Search

District nursing services are relentless in adapting to change and continuously seek different methods to improve the care they offer to service users. One of the relatively new changes where adaptation has been necessary is the use of digital devices to record assessments and work in real time. As documented in The NHS Long Term Plan (NHS) (2019), the NHS is being required to grow into the new digital age and, subsequently, district nurses (DNs) have needed to embrace the use of a paper-lite system. Although beneficial overall, the use of technology is a threat to the person-centred nursing approach used in assessment, which is the highest fundamental value in nursing. This article will seek to conceptualise the importance of a person-centred initial assessment with a service user who has either a long-term and/or a lifelong condition whilst considering the use of digital devices, benefits, and drawbacks of their use. It will emphasise the role of the DN, encompassing the multidisciplinary services throughout, whilst also considering any recommendations for further development. [ABSTRACT FROM AUTHOR]

Objectives: This research aims to present and assess the Primary Functions of autoinjectors introduced in ISO 11608-1:2022. Investigate the risks in current autoinjector technology, identify and assess risks and benefits associated with Artificial Intelligence (AI) powered autoinjectors, and propose a framework for mitigating these risks. ISO 11608-1:2022 is a standard that specifies requirements and test methods for needle-based injection systems intended to deliver drugs, focusing on design and function to ensure patient safety and product effectiveness. 'KZH' is an FDA product code used to classify autoinjectors, for regulatory purposes, ensuring they meet defined safety and efficacy standards before being marketed. Method: A comprehensive analysis of autoinjectors problems is conducted using data from the United States Food and Drug Administration (FDA) database. This database records medical device reporting events, including those related to autoinjectors, reported by various sources. The analysis focuses on events associated with the product code KZH, covering data from January 1, 2008, to September 30, 2023. This research employs statistical frequency analysis and incorporates pertinent the FDA, United Kingdom, European Commission regulations, and ISO standards. Results: 500 medical device reporting events are assessed for autoinjectors under the KZH code. Ultimately, 188 of these events are confirmed to be associated with autoinjectors, all 500 medical devices were seen to lack AI capabilities. An analysis of these events for traditional mechanical autoinjectors revealed a predominant occurrence of malfunctions (72%) and injuries (26%) among event types. Device problems, such as breakage, defects, jams, and others, accounted for 45% of incidents, while 10% are attributed to patient problems, particularly missed and underdoses. Conclusion: Traditional autoinjectors are designed to assist patients in medication administration, underscoring the need for quality control, reliability, and design enhancements. AI autoinjectors, sharing this goal, bring additional cybersecurity and software risks, requiring a comprehensive risk management framework that includes standards, tools, training, and ongoing monitoring. The integration of AI promises to improve functionality, enable real-time monitoring, and facilitate remote clinical trials, timely interventions, and tailored medical treatments. [ABSTRACT FROM AUTHOR]