{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T22:23:23Z","timestamp":1785882203104,"version":"3.56.0"},"reference-count":20,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T00:00:00Z","timestamp":1723075200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Transfusion Medicine"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Artificial intelligence (AI) uses sophisticated algorithms to \u201clearn\u201d from large volumes of data. This could be used to optimise recruitment of blood donors through predictive modelling of future blood supply, based on previous donation and transfusion demand. We sought to assess utilisation of predictive modelling and AI blood establishments (BE) and conducted predictive modelling to illustrate its use. A BE survey of data modelling and AI was disseminated to the International Society of Blood transfusion members. Additional anonymzed data were obtained from Italy, Singapore and the United States (US) to build predictive models for each region, using January 2018 through August 2019 data to determine likelihood of donation within a prescribed number of months. Donations were from March 2020 to June 2021. Ninety ISBT members responded to the survey. Predictive modelling was used by 33 (36.7%) respondents and 12 (13.3%) reported AI use. Forty\u2010four (48.9%) indicated their institutions do not utilise predictive modelling nor AI to predict transfusion demand or optimise donor recruitment. In the predictive modelling case study involving three sites, the most important variable for predicting donor return was number of previous donations for Italy and the US, and donation frequency for Singapore. Donation rates declined in each region during COVID\u201019. Throughout the observation period the predictive model was able to consistently identify those individuals who were most likely to return to donate blood. The majority of BE do not use predictive modelling and AI. The effectiveness of predictive model in determining likelihood of donor return was validated; implementation of this method could prove useful for BE operations.<\/jats:p>","DOI":"10.1111\/tme.13071","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T08:10:49Z","timestamp":1723104649000},"page":"333-343","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["The use of predictive modelling to determine the likelihood of donor return during the <scp>COVID<\/scp>\u201019 pandemic"],"prefix":"10.1111","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1175-9579","authenticated-orcid":false,"given":"Richard R.","family":"Gammon","sequence":"first","affiliation":[{"name":"OneBlood, Scientific, Medical, Technical Direction  Orlando Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3804-5434","authenticated-orcid":false,"given":"Salwa","family":"Hindawi","sequence":"additional","affiliation":[{"name":"Department of Hematology King Abdulaziz University  Jeddah Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arwa Z.","family":"Al\u2010Riyami","sequence":"additional","affiliation":[{"name":"Department of Hematology Sultan Qaboos University Hospital  Muscat Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ai Leen","family":"Ang","sequence":"additional","affiliation":[{"name":"Blood Services Group, Health Sciences Authority  Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renee","family":"Bazin","sequence":"additional","affiliation":[{"name":"H\u00e9ma\u2010Qu\u00e9bec, Medical Affairs and Innovation  Qu\u00e9bec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evan M.","family":"Bloch","sequence":"additional","affiliation":[{"name":"Department of Pathology, Transfusion Medicine Division Johns Hopkins University School of Medicine  Baltimore Maryland USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kelley","family":"Counts","sequence":"additional","affiliation":[{"name":"OneBlood, Information Technology Administration  Saint Petersburg Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincenzo","family":"de Angelis","sequence":"additional","affiliation":[{"name":"National Blood Centre Italian National Institute of Health  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruchika","family":"Goel","sequence":"additional","affiliation":[{"name":"Department of Pathology, Transfusion Medicine Division Johns Hopkins University School of Medicine  Baltimore Maryland USA"},{"name":"Department of Biology University of Illinois  Springfield Illinois 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Italian National Institute of Health  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlo","family":"Mengoli","sequence":"additional","affiliation":[{"name":"National Blood Centre Italian National Institute of Health  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9141-8215","authenticated-orcid":false,"given":"Adaeze","family":"Oreh","sequence":"additional","affiliation":[{"name":"National Planning Commission  Abuja Nigeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9681-3898","authenticated-orcid":false,"given":"Gopal Kumar","family":"Patidar","sequence":"additional","affiliation":[{"name":"Department of Transfusion Medicine All India Institute of Medical Sciences  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naomi","family":"Rahimi\u2010Levene","sequence":"additional","affiliation":[{"name":"Blood Bank, 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