Objectives: To understand the use, functionality and interoperability of laboratory information management systems (LIMS) in UK transfusion laboratories.

Background: LIMS are widely used to support safe transfusion practice. LIMS have the potential to reduce the risk of laboratory error using algorithms, flags and alerts that support compliance with best practice guidelines and regulatory standards. Reporting to Serious Hazards of Transfusion (SHOT), the United Kingdom (UK) haemovigilance scheme, has identified cases where the LIMS could have prevented errors but did not. Shared care of patients across different organisations and the development of pathology networks has raised challenges relating to interoperability of IT systems both within, and between, organisations.

Methods And Materials: A survey was distributed to all SHOT-reporting organisations to understand the current state of LIMS in the UK, prevalence of expertise in transfusion IT, and barriers to progress. Survey questions covered LIMS interoperability with other IT systems used in the healthcare setting.

Results: A variety of LIMS and version numbers are in use in transfusion laboratories, LIMS are not always updated due to resource constraints. Respondents identified interoperability and improved functionality as the main requirements for transfusion safety.

Conclusion: A nationally agreed set of minimum standards for transfusion LIMS is required for safe practice. Adequate resources, training and expertise should be provided to support the effective use and timely updates of LIMS. A single LIMS solution should be in place for transfusion laboratories working within a network and interoperability with other systems should be explored to further improve practice.

Download full-text PDF

Source
http://dx.doi.org/10.1111/tme.13010DOI Listing

Publication Analysis

Top Keywords

interoperability systems
12
lims
11
transfusion
9
laboratory management
8
management systems
8
transfusion laboratories
8
systems
5
interoperability
5
shot collaborative
4
collaborative reviewing
4

Similar Publications

The Impact of Artificial Intelligence and Machine Learning in Organ Retrieval and Transplantation: A Comprehensive Review.

Curr Res Transl Med

January 2025

Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom.

This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks.

View Article and Find Full Text PDF

The development and implementation of a digital platform in a fracture liaison service.

Arch Osteoporos

January 2025

HSE North East, St. Brigid's Complex, Ardee, Co Louth, Ireland.

Unlabelled: The fracture liaison service in the study hospital developed and successfully implemented a digital platform to support the identification of patients while concurrently optimizing participation in the National Fracture Liaison Database. This initiative provides additional evidence of the capacity of digital health to support fracture liaison services.

Purpose: Resourced fracture liaison services (FLSs) are accepted internationally as the preeminent means of reducing the risk of future fragility fractures.

View Article and Find Full Text PDF

Open Ecosystem Through Secure Plug and Play Interoperability: An Overview.

J Diabetes Sci Technol

January 2025

Roche Diabetes Care GmbH, Mannheim, Germany.

Background: Interoperability is a critical enabler for integrated Personalized Diabetes Management (iPDM), automated insulin delivery (AID), and the digital transformation of healthcare in general. However, manufacturers still create closed ecosystems (ie, solutions designed to work end-to-end minimizing collaboration with other organizations) with proprietary interfaces because of various interoperability challenges. Therefore, the aim of this article is to provide an overview of how to achieve organizational interoperability in an open ecosystem (ie, solutions designed to integrate different organizations via interoperability standards) for diabetes management.

View Article and Find Full Text PDF

Federated learning (FL) is revolutionizing healthcare by enabling collaborative machine learning across institutions while preserving patient privacy and meeting regulatory standards. This review delves into FL's applications within smart health systems, particularly its integration with IoT devices, wearables, and remote monitoring, which empower real-time, decentralized data processing for predictive analytics and personalized care. It addresses key challenges, including security risks like adversarial attacks, data poisoning, and model inversion.

View Article and Find Full Text PDF
Article Synopsis
  • The use of well-structured ontologies and ontology-aware tools enhances data and analyses to be FAIR (Findable, Accessible, Interoperable, Reusable), supporting effective lexical searches and biologically meaningful annotation grouping.
  • Researchers face challenges in adopting ontologies, primarily due to their complexity and the tendency to create simplified hierarchies that may misuse relationship types, leading to ineffective organization.
  • A suite of validation tools is introduced to help users align their hierarchies with established ontology structures, providing graphical reports and tailored views for various atlases like the HuBMAP Human Reference Atlas and the Human Developmental Cell Atlas.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!