Publicação
Personalizing total quality management strategies for transfusion services: integrating artificial intelligence, Big Data, and the SoHO framework
| datacite.subject.sdg | 03:Saúde de Qualidade | |
| dc.contributor.author | Pereira, Paulo | |
| dc.date.accessioned | 2026-01-06T10:11:59Z | |
| dc.date.available | 2026-01-06T10:11:59Z | |
| dc.date.issued | 2026-02 | |
| dc.description.abstract | Over the past three decades, Total Quality Management (TQM) has evolved from a management philosophy rooted in industrial engineering to a foundational paradigm in healthcare and transfusion medicine. Initially designed to optimize production consistency, reduce waste, and enhance customer satisfaction, TQM has been progressively adapted to the complex, high-stakes environment of blood establishments and transfusion services, where patient and donor safety are inseparable from process quality and regulatory compliance. The implementation of TQM in transfusion medicine has traditionally relied on statistical process control (SPC), process capability assessment, and continuous improvement cycles, ensuring that blood components meet specifications defined by the European Directorate for the Quality of Medicines & HealthCare (EDQM) and national competent authorities. These methods, rooted in classical quality engineering, have proven effective for standardization but are now being challenged by the growing complexity of data generated across the blood chain - from donor screening and laboratory testing to storage and clinical use. | eng |
| dc.identifier.citation | Pereira P. Personalizing total quality management strategies for transfusion services: integrating artificial intelligence, Big Data, and the SoHO framework. Transfus Apher Sci. 2026;65(1):104303. | |
| dc.identifier.doi | 10.1016/j.transci.2025.104303 | |
| dc.identifier.issn | 1473-0502 | |
| dc.identifier.uri | http://hdl.handle.net/10400.21/22436 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Elsevier BV | |
| dc.relation.hasversion | https://www.sciencedirect.com/science/article/pii/S1473050225002460 | |
| dc.relation.ispartof | Transfusion and Apheresis Science | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Healthcare management | |
| dc.subject | Total quality management | |
| dc.subject | Transfusion service | |
| dc.subject | Artificial Intelligence | |
| dc.subject | Big data | |
| dc.subject | SoHO framework | |
| dc.title | Personalizing total quality management strategies for transfusion services: integrating artificial intelligence, Big Data, and the SoHO framework | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 1 | |
| oaire.citation.startPage | 104303 | |
| oaire.citation.title | Transfusion and Apheresis Science | |
| oaire.citation.volume | 65 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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