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Rusanganwa V, Nzabahimana I, Evander M. Quality and resilience of clinical laboratories in Rwanda: a need for sustainable strategies. Glob Health Action 2024; 17:2358633. [PMID: 38828509 PMCID: PMC11149573 DOI: 10.1080/16549716.2024.2358633] [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: 03/06/2024] [Accepted: 05/20/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND Quality healthcare is a global priority, reliant on robust health systems for evidence-based medicine. Clinical laboratories are the backbone of quality healthcare facilitating diagnostics, treatment, patient monitoring, and disease surveillance. Their effectiveness depends on sustainable delivery of accurate test results. Although the Strengthening Laboratory Management Towards Accreditation (SLMTA) programme has enhanced laboratory quality in low-income countries, the long-term sustainability of this improvement remains uncertain. OBJECTIVE To explore the sustainability of quality performance in clinical laboratories in Rwanda following the conclusion of SLMTA. METHODS A quasi-experimental design was adopted, involving 47 laboratories divided into three groups with distinct interventions. While one group received continuous mentorship and annual assessments (group two), interventions for the other groups (groups one and three) ceased following the conclusion of SLMTA. SLMTA experts collected data for 10 years through assessments using WHO's StepwiseLaboratory Quality Improvement Process Towards Accreditation (SLIPTA) checklist. Descriptive and t-test analyses were conducted for statistical evaluation. RESULTS Improvements in quality were noted between baseline and exit assessments across all laboratory groups (mean baseline: 35.3%, exit: 65.8%, p < 0.001). However, groups one and three experienced performance declines following SLMTA phase-out (mean group one: 64.6% in reference to 85.8%, p = 0.01; mean group three: 57.3% in reference to 64.7%, p < 0.001). In contrast, group two continued to enhance performance even years later (mean: 86.6%compared to 70.6%, p = 0.03). CONCLUSION A coordinated implementation of quality improvement plan that enables regular laboratory assessments to pinpoint and address the quality gaps is essential for sustaining quality services in clinical laboratories.
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Affiliation(s)
| | | | - Magnus Evander
- Department of Clinical Microbiology, Umeå University, Umeå, Sweden
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2
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Astărăstoae V, Rogozea LM, Leaşu F, Ioan BG. Ethical Dilemmas of Using Artificial Intelligence in Medicine. Am J Ther 2024; 31:e388-e397. [PMID: 38662923 DOI: 10.1097/mjt.0000000000001693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is considered the fourth industrial revolution that will change the evolution of humanity technically and relationally. Although the term has been around since 1956, it has only recently become apparent that AI can revolutionize technologies and has many applications in the medical field. AREAS OF UNCERTAINTY The ethical dilemmas posed by the use of AI in medicine revolve around issues related to informed consent, respect for confidentiality, protection of personal data, and last but not least the accuracy of the information it uses. DATA SOURCES A literature search was conducted through PubMed, MEDLINE, Plus, Scopus, and Web of Science (2015-2022) using combinations of keywords, including: AI, future in medicine, and machine learning plus ethical dilemma. ETHICS AND THERAPEUTIC ADVANCES The ethical analysis of the issues raised by AI used in medicine must mainly address nonmaleficence and beneficence, both in correlation with patient safety risks, ability versus inability to detect correct information from inadequate or even incorrect information. The development of AI tools that can support medical practice can increase people's access to medical information, to obtain a second opinion, for example, but it is also a source of concern among health care professionals and especially bioethicists about how confidentiality is maintained and how to maintain cybersecurity. Another major risk may be related to the dehumanization of the medical act, given that, at least for now, empathy and compassion are accessible only to human beings. CONCLUSIONS AI has not yet managed to overcome certain limits, lacking moral subjectivity, empathy, the level of critical thinking is still insufficient, but no matter who will practice preventive or curative medicine in the next period, they will not be able to ignore AI, which under human control can be an important tool in medical practice.
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Affiliation(s)
- Vasile Astărăstoae
- Faculty of Medicine, Grigore T Popa University of Medicine & Pharmacy, Iasi, Romania; and
| | - Liliana M Rogozea
- Basic, Preventive and Clinical Sciences Department, Transilvania University, Brasov, Romania
| | - Florin Leaşu
- Basic, Preventive and Clinical Sciences Department, Transilvania University, Brasov, Romania
| | - Beatrice Gabriela Ioan
- Faculty of Medicine, Grigore T Popa University of Medicine & Pharmacy, Iasi, Romania; and
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3
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Devis L, Catry E, Honore PM, Mansour A, Lippi G, Mullier F, Closset M. Interventions to improve appropriateness of laboratory testing in the intensive care unit: a narrative review. Ann Intensive Care 2024; 14:9. [PMID: 38224401 PMCID: PMC10789714 DOI: 10.1186/s13613-024-01244-y] [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: 09/11/2023] [Accepted: 01/03/2024] [Indexed: 01/16/2024] Open
Abstract
Healthcare expenses are increasing, as is the utilization of laboratory resources. Despite this, between 20% and 40% of requested tests are deemed inappropriate. Improper use of laboratory resources leads to unwanted consequences such as hospital-acquired anemia, infections, increased costs, staff workload and patient stress and discomfort. The most unfavorable consequences result from unnecessary follow-up tests and treatments (overuse) and missed or delayed diagnoses (underuse). In this context, several interventions have been carried out to improve the appropriateness of laboratory testing. To date, there have been few published assessments of interventions specific to the intensive care unit. We reviewed the literature for interventions implemented in the ICU to improve the appropriateness of laboratory testing. We searched literature from 2008 to 2023 in PubMed, Embase, Scopus, and Google Scholar databases between April and June 2023. Five intervention categories were identified: education and guidance (E&G), audit and feedback, gatekeeping, computerized physician order entry (including reshaping of ordering panels), and multifaceted interventions (MFI). We included a sixth category exploring the potential role of artificial intelligence and machine learning (AI/ML)-based assisting tools in such interventions. E&G-based interventions and MFI are the most frequently used approaches. MFI is the most effective type of intervention, and shows the strongest persistence of effect over time. AI/ML-based tools may offer valuable assistance to the improvement of appropriate laboratory testing in the near future. Patient safety outcomes are not impaired by interventions to reduce inappropriate testing. The literature focuses mainly on reducing overuse of laboratory tests, with only one intervention mentioning underuse. We highlight an overall poor quality of methodological design and reporting and argue for standardization of intervention methods. Collaboration between clinicians and laboratory staff is key to improve appropriate laboratory utilization. This article offers practical guidance for optimizing the effectiveness of an intervention protocol designed to limit inappropriate use of laboratory resources.
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Affiliation(s)
- Luigi Devis
- Department of Laboratory Medicine, Biochemistry, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium
| | - Emilie Catry
- Department of Laboratory Medicine, Biochemistry, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium
- Institute for Experimental and Clinical Research (IREC), Pôle Mont Godinne (MONT), UCLouvain, Yvoir, Belgium
| | - Patrick M Honore
- Department of Intensive Care, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium
| | - Alexandre Mansour
- Department of Anesthesia and Critical Care, Pontchaillou University Hospital of Rennes, Rennes, France
- IRSET-INSERM-1085, Univ Rennes, Rennes, France
| | - Giuseppe Lippi
- Section of Clinical Biochemistry and School of Medicine, University Hospital of Verona, Verona, Italy
| | - François Mullier
- Department of Laboratory Medicine, Hematology, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium
- Namur Thrombosis and Hemostasis Center (NTHC), Namur Research Institute for Life Sciences (NARILIS), Namur, Belgium
- Institute for Experimental and Clinical Research (IREC), Pôle Mont Godinne (MONT), UCLouvain, Yvoir, Belgium
| | - Mélanie Closset
- Department of Laboratory Medicine, Biochemistry, CHU UCL Namur, Université catholique de Louvain, Yvoir, Belgium.
- Institute for Experimental and Clinical Research (IREC), Pôle Mont Godinne (MONT), UCLouvain, Yvoir, Belgium.
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4
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Daher OA, Dabbousi AA, Chamroukh R, Saab AY, Al Ayoubi AR, Salameh P. Artificial Intelligence: Knowledge and Attitude Among Lebanese Medical Students. Cureus 2024; 16:e51466. [PMID: 38298326 PMCID: PMC10829838 DOI: 10.7759/cureus.51466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/01/2024] [Indexed: 02/02/2024] Open
Abstract
Background Artificial intelligence (AI) has taken on a variety of functions in the medical field, and research has proven that it can address complicated issues in various applications. It is unknown whether Lebanese medical students and residents have a detailed understanding of this concept, and little is known about their attitudes toward AI. Aim This study fills a critical gap by revealing the knowledge and attitude of Lebanese medical students toward AI. Methods A multi-centric survey targeting 365 medical students from seven medical schools across Lebanon was conducted to assess their knowledge of and attitudes toward AI in medicine. The survey consists of five sections: the first part includes socio-demographic variables, while the second comprises the 'Medical Artificial Intelligence Readiness Scale' for medical students. The third part focuses on attitudes toward AI in medicine, the fourth assesses understanding of deep learning, and the fifth targets considerations of radiology as a specialization. Results There is a notable awareness of AI among students who are eager to learn about it. Despite this interest, there exists a gap in knowledge regarding deep learning, albeit alongside a positive attitude towards it. Students who are more open to embracing AI technology tend to have a better understanding of AI concepts (p=0.001). Additionally, a higher percentage of students from Mount Lebanon (71.6%) showed an inclination towards using AI compared to Beirut (63.2%) (p=0.03). Noteworthy are the Lebanese University and Saint Joseph University, where the highest proportions of students are willing to integrate AI into the medical field (79.4% and 76.7%, respectively; p=0.001). Conclusion It was concluded that most Lebanese medical students might not necessarily comprehend the core technological ideas of AI and deep learning. This lack of understanding was evident from the substantial amount of misinformation among the students. Consequently, there appears to be a significant demand for the inclusion of AI technologies in Lebanese medical school courses.
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Affiliation(s)
- Omar A Daher
- Faculty of Medicine, Beirut Arab University, Beirut, LBN
| | | | | | | | - Amir Rabih Al Ayoubi
- Department of General Medicine, Faculty of Medical Sciences, Lebanese University, Beirut, LBN
| | - Pascale Salameh
- Department of Primary Care and Population Health, University of Nicosia Medical School, Nicosia, CYP
- Department of Public Health, Institut National de Santé Publique, d'Épidémiologie Clinique et de Toxicologie (INSPECT-LB), Beirut, LBN
- Department of Pharmacy Practice, Lebanese University, Beirut, LBN
- School of Medicine, Lebanese American University, Beirut, LBN
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Miragall MF, Knoedler S, Kauke-Navarro M, Saadoun R, Grabenhorst A, Grill FD, Ritschl LM, Fichter AM, Safi AF, Knoedler L. Face the Future-Artificial Intelligence in Oral and Maxillofacial Surgery. J Clin Med 2023; 12:6843. [PMID: 37959310 PMCID: PMC10649053 DOI: 10.3390/jcm12216843] [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: 10/06/2023] [Revised: 10/24/2023] [Accepted: 10/28/2023] [Indexed: 11/15/2023] Open
Abstract
Artificial intelligence (AI) has emerged as a versatile health-technology tool revolutionizing medical services through the implementation of predictive, preventative, individualized, and participatory approaches. AI encompasses different computational concepts such as machine learning, deep learning techniques, and neural networks. AI also presents a broad platform for improving preoperative planning, intraoperative workflow, and postoperative patient outcomes in the field of oral and maxillofacial surgery (OMFS). The purpose of this review is to present a comprehensive summary of the existing scientific knowledge. The authors thoroughly reviewed English-language PubMed/MEDLINE and Embase papers from their establishment to 1 December 2022. The search terms were (1) "OMFS" OR "oral and maxillofacial" OR "oral and maxillofacial surgery" OR "oral surgery" AND (2) "AI" OR "artificial intelligence". The search format was tailored to each database's syntax. To find pertinent material, each retrieved article and systematic review's reference list was thoroughly examined. According to the literature, AI is already being used in certain areas of OMFS, such as radiographic image quality improvement, diagnosis of cysts and tumors, and localization of cephalometric landmarks. Through additional research, it may be possible to provide practitioners in numerous disciplines with additional assistance to enhance preoperative planning, intraoperative screening, and postoperative monitoring. Overall, AI carries promising potential to advance the field of OMFS and generate novel solution possibilities for persisting clinical challenges. Herein, this review provides a comprehensive summary of AI in OMFS and sheds light on future research efforts. Further, the advanced analysis of complex medical imaging data can support surgeons in preoperative assessments, virtual surgical simulations, and individualized treatment strategies. AI also assists surgeons during intraoperative decision-making by offering immediate feedback and guidance to enhance surgical accuracy and reduce complication rates, for instance by predicting the risk of bleeding.
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Affiliation(s)
- Maximilian F. Miragall
- Department of Oral and Maxillofacial Surgery, University Hospital Regensburg, 93053 Regensburg, Germany
- Department of Oral and Maxillofacial Surgery, School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Samuel Knoedler
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT 06510, USA
| | - Martin Kauke-Navarro
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT 06510, USA
| | - Rakan Saadoun
- Department of Plastic Surgery, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Alex Grabenhorst
- Department of Oral and Maxillofacial Surgery, School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Florian D. Grill
- Department of Oral and Maxillofacial Surgery, School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Lucas M. Ritschl
- Department of Oral and Maxillofacial Surgery, School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Andreas M. Fichter
- Department of Oral and Maxillofacial Surgery, School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Ali-Farid Safi
- Craniologicum, Center for Cranio-Maxillo-Facial Surgery, 3011 Bern, Switzerland;
- Faculty of Medicine, University of Bern, 3010 Bern, Switzerland
| | - Leonard Knoedler
- Division of Plastic Surgery, Department of Surgery, Yale New Haven Hospital, Yale School of Medicine, New Haven, CT 06510, USA
- Department of Plastic, Hand and Reconstructive Surgery, University Hospital Regensburg, 93053 Regensburg, Germany
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Shin TY, Han H, Min HS, Cho H, Kim S, Park SY, Kim HJ, Kim JH, Lee YS. Prediction of Postoperative Creatinine Levels by Artificial Intelligence after Partial Nephrectomy. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1402. [PMID: 37629692 PMCID: PMC10456500 DOI: 10.3390/medicina59081402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/20/2023] [Accepted: 07/28/2023] [Indexed: 08/27/2023]
Abstract
Background and Objectives: Multiple factors are associated with postoperative functional outcomes, such as acute kidney injury (AKI), following partial nephrectomy (PN). The pre-, peri-, and postoperative factors are heavily intertwined and change dynamically, making it difficult to predict postoperative renal function. Therefore, we aimed to build an artificial intelligence (AI) model that utilizes perioperative factors to predict residual renal function and incidence of AKI following PN. Methods and Materials: This retrospective study included 785 patients (training set 706, test set 79) from six tertiary referral centers who underwent open or robotic PN. Forty-four perioperative features were used as inputs to train the AI prediction model. XG-Boost and genetic algorithms were used for the final model selection and to determine feature importance. The primary outcome measure was immediate postoperative serum creatinine (Cr) level. The secondary outcome was the incidence of AKI (estimated glomerular filtration rate (eGFR) < 60 mL/h). The average difference between the true and predicted serum Cr levels was considered the mean absolute error (MAE) and was used as a model evaluation parameter. Results: An AI model for predicting immediate postoperative serum Cr levels was selected from 2000 candidates by providing the lowest MAE (0.03 mg/dL). The model-predicted immediate postoperative serum Cr levels correlated closely with the measured values (R2 = 0.9669). The sensitivity and specificity of the model for predicting AKI were 85.5% and 99.7% in the training set, and 100.0% and 100.0% in the test set, respectively. The limitations of this study included its retrospective design. Conclusions: Our AI model successfully predicted accurate serum Cr levels and the likelihood of AKI. The accuracy of our model suggests that personalized guidelines to optimize multidisciplinary plans involving pre- and postoperative care need to be developed.
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Affiliation(s)
- Tae Young Shin
- Synergy A.I. Co., Ltd., Seoul 07985, Republic of Korea;
- Department of Urology, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea;
- Department of Urology, College of Medicine, Ewha Womans University, Seoul 07985, Republic of Korea
| | - Hyunho Han
- Department of Urology, College of Medicine, Yonsei University, Seoul 03722, Republic of Korea;
| | - Hyun-Seok Min
- Tomocube, Inc., Daejeon 34109, Republic of Korea; (H.-S.M.); (H.C.)
| | - Hyungjoo Cho
- Tomocube, Inc., Daejeon 34109, Republic of Korea; (H.-S.M.); (H.C.)
| | - Seonggyun Kim
- Department of Urology, College of Medicine, Hallym University, Chuncheon 24253, Republic of Korea;
| | - Sung Yul Park
- Department of Urology, College of Medicine, Hanyang University, Seoul 04763, Republic of Korea;
| | - Hyung Joon Kim
- Department of Urology, College of Medicine, Konyang University, Daejeon 35365, Republic of Korea;
| | - Jung Hoon Kim
- Department of Urology, Chung-Ang University Gwangmyeong Hospital, College of Medicine, Chung-Ang University, Gwangmyeong 14353, Republic of Korea;
| | - Yong Seong Lee
- Department of Urology, Chung-Ang University Gwangmyeong Hospital, College of Medicine, Chung-Ang University, Gwangmyeong 14353, Republic of Korea;
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Grech V, Cuschieri S, Eldawlatly AA. Artificial intelligence in medicine and research - the good, the bad, and the ugly. Saudi J Anaesth 2023; 17:401-406. [PMID: 37601525 PMCID: PMC10435812 DOI: 10.4103/sja.sja_344_23] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 04/26/2023] [Indexed: 08/22/2023] Open
Abstract
Artificial intelligence (AI) broadly refers to machines that simulate intelligent human behavior, and research into this field is exponential and worldwide, with global players such as Microsoft battling with Google for supremacy and market share. This paper reviews the "good" aspects of AI in medicine for individuals who embrace the 4P model of medicine (Predictive, Preventive, Personalized, and Participatory) to medical assistants in diagnostics, surgery, and research. The "bad" aspects relate to the potential for errors, culpability, ethics, data loss and data breaches, and so on. The "ugly" aspects are deliberate personal malfeasances and outright scientific misconduct including the ease of plagiarism and fabrication, with particular reference to the novel ChatGPT as well as AI software that can also fabricate graphs and images. The issues pertaining to the potential dangers of creating rogue, super-intelligent AI systems that lead to a technological singularity and the ensuing perceived existential threat to mankind by leading AI researchers are also briefly discussed.
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8
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Al Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, Subbarayalu AV, Al Muhanna D, Al-Muhanna FA. A Review of the Role of Artificial Intelligence in Healthcare. J Pers Med 2023; 13:951. [PMID: 37373940 PMCID: PMC10301994 DOI: 10.3390/jpm13060951] [Citation(s) in RCA: 46] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 06/29/2023] Open
Abstract
Artificial intelligence (AI) applications have transformed healthcare. This study is based on a general literature review uncovering the role of AI in healthcare and focuses on the following key aspects: (i) medical imaging and diagnostics, (ii) virtual patient care, (iii) medical research and drug discovery, (iv) patient engagement and compliance, (v) rehabilitation, and (vi) other administrative applications. The impact of AI is observed in detecting clinical conditions in medical imaging and diagnostic services, controlling the outbreak of coronavirus disease 2019 (COVID-19) with early diagnosis, providing virtual patient care using AI-powered tools, managing electronic health records, augmenting patient engagement and compliance with the treatment plan, reducing the administrative workload of healthcare professionals (HCPs), discovering new drugs and vaccines, spotting medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. Nevertheless, this science pitch meets several technical, ethical, and social challenges, including privacy, safety, the right to decide and try, costs, information and consent, access, and efficacy, while integrating AI into healthcare. The governance of AI applications is crucial for patient safety and accountability and for raising HCPs' belief in enhancing acceptance and boosting significant health consequences. Effective governance is a prerequisite to precisely address regulatory, ethical, and trust issues while advancing the acceptance and implementation of AI. Since COVID-19 hit the global health system, the concept of AI has created a revolution in healthcare, and such an uprising could be another step forward to meet future healthcare needs.
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Affiliation(s)
- Ahmed Al Kuwaiti
- Department of Dental Education, College of Dentistry, Deanship of Quality and Academic Accreditation, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Khalid Nazer
- Department of Information and Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
- Health Information Department, King Fahad hospital of the University, Al-Khobar 31952, Saudi Arabia
| | - Abdullah Al-Reedy
- Department of Information and Technology, Family and Community Medicine Department, Family and Community Medicine Centre, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Shaher Al-Shehri
- Faculty of Medicine, Family and Community Medicine Department, Family and Community Medicine Centre, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Afnan Al-Muhanna
- Breast Imaging Division, Department of Radiology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
- Radiology Department, King Fahad hospital of the University, Al-Khobar 31952, Saudi Arabia
| | - Arun Vijay Subbarayalu
- Quality Studies and Research Unit, Vice Deanship of Quality, Deanship of Quality and Academic Accreditation, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Dhoha Al Muhanna
- NDirectorate of Quality and Patient Safety, Family and Community Medicine Center, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Fahad A. Al-Muhanna
- Nephrology Division, Department of Internal Medicine, Faculty of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
- Medicine Department, King Fahad hospital of the University, Al-Khobar 31952, Saudi Arabia
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9
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Vanstapel FJLA, Orth M, Streichert T, Capoluongo ED, Oosterhuis WP, Çubukçu HC, Bernabeu-Andreu FA, Thelen M, Jacobs LHJ, Linko S, Bhattoa HP, Bossuyt PMM, Meško Brguljan P, Boursier G, Cobbaert CM, Neumaier M. ISO 15189 is a sufficient instrument to guarantee high-quality manufacture of laboratory developed tests for in-house-use conform requirements of the European In-Vitro-Diagnostics Regulation. Clin Chem Lab Med 2023; 61:608-626. [PMID: 36716120 DOI: 10.1515/cclm-2023-0045] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 01/15/2023] [Indexed: 01/31/2023]
Abstract
The EU In-Vitro Diagnostic Device Regulation (IVDR) aims for transparent risk-and purpose-based validation of diagnostic devices, traceability of results to uniquely identified devices, and post-market surveillance. The IVDR regulates design, manufacture and putting into use of devices, but not medical services using these devices. In the absence of suitable commercial devices, the laboratory can resort to laboratory-developed tests (LDT) for in-house use. Documentary obligations (IVDR Art 5.5), the performance and safety specifications of ANNEX I, and development and manufacture under an ISO 15189-equivalent quality system apply. LDTs serve specific clinical needs, often for low volume niche applications, or correspond to the translational phase of new tests and treatments, often extremely relevant for patient care. As some commercial tests may disappear with the IVDR roll-out, many will require urgent LDT replacement. The workload will also depend on which modifications to commercial tests turns them into an LDT, and on how national legislators and competent authorities (CA) will handle new competences and responsibilities. We discuss appropriate interpretation of ISO 15189 to cover IVDR requirements. Selected cases illustrate LDT implementation covering medical needs with commensurate management of risk emanating from intended use and/or design of devices. Unintended collateral damage of the IVDR comprises loss of non-profitable niche applications, increases of costs and wasted resources, and migration of innovative research to more cost-efficient environments. Taking into account local specifics, the legislative framework should reduce the burden on and associated opportunity costs for the health care system, by making diligent use of existing frameworks.
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Key Words
- AB, accrediting body
- BRCA1/2, breast cancer genes 1 and 2
- CA, competent authority
- CAPA, corrective and preventive actions
- CDx, companion diagnostics
- CGP, comprehensive genomic profile
- CRGA, clinically relevant genomic alterations
- EEA, European economic area
- EFLM, European Federation of Clinical Chemistry and Laboratory Medicine
- EMA, European Medicines Agency
- EU, European Union
- European Regulation 2017/746 on In-Vitro-Diagnostic Devices
- FMEA, failure-mode effects analysis
- GA, genomic alterations
- GDPR, General Data Protection Regulation
- HI, health institution
- HRD, homologous recombination deficiency
- HRR, homologous recombination repair
- ISO 15189:2012
- ISO, International Organization for Standardization
- IVDD, In-Vitro Diagnostic Device Directive
- IVDR, In-Vitro Diagnostic Device Regulation
- LDT, laboratory-developed test
- MDCG, Medical Device Coordination Group
- MSI, micro satellite instability
- MU, measurement uncertainty
- NB, notified body
- NGS, next generation sequencing
- NTRK, neurotrophic tyrosine receptor kinase
- PARPi, poly (ADP-ribose) polymerase inhibitors
- PRRC, person responsible for regulatory compliance
- PT, proficiency testing
- RUO, research use only
- RiliBÄk, Richtlinie der Bundesärztekammer zur Qualitätssicherung Laboratoriums medizinischer Untersuchungen
- SOP, standard operating procedure
- TMB, tumor mutational burden
- UDI, unique device identifier
- VAF, variant allele frequency
- iQC, internal quality control
- laboratory-developed tests for in-house use
- method validation
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Affiliation(s)
- Florent J L A Vanstapel
- Laboratory Medicine, University Hospital Leuven, Leuven, Belgium
- Department of Public Health, Biomedical Sciences Group, Catholic University Leuven, Leuven, Belgium
| | - Matthias Orth
- Institute of Laboratory Medicine, Vinzenz von Paul Kliniken gGmbH, Stuttgart, Germany
- Medical Faculty Mannheim of Heidelberg University, Mannheim, Germany
| | - Thomas Streichert
- University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Ettore D Capoluongo
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche, Università Federico II, Naples, Italy
| | - Wytze P Oosterhuis
- Department of Clinical Chemistry, Reinier Haga Medical Diagnostic Centre, Delft, The Netherlands
| | - Hikmet Can Çubukçu
- Ankara University Stem Cell Institute, Ankara, Türkiye
- Department of Rare Diseases, General Directorate of Health Services, Turkish Ministry of Health, Ankara, Türkiye
| | - Francisco A Bernabeu-Andreu
- Servicio Bioquímica Análisis Clínicos, Hospital Universitario Puerta de Hierro Majadahonda (Madrid), Majadahonda, Spain
| | - Marc Thelen
- Result Laboratory for Clinical Chemistry, Amphia Hospital, Breda, The Netherlands
- Department of Laboratory Medicine, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Leo H J Jacobs
- Laboratory for Clinical Chemistry and Hematology, Meander Medical Centre, Amersfoort, The Netherlands
| | | | - Harjit Pal Bhattoa
- Department of Laboratory Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Patrick M M Bossuyt
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Pika Meško Brguljan
- Department of Clinical Chemistry, University Clinic for Respiratory and Allergic Diseases Golnik, Golnik, Slovenia
| | - Guilaine Boursier
- Department of Molecular Genetics and Cytogenomics, Rare and Autoinflammatory Diseases Unit, CHU Montpellier, Univ Montpellier, Montpellier, France
| | - Christa M Cobbaert
- Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Centre, Leiden, The Netherlands
| | - Michael Neumaier
- Institute for Clinical Chemistry, Medical Faculty Mannheim of Heidelberg University, Mannheim, Germany
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10
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Orth M, Vollebregt E, Trenti T, Shih P, Tollanes M, Sandberg S. Direct-to-consumer laboratory testing (DTCT): challenges and implications for specialists in laboratory medicine. Clin Chem Lab Med 2023; 61:696-702. [PMID: 36565304 DOI: 10.1515/cclm-2022-1227] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 12/13/2022] [Indexed: 12/25/2022]
Abstract
In vitro diagnostics (IVD) testing is a powerful tool for medical diagnosis, and patients' safety is guaranteed by a complex system of personnel qualification of the specialist in laboratory medicine, of process control, and legal restrictions in healthcare, most of them under national regulation. Direct-to-consumer laboratory testing (DTCT) is testing ordered by the consumer and performed either by the consumer at home or analysis of self-collected samples in a laboratory. However, since DTCT are not always subject to effective competent authority oversight, DTCT may pose risks to lay persons using and relying on it for healthcare decision-making. Laboratory medicine specialists should be very cautious when new DTCTs are introduced. As qualified professionals, they should feel obliged to warn and educate patients and the public about the risks of inappropriate and harmful DTCT.
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Affiliation(s)
- Matthias Orth
- Vinzenz von Paul Kliniken gGmbH, Institut für Laboratoriumsmedizin, Stuttgart, Germany
- Medizinische Fakultät Mannheim, Ruprecht Karls Universität, Mannheim, Germany
| | | | - Tomaso Trenti
- Dipartimento Integrato Interaziendale di Medicina di Laboratorio e Anatomia Patologica, Azienda Ospedaliera Universitaria e Azienda USL di Modena, Modena, Italy
| | - Patti Shih
- Australian Centre for Health Engagement Evidence and Values (ACHEEV), School of Health and Society, University of Wollongong NSW, Wollongong, Australia
| | - Mette Tollanes
- Norwegian Organisation for Quality Improvement of Laboratory Examinations (NOKLUS), Bergen, Norway
- Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Sverre Sandberg
- Norwegian Organisation for Quality Improvement of Laboratory Examinations (NOKLUS), Bergen, Norway
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11
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Hulsen T. Literature analysis of artificial intelligence in biomedicine. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:1284. [PMID: 36618779 PMCID: PMC9816850 DOI: 10.21037/atm-2022-50] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 10/19/2022] [Indexed: 11/20/2022]
Abstract
Artificial intelligence (AI) refers to the simulation of human intelligence in machines, using machine learning (ML), deep learning (DL) and neural networks (NNs). AI enables machines to learn from experience and perform human-like tasks. The field of AI research has been developing fast over the past five to ten years, due to the rise of 'big data' and increasing computing power. In the medical area, AI can be used to improve diagnosis, prognosis, treatment, surgery, drug discovery, or for other applications. Therefore, both academia and industry are investing a lot in AI. This review investigates the biomedical literature (in the PubMed and Embase databases) by looking at bibliographical data, observing trends over time and occurrences of keywords. Some observations are made: AI has been growing exponentially over the past few years; it is used mostly for diagnosis; COVID-19 is already in the top-3 of diseases studied using AI; China, the United States, South Korea, the United Kingdom and Canada are publishing the most articles in AI research; Stanford University is the world's leading university in AI research; and convolutional NNs are by far the most popular DL algorithms at this moment. These trends could be studied in more detail, by studying more literature databases or by including patent databases. More advanced analyses could be used to predict in which direction AI will develop over the coming years. The expectation is that AI will keep on growing, in spite of stricter privacy laws, more need for standardization, bias in the data, and the need for building trust.
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12
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Novel artificial intelligent transformer U-NET for better identification and management of prostate cancer. Mol Cell Biochem 2022; 478:1439-1445. [DOI: 10.1007/s11010-022-04600-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 10/24/2022] [Indexed: 11/10/2022]
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13
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Mitek A. Technology Basics for Telemedicine: What Practitioners Need to Know. Vet Clin North Am Small Anim Pract 2022; 52:1109-1122. [PMID: 36150788 DOI: 10.1016/j.cvsm.2022.06.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Veterinary medical technology is rapidly evolving and provides exciting opportunities for veterinarians to practice medicine in new ways. This article reviews the basic components of telemedicine technology that practitioners should be aware of.
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Affiliation(s)
- Ashley Mitek
- Co-Founder, Stratocyte.com, Owner, AnesthesiaDiva.com, 48-113 Angel Wing Peak Glacier National Park, MT, USA.
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14
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Azmanov H, Bayatra A, Ilan Y. Digital Analgesic Comprising a Second-Generation Digital Health System: Increasing Effectiveness by Optimizing the Dosing and Minimizing Side Effects. J Pain Res 2022; 15:1051-1060. [PMID: 35444460 PMCID: PMC9013915 DOI: 10.2147/jpr.s356319] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 03/02/2022] [Indexed: 12/30/2022] Open
Abstract
Opioids remain an essential part of the treatment of chronic pain. However, their use and increasing rates of misuse are associated with high morbidity and mortality. The development of tolerance to opioids and analgesics further complicates dosing and the need to reduce side effects. First-generation digital systems were developed to improve analgesics but are not always capable of making clinically relevant associations and do not necessarily lead to better clinical efficacy. A lack of improved clinical outcomes makes these systems less applicable for adoption by clinicians and patients. There is a need to enhance the therapeutic regimens of opioids. In the present paper, we present the use of a digital analgesic that consists of an analgesic administered under the control of a second-generation artificial intelligence system. Second-generation systems focus on improved patient outcomes measured based on clinical response and reduced side effects in a single subject. The algorithm regulates the administration of analgesics in a personalized manner. The digital analgesic provides advantages for both users and providers. The system enables dose optimization, improving effectiveness, and minimizing side effects while increasing adherence to beneficial therapeutic regimens. The algorithm improves the clinicians’ experience and assists them in managing chronic pain. The system reduces the financial burden on healthcare providers by lowering opioid-related morbidity and provides a market disruptor for pharma companies.
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Affiliation(s)
- Henny Azmanov
- Hebrew University, Faculty of Medicine, Hadassah Medical Center, Jerusalem, Israel
| | - Areej Bayatra
- Hebrew University, Faculty of Medicine, Hadassah Medical Center, Jerusalem, Israel
| | - Yaron Ilan
- Hebrew University, Faculty of Medicine, Hadassah Medical Center, Jerusalem, Israel
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15
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Ilan Y. Digital Medical Cannabis as Market Differentiator: Second-Generation Artificial Intelligence Systems to Improve Response. Front Med (Lausanne) 2022; 8:788777. [PMID: 35141242 PMCID: PMC8818992 DOI: 10.3389/fmed.2021.788777] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 12/20/2021] [Indexed: 12/19/2022] Open
Abstract
Legalized use of cannabis products and the rising interest in their therapeutic benefits have opened up new opportunities for therapy and marketing. However, the marked variability in formulations, administration modes, therapeutic regimens, and inter- and intra-subject responses make the standardization of medical cannabis-based regimens difficult. Legalization has made the cannabis market highly competitive and lowered the revenue margins. This study reviews some of the challenges in medical cannabis use and difficulties in standardizing its therapeutic regimens that hinder maximizing its beneficial effects. The development of tolerance toward cannabis and low adherence to chronic administration further impair its long-term beneficial effects. Digital medical cannabis is a cannabis product controlled by a second-generation artificial intelligence (AI) system that improves patient responses by increasing adherence and dealing with tolerance. Second-generation AI systems focus on a single patient's outcome and deal with the inter- and intra-subject variability in responses. The use of digital medical cannabis is expected to improve product standardization, maximize therapeutic benefits, reduce health care costs, and increase the revenue of companies. Digital medical cannabis offers several market differentiators for cannabis companies. This study presents a model for promoting the use of digital medical cannabis and presents its advantages for patients, clinicians, health care authorities, insurance companies, and cannabis manufacturers. Ongoing trials and real-world data on the use of these systems further support the use of digital medical cannabis for improved global health.
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Affiliation(s)
- Yaron Ilan
- Faculty of Medicine, Hebrew University, Jerusalem, Israel
- Department of Medicine, Hadassah Medical Center, Jerusalem, Israel
- *Correspondence: Yaron Ilan
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16
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Orth M. Direct to Consumer Laboratory Testing (DTCT) - Opportunities and Concerns. EJIFCC 2021; 32:209-215. [PMID: 34421490 PMCID: PMC8343044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Direct to consumer laboratory testing has the potential for self-empowerment of patients. However, the Direct to consumer laboratory testing (DTCT) uses loopholes which are related to the particular situation of healthcare: While advertisements and claims for medical usefulness are very high regulated in healthcare, essentially no regulations safeguard the consumers in DTCT. The same is true for the quality of testing services since quality regulations are only mandatory in healthcare. Another problem is the lack of medical interpretation of test results. Besides being very risky for the consumers, healthcare professionals relying on test results obtained by DTCT must be aware about the risks of these data.
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Affiliation(s)
- Matthias Orth
- Vinzenz von Paul Kliniken gGmbH, Institut für Laboratoriumsmedizin, Stuttgart, Germany, Medizinische Fakultät Mannheim, Ruprecht Karls Universität, Mannheim, Germany,Corresponding author: Matthias Orth Institut für Laboratoriumsmedizin Vinzenz von Paul Kliniken gGmbH Postfach 103163 70027 Stuttgart Germany E-mail:
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17
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Žaliauskaitė M. Role of ruler or intruder? Patient’s right to autonomy in the age of innovation and technologies. AI & SOCIETY 2021. [DOI: 10.1007/s00146-020-01034-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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18
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Robinson T, Harkin J, Shukla P. Hardware Acceleration of Genomics Data Analysis: Challenges and Opportunities. Bioinformatics 2021; 37:1785-1795. [PMID: 34037688 PMCID: PMC8317111 DOI: 10.1093/bioinformatics/btab017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 11/03/2020] [Accepted: 05/24/2021] [Indexed: 12/11/2022] Open
Abstract
The significant decline in the cost of genome sequencing has dramatically changed the typical bioinformatics pipeline for analysing sequencing data. Where traditionally, the computational challenge of sequencing is now secondary to genomic data analysis. Short read alignment (SRA) is a ubiquitous process within every modern bioinformatics pipeline in the field of genomics and is often regarded as the principal computational bottleneck. Many hardware and software approaches have been provided to solve the challenge of acceleration. However, previous attempts to increase throughput using many-core processing strategies have enjoyed limited success, mainly due to a dependence on global memory for each computational block. The limited scalability and high energy costs of many-core SRA implementations pose a significant constraint in maintaining acceleration. The Networks-On-Chip (NoC) hardware interconnect mechanism has advanced the scalability of many-core computing systems and, more recently, has demonstrated potential in SRA implementations by integrating multiple computational blocks such as pre-alignment filtering and sequence alignment efficiently, while minimising memory latency and global memory access. This paper provides a state of the art review on current hardware acceleration strategies for genomic data analysis, and it establishes the challenges and opportunities of utilising NoCs as a critical building block in next-generation sequencing (NGS) technologies for advancing the speed of analysis.
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Affiliation(s)
- Tony Robinson
- School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Derry/Londonderry, BT48 7JL, UK
| | - Jim Harkin
- School of Computing, Engineering and Intelligent Systems, Ulster University, Magee Campus, Derry/Londonderry, BT48 7JL, UK
| | - Priyank Shukla
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, C-TRIC Building, Altnagelvin Area Hospital, Derry/Londonderry, BT47 6SB, UK
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19
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Mrazek C, Lippi G, Keppel MH, Felder TK, Oberkofler H, Haschke-Becher E, Cadamuro J. Errors within the total laboratory testing process, from test selection to medical decision-making - A review of causes, consequences, surveillance and solutions. Biochem Med (Zagreb) 2021; 30:020502. [PMID: 32550813 PMCID: PMC7271754 DOI: 10.11613/bm.2020.020502] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 04/23/2020] [Indexed: 12/18/2022] Open
Abstract
Laboratory analyses are crucial for diagnosis, follow-up and treatment decisions. Since mistakes in every step of the total testing process may potentially affect patient safety, a broad knowledge and systematic assessment of laboratory errors is essential for future improvement. In this review, we aim to discuss the types and frequencies of potential errors in the total testing process, quality management options, as well as tentative solutions for improvement. Unlike most currently available reviews on this topic, we also include errors in test-selection, reporting and interpretation/action of test results. We believe that laboratory specialists will need to refocus on many process steps belonging to the extra-analytical phases, intensifying collaborations with clinicians and supporting test selection and interpretation. This would hopefully lead to substantial improvements in these activities, but may also bring more value to the role of laboratory specialists within the health care setting.
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Affiliation(s)
- Cornelia Mrazek
- Department of Laboratory Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Giuseppe Lippi
- Section of Clinical Chemistry, University of Verona, Verona, Italy
| | - Martin H Keppel
- Department of Laboratory Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Thomas K Felder
- Department of Laboratory Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Hannes Oberkofler
- Department of Laboratory Medicine, Paracelsus Medical University, Salzburg, Austria
| | | | - Janne Cadamuro
- Department of Laboratory Medicine, Paracelsus Medical University, Salzburg, Austria
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20
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Ilan Y. Improving Global Healthcare and Reducing Costs Using Second-Generation Artificial Intelligence-Based Digital Pills: A Market Disruptor. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:811. [PMID: 33477865 PMCID: PMC7832873 DOI: 10.3390/ijerph18020811] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 01/16/2021] [Accepted: 01/17/2021] [Indexed: 12/12/2022]
Abstract
Background and Aims: Improving global health requires making current and future drugs more effective and affordable. While healthcare systems around the world are faced with increasing costs, branded and generic drug companies are facing the challenge of creating market differentiators. Two of the problems associated with the partial or complete loss of response to chronic medications are a lack of adherence and compensatory responses to chronic drug administration, which leads to tolerance and loss of effectiveness. Approach and Results: First-generation artificial intelligence (AI) systems do not address these needs and suffer from a low adoption rate by patients and clinicians. Second-generation AI systems are focused on a single subject and on improving patients' clinical outcomes. The digital pill, which combines a personalized second-generation AI system with a branded or generic drug, improves the patient response to drugs by increasing adherence and overcoming the loss of response to chronic medications. By improving the effectiveness of drugs, the digital pill reduces healthcare costs and increases end-user adoption. The digital pill also provides a market differentiator for branded and generic drug companies. Conclusions: Implementing the use of a digital pill is expected to reduce healthcare costs, providing advantages for all the players in the healthcare system including patients, clinicians, healthcare authorities, insurance companies, and drug manufacturers. The described business model for the digital pill is based on distributing the savings across all stakeholders, thereby enabling improved global health.
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Affiliation(s)
- Yaron Ilan
- Department of Medicine, The Hebrew University of Jerusalem-Hadassah Medical Center, Jerusalem 12000, Israel
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21
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Ilan Y. Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes. Front Digit Health 2020; 2:569178. [PMID: 34713042 PMCID: PMC8521820 DOI: 10.3389/fdgth.2020.569178] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Accepted: 10/02/2020] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.
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22
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Mukhopadhyay T, Subramanian A. An overview of the potential sources of diagnostic errors in (classic) thromboelastography curve interpretation and preventive measures. Pract Lab Med 2020; 22:e00193. [PMID: 33319008 PMCID: PMC7723805 DOI: 10.1016/j.plabm.2020.e00193] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 11/18/2020] [Indexed: 11/17/2022] Open
Abstract
Thromboelastography (TEG), a hemostatic point-of-care assay, provides global information about fibrin formation, platelet activation, and clot retraction in real-time. As it is an operator-dependent technique, error in any phase of the testing process can result in the misinterpretation of the thromboelastogram, and subsequently lead to mismanagement of the patient, wastage of blood products besides increasing the financial burden on the hospital and the patient. The present paper describes the possible errors leading to wrong thromboelastogram interpretation, and the respective preventive measure. In the light of limited resources available for operational challenges in TEG, this review paper can prove to be helpful.
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Affiliation(s)
- Tapasyapreeti Mukhopadhyay
- Department of Laboratory Medicine, Jai Prakash Narayan Apex Trauma Centre, All India Institute Medical Sciences, New Delhi, 110029, India
| | - Arulselvi Subramanian
- Room No. 207, Department of Laboratory Medicine, Jai Prakash Narayan Apex Trauma Centre, All India Institute Medical Sciences, New Delhi, 110029, India
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23
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Reeve JLV, Twomey PJ, Borovickova I. The role of the Clinical Chemistry laboratory in facilitating earlier diagnosis of dyslipidaemia-associated inherited metabolic disease. Clin Mol Pathol 2020; 73:363-365. [DOI: 10.1136/jclinpath-2019-206254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 04/30/2020] [Indexed: 11/04/2022]
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24
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Aubourg T, Demongeot J, Provost H, Vuillerme N. Circadian Rhythms in the Telephone Calls of Older Adults: Observational Descriptive Study. JMIR Mhealth Uhealth 2020; 8:e12452. [PMID: 32130156 PMCID: PMC7064945 DOI: 10.2196/12452] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 05/24/2019] [Accepted: 06/28/2019] [Indexed: 12/26/2022] Open
Abstract
Background Recent studies have thoughtfully and convincingly demonstrated the possibility of estimating the circadian rhythms of young adults’ social activity by analyzing their telephone call-detail records (CDRs). In the field of health monitoring, this development may offer new opportunities for supervising a patient’s health status by collecting objective, unobtrusive data about their daily social interactions. However, before considering this future perspective, whether and how similar results could be observed in other populations, including older ones, should be established. Objective This study was designed specifically to address the circadian rhythms in the telephone calls of older adults. Methods A longitudinal, 12-month dataset combining CDRs and questionnaire data from 26 volunteers aged 65 years or older was used to examine individual differences in the daily rhythms of telephone call activity. The study used outgoing CDRs only and worked with three specific telecommunication parameters: (1) call recipient (alter), (2) time of day, and (3) call duration. As did the studies involving young adults, we analyzed three issues: (1) the existence of circadian rhythms in the telephone call activity of older adults, (2) their persistence over time, and (3) the alter-specificity of calls by calculating relative entropy. Results We discovered that older adults had their own specific circadian rhythms of outgoing telephone call activity whose salient features and preferences varied across individuals, from morning until night. We demonstrated that rhythms were consistent, as reflected by their persistence over time. Finally, results suggested that the circadian rhythms of outgoing telephone call activity were partly structured by how older adults allocated their communication time across their social network. Conclusions Overall, these results are the first to have demonstrated the existence, persistence, and alter-specificity of the circadian rhythms of the outgoing telephone call activity of older adults. These findings suggest an opportunity to consider modern telephone technologies as potential sensors of daily activity. From a health care perspective, these sensors could be harnessed for unobtrusive monitoring purposes.
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Affiliation(s)
- Timothée Aubourg
- Orange Labs, Chemin du Vieux Chêne, Meylan, France.,University Grenoble Alpes, AGEIS, Grenoble, France.,LabCom Telecom4Health, University Grenoble Apes & Orange Labs, Grenoble, France
| | - Jacques Demongeot
- University Grenoble Alpes, AGEIS, Grenoble, France.,LabCom Telecom4Health, University Grenoble Apes & Orange Labs, Grenoble, France.,Institut Universitaire de France, Paris, France
| | - Hervé Provost
- Orange Labs, Chemin du Vieux Chêne, Meylan, France.,LabCom Telecom4Health, University Grenoble Apes & Orange Labs, Grenoble, France
| | - Nicolas Vuillerme
- University Grenoble Alpes, AGEIS, Grenoble, France.,LabCom Telecom4Health, University Grenoble Apes & Orange Labs, Grenoble, France.,Institut Universitaire de France, Paris, France
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25
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Briganti G, Le Moine O. Artificial Intelligence in Medicine: Today and Tomorrow. Front Med (Lausanne) 2020; 7:27. [PMID: 32118012 PMCID: PMC7012990 DOI: 10.3389/fmed.2020.00027] [Citation(s) in RCA: 176] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 01/17/2020] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence-powered medical technologies are rapidly evolving into applicable solutions for clinical practice. Deep learning algorithms can deal with increasing amounts of data provided by wearables, smartphones, and other mobile monitoring sensors in different areas of medicine. Currently, only very specific settings in clinical practice benefit from the application of artificial intelligence, such as the detection of atrial fibrillation, epilepsy seizures, and hypoglycemia, or the diagnosis of disease based on histopathological examination or medical imaging. The implementation of augmented medicine is long-awaited by patients because it allows for a greater autonomy and a more personalized treatment, however, it is met with resistance from physicians which were not prepared for such an evolution of clinical practice. This phenomenon also creates the need to validate these modern tools with traditional clinical trials, debate the educational upgrade of the medical curriculum in light of digital medicine as well as ethical consideration of the ongoing connected monitoring. The aim of this paper is to discuss recent scientific literature and provide a perspective on the benefits, future opportunities and risks of established artificial intelligence applications in clinical practice on physicians, healthcare institutions, medical education, and bioethics.
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Affiliation(s)
- Giovanni Briganti
- Medical Informatics, School of Medicine, Université Libre de Bruxelles, Brussels, Belgium
- Unit of Epidemiology, Biostatistics and Clinical Research, School of Public Health, Université Libre de Bruxelles, Brussels, Belgium
| | - Olivier Le Moine
- Medical Informatics, School of Medicine, Université Libre de Bruxelles, Brussels, Belgium
- Hopital Erasme, Université Libre de Bruxelles, Brussels, Belgium
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26
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Nguyen DV, Hugoni L, Filippi M, Perton F, Shi D, Voirin E, Power L, Cotin G, Krafft MP, Scherberich A, Lavalle P, Begin-Colin S, Felder-Flesch D. Mastering bioactive coatings of metal oxide nanoparticles and surfaces through phosphonate dendrons. NEW J CHEM 2020. [DOI: 10.1039/c9nj05565g] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Dendritic phosphonates are versatile coatings of several nanomaterials for health applications ranging from implants to nanoparticles and microbubbles.
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27
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Cadamuro J, Lippi G, von Meyer A, Ibarz M, van Dongen E, Cornes M, Nybo M, Vermeersch P, Grankvist K, Guimaraes JT, Kristensen GBB, de la Salle B, Simundic AM. European survey on preanalytical sample handling - Part 1: How do European laboratories monitor the preanalytical phase? On behalf of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group for the Preanalytical Phase (WG-PRE). Biochem Med (Zagreb) 2019; 29:020704. [PMID: 31223258 PMCID: PMC6559617 DOI: 10.11613/bm.2019.020704] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 03/12/2019] [Indexed: 11/30/2022] Open
Abstract
Introduction Compared to other activities of the testing process, the preanalytical phase is plagued by a lower degree of standardization, which makes it more vulnerable to errors. With the aim of providing guidelines and recommendations, the EFLM WG-PRE issued a survey across European medical laboratories, to gather information on local preanalytical practices. This is part one of two coherent articles, which covers all practices on monitoring preanalytical quality except haemolysis, icterus and lipemia (HIL). Materials and methods An online survey, containing 39 questions dealing with a broad spectrum of preanalytical issues, was disseminated to EFLM member countries. The survey included questions on willingness of laboratories to engage in preanalytical issues. Results Overall, 1405 valid responses were received from 37 countries. 1265 (94%) responders declared to monitor preanalytical errors. Assessment, documentation and further use of this information varied widely among respondents and partially among countries. Many responders were interested in a preanalytical online platform, holding information on various aspects of the preanalytical phase (N = 1177; 87%), in a guideline for measurement and evaluation of preanalytical variables (N = 1235; 92%), and in preanalytical e-learning programs or webinars (N = 1125; 84%). Fewer responders were interested in, or already participating in, preanalytical EQA programs (N = 951; 71%). Conclusion Although substantial heterogeneity was found across European laboratories on preanalytical phase monitoring, the interest in preanalytical issues was high. A large majority of participants indicated an interest in new guidelines regarding preanalytical variables and learning activities. This important data will be used by the WG-PRE for providing recommendations on the most critical issues.
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Affiliation(s)
- Janne Cadamuro
- Department of Laboratory Medicine, Paracelsus Medical University, Salzburg, Austria
| | - Giuseppe Lippi
- Section of Clinical Chemistry, University of Verona, Verona, Italy
| | - Alexander von Meyer
- Institute of Laboratory Medicine, Kliniken Nordoberpfalz AG and Klinikum St. Marien, Weiden and Amberg, Germany
| | - Mercedes Ibarz
- Department of Laboratory Medicine, University Hospital Arnau de Vilanova, IRBLleida, Lleida, Spain
| | - Edmee van Dongen
- Department of Laboratory Medicine, Paracelsus Medical University, Salzburg, Austria.,Section of Clinical Chemistry, University of Verona, Verona, Italy.,Institute of Laboratory Medicine, Kliniken Nordoberpfalz AG and Klinikum St. Marien, Weiden and Amberg, Germany.,Department of Laboratory Medicine, University Hospital Arnau de Vilanova, IRBLleida, Lleida, Spain.,Department of Clinical Chemistry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.,Clinical Chemistry Department, Worcestershire Acute Hospitals NHS Trust, Worcester, UK.,Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark.,Clinical Department of Laboratory Medicine, University Hospitals Leuven, Leuven, Belgium.,Department of Medical Biosciences, Clinical Chemistry, Umea University, Umea, Sweden.,Department of Clinical Pathology, São João Hospital Center, Department of Biomedicine, Faculty of Medicine, and EPI Unit, Institute of Public Health, University of Porto, Porto, Portugal.,Norwegian Quality Improvement of laboratory examinations (Noklus), Bergen, Norway.,UK NEQAS Haematology, West Hertfordshire Hospitals NHS Trust, operating UK NEQAS for Haematology and Transfusion, Watford, UK.,Department of Medical Laboratory Diagnostics, University Hospital Sveti Duh, Zagreb, Croatia
| | | | - Michael Cornes
- Clinical Chemistry Department, Worcestershire Acute Hospitals NHS Trust, Worcester, UK
| | - Mads Nybo
- Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark
| | - Pieter Vermeersch
- Clinical Department of Laboratory Medicine, University Hospitals Leuven, Leuven, Belgium
| | - Kjell Grankvist
- Department of Medical Biosciences, Clinical Chemistry, Umea University, Umea, Sweden
| | - Joao Tiago Guimaraes
- Department of Clinical Pathology, São João Hospital Center, Department of Biomedicine, Faculty of Medicine, and EPI Unit, Institute of Public Health, University of Porto, Porto, Portugal
| | - Gunn B B Kristensen
- Norwegian Quality Improvement of laboratory examinations (Noklus), Bergen, Norway
| | - Barbara de la Salle
- UK NEQAS Haematology, West Hertfordshire Hospitals NHS Trust, operating UK NEQAS for Haematology and Transfusion, Watford, UK
| | - Ana-Maria Simundic
- Department of Medical Laboratory Diagnostics, University Hospital Sveti Duh, Zagreb, Croatia
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28
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Cadamuro J, Ibarz M, Cornes M, Nybo M, Haschke-Becher E, von Meyer A, Lippi G, Simundic AM. Managing inappropriate utilization of laboratory resources. ACTA ACUST UNITED AC 2019; 6:5-13. [PMID: 30096052 DOI: 10.1515/dx-2018-0029] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 06/27/2018] [Indexed: 12/29/2022]
Abstract
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.
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Affiliation(s)
- Janne Cadamuro
- Department of Laboratory Medicine, Paracelsus Medical University, Müllner Hauptstr. 48, 5020 Salzburg, Austria, Phone: +43-57255-57263, Fax: +43-57255-23199
| | - Mercedes Ibarz
- Laboratory Medicine Department, University Hospital Arnau de Vilanova, IRBLleida, Lleida, Spain
| | - Michael Cornes
- Clinical Chemistry Department, Worcester Acute Hospitals NHS Trust, Worcester, UK
| | - Mads Nybo
- Department of Clinical Biochemistry and Pharmacology, Odense University Hospital, Odense, Denmark
| | | | - Alexander von Meyer
- Institute of Laboratory Medicine, Kliniken Nordoberpfalz AG and Klinikum St. Marien, Weiden and Amberg, Germany
| | - Giuseppe Lippi
- Section of Clinical Biochemistry, University of Verona, Verona, Italy
| | - Ana-Maria Simundic
- Department of Medical Laboratory Diagnostics, University Hospital "Sveti Duh", Zagreb, Croatia
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29
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Makris K. Is the Profession of Laboratory Medicine Uniform Across the North Mediterranean Countries? EJIFCC 2018; 29:180-190. [PMID: 30479601 PMCID: PMC6247125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Harmonization of the postgraduate training of both Clinical Scientists and Physicians, in Laboratory Medicine (LM) has been a goal for many years, for the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) and the Union Européenne de Médecins Spécialistes (UEMS), Section of Laboratory Medicine/Medical Biopathology. This was based on the concept of free movement of people within the European Union. Much has been achieved within the respective European organizations in the development of curricula that will harmonize the postgraduate training at least within the European Union (EU). Advances in the area of diagnostics and the need for particular expertise in distinct areas have led to the emergence of laboratory scientists and physicians specialized in hematology (including transfusion medicine), clinical biochemistry, immunology, and microbiology. However, the training and specialization is varying and practice is of laboratory medicine is polyvalent in some countries and single specialties in others countries. Moreover, these advances have led to the involvement of non-medical scientists in the clinical laboratories. However, the training and the roles of Medical Doctors and Clinical Scientists in a Clinical laboratory, differ from country to country. These differences still remain today throughout Europe and even within the EU.
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Affiliation(s)
- Konstantinos Makris
- Corresponding author: Konstantinos Makris Clinical Biochemist Clinical Biochemistry Department KAT General Hospital 2 Nikis street, Kifissia, 14561 Greece Email1: Email2:
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