From demand forecasting to inventory ordering decisions for red blood cells through integrating machine learning, statistical modeling, and inventory optimization

Forecasting demands of blood components based on prediction models

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.

Grand rounds in methodology: key considerations for implementing machine learning solutions in quality improvement initiatives

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.

Machine learning applications and challenges in graft-versus-host disease: a scoping review

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.

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

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.

A data-driven approach to determine daily platelet order quantities at hospitals

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.