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Rahman Z, Pasam T, Rishab, Dandekar MP. Binary classification model of machine learning detected altered gut integrity in controlled-cortical impact model of traumatic brain injury. Int J Neurosci 2024; 134:163-174. [PMID: 35758006 DOI: 10.1080/00207454.2022.2095271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/23/2022] [Indexed: 10/17/2022]
Abstract
Aim of the study: To examine the effect of controlled-cortical impact (CCI), a preclinical model of traumatic brain injury (TBI), on intestinal integrity using a binary classification model of machine learning (ML).Materials and methods: Adult, male C57BL/6 mice were subjected to CCI surgery using a stereotaxic impactor (Impact One™). The rotarod and hot-plate tests were performed to assess the neurological deficits.Results: Mice underwent CCI displayed a remarkable neurological deficit as noticed by decreased latency to fall and lesser paw withdrawal latency in rotarod and hot plate test, respectively. Animals were sacrificed 3 days post-injury (dpi). The colon sections were stained with hematoxylin and eosin (H&E) to integrate with machinery tool-based algorithms. Several stained colon images were captured to build a dataset for ML model to predict the impact of CCI vs sham procedure. The best results were obtained with VGG16 features with SVM RBF kernel and VGG16 features with stacked fully connected layers on top. We achieved a test accuracy of 84% and predicted the disrupted gut permeability and epithelium wall of colon in CCI group as compared to sham-operated mice.Conclusion: We suggest that ML may become an important tool in the development of preclinical TBI model and discovery of newer therapeutics.
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Affiliation(s)
- Zara Rahman
- Department of Pharmacology & Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Balanagar, Hyderabad, India
| | - Tulasi Pasam
- Department of Pharmacology & Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Balanagar, Hyderabad, India
| | - Rishab
- Department of Computer Science and Engineering, International Institute of Information Technology (IIIT), Hyderabad, India
| | - Manoj P Dandekar
- Department of Pharmacology & Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Balanagar, Hyderabad, India
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2
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Cadamuro J, Cabitza F, Debeljak Z, De Bruyne S, Frans G, Perez SM, Ozdemir H, Tolios A, Carobene A, Padoan A. 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). Clin Chem Lab Med 2023; 61:1158-1166. [PMID: 37083166 DOI: 10.1515/cclm-2023-0355] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 04/12/2023] [Indexed: 04/22/2023]
Abstract
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.
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Affiliation(s)
- Janne Cadamuro
- Department of Laboratory Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria
| | - Federico Cabitza
- DISCo, Università degli Studi di Milano-Bicocca, Milano, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | - Zeljko Debeljak
- Faculty of Medicine, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia
- Clinical Institute of Laboratory Diagnostics, University Hospital Center Osijek, Osijek, Croatia
| | - Sander De Bruyne
- Department of Laboratory Medicine, Ghent University Hospital, Ghent, Belgium
| | - Glynis Frans
- Department of Laboratory Medicine, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Salomon Martin Perez
- Unidad de Bioquímica Clínica, Hospital Universitario Virgen Macarena, Sevilla, Spain
| | - Habib Ozdemir
- Department of Medical Biochemistry, Faculty of Medicine, Manisa Celal Bayar University, Manisa, Türkiye
| | - Alexander Tolios
- Department of Transfusion Medicine and Cell Therapy, Medical University of Vienna, Vienna, Austria
| | - Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Andrea Padoan
- Department of Medicine (DIMED), University of Padova, Padova, Italy
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3
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Galozzi P, Basso D, Plebani M, Padoan A. Artificial Intelligence and laboratory data in rheumatic diseases. Clin Chim Acta 2023; 546:117388. [PMID: 37187221 DOI: 10.1016/j.cca.2023.117388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 05/17/2023]
Abstract
Artificial intelligence (AI)-based medical technologies are rapidly evolving into actionable solutions for clinical practice. Machine learning (ML) algorithms can process increasing amounts of laboratory data such as gene expression immunophenotyping data and biomarkers. In recent years, the analysis of ML has become particularly useful for the study of complex chronic diseases, such as rheumatic diseases, heterogenous conditions with multiple triggers. Numerous studies have used ML to classify patients and improve diagnosis, to stratify the risk and determine disease subtypes, as well as to discover biomarkers and gene signatures. This review aims to provide examples of ML models for specific rheumatic diseases using laboratory data and some insights into relevant strengths and limitations. A better understanding and future application of these analytical strategies could facilitate the development of precision medicine for rheumatic patients.
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Affiliation(s)
- Paola Galozzi
- Department of Medicine-DIMED, University of Padova, Padova, Italy.
| | - Daniela Basso
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Mario Plebani
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Andrea Padoan
- Department of Medicine-DIMED, University of Padova, Padova, Italy; Laboratory Medicine Unit, University Hospital of Padova, Padova, Italy
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4
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Carobene A, Cabitza F, Bernardini S, Gopalan R, Lennerz JK, Weir C, Cadamuro J. Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories. Clin Chem Lab Med 2023; 61:535-543. [PMID: 36327445 DOI: 10.1515/cclm-2022-1030] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022]
Abstract
OBJECTIVES The field of artificial intelligence (AI) has grown in the past 10 years. Despite the crucial role of laboratory diagnostics in clinical decision-making, we found that the majority of AI studies focus on surgery, radiology, and oncology, and there is little attention given to AI integration into laboratory medicine. METHODS We dedicated a session at the 3rd annual European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) strategic conference in 2022 to the topic of AI in the laboratory of the future. The speakers collaborated on generating a concise summary of the content that is presented in this paper. RESULTS The five key messages are (1) Laboratory specialists and technicians will continue to improve the analytical portfolio, diagnostic quality and laboratory turnaround times; (2) The modularized nature of laboratory processes is amenable to AI solutions; (3) Laboratory sub-specialization continues and from test selection to interpretation, tasks increase in complexity; (4) Expertise in AI implementation and partnerships with industry will emerge as a professional competency and require novel educational strategies for broad implementation; and (5) regulatory frameworks and guidances have to be adopted to new computational paradigms. CONCLUSIONS In summary, the speakers opine that the ability to convert the value-proposition of AI in the laboratory will rely heavily on hands-on expertise and well designed quality improvement initiative from within laboratory for improved patient care.
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Affiliation(s)
- Anna Carobene
- IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federico Cabitza
- IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milan, Italy.,DISCo, Università Degli Studi di Milano-Bicocca, Milan, Italy
| | - Sergio Bernardini
- Unit of Laboratory Medicine, Tor Vergata University Hospital, Rome, Italy.,Department of Experimental Medicine, University of Tor Vergata, Rome, Italy
| | - Raj Gopalan
- Siemens Healthcare Diagnostics, Siemens Healthineers, Malvern, PA, USA
| | - Jochen K Lennerz
- Department of Pathology, Center for Integrated Diagnostics, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | | | - Janne Cadamuro
- Department of Laboratory Medicine, Paracelsus Medical University Salzburg, Salzburg, Austria
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5
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Ahmad MN, Abdallah SA, Abbasi SA, Abdallah AM. Student perspectives on the integration of artificial intelligence into healthcare services. Digit Health 2023; 9:20552076231174095. [PMID: 37312954 PMCID: PMC10259127 DOI: 10.1177/20552076231174095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 04/19/2023] [Indexed: 06/15/2023] Open
Abstract
Background Healthcare workers are often overworked, underfunded, and face many challenges. Integration of artificial intelligence into healthcare service provision can tackle these challenges by relieving burdens on healthcare workers. Since healthcare students are our future healthcare workers, we assessed the knowledge, attitudes, and perspectives of current healthcare students at Qatar University on the implementation of artificial intelligence into healthcare services. Methods This was a cross-sectional study of QU-Health Cluster students via an online survey over a three-week period in November 2021. Chi-squared tests and gamma coefficients were used to compare differences between categorical variables. Results One hundred and ninety-three QU-Health students responded. Most participants had a positive attitude towards artificial intelligence, finding it useful and reliable. The most popular perceived advantage of artificial intelligence was its ability to speed up work processes. Around 40% expressed concern about a threat to job security from artificial intelligence, and a majority believed that artificial intelligence cannot provide sympathetic care (57.9%). Participants who felt that artificial intelligence can better make diagnoses than humans also agreed that artificial intelligence could replace their job (p = 0.005). Male students had more knowledge (p = 0.005) and received more training (p = 0.005) about healthcare artificial intelligence. Participants cited a lack of expert mentorship as a barrier to obtaining knowledge about artificial intelligence, followed by lack of dedicated courses and funding. Conclusions More resources are required for students to develop a good understanding about artificial intelligence. Education needs to be supported by expert mentorship. Further work is needed on how best to integrate artificial intelligence teaching into university curricula.
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Affiliation(s)
- Muna N Ahmad
- Department of Biomedical Sciences, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Saja A Abdallah
- University of Birmingham Medical School, Edgbaston Campus, Birmingham, UK
| | - Saddam A Abbasi
- Department of Mathematics, Statistics, and Physics, Qatar University, Doha, Qatar
- Statistical Consulting Unit, College of Arts and Science, Qatar University, Doha, Qatar
| | - Atiyeh M Abdallah
- Department of Biomedical Sciences, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
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6
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Mosch L, Fürstenau D, Brandt J, Wagnitz J, Klopfenstein SAI, Poncette AS, Balzer F. The medical profession transformed by artificial intelligence: Qualitative study. Digit Health 2022; 8:20552076221143903. [PMID: 36532112 PMCID: PMC9756357 DOI: 10.1177/20552076221143903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 11/18/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Healthcaare delivery will change through the increasing use of artificial intelligence (AI). Physicians are likely to be among the professions most affected, though to what extent is not yet clear. OBJECTIVE We analyzed physicians' and AI experts' stances towards AI-induced changes. This concerned (1) physicians' tasks, (2) job replacement risk, and (3) implications for the ways of working, including human-AI interaction, changes in job profiles, and hierarchical and cross-professional collaboration patterns. METHODS We adopted an exploratory, qualitative research approach, using semi-structured interviews with 24 experts in the fields of AI and medicine, medical informatics, digital medicine, and medical education and training. Thematic analysis of the interview transcripts was performed. RESULTS Specialized tasks currently performed by physicians in all areas of medicine would likely be taken over by AI, including bureaucratic tasks, clinical decision support, and research. However, the concern that physicians will be replaced by an AI system is unfounded, according to experts; AI systems today would be designed only for a specific use case and could not replace the human factor in the patient-physician relationship. Nevertheless, the job profile and professional role of physicians would be transformed as a result of new forms of human-AI collaboration and shifts to higher-value activities. AI could spur novel, more interprofessional teams in medical practice and research and, eventually, democratization and de-hierarchization. CONCLUSIONS The study highlights changes in job profiles of physicians and outlines demands for new categories of medical professionals considering AI-induced changes of work. Physicians should redefine their self-image and assume more responsibility in the age of AI-supported medicine. There is a need for the development of scenarios and concepts for future job profiles in the health professions as well as their education and training.
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Affiliation(s)
- Lina Mosch
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany,Department of Anesthesiology and Intensive Care Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany,Lina Mosch, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Charitéplatz 1, 10117 Berlin, Germany
| | - Daniel Fürstenau
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany,Department of Business IT, IT University of Copenhagen, København, Denmark
| | - Jenny Brandt
- Universitätsmedizin Mainz, corporate member of Johannes Gutenberg University, Mainz, Germany
| | - Jasper Wagnitz
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany
| | - Sophie AI Klopfenstein
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany,Core Facility Digital Medicine and Interoperability, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Akira-Sebastian Poncette
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany,Department of Anesthesiology and Intensive Care Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Felix Balzer
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany
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7
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Cadamuro J, Simundic AM. The preanalytical phase – from an instrument-centred to a patient-centred laboratory medicine. Clin Chem Lab Med 2022; 61:732-740. [PMID: 36330758 DOI: 10.1515/cclm-2022-1036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 10/16/2022] [Indexed: 11/06/2022]
Abstract
Abstract
In order to guarantee patient safety, medical laboratories around the world strive to provide highest quality in the shortest amount of time. A major leap in quality improvement was achieved by aiming to avoid preanalytical errors within the total testing process. Although these errors were first described in the 1970s, it took additional years/decades for large-scale efforts, aiming to improve preanalytical quality by standardisation and/or harmonisation. Initially these initiatives were mostly on the local or national level. Aiming to fill this void, in 2011 the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) working group “Preanalytical Phase” (WG-PRE) was founded. In the 11 years of its existence this group was able to provide several recommendations on various preanalytical topics. One major achievement of the WG-PRE was the development of an European consensus guideline on venous blood collection. In recent years the definition of the preanalytical phase has been extended, including laboratory test selection, thereby opening a huge field for improvement, by implementing strategies to overcome misuse of laboratory testing, ideally with the support of artificial intelligence models. In this narrative review, we discuss important aspects and milestones in the endeavour of preanalytical process improvement, which would not have been possible without the support of the Clinical Chemistry and Laboratory Medicine (CCLM) journal, which was one of the first scientific journals recognising the importance of the preanalytical phase and its impact on laboratory testing quality and ultimately patient safety.
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Affiliation(s)
- Janne Cadamuro
- Department of Laboratory Medicine , Paracelsus Medical University Salzburg , Salzburg , Austria
| | - Ana-Maria Simundic
- Department of Medical Laboratory Diagnostics , University Hospital “Sveti Duh”, University of Zagreb, Faculty of Pharmacy and Biochemistry , Zagreb , Croatia
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Big Data in Laboratory Medicine—FAIR Quality for AI? Diagnostics (Basel) 2022; 12:diagnostics12081923. [PMID: 36010273 PMCID: PMC9406962 DOI: 10.3390/diagnostics12081923] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/05/2022] [Accepted: 08/06/2022] [Indexed: 12/22/2022] Open
Abstract
Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day—from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and metadata. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved, for example, by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: the growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research.
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9
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Padoan A, Plebani M. Flowing through laboratory clinical data: the role of artificial intelligence and big data. Clin Chem Lab Med 2022; 60:1875-1880. [PMID: 35850928 DOI: 10.1515/cclm-2022-0653] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 07/08/2022] [Indexed: 12/11/2022]
Abstract
During the last few years, clinical laboratories have faced a sea change, from facilities producing a high volume of low-cost test results, toward a more integrated and patient-centered service. Parallel to this paradigm change, the digitalization of healthcare data has made an enormous quantity of patients' data easily accessible, thus opening new scenarios for the utilization of artificial intelligence (AI) tools. Every day, clinical laboratories produce a huge amount of information, of which patients' results are only a part. The laboratory information system (LIS) may include other "relevant" compounding data, such as internal quality control or external quality assessment (EQA) results, as well as, for example, timing of test requests and of blood collection and exams transmission, these data having peculiar characteristics typical of big data, as volume, velocity, variety, and veracity, potentially being used to generate value in patients' care. Despite the increasing interest expressed in AI and big data in laboratory medicine, these topics are approaching the discipline slowly for several reasons, attributable to lack of knowledge and skills but also to poor or absent standardization, harmonization and problematic regulatory and ethical issues. Finally, it is important to bear in mind that the mathematical postulation of algorithms is not sufficient for obtaining useful clinical tools, especially when biological parameters are not evaluated in the appropriate context. It is therefore necessary to enhance cooperation between laboratory and AI experts, and to coordinate and govern processes, thus favoring the development of valuable clinical tools.
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Affiliation(s)
- Andrea Padoan
- Department of Laboratory Medicine, University-Hospital of Padova, Padova, Italy.,Department of Medicine, University of Padova, Padova, Italy
| | - Mario Plebani
- Department of Laboratory Medicine, University-Hospital of Padova, Padova, Italy.,Department of Medicine, University of Padova, Padova, Italy
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10
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Cadamuro J, Baird G, Baumann G, Bolenius K, Cornes M, Ibarz M, Lewis T, Oliveira GL, Lippi G, Plebani M, Simundic AM, von Meyer A. Preanalytical quality improvement - an interdisciplinary journey, on behalf of the European Federation for Clinical Chemistry and Laboratory Medicine (EFLM) Working Group for Preanalytical Phase (WG-PRE). Clin Chem Lab Med 2022; 60:cclm-2022-0117. [PMID: 35258235 DOI: 10.1515/cclm-2022-0117] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 11/15/2022]
Abstract
Since the beginning of laboratory medicine, the main focus was to provide high quality analytics. Over time the importance of the extra-analytical phases and their contribution to the overall quality became evident. However, as the initial preanalytical processes take place outside of the laboratory and mostly without its supervision, all professions participating in these process steps, from test selection to sample collection and transport, need to engage accordingly. Focusing solely on intra-laboratory processes will not be sufficient to achieve the best possible preanalytical quality. The Working Group for the Preanalytical Phase (WG-PRE) of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) has provided several recommendations, opinion papers and scientific evidence over the past years, aiming to standardize the preanalytical phase across Europe. One of its strategies to reach this goal are educational efforts. As such, the WG-PRE has organized five conferences in the past decade with the sole focus on preanalytical quality. This year's conference mainly aims to depict the views of different professions on preanalytical processes in order to acquire common ground as basis for further improvements. This article summarizes the content of this 6th preanalytical conference.
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Affiliation(s)
- Janne Cadamuro
- Department of Laboratory Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Geoffrey Baird
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Gabriele Baumann
- Department of Laboratory Medicine, Pyhrn-Eisenwurzen Klinikum, General Hospital Steyr, Steyr, Austria
| | - Karin Bolenius
- Department of Nursing, Umeå University, The Unit of Research and Education, The County Council of Västerbotten, Umeå, Sweden
| | - Michael Cornes
- Clinical Chemistry Department, Worcester Acute Hospitals NHS Trust, Worcester, UK
| | - Mercedes Ibarz
- Laboratory Medicine Department, University Hospital Arnau de Vilanova, IRBLleida, Lleida, Spain
| | - Tom Lewis
- North Devon District Hospital, Devon, UK
| | - Gabriel Lima Oliveira
- Clinical Laboratory, Carlo Poma Hospital, Mantua, Italy
- Latin American Working Group for Preanalytical Phase (WG-PRE-LATAM), Latin America Confederation of Clinical Biochemistry (COLABIOCLI), Montevideo, Uruguay
| | - Giuseppe Lippi
- Section of Clinical Biochemistry and School of Medicine, University of Verona, Verona, Italy
| | - Mario Plebani
- Honorary Professor of Clinical Biochemistry and Clinical Molecular Biology, School of Medicine, University of Padova, Padova, Italy
| | - Ana-Maria Simundic
- Department of Medical Laboratory Diagnostics, University Hospital "Sveti Duh", Zagreb, Croatia
- Faculty of Pharmacy and Biochemistry, University of Zagreb, Zagreb, Croatia
| | - Alexander von Meyer
- Institute for Laboratory Medicine and Medical Microbiology, Munich Clinics, Munich, Germany
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