The use of predictive modelling to determine the likelihood of donor return during the COVID ‐19 pandemic

Richard R. Gammon · Salwa Hindawi · Arwa Z. Al‐Riyami · Ai Leen Ang · Renée Bazin · Evan M. Bloch +17 more

4 citations0/0 files storedTransfusion Medicine

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Blood donation and transfusion practices Blood transfusion and management Organ Donation and Transplantation

Abstract

Artificial intelligence (AI) uses sophisticated algorithms to "learn" 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-four (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-19. 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.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 4 citations exist upstream.

References

2013Challenges in the management of the blood supply 2020Impact of COVID‐19 on blood centres in Zhejiang province China 2020The impact of COVID-19 pandemic on blood supplies and transfusion services in Eastern Mediterranean Region 2021Impact of the COVID‐19 pandemic on blood supply and demand in the WHO African Region 2020The impact of the SARS‐CoV‐2 outbreak on the safety and availability of blood transfusions in Italy 2014A generic modelling framework to evaluate network blood management policies: The Canadian Blood Services experience 2020Blood supply management in times of SARS‐CoV‐2 pandemic – challenges, strategies adopted, and the lessons learned from the experience of a hospital‐based blood centre 2016Blood shortages and donation in China

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.