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Wang Y, Zhang W, Rao Q, Ma Y, Ding X, Zhang X, Li X. Forecasting demands of blood components based on prediction models. Transfus Clin Biol 2024; 31:141-148. [PMID: 38670448 DOI: 10.1016/j.tracli.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 04/07/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND An adequate blood supply is an important guarantee for saving lives and protecting health. In order to manage the blood supply more effectively when the condition of demand and supply are uncertainty, it is very important to forecast the demands of blood resources. MATERIALS AND METHODS SARIMAX model and LSTM model were integrated into the prediction system of blood station. The collection and supply data of blood components was directly imported into the forecasting models to achieve automatic data update and model update. The forecasting daily demands of apheresis platelets, washing red blood cells (RBCs), suspended RBCs and plasma were recorded from January to June 2023 and compared with real data. RESULTS The prediction models had good forecasting performances. In the goodness of fit results of apheresis platelet model, the maximum value of coefficient of determination (R2) could reach 87.6%, and the minimum value of the mean absolute percentage error (MAPE) was only 0.0037. The predicted data of washing RBCs could be basically fitted, and the MAPE was 0.0121. For the prediction of suspended RBCs, the R2 was greater than 66%, and the MAPE could be 0.0372. The plasma model generated very high goodness of fit results, with R2 of over 90% and the lowest MAPE of 0.0394. CONCLUSION The forecasting models, which predicts future demands of different blood components based on historical data, can help managers to overcome the challenges of blood stock control more effectively, thereby reducing blood waste and blood shortages.
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Affiliation(s)
- Yajie Wang
- Department of Blood Transfusion, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Wei Zhang
- Beijing Tongzhou Central Blood Station, Beijing 101100, China
| | - Quan Rao
- Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Yiming Ma
- Department of Blood Transfusion, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Xinyi Ding
- The Information Department, Beijing University Of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing 100124, China
| | - Xiao Zhang
- The Information Department, Beijing University Of Technology, No. 100, Pingleyuan, Chaoyang District, Beijing 100124, China.
| | - Xiaofei Li
- Department of Blood Transfusion, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
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Verma AA, Trbovich P, Mamdani M, Shojania KG. Grand rounds in methodology: key considerations for implementing machine learning solutions in quality improvement initiatives. BMJ Qual Saf 2024; 33:121-131. [PMID: 38050138 DOI: 10.1136/bmjqs-2022-015713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 11/04/2023] [Indexed: 12/06/2023]
Abstract
Machine learning (ML) solutions are increasingly entering healthcare. They are complex, sociotechnical systems that include data inputs, ML models, technical infrastructure and human interactions. They have promise for improving care across a wide range of clinical applications but if poorly implemented, they may disrupt clinical workflows, exacerbate inequities in care and harm patients. Many aspects of ML solutions are similar to other digital technologies, which have well-established approaches to implementation. However, ML applications present distinct implementation challenges, given that their predictions are often complex and difficult to understand, they can be influenced by biases in the data sets used to develop them, and their impacts on human behaviour are poorly understood. This manuscript summarises the current state of knowledge about implementing ML solutions in clinical care and offers practical guidance for implementation. We propose three overarching questions for potential users to consider when deploying ML solutions in clinical care: (1) Is a clinical or operational problem likely to be addressed by an ML solution? (2) How can an ML solution be evaluated to determine its readiness for deployment? (3) How can an ML solution be deployed and maintained optimally? The Quality Improvement community has an essential role to play in ensuring that ML solutions are translated into clinical practice safely, effectively, and ethically.
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Affiliation(s)
- Amol A Verma
- Unity Health Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
- Medicine, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada
| | - Patricia Trbovich
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Centre for Quality Improvement and Patient Safety, Department of Medicine, University of Toronto, Toronto, ON, Canada
- North York General Hospital, Toronto, ON, Canada
| | - Muhammad Mamdani
- Unity Health Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Medicine, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada
| | - Kaveh G Shojania
- Medicine, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada
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Maynard S, Farrington J, Alimam S, Evans H, Li K, Wong WK, Stanworth SJ. Machine learning in transfusion medicine: A scoping review. Transfusion 2024; 64:162-184. [PMID: 37950535 PMCID: PMC11497333 DOI: 10.1111/trf.17582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 11/12/2023]
Affiliation(s)
- Suzanne Maynard
- Medical Sciences Division, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- NHSBT and Oxford University Hospitals NHS Foundation TrustOxfordUK
| | | | - Samah Alimam
- Haematology DepartmentUniversity College London Hospitals NHS Foundation TrustLondonUK
| | - Hayley Evans
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
| | - Kezhi Li
- Institute of Health InformaticsUniversity College LondonLondonUK
| | - Wai Keong Wong
- Director of DigitalCambridge University Hospitals NHS Foundation TrustCambridgeUK
| | - Simon J. Stanworth
- Medical Sciences Division, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- NIHR Blood and Transplant Research Unit in Data Driven Transfusion Practice, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- NHSBT and Oxford University Hospitals NHS Foundation TrustOxfordUK
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Mushtaq AH, Shafqat A, Salah HT, Hashmi SK, Muhsen IN. Machine learning applications and challenges in graft-versus-host disease: a scoping review. Curr Opin Oncol 2023; 35:594-600. [PMID: 37820094 DOI: 10.1097/cco.0000000000000996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
PURPOSE OF REVIEW This review delves into the potential of artificial intelligence (AI), particularly machine learning (ML), in enhancing graft-versus-host disease (GVHD) risk assessment, diagnosis, and personalized treatment. RECENT FINDINGS Recent studies have demonstrated the superiority of ML algorithms over traditional multivariate statistical models in donor selection for allogeneic hematopoietic stem cell transplantation. ML has recently enabled dynamic risk assessment by modeling time-series data, an upgrade from the static, "snapshot" assessment of patients that conventional statistical models and older ML algorithms offer. Regarding diagnosis, a deep learning model, a subset of ML, can accurately identify skin segments affected with chronic GVHD with satisfactory results. ML methods such as Q-learning and deep reinforcement learning have been utilized to develop adaptive treatment strategies (ATS) for the personalized prevention and treatment of acute and chronic GVHD. SUMMARY To capitalize on these promising advancements, there is a need for large-scale, multicenter collaborations to develop generalizable ML models. Furthermore, addressing pertinent issues such as the implementation of stringent ethical guidelines is crucial before the widespread introduction of AI into GVHD care.
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Affiliation(s)
- Ali Hassan Mushtaq
- Department of Internal Medicine, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Areez Shafqat
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Haneen T Salah
- Department of Pathology and Genomic Medicine, Houston Methodist Hospital, Houston, Texas
| | - Shahrukh K Hashmi
- Division of Hematology, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Department of Medicine, Sheikh Shakbout Medical City
- Medical Affairs, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Ibrahim N Muhsen
- Section of Hematology and Oncology, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA
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Li N, Pham T, Cheng C, McElfresh DC, Metcalf RA, Russell WA, Birch R, Yurkovich JT, Montemayor-Garcia C, Lane WJ, Tobian AAR, Roubinian N, Seheult J, Goel R. Blood Demand Forecasting and Supply Management: An Analytical Assessment of Key Studies Utilizing Novel Computational Techniques. Transfus Med Rev 2023; 37:150768. [PMID: 37980192 DOI: 10.1016/j.tmrv.2023.150768] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 08/30/2023] [Accepted: 09/01/2023] [Indexed: 11/20/2023]
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.
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Affiliation(s)
- Na Li
- 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
| | - Tho Pham
- Stanford Blood Center and Department of Pathology, Stanford Health Care, CA, USA
| | - Calvino Cheng
- Department of Pathology and Laboratory Medicine, Dalhousie University; Nova Scotia, Canada
| | - Duncan C McElfresh
- VA Center for Innovation to Implementation & Stanford Health Policy, USA
| | - Ryan A Metcalf
- Department of Pathology University of Utah Health and ARUP Laboratories, Salt Lake City, UT, USA
| | - W Alton Russell
- School of Population and Global Health, McGill University, Montreal, Quebec, Canada
| | | | | | | | - William J Lane
- Department of Pathology, Brigham and Women 's Hospital, Harvard Medical School, Massachusetts, MA, USA
| | - Aaron A R Tobian
- Division of Transfusion Medicine, Department of Pathology, Johns Hopkins University, Baltimore, MD, USA
| | - Nareg Roubinian
- Department of Laboratory Medicine, UCSF, San Francisco, CA, USA; Vitalant Research Institute, San Francisco, CA, USA
| | - Jansen Seheult
- Department of Laboratory Medicine and Pathology, Mayo Clinic, MN, USA
| | - Ruchika Goel
- 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.
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Mirjalili M, Abouee-Mehrizi H, Barty R, Heddle NM, Sarhangian V. A data-driven approach to determine daily platelet order quantities at hospitals. Transfusion 2022; 62:2048-2056. [PMID: 36062955 DOI: 10.1111/trf.17080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/05/2022] [Accepted: 08/05/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Determining the required daily number of platelet units in hospitals is a challenging task due to the high uncertainty in daily usage and short shelf life of platelets. STUDY DESIGN AND METHODS We developed a linear prediction model to guide the daily ordering quantity of platelet units at a hospital that orders the required units from a central supplier. The predictive model relies on historical demand data and other information from the hospital's information system. The ordering strategy is to place an order at the end of each day to bring the platelet inventory to the predicted demand for the next day. Unlike typical prediction models, the quality of the predictions is measured with respect to the resulting inventory costs of wastage and shortage. We used data from two hospitals in Hamilton, Ontario from 2015 to 2016 to train our model and evaluated its performance based on the resulting wastage and shortage rates in 2017. RESULTS In 2017, respectively 1915 and 4305 platelet units were transfused at the two hospitals, with daily average (SD) usage of 5.2 (3.7) and 11.8 (4.4). The expiry (estimated shortage) rates were 8.67% (13.86%), and 2.28% (8.48%) at the two hospitals, respectively. Our baseline model would have reduced the expiry (shortage) rates to 2.54% (4.01%) and 0.05% (0.44%) for the two hospitals, respectively. DISCUSSION Guiding daily ordering decisions for platelets using our proposed model could lead to a significant reduction of wastage and shortage rates at hospitals.
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Affiliation(s)
- Mahdi Mirjalili
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | | | - Rebecca Barty
- McMaster Centre for Transfusion Research, Department of Medicine, McMaster University, Hamilton, Ontario, Canada.,Southwest Region, Ontario Regional Blood Coordinating Network, Hamilton, Ontario, Canada
| | - Nancy M Heddle
- McMaster Centre for Transfusion Research, Department of Medicine, McMaster University, Hamilton, Ontario, Canada.,Centre for Innovation, Canadian Blood Services, Ottawa, Ontario, Canada
| | - Vahid Sarhangian
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
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