Future healthcare journal最新文献

This article assesses the cyclical failures of NHS data modernisation programmes, and considers that they fail because they proceed from a faulty - excessively paternalistic - governance model. Bias in algorithmic delivery of healthcare, a demonstrated problem with many existing health applications, is another serious risk. To regain trust and move towards better use of data in the NHS, we should democratise the development of these systems, and de-risk operational systems from issues such as automation bias. As a comparison, the essay explores two approaches to trust and bias problems in other contexts: Taiwan's digital democracy, and American Airlines' struggles to overcome automation bias in their pilots.

We challenge the dominant technology-centric narrative around clinical AI. To realise the true potential of the technology, clinicians must be empowered to take a whole-system perspective and assess the suitability of AI-supported tasks for their specific complex clinical setting. Key factors include the AI's capacity to augment human capabilities, evidence of clinical safety beyond general performance metrics and equitable clinical decision-making by the human-AI team. Proactively addressing these issues could pave the way for an accountable clinical buy-in and a trustworthy deployment of the technology.

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.

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).