels of performance quality: 25th, 50th and 75th percentile values. RESULTS: When the pre- and during pandemic periods were compared, the sigma value of the samples not received was significantly lower in pre-pandemic group than during pandemic group (4.7σ vs. 5.4σ, P = 0.003). The sigma values of samples transported inappropriately and haemolysed samples were significantly higher in pre-pandemic period than during pandemic (5.0σ vs. 4.9σ, 4.3σ vs. 4.1σ; P = 0.046 and P = 0.044, respectively). Sigma value of tests with inappropriate IQC performances was lower during pandemic compared to the pre-pandemic period (3.3σ vs. 3.2σ, P = 0.081). Sigma value of the reports delivered outside the specified time was higher during pandemic than pre-pandemic period (3.0σ vs. 3.1σ, P = 0.030). CONCLUSION: In all TTP phases, some quality indicators improved while others regressed during the pandemic period. It was observed that preanalytical phase was affected more by the pandemic.

A model to establish autoverification in the clinical laboratory.

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.

Real-world use of key performance indicators for point-of-Care Testing network accredited by ISO 22870.

OBJECTIVE: We aimed to evaluate the results of key performance indicators (KPIs) for a period of over three years, as well as their effectiveness as an improvement tool, to provide information about Point-of-Care Testing (POCT) management system performance and quality assurance. DESIGN AND METHODS: KPIs regarding the global POCT process, extra-analytical phase, quality assurance and staff training and competency were evaluated for blood gases, HbA1c, sweat test and non-connected and connected glucose in an ISO 22870 accredited network. We established the definition of every KPI and its corresponding target. The results of KPIs from all clinical settings were appraised every month during the study period, taking corrective actions when necessary. RESULTS: Annual global results were generally acceptable. However, some clinical areas displayed deviations in specific months. The monitoring of these KPIs allowed us to detect the deviations immediately and identify their causes. These included errors in patient identification, consumables, strips, reagents, analyzers, calibration, internal and external quality control, sample management, connectivity, and operator identification strategy, among others. CONCLUSIONS: The evaluation of these KPIs over time has shown their appropriateness. This set of quality indicators could be a useful tool for laboratory medicine leading POCT networks for better and safer patient care.