Older adults often face significant barriers to engaging with and critically evaluating complex, AI-driven systems, leading to potential exclusion and marginalisation. This paper introduces a novel approach leveraging a nostalgic vintage radio platform as a familiar and accessible "bridge" to provoke and facilitate meaningful participatory design discussions about such a system. We present a multidisciplinary design project that utilises the tactile and accessible metaphor of an old-fashioned, table-top wood-cased radio to engage older adults in conversations about the ethical impact and accessibility of contemporary media systems. The platform is used as a discussion toolkit to explore the pros and cons of AI driven recommender systems for this demographic, including issues of personalisation, filter bubbles and data transparency. Our work demonstrates the efficacy of using a familiar, emotionally resonant, tangible interface to enable deeper engagement and critical reflection on the impact of AI generated media content. We detail the design and intended evaluation of this interactive radio to provide valuable insights and methodologies for Human Computer Interfaces (HCI) and media studies researchers aiming to design more inclusive and accessible interactive media systems and foster informed dialogue with older user populations.

Patient-Generated Health Data (PGHD) holds promise for enhancing personalized healthcare, but its diverse origins pose trust challenges for clinicians. This article outlines the collaborative creation of the PGHD Trust Canvas, a tool devised to help clinicians evaluate the trustworthiness of PGHD in their decision-making processes. Developed through an iterative design process and informed by interviews with nine clinicians from various disciplines, the PGHD Trust Canvas draws inspiration from similar existing models like the Business Model Canvas, the Trust Canvas and the Ethics Canvas. It offers a structured approach for clinicians to assess PGHD systematically, aiming to facilitate its appropriate integration into clinical decisions. This initiative represents a significant step toward leveraging PGHD safely and effectively in healthcare.

This article explores the concept of patient-generated health data (PGHD) and its potential to be used in clinical decision-making. PGHD is data created and recorded by patients outside of clinical settings, such as through wearable technology or mobile apps. While PGHD has the potential to provide valuable insights into patients' health and behaviour, little is known about the perspective of clinicians when facing with the decision whether to trust or distrust PGHD. 13 clinicians of various disciplines, including nurses and doctors were recruited using the snowball sampling method. Qualitative methods were used in this study to analyse the results. The results suggest that some aspects of Quality, Provenance and Risk are important to clinicians and that there is a need for guidance and governance to assist clinicians in this decision.

In precision agriculture, having knowledge of pastureland forage biomass and moisture content prior to an ensiling process enables pastoralists to enhance silage production. While traditional trait measurement estimation methods relied on hand-crafted vegetation indices, manual measurements, or even destructive methods, remote sensing technology coupled with state-of-the-art deep learning algorithms can enable estimation using a broader spectrum of data, but generally require large volumes of labelled data, which is lacking in this domain. This work investigates the performance of a range of deep learning algorithms on a small dataset for biomass and moisture estimation that was collected with a compact remote sensing system designed to work in real time. Our results showed that applying transfer learning to Inception ResNet improved minimum mean average percentage error from 45.58% on a basic CNN, to 28.07% on biomass, and from 29.33% to 8.03% on moisture content. From scratch models and models optimised for mobile remote sensing applications (MobileNet) failed to produce the same level of improvement.

Term bindings in archetypes are at a boundary between health information models and health terminology for dual model-based electronic health-care record (EHR) systems. The development of archetypes and the population of archetypes with bound terms is in its infancy. Terminological binding is currently performed “manually” by the teams who create archetypes. This process could be made more efficient, if it was supported by automatic tools. This paper presents a method for evaluating the performance of automatic code search approaches. In order to assess the quality of the automatic search, the authors extracted all the unique bound codes from 1133 archetypes from an archetype repository. These “manually bound ”SNOMED-CT codes were compared against the codes suggested by the authors' automatic search and used for assessing the algorithm's performance in terms of accuracy and category matching. The result of this study shows a sensitivity analysis of a set of parameters relevant to the matching process.

The ability to exchange in a meaningful, secure, and simple fashion relevant healthcare data about patients is seen as vital in the context of efficient and cost-effective shared or team-based care. The electronic healthcare record (EHCR) lies at the heart of this information exchange, and it follows that there is an urgent need to address the ability to share EHCR's or parts of records between carers and across distributed health information systems. This paper presents the Synapses approach to sharing based on a standardized shared record, the Federated Healthcare Record, which is implemented in an open and flexible manner using the Common Object Request Broker Architecture (CORBA). The architecture of the Federated Healthcare Record is based on the architecture proposed by the Technical Committee 251 of the European Committee for Standardization.

This paper describes the approach to information sharing being adopted by Synapses, a pan-European project funded under the EU Health Telematics Programme. The emphasis in Synapses is on developing standards to enable sharing, in whole or in part, of electronic healthcare records between distributed healthcare providers and institutions. The current technological solutions available for data sharing, including federated database systems, gateways, data warehousing, messaging and the web are insufficient on their own to meet the needs of Synapses. Thus in Synapses several different, but complementary technologies are merged in an open, distributed object-oriented environment. Furthermore, in order to safeguard the legal, ethical and clinical integrity of the record, it is essential not only to preserve the meaning of the data being transferred, but also its context and structure. Synapses solves this problem by defining a standard architecture for the federated electronic healthcare record.