Future healthcare journal最新文献

This article contributes to the ongoing debate about legal liability and responsibility for patient harm in scenarios where artificial intelligence (AI) is used in healthcare.We note that due to the structure of negligence liability in England and Wales, it is likely that clinicians would be held solely negligent for patient harms arising from software defects, even though AI algorithms will share the decision-making space with clinicians.Drawing on previous research, we argue that the traditional model of negligence liability for clinical malpractice cannot be relied upon to offer justice for clinicians and patients. There is a pressing need for law reform to consider the use of risk pooling, alongside detailed professional guidance for the use of AI in healthcare spaces.

Flexible bronchoscopy is a vital tool that is used for both diagnostic and therapeutic indications on the intensive care unit. During the COVID pandemic, training opportunities to perform such a skill were decreased and novel methods of teaching, including using bronchoscopy simulators, were explored out of necessity. The aim of this quality improvement project was to investigate how training on an intensive care unit prepared trainees for carrying out bronchoscopy, and whether a simulated bronchoscopy session with a high-fidelity simulator could be a useful adjunct to gain experience. Our pilot, although limited in patient numbers, suggested that current teaching methods might not be adequate for building trainee confidence and that a session with a bronchoscopy simulator could serve as a remedy to this with a significant increase in self-reported confidence. Moving forwards in a post-pandemic era, this serves as a lesson learnt from COVID and would benefit from further development in this area.

Objective: The presence of artificial intelligence (AI) in healthcare is a powerful and game-changing force that is completely transforming the industry as a whole. Using sophisticated algorithms and data analytics, AI has unparalleled prospects for improving patient care, streamlining operational efficiency, and fostering innovation across the healthcare ecosystem. This study conducts a comprehensive bibliometric analysis of research on AI in healthcare, utilising the SCOPUS database as the primary data source.

Methods: Preliminary findings from 2013 identified 153 publications on AI and healthcare. Between 2019 and 2023, the number of publications increased exponentially, indicating significant growth and development in the field. The analysis employs various bibliometric indicators to assess research production performance, science mapping techniques, and thematic mapping analysis.

Results: The study reveals insights into research hotspots, thematic focus, and emerging trends in AI and healthcare research. Based on an extensive examination of the Scopus database provides a brief overview and suggests potential avenues for further investigation.

Conclusion: This article provides valuable contributions to understanding the current landscape of AI in healthcare, offering insights for future research directions and informing strategic decision making in the field.

Introduction: Artificial intelligence (AI) has the potential to improve healthcare quality when thoughtfully integrated into clinical practice. Current evaluations of AI solutions tend to focus solely on model performance. There is a critical knowledge gap in the assessment of AI-clinician interactions. We systematically reviewed existing literature to identify interaction traits that can be used to assess the quality of AI-clinician interactions.

Methods: We performed a systematic review of published studies to June 2022 that reported elements of interactions that impacted the relationship between clinicians and AI-enabled clinical decision support systems. Due to study heterogeneity, we conducted a narrative synthesis of the different interaction traits identified from this review. Two study authors categorised the AI-clinician interaction traits based on their shared constructs independently. After the independent categorisation, both authors engaged in a discussion to finalise the categories.

Results: From 34 included studies, we identified 210 interaction traits. The most common interaction traits included usefulness, ease of use, trust, satisfaction, willingness to use and usability. After removing duplicate or redundant traits, 90 unique interaction traits were identified. Unique interaction traits were then classified into seven categories: usability and user experience, system performance, clinician trust and acceptance, impact on patient care, communication, ethical and professional concerns, and clinician engagement and workflow.

Discussion: We identified seven categories of interaction traits between clinicians and AI systems. The proposed categories may serve as a foundation for a framework assessing the quality of AI-clinician interactions.

Several publications have indicated potential benefit from collaboration with industry regarding wider use of anonymised routine NHS healthcare data. However, there is limited guidance regarding exactly how such collaborations between NHS hospitals and industry partners should best be carried out, and specific issues that need to be addressed at an individual project or collaboration level to achieve desired benefit. Specifically, routine health data are complex, not collected in a format optimised for secondary use, and often require interpretation based on clinical understanding of the medical conditions or patients. In order to address these issues, a formal partnership collaboration was established between an NHS organisation (Great Ormond Street Hospital for Children) and a pharmaceutical company (Roche Products Limited), to jointly understand the problems that require solving in order to maximise such use of NHS data to support improved patient outcomes and other patient/NHS benefit in a more sustainable way. We present the learnings from the first 2 years of the 5-year collaboration addressing aspects such as complexities of NHS Electronic Patient Record (EPR), data engineering and use of modern technology to optimise such data. Plus, the development of appropriate technology and data infrastructure within the NHS to support interoperability and prepare the NHS for wider application of artificial intelligence. We also highlight the staff skills and training needed to support such systems in the NHS, governance structures and processes needed to ensure appropriate use of tools and data and how best to co-design with patients, their families, and clinical teams. It is hoped that this review may provide useful information for both healthcare organisations and industry partners working towards the future of optimal use of data and technology for healthcare benefit.

Effective healthcare management for addressing complex organisational challenges is crucial for efficient healthcare delivery. Healthcare management involves organising, coordinating, planning and operationalising healthcare services, as well as leading people to ensure the delivery of effective patient care. Healthcare management applies management principles and practices to various healthcare organisations, such as hospitals, functional departments, clinics, cross-functional departments and public health organisations. Recognising a gap in management training, especially for clinicians having managerial responsibilities, is a call for global professionalisation of healthcare management to equip leaders with essential skills. In many healthcare settings across the globe, healthcare management does not always require professional management qualifications. This article advocates for the need for a structured approach towards professionalising healthcare management globally and especially in the Eastern Mediterranean Region (EMR).

General internal medicine (GIM) has predominantly been perceived as a joint specialty to be completed alongside specialty training in the United Kingdom. The Internal Medicine (IMT) Stage 2 curriculum was released in August 2022;¹ in October 2022 the Round 2 Specialty Training recruitment included applications for the pilot GIM training programme, and in February 2023 NHS England commenced the 3-year programme. The GIM programme aims to provide run-through training for GIM to Certificate of Completion of Training (CTT). This report establishes the initial recruitment demographics of trainees, as well as an analysis of the successful applicants' perspectives on the programme and their motivations for applying.