Medical Centre

In an era where artificial intelligence (AI) is rapidly reshaping the landscape of healthcare diagnostics, our recent BMJ article sheds light on a critical issue: the equity gap in AI healthcare diagnostics. The UK's substantial investment in AI technologies underscores the nation's commitment to enhancing healthcare delivery through innovations. However, this evolution brings to the forefront the need for equity: defined as fair access to medical technologies and unbiased treatment outcomes for all. AI's potential in diagnosing clinical conditions like cancer, diabetes, and Alzheimer’s Disease is promising. Yet, the challenges of data representation, algorithmic bias, and accessibility of AI-driven technologies loom large, threatening to perpetuate existing healthcare disparities. Our article highlights that the quality and inclusivity of data used to train AI tools are often problematic, leading to less representative data and biases in AI models. These biases can advers

During the COVID-19 pandemic, healthcare systems worldwide grappled with unprecedented challenges, particularly in managing vulnerable populations. Among these, older adults with multimorbidity and depression faced heightened risks, underscoring the need for targeted healthcare interventions to improve their health outcomes. Our recent study published in PLOS ONE offers helpful insights into this issue, focusing on unplanned emergency hospital admissions among patients aged 65 and older with multimorbidity and depression in Northwest London during and after the COVID-19 lockdown. The study used retrospective cross-sectional data analysis, leveraging the Discover-NOW database for Northwest London. It included a sample of 20,165 registered patients aged 65+ with depression, analysing data across two periods: during the COVID-19 lockdown (23rd March 2020 to 21st June 2021) and an equivalent-length post-lockdown period (22nd June 2021 to 19th September 2022). Using multivariate logistic r

Our study in published in  BMJ Medicine  in February 2024 examined the effect of defining timeframes for long-term conditions on the prevalence of multimorbidity in England, and on the role played by sociodemographic factors. Using primary care electronic health records from the Clinical Practice Research Datalink Aurum, the study included over 9.7 million adults registered in England as of 1 January 2020, focusing on 212 long-term conditions. Key Findings Varying Prevalence Rates: The prevalence of multimorbidity, defined as the coexistence of two or more long-term conditions, varied widely based on the timeframe used for definition. It ranged from 41% with stricter criteria (requiring three codes within any 12-month period) to a 74% when a single diagnostic code was deemed sufficient. Using conditions marked as active problems resulted in the lowest prevalence rate at 35%. Sociodemographic Influences: The study revealed that younger individuals, certain minority ethnic groups, and th

The National Health Service (NHS) in England requires the ability to maintain adequate staffing levels across all professional groups. A crucial aspect of this challenge is managing sickness absence rates among NHS staff, which not only impacts patient care and operational costs but also plays a pivotal role in workforce retention and overall healthcare efficacy. Our recent paper in the Journal of the Royal Society of Medicine discusses this important challenge for the NHS. Recent data published by NHS Digital indicates a worrying trend: sickness absence rates have been on a steady rise across all NHS staff groups since 2009, with a notable surge during the COVID-19 pandemic. This trend has resulted in absence rates remaining elevated above pre-pandemic levels, signaling a potential crisis in staffing and healthcare delivery. The Dynamics of Sickness Absence Rates Before the pandemic, monthly sickness absence rates typically varied between 4% and 5%, with expected seasonal variations.