Defining a roadmap for harmonizing quality indicators in Laboratory Medicine: A consensus statement on behalf of the IFCC Working Group "laboratory Error and Patient Safety" and EFLM Task and Finish Group "performance specifications for the extra-analytical phases"

The improving quality of laboratory testing requires a deep understanding of the many vulnerable steps involved in the total examination process (TEP), along with the identification of a hierarchy of risks and challenges that need to be addressed. From this perspective, the Working Group \u201cLaboratory Errors and Patient Safety\u201d (WG-LEPS) of International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) is focusing its activity on implementation of an efficient tool for obtaining meaningful information on the risk of errors developing throughout the TEP, and for establishing reliable information about error frequencies and their distribution. More recently, the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) has created the Task and Finish Group \u201cPerformance specifications for the extraanalytical phases\u201d (TFG-PSEP) for defining performance specifications for extra-analytical phases. Both the IFCC and EFLM groups are working to provide laboratories with a system to evaluate their performances and recognize the critical aspects where improvement actions are needed. A Consensus Conference was organized in Padova, Italy, in 2016 in order to bring together all the experts and interested parties to achieve a consensus for effective harmonization of quality indicators (QIs). A general agreement was achieved and the main outcomes have been the release of a new version of model of quality indicators (MQI), the approval of a criterion for establishing performance specifications and the definition of the type of information that should be provided within the report to the clinical laboratories participating to the QIs project

Defining a roadmap for harmonizing quality indicators in Laboratory Medicine: A consensus statement on behalf of the IFCC Working Group "laboratory Error and Patient Safety" and EFLM Task and Finish Group "performance specifications for the extra-analytical phases"

The improving quality of laboratory testing requires a deep understanding of the many vulnerable steps involved in the total examination process (TEP), along with the identification of a hierarchy of risks and challenges that need to be addressed. From this perspective, the Working Group “Laboratory Errors and Patient Safety” (WG-LEPS) of International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) is focusing its activity on implementation of an efficient tool for obtaining meaningful information on the risk of errors developing throughout the TEP, and for establishing reliable information about error frequencies and their distribution. More recently, the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) has created the Task and Finish Group “Performance specifications for the extraanalytical phases” (TFG-PSEP) for defining performance specifications for extra-analytical phases. Both the IFCC and EFLM groups are working to provide laboratories with a system to evaluate their performances and recognize the critical aspects where improvement actions are needed. A Consensus Conference was organized in Padova, Italy, in 2016 in order to bring together all the experts and interested parties to achieve a consensus for effective harmonization of quality indicators (QIs). A general agreement was achieved and the main outcomes have been the release of a new version of model of quality indicators (MQI), the approval of a criterion for establishing performance specifications and the definition of the type of information that should be provided within the report to the clinical laboratories participating to the QIs project

Statistical learning and big data applications

The amount of data generated in the field of laboratory medicine has grown to an extent that conventional laboratory information systems (LISs) are struggling to manage and analyze this complex, entangled information (“Big Data”). Statistical learning, a generalized framework from machine learning (ML) and artificial intelligence (AI) is predestined for processing “Big Data” and holds the potential to revolutionize the field of laboratory medicine. Personalized medicine may in particular benefit from AI-based systems, especially when coupled with readily available wearables and smartphones which can collect health data from individual patients and offer new, cost-effective access routes to healthcare for patients worldwide. The amount of personal data collected, however, also raises concerns about patient-privacy and calls for clear ethical guidelines for “Big Data” research, including rigorous quality checks of data and algorithms to eliminate underlying bias and enable transparency. Likewise, novel federated privacy-preserving data processing approaches may reduce the need for centralized data storage. Generative AI-systems including large language models such as ChatGPT currently enter the stage to reshape clinical research, clinical decision-support systems, and healthcare delivery. In our opinion, AI-based systems have a tremendous potential to transform laboratory medicine, however, their opportunities should be weighed against the risks carefully. Despite all enthusiasm, we advocate for stringent added-value assessments, just as for any new drug or treatment. Human experts should carefully validate AI-based systems, including patient-privacy protection, to ensure quality, transparency, and public acceptance. In this opinion paper, data prerequisites, recent developments, chances, and limitations of statistical learning approaches are highlighted

Managing inappropriate utilization of laboratory resource

Background The inappropriate use of laboratory resources, due to excessive number of tests not really necessary for patient care or by failure to order the appropriate diagnostic test, may lead to wrong, missed or delayed diagnosis, thus potentially jeopardizing patient safety. It is estimated that 5-95% of tests are currently used inappropriately, depending on the appropriateness criteria, thus significantly contributing to the potential of generating medical errors, the third leading cause of death in the US. Content In this review, we discuss the reasons as well as the medical and financial consequences of inappropriate utilization of laboratory tests. We then provide demand management (DM) tools as a means for overcoming this issue and also discuss their benefits, challenges, limitations and requirements for successful implementation. Summary and outlook When based on current evidence, adapted to local conditions and developed in close collaboration with clinicians, DM is a reasonable strategy for progressing toward better management of over- and underuse of laboratory resources

Disruption of laboratory activities during the COVID-19 pandemic: results of an EFLM Task Force Preparation of Labs for Emergencies (TF-PLE) survey

Background: The EFLM Task Force Preparation of Labs for Emergencies (TF-PLE) created a survey that has been distributed to its members for gathering information on the key hazards experienced by European medical laboratories during the COVID-19 pandemic. Methods: The survey was distributed to over 12,000 potential contacts (laboratory workers) via an EFLM newsletter, with responses collected between May 8 and June 8, 2023. Results: Two hundred replies were collected and examined from European laboratories. 69.7% and 78.1% of all responders said they were short on non-COVID and COVID reagents, respectively. Exactly half of respondents (50.0%) said that they could not complete all laboratory tests required for a specific period, but this figure climbed to 61.2% for COVID tests. Finally, 72.3% of respondents expressed exhaustion during the pandemic, and 61.2% reported increasing patient hostility. Conclusions: The COVID-19 pandemic had a significant impact on laboratory medicine in Europe. Cultural change, proactive planning, and even re-engineering in some parts of the laboratory industry may thus be necessary to prepare for future challenges

Results of the first survey of the EFLM Task Force Preparation of Labs for Emergencies (TF-PLE)

This survey includes a series of questions about the nature, organization, and preparedness for emergencies, was created using Google forms, officially mailed to over 12,000 potential EFLM contacts with an official newsletter to collect responses between May 8 and June 8, 2023. Although obviously not representative of all European countries, the collected data provides an overview of the current situation with respect to laboratory readiness for emergencies

Potentials and pitfalls of ChatGPT and natural-language artificial intelligence models for the understanding of laboratory medicine test results. An assessment by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group on Artificial Intelligence (WG-AI)

Objectives: ChatGPT, a tool based on natural language processing (NLP), is on everyone's mind, and several potential applications in healthcare have been already proposed. However, since the ability of this tool to interpret laboratory test results has not yet been tested, the EFLM Working group on Artificial Intelligence (WG-AI) has set itself the task of closing this gap with a systematic approach.Methods: WG-AI members generated 10 simulated laboratory reports of common parameters, which were then passed to ChatGPT for interpretation, according to reference intervals (RI) and units, using an optimized prompt. The results were subsequently evaluated independently by all WG-AI members with respect to relevance, correctness, helpfulness and safety.Results: ChatGPT recognized all laboratory tests, it could detect if they deviated from the RI and gave a test-by-test as well as an overall interpretation. The interpretations were rather superficial, not always correct, and, only in some cases, judged coherently. The magnitude of the deviation from the RI seldom plays a role in the interpretation of laboratory tests, and artificial intelligence (AI) did not make any meaningful suggestion regarding follow-up diagnostics or further procedures in general.Conclusions: ChatGPT in its current form, being not specifically trained on medical data or laboratory data in particular, may only be considered a tool capable of interpreting a laboratory report on a test-by-test basis at best, but not on the interpretation of an overall diagnostic picture. Future generations of similar AIs with medical ground truth training data might surely revolutionize current processes in healthcare, despite this implementation is not ready yet

The CRESS checklist for reporting stability studies:on behalf of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group for the Preanalytical Phase (WG-PRE)

To ensure that clinical laboratories produce results that are both accurate and of clinical utility it is essential that only samples of adequate quality are analysed. Although various studies and databases assessing the stability of analytes in different settings do exist, guidance on how to perform and report stability studies is lacking. This results in studies that often do not report essential information, thus compromising transferability of the data. The aim of this manuscript is to describe the C hecklist for R eporting S tability S tudies (CRESS) against which future studies should be reported to ensure standardisation of reporting and easy assessment of transferability of studies to other healthcare settings. The EFLM WG-PRE (European Federation of Clinical Chemistry and Laboratory Medicine Working Group for the Preanalytical Phase) produced the CRESS checklist following a detailed literature review and extensive discussions resulting in consensus agreement. The checklist consists of 20 items covering all the aspects that should be considered when producing a report on a stability study including details of what should be included for each item and a rationale as to why. Adherence to the CRESS checklist will ensure that studies are reported in a transparent and replicable way. This will allow other laboratories to assess whether published data meet the stability criteria required in their own particular healthcare scenario. The EFLM WG-PRE encourage researchers and authors to use the CRESS checklist as a guide to planning stability studies and to produce standardised reporting of future stability studies.</p

Recommendation for the design of stability studies on clinical specimens

OBJECTIVES: Knowledge of the stability of analytes in clinical specimens is a prerequisite for proper transport and preservation of samples to avoid laboratory errors. The new version of ISO 15189:2022 and the European directive 2017/746 increase the requirements on this topic for manufacturers and laboratories. Within the project to generate a stability database of European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group Preanalytical Phase (WG-PRE), the need to standardise and improve the quality of published stability studies has been detected, being a manifest deficit the absence of international guidelines for the performance of stability studies on clinical specimens.METHODS: These recommendations have been developed and summarised by consensus of the WG-PRE and are intended primarily to improve the quality of sample stability claims included in information for users provided by assay supplier companies, according to the requirements of the new European regulations and standards for accreditation.RESULTS: This document provides general recommendations for the performance of stability studies, oriented to the estimation of instability equations in the usual working conditions, allowing flexible adaptation of the maximum permissible error specifications to obtain stability limits adapted to the intended use.CONCLUSIONS: We present this recommendation based on the opinions of the EFLM WG-PRE group for the standardisation and improvement of stability studies, with the intention to improve the quality of the studies and the transferability of their results to laboratories.</p