Blood Demand Forecasting and Supply Management: An Analytical Assessment of Key Studies Utilizing Novel Computational Techniques

Na Li; Tho Pham; Calvino Cheng; Duncan C McElfresh; Ryan A Metcalf; W Alton Russell; Rebecca Birch; James T Yurkovich; Celina Montemayor-Garcia; William J Lane; Aaron A R Tobian; Nareg Roubinian; Jansen Seheult; Ruchika Goel

doi:10.1016/j.tmrv.2023.150768

Blood Demand Forecasting and Supply Management: An Analytical Assessment of Key Studies Utilizing Novel Computational Techniques

Transfus Med Rev. 2023 Oct;37(4):150768. doi: 10.1016/j.tmrv.2023.150768. Epub 2023 Oct 2.

Authors

Affiliations

¹ Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada; Michael G. DeGroote Centre for Transfusion Research, Department of Medicine, McMaster University, Hamilton, Ontario, Canada; Department of Computing and Software, McMaster University, Hamilton, Ontario, Canada.
² Stanford Blood Center and Department of Pathology, Stanford Health Care, CA, USA.
³ Department of Pathology and Laboratory Medicine, Dalhousie University; Nova Scotia, Canada.
⁴ VA Center for Innovation to Implementation & Stanford Health Policy, USA.
⁵ Department of Pathology University of Utah Health and ARUP Laboratories, Salt Lake City, UT, USA.
⁶ School of Population and Global Health, McGill University, Montreal, Quebec, Canada.
⁷ Westat, Rockville, MD, USA.
⁸ Phenome Health, Seattle, WA, USA.
⁹ Canadian Blood Services, Ottawa, Ontario, Canada.
¹⁰ Department of Pathology, Brigham and Women 's Hospital, Harvard Medical School, Massachusetts, MA, USA.
¹¹ Division of Transfusion Medicine, Department of Pathology, Johns Hopkins University, Baltimore, MD, USA.
¹² Department of Laboratory Medicine, UCSF, San Francisco, CA, USA; Vitalant Research Institute, San Francisco, CA, USA.
¹³ Department of Laboratory Medicine and Pathology, Mayo Clinic, MN, USA.
¹⁴ Division of Transfusion Medicine, Department of Pathology, Johns Hopkins University, Baltimore, MD, USA; Simmons Cancer Institute, at SIU School of Medicine, Springfield, IL, USA; Corporate Medical Affairs, Vitalant, Scottsdale, AZ, USA. Electronic address: [email protected].

PMID: 37980192
DOI: 10.1016/j.tmrv.2023.150768

Abstract

Use of data-driven methodologies in enhancing blood transfusion practices is rising, leveraging big data, machine learning, and optimization techniques to improve demand forecasting and supply chain management. This review used a narrative approach to identify, evaluate, and synthesize key studies that considered novel computational techniques for blood demand forecasting and inventory management through a search of PubMed and Web of Sciences databases for studies published from January 01, 2016, to March 30, 2023. The studies were analyzed for their utilization of various techniques, and their strengths, limitations, and areas for improvement. Seven key studies were identified. The studies focused on different blood components using various computational methods, such as regression, machine learning, hybrid models, and time series models, across different locations and time periods. Key variables used for demand forecasting were largely derived from electronic health record data, including clinical related predictors such as laboratory test results and hospital census by location. Each study offered unique strengths and valuable insights into the use of data-driven methods in blood bank management. Common limitations were unknown generalizability to other healthcare settings or blood components, need for field-specific performance measures, lack of ABO compatibility consideration, and ethical challenges in resource allocation. While data-driven research in blood demand forecasting and management has progressed, limitations persist and further exploration is needed. Understanding these innovative, interdisciplinary methods and their complexities can help refine inventory strategies and address healthcare challenges more effectively, leading to more robust, accurate models to enhance blood management across diverse healthcare scenarios.

Keywords: Blood transfusion; Computational methods; Data sciences; Demand forecasting; Plasma; Platelets; Red blood cells; Supply management.

Publication types

Review

MeSH terms

Blood Banks*
Blood Transfusion*
Forecasting
Hospitals
Humans