elpais.com
Algorithm Reduces Blood Bank Waste, Improves Patient Care
Irene Ayerra's algorithm, LHEMA, optimizes blood bank operations by improving blood component allocation, resulting in less waste and increased medication production; this was implemented in Aragon in 2022 and has proven successful.
- What is the immediate impact of LHEMA on blood bank efficiency and patient care?
- Irene Ayerra, founder of Hemotic, developed LHEMA, an algorithm optimizing blood bank operations. It improves blood component allocation, reducing waste and ensuring sufficient supply for patients. This has already led to increased plasma for medication production at the Aragón Blood and Tissue Bank.
- How does LHEMA address the challenges of blood component perishability and variable demand, and what are the broader implications for healthcare resource management?
- LHEMA addresses the challenge of blood component expiry and fluctuating demand in blood banks. By providing recommendations to healthcare professionals, it maximizes plasma yield for pharmaceutical use and minimizes waste, as evidenced by the Aragón Blood and Tissue Bank's success since 2022. This reduces waste, similar to the 11% of platelet concentrates that expired in Spanish blood banks in 2022, according to a Ministry of Health report.
- What are the potential future applications of Hemotic's algorithm beyond blood banks, and what challenges might the company face in scaling its operations and expanding into new markets?
- Hemotic's algorithm has the potential for broader applications beyond blood banks, such as optimizing healthcare resource allocation and managing waiting lists. Its adaptability to various data-driven decision-making processes suggests further expansion into diverse sectors. The success in a small rural town automating library services demonstrates its versatility.
Cognitive Concepts
Framing Bias
The narrative strongly frames Irene Ayerra and her algorithm, LHEMA, as a success story. The headline implicitly celebrates her awards. The focus remains primarily on her achievements and the positive impact of her innovation, which is understandable given the context of a profile piece, but it could benefit from a more balanced perspective on broader challenges in blood management.
Language Bias
The language used is largely neutral and descriptive. Words like "pioneer" and "innovative" are used, but these are justified given the nature of the algorithm and its impact. There is no evidence of loaded or biased language.
Bias by Omission
The article focuses heavily on Irene Ayerra's achievements and the success of her algorithm. While it mentions the 11% expiry rate of platelets in 2022, it doesn't delve into the reasons behind this wastage beyond the complexity of blood fractionation. Further exploration of systemic issues within blood banks, beyond the solution offered by LHEMA, would provide a more complete picture. Additionally, the article doesn't discuss potential drawbacks or limitations of the algorithm, which could help readers assess its overall impact.
Sustainable Development Goals
The algorithm reduces blood waste in blood banks, leading to more efficient use of blood donations and improved healthcare. This directly contributes to better health outcomes and saves lives by ensuring sufficient blood supplies for transfusions and producing more medicines from plasma.