Before we begin
In This Issue
- International survey on current home transfusion practices
- The Possibilities of Machine Learning Applications in Home Transfusion Services
- Save the Date: Bootcamp for Nurses
- Upcoming Events
- Register Now: U of T Rounds
- Register Now: ORBCoN Fall Symposium
International survey on current home transfusion practices
Wen Lu, MD1, Katie Hands, MD2,Andrew Shih MD, FRCPC, DRCPSC, HRM MSc3-5,Simon Stanworth MA, FRCP (Paeds, UK), PhD, FRCPath6-8
- Center for Regenerative Biotherapeutics, Division of Transfusion Medicine, Department of Laboratory Medicine and Pathology
- Scottish National Blood Transfusion Service
- University of British Columbia
- Transfusion Medicine, Vancouver Coastal Health Authority
- British Columbia Provincial Blood Coordinating Office
- National Health Service Blood & Transplant
- Oxford University Hospitals NHS Trust
- University of Oxford
Take home message: We are conducting an international survey on home transfusion programs to determine how many exist and how they function; to help the BEST Collaborative develop practical frameworks for those that wish to develop such programs.
Medical care, including transfusion, predominantly occurs in the inpatient and ambulatory healthcare settings. Hospital at home programs have been shown to have better outcomes with regard to improved quality of life, decreased readmission, and reduced mortality. While such programs typically have not included transfusion support, home transfusion programs have been implemented sporadically around the world.
Due to the COVID-19 pandemic, there has been renewed interest and demand for home transfusion which has led to further implementation of home transfusion programs. Therefore, there is a need to understand the current extent to which home transfusion has become available in different countries. Additionally, there is an opportunity to understand how experienced sites have made home transfusion feasible and safe for patients and potentially share what has been learned.
The Biomedical Excellence for Safer Transfusion (BEST) Collaborative is working on three projects with the overarching aim to characterize the demand for and practice of home transfusion. This international survey is part of this triad of home transfusion projects. The goals of this survey project are as follows:
- Determine how “common” the practice of home transfusion is internationally in terms of a) proportion of transfusion services and b) proportion of countries surveyed that provide transfusion at home.
- Identify the policies, processes, and workflows used by home transfusion programs to minimize the potential risks to patients receiving transfusions at home.
- Collect data to help develop a practical framework for sites wanting to establish new home transfusion programs in the form of a BEST home transfusion guidance.
Hospitals and healthcare institutions around the world will be invited to participate in this survey through international and national transfusion medicine organizations and associations. One of the organizations that the study team has had the honor of collaborating with is the Canadian Society for Transfusion Medicine (CSTM). CSTM has helped make the survey available in French in addition to English and will be distributing the survey when it becomes available at the end of 2023.
We look forward to your participation. If you have any comments or questions about the home transfusion survey study, please contact Wen Lu (firstname.lastname@example.org).
Save the Date
2023 TM Boot Camp for Nurses
The Possibilities of Machine Learning Applications in Home Transfusion Services
Na Li, PhD
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
Home transfusion services, first introduced in 1986, has had a slow global uptake. The COVID-19 pandemic has brought the importance of innovative, patient-centered care into sharp focus, leading to a revitalized emphasis on expanding home transfusion. Initial worries surrounding patient safety and cost-effectiveness acted as stumbling blocks to widespread implementation, but recent evidence has started to allay such fears.
Recent research, with findings from three major studies, has put to rest many of these fears. The studies show that the rate of adverse events connected to home transfusions of red blood cells and platelets is low, between 0.2% and 2.7%. These promising findings have done much to alleviate safety concerns.
The patient selection process for home transfusion services is far from straightforward. There is no universal suitability – careful analysis of various patient and clinical factors is vital to make informed decisions. Moreover, the substantial cost implications cannot be ignored. The one-to-one care inherent to home transfusion services demands substantial resources, requiring strategic planning for optimal usage.
In this burgeoning era of technology, artificial intelligence (AI) can play a pivotal role in advancing home transfusion services. Leveraging machine learning and electronic health records (EHRs) can make the system more efficient and data-driven. AI technology can sift through patient data, anticipate patient needs, and facilitate strategic resource planning.
Through analyzing data variables such as patient age, medical history, transfusion frequency, and past transfusion reactions, these models can predict the timing and volume of the next transfusion, as well as the likelihood of severe reactions. Consequently, they can help pinpoint which patients would be best suited to home transfusion services by discerning patterns in vast troves of healthcare data.
Furthermore, these models could streamline resource management, including staff numbers and appointment scheduling, thereby optimizing funding and human resources. They could also enhance the safety of home transfusions by enabling real-time remote patient monitoring.
The use of machine learning methods and technologies may result in more personalized care, with services adjusted to each patient’s specific needs. Instead of generic treatment plans, each patient gets a tailored transfusion schedule and care plan. This individualized care could maximize the benefits of transfusion while minimizing potential complications.
In summary, the fusion of technology and transfusion services provides the potential of turning home into the new frontier of effective patient care. As we move into the future, we can expect even more sophisticated and beneficial applications of these technologies, making transfusion services more personalized, accessible, safe, and convenient.