BMJ Health & Care Informatics最新文献

The integration of artificial intelligence (AI) into healthcare is progressively becoming pivotal, especially with its potential to enhance patient care and operational workflows. This paper navigates through the complexities and potentials of AI in healthcare, emphasising the necessity of explainability, trustworthiness, usability, transparency and fairness in developing and implementing AI models. It underscores the 'black box' challenge, highlighting the gap between algorithmic outputs and human interpretability, and articulates the pivotal role of explainable AI in enhancing the transparency and accountability of AI applications in healthcare. The discourse extends to ethical considerations, exploring the potential biases and ethical dilemmas that may arise in AI application, with a keen focus on ensuring equitable and ethical AI use across diverse global regions. Furthermore, the paper explores the concept of responsible AI in healthcare, advocating for a balanced approach that leverages AI's capabilities for enhanced healthcare delivery and ensures ethical, transparent and accountable use of technology, particularly in clinical decision-making and patient care.

Background: Health information technology (HIT) is increasingly used to enable health service/system transformation. Most HIT implementations fail to some degree; very few demonstrate sustainable success. No guidelines exist for health service leaders to leverage factors associated with success. The purpose of this paper is to present an evidence-based guideline for leaders to test and leverage in practice.

Methods: This guideline was developed from a literature review and refined by a set of eight interviews with people in senior HIT roles, which were thematically analysed. It was refined in the consultancy work of the first author and confirmed after minor refinements.

Results: Five key actions were identified: relationships, vision, HIT system attributes, constant evaluation and learning culture.

Conclusions: This guideline presents a significant opportunity for health system leaders to systematically check relevant success factors during the implementation process of single projects and regional/national programmes.

Objectives: Digital health inequality, observed as differential utilisation of digital tools between population groups, has not previously been quantified in the National Health Service (NHS). Deployment of universal digital health interventions, including a national smartphone app and online primary care services, allows measurement of digital inequality across a nation. We aimed to measure population factors associated with digital utilisation across 6356 primary care providers serving the population of England.

Methods: We used multivariable regression to test association of population and provider characteristics (including patient demographics, socioeconomic deprivation, disease burden, prescribing burden, geography and healthcare provider resource) with activation of two independent digital services during 2021/2022.

Results: We find a significant adjusted association between increased population deprivation and reduced digital utilisation across both interventions. Multivariable regression coefficients for most deprived quintiles correspond to 4.27 million patients across England where deprivation is associated with non-activation of the NHS App.

Conclusion: Results are concerning for technologically driven widening of healthcare inequalities. Targeted incentive to digital is necessary to prevent digital disparity from becoming health outcomes disparity.

Objectives: To overcome the barriers of interoperability by sharing simulated patient data from different electronic health records systems and presenting them in an intuitive timeline of events.

Methods: The 'Patient Story' software comprising database and blockchain, PS Timeline Windows interface, PS Timeline Web interface and network relays on Azure cloud was customised for Epic and Lorenzo electonic patient record (EPR) systems used at different hospitals, using site-specific adapters.

Results: Each site could view their own clinical documents and view each other's site specific, fully coded test sets of (Care Connect) medications, conditions and allergies, in an aggregated single view.

Discussion: This work has shown that clinical data from different EPR systems can be successfully integrated and visualised on a single timeline, accessible by clinicians and patients.

Conclusion: The Patient Story system combined the timeline visualisation with successful interoperability across healthcare settings, as well giving patients the ability to directly interact with their timeline.