Data flow in clinical laboratories: could metadata and peridata bridge the gap to new AI-based applications?
artificial intelligence
clinical laboratory
laboratory medicine
metadata
peridata
total testing process
Journal
Clinical chemistry and laboratory medicine
ISSN: 1437-4331
Titre abrégé: Clin Chem Lab Med
Pays: Germany
ID NLM: 9806306
Informations de publication
Date de publication:
07 Oct 2024
07 Oct 2024
Historique:
received:
20
08
2024
accepted:
18
09
2024
medline:
6
10
2024
pubmed:
6
10
2024
entrez:
5
10
2024
Statut:
aheadofprint
Résumé
In the last decades, clinical laboratories have significantly advanced their technological capabilities, through the use of interconnected systems and advanced software. Laboratory Information Systems (LIS), introduced in the 1970s, have transformed into sophisticated information technology (IT) components that integrate with various digital tools, enhancing data retrieval and exchange. However, the current capabilities of LIS are not sufficient to rapidly save the extensive data, generated during the total testing process (TTP), beyond just test results. This opinion paper discusses qualitative types of TTP data, proposing how to divide laboratory-generated information into two categories, namely metadata and peridata. Being both metadata and peridata information derived from the testing process, it is proposed that the first is useful to describe the characteristics of data, while the second is for interpretation of test results. Together with standardizing preanalytical coding, the subdivision of laboratory-generated information into metadata or peridata might enhance ML studies, also by facilitating the adherence of laboratory-derived data to the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles. Finally, integrating metadata and peridata into LIS can improve data usability, support clinical utility, and advance AI model development in healthcare, emphasizing the need for standardized data management practices.
Identifiants
pubmed: 39367764
pii: cclm-2024-0971
doi: 10.1515/cclm-2024-0971
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024 the author(s), published by De Gruyter, Berlin/Boston.
Références
Kammergruber, R, Durner, J. Laboratory information system and necessary improvements in function and programming. LaboratoriumsMedizin 2018;42:277–87. https://doi.org/10.1515/labmed-2018-0038 .
doi: 10.1515/labmed-2018-0038
Padoan, A, Plebani, M. Flowing through laboratory clinical data: the role of artificial intelligence and big data. Clin Chem Lab Med 2022;60:1875–80. https://doi.org/10.1515/cclm-2022-0653 .
doi: 10.1515/cclm-2022-0653
Plebani, M, Laposata, M, Lundberg, GD. The brain-to-brain loop concept for laboratory testing 40 years after its introduction. Am J Clin Pathol 2011;136:829–33. https://doi.org/10.1309/ajcpr28hwhssdnon .
doi: 10.1309/ajcpr28hwhssdnon
Bellini, C, Padoan, A, Carobene, A, Guerranti, R. Moving towards total health data integration including quality management: insights from the SIBioC Working Group “Big Data and Artificial Intelligence” survey. Biochim Clin 2024;48:46–52.
Plebani, M. Exploring the iceberg of errors in laboratory medicine. Clin Chim Acta 2009;404:16–23. https://doi.org/10.1016/j.cca.2009.03.022 .
doi: 10.1016/j.cca.2009.03.022
Cadamuro, J, Simundic, AM. The preanalytical phase – from an instrument-centred to a patient-centred laboratory medicine. Clin Chem Lab Med 2023;61:732–40. https://doi.org/10.1515/cclm-2022-1036 .
doi: 10.1515/cclm-2022-1036
Sepulveda, JL, Young, DS. The ideal laboratory information system. Arch Pathol Lab Med 2013;137:1129–40. https://doi.org/10.5858/arpa.2012-0362-ra .
doi: 10.5858/arpa.2012-0362-ra
Aronson, S, Mahanta, L, Ros, LL, Clark, E, Babb, L, Oates, M, et al.. Information technology support for clinical genetic testing within an academic medical center. J Personalized Med 2016;6:1–9. https://doi.org/10.3390/jpm6010004 .
doi: 10.3390/jpm6010004
Kilkenny, MF, Robinson, KM. Data quality: “garbage in – garbage out”. Health Inf Manag J 2018;47:103–5. https://doi.org/10.1177/1833358318774357 .
doi: 10.1177/1833358318774357
Javaid, M, Haleem, A, Pratap Singh, R, Suman, R, Rab, S. Significance of machine learning in healthcare: features, pillars and applications. Int J Intell Network 2022;3:58–73. https://doi.org/10.1016/j.ijin.2022.05.002 .
doi: 10.1016/j.ijin.2022.05.002
Carobene, A, Milella, F, Famiglini, L, Cabitza, F. How is test laboratory data used and characterised by machine learning models? A systematic review of diagnostic and prognostic models developed for COVID-19 patients using only laboratory data. Clin Chem Lab Med 2022;60:1887–901. https://doi.org/10.1515/cclm-2022-0182 .
doi: 10.1515/cclm-2022-0182
Agnello, L, Vidali, M, Padoan, A, Lucis, R, Mancini, A, Guerranti, R, et al.. Machine learning algorithms in sepsis. Clin Chim Acta 2024;553:117738. https://doi.org/10.1016/j.cca.2023.117738 .
doi: 10.1016/j.cca.2023.117738
Azimi, V, Zaydman, MA. Optimizing equity: working towards fair machine learning algorithms in laboratory medicine. J Appl Lab Med 32023;8:113–28. https://doi.org/10.1093/jalm/jfac085 .
doi: 10.1093/jalm/jfac085
Cabitza, F, Campagner, A, Soares, F, García de Guadiana-Romualdo, L, Challa, F, Sulejmani, A, et al.. The importance of being external methodological insights for the external validation of machine learning models in medicine. Comput Methods Progr Biomed 2021;208:106288. https://doi.org/10.1016/j.cmpb.2021.106288 .
doi: 10.1016/j.cmpb.2021.106288
Riley, J. Understanding metadata what is metadata, and what is it for? A primer publication of the National Information Standards Organization . National Information Standards Organization (NISO); 2017. Available from: https://groups.niso.org/higherlogic/ws/public/download/17446/Understanding%20Metadata.pdf .
ISO/IEC 2382 . Information technology — vocabulary ; 2015. Available from: https://www.iso.org/obp/ui/#iso:std:iso-iec:2382:ed-1:v2:en .
Ghiringhelli, LM, Baldauf, C, Bereau, T, Brockhauser, S, Carbogno, C, Chamanara, J, et al.. Shared metadata for data-centric materials science. Sci Data 2023;10:1–18. https://doi.org/10.1038/s41597-023-02501-8 .
doi: 10.1038/s41597-023-02501-8
Johnson, CS, Badger, ML, Waltermire, DA, Snyder, J, Skorupka, C. Guide to cyber threat information sharing . National Institute of Standards and Technology (NIST); 2016. Available from: https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-150.pdf .
Grassi, PA, Lefkovitz, NB, Nadeau, EM, Galluzzo, RJ, Dinh, AT. Attribute metadata: a proposed schema for evaluating federated attributes . National Institute of Standards and Technology (NIST); 2018.
Blatter, TU, Witte, H, Nakas, CT, Leichtle, AB. Big data in laboratory medicine—FAIR quality for AI? Diagnostics 2022;12:1–13. https://doi.org/10.3390/diagnostics12081923 .
doi: 10.3390/diagnostics12081923
GO FAIR . FAIR principles . https://www.go-fair.org/fair-principles/ [Accessed 23 July 2024].
Wilkinson, MD, Dumontier, M, Aalbersberg, IJ, Appleton, G, Axton, M, Baak, A, et al.. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 2016;3:160018. https://doi.org/10.1038/sdata.2016.18 .
doi: 10.1038/sdata.2016.18
Overmars, LM, Niemantsverdriet, MSA, Groenhof, TKJ, De Groot, MCH, Hulsbergen-Veelken, CAR, Van Solinge, WW, et al.. A wolf in sheep’s clothing: reuse of routinely obtained laboratory data in research. J Med Internet Res 2022;24:e40516. https://doi.org/10.2196/40516 .
doi: 10.2196/40516
Ravi, N, Chaturvedi, P, Huerta, EA, Liu, Z, Chard, R, Scourtas, A, et al.. FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy. Sci Data 2022;9:1–9. https://doi.org/10.1038/s41597-022-01712-9 .
doi: 10.1038/s41597-022-01712-9
Huerta, EA, Blaiszik, B, Brinson, LC, Bouchard, KE, Diaz, D, Doglioni, C, et al.. FAIR for AI: an interdisciplinary and international community building perspective. Sci Data 2023;10:1–10. https://doi.org/10.1038/s41597-023-02298-6 .
doi: 10.1038/s41597-023-02298-6
European Commission . Cost-benefit analysis for FAIR research data: cost of not having FAIR research data ; 2019. [Online]. Available from: https://op.europa.eu/en/publication-detail/-/publication/d375368c-1a0a-11e9-8d04-01aa75ed71a1/language-en .
Allen, B, Dreyer, K, Stibolt, R, Agarwal, S, Coombs, L, Treml, C, et al.. Evaluation and real-world performance monitoring of artificial intelligence models in clinical practice: try it, buy it, check it. J Am Coll Radiol 2021;18:1489–96. https://doi.org/10.1016/j.jacr.2021.08.022 .
doi: 10.1016/j.jacr.2021.08.022
Dreyer, KJ, Allen, B, Wald, C. Real-world surveillance of FDA-cleared artificial intelligence models: rationale and logistics. J Am Coll Radiol 2022;19:274–7. https://doi.org/10.1016/j.jacr.2021.06.025 .
doi: 10.1016/j.jacr.2021.06.025
D’Amico, S, Dall’Olio, D, Sala, C, Dall’Olio, L, Sauta, E, Zampini, M, et al.. Synthetic data generation by artificial intelligence to accelerate research and precision medicine in hematology. JCO Clin Cancer Inf 2023:e2300021. https://doi.org/10.1200/CCI.23.00021 .
doi: 10.1200/CCI.23.00021
The international standard for identifying health measurements, observations, and documents . https://loinc.org/ [Accessed 23 July 2024].
Use SNOMED CT . SNOMED International. https://www.snomed.org/use-snomed-ct [Accessed 23 July 2024].
Park, HA. Why terminology standards matter for data-driven artificial intelligence in healthcare. Ann Lab Med 2024;44:467–71. https://doi.org/10.3343/alm.2004.0105 .
doi: 10.3343/alm.2004.0105
The Observational Health Data Sciences and Informatics (OHDSI) . https://www.ohdsi.org [Accessed 17 September 2024].
Lehmann, S, Guadagni, F, Moore, H, Ashton, G, Barnes, M, Benson, E, et al.. Standard preanalytical coding for biospecimens: review and implementation of the Sample PREanalytical Code (SPREC). Biopreserv Biobanking 2012;10:366–74. https://doi.org/10.1089/bio.2012.0012 .
doi: 10.1089/bio.2012.0012
Cadamuro, J, Carobene, A, Cabitza, F, Debeljak, Z, De Bruyne, S, van Doorn, W, et al.. A comprehensive survey of Artificial Intelligence adoption in European Laboratory Medicine: current utilization and prospects. Clin Chem Lab Med 2024.
Bellini, C, Padoan, A, Carobene, A, Guerranti, R. A survey on Artificial Intelligence and Big Data utilisation in Italian clinical laboratories. Clin Chem Lab Med 2022;60:2017–26. https://doi.org/10.1515/cclm-2022-0680 .
doi: 10.1515/cclm-2022-0680
Badrick, T, Banfi, G, Bietenbeck, A, Cervinski, MA, Loh, TP, Sikaris, K. Machine learning for clinical chemists. Clin Chem 2019;65:1350–6. https://doi.org/10.1373/clinchem.2019.307512 .
doi: 10.1373/clinchem.2019.307512
Ferrari, A, Pennestrì, F, Bonciani, M, Banfi, G, Vainieri, M, Tomaiuolo, R. The role of patient-reported experiences in disclosing genetic prenatal testing: findings from a large-scale survey on pregnant women. Eur J Obstet Gynecol Reprod Biol X 2024;23:100327. https://doi.org/10.1016/j.eurox.2024.100327 .
doi: 10.1016/j.eurox.2024.100327