Month: February 2024

Bridging the Equity Gap in AI Healthcare Diagnostics

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 adversely affect diagnostic accuracy and treatment outcomes, particularly for people from ethnic minority groups and women, who are often under-represented in medical research.

To bridge this equity gap, we advocate for a multi-dimensional systems approach rooted in strong ethical foundations, as outlined by the World Health Organization. This includes ensuring diversity in data collection, adopting unbiased algorithms, and continually monitoring and adjusting AI tools post-deployment. We also suggest establishing digital healthcare testbeds for systematic evaluation of AI algorithms and promoting community engagement through participatory design to tailor AI tools to diverse health needs.

A notable innovation would be the creation of a Health Equity Advisory and Algorithmic Stewardship Committee, spearheaded by national health authorities. This committee would set and oversee compliance with ethical and equity guidelines, ensuring AI tools are developed and implemented conscientiously to manage bias and promote transparency.

The advancement of AI in healthcare diagnostics holds immense potential for improving patient outcomes and healthcare delivery. However, realising this potential requires a concerted effort to address and mitigate biases, ensuring that AI tools are equitable and representative of the diverse populations they serve. As we move forward, prioritising rigorous data assessment, active community engagement, and robust regulatory oversight will be key to reducing health inequalities and fostering a more equitable healthcare landscape through the use of AI in healthcare diagnostics.

Understanding the Impact of COVID-19 on Emergency Hospital Admissions in Older Adults with Multimorbidity and Depression

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 regression, we examined the impact of sociodemographic and multimorbidity-related characteristics on the likelihood of at least one emergency hospital admission during each period.

Key Findings:

– Men had a higher risk of emergency hospitalisation compared to women in both periods, with a noticeable increase post-lockdown.

– The risk of hospitalisation significantly increased with age, higher levels of deprivation, and a greater number of comorbidities across both periods.

– Asian and Black ethnicities showed a statistically significant protective effect compared to White patients during the post-lockdown period only.

The study’s conclusions highlight the need for proactive case reviews by multidisciplinary teams, especially for men with multimorbidity and depression, patients with a higher number of comorbidities, and those experiencing greater deprivation. The findings underscore the importance of understanding the specific healthcare needs of vulnerable populations during health crises like the COVID-19 pandemic to prevent unplanned admissions, improve health outcomes and reduce pressures on health systems.

This research not only contributes to the body of knowledge on healthcare use during the COVID-19 pandemic but also provides valuable insights for healthcare providers, policymakers, and researchers on the care of older adults with multimorbidity and depression. The findings emphasise the importance of tailored healthcare strategies to address the complex health needs of these patients, thereby ensuring that healthcare systems are better prepared for future public health emergencies.

Exploring the Impact of Diagnostic Timeframes on Multimorbidity Prevalence in England

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 those living in areas of lower socioeconomic deprivation were more likely to be reclassified as not multimorbid under timeframes that required more than one diagnostic code. This suggests that these groups are disproportionately affected by the criteria used to define long-term conditions.

Implications for Healthcare Policy and Research: The substantial variation in multimorbidity prevalence underscores the challenges in directly comparing estimates of multimorbidity between studies. It highlights the need for clear rationales behind the choice of timeframe and suggests a potential bias introduced by definitions requiring multiple codes. We recommended that researchers provide their reasoning for the timeframe choice and consider sensitivity analyses to explore the impact on different patient groups.

Addressing Multimorbidity in Healthcare

The findings emphasize the complexity of measuring multimorbidity and the influence of methodological decisions on prevalence estimates. This has important implications for healthcare policy, practice, and research; stressing the importance of adopting a nuanced approach to understanding and addressing the needs of people with multiple health conditions. It calls for a balance between the granularity of condition definitions and the practicality of healthcare delivery, ensuring that healthcare systems can adequately respond to the nuanced needs of its diverse patient population.


The study serves as a critical reminder of the dynamic nature of health conditions and the need for healthcare systems to adapt their approaches to effectively manage multimorbidity. It opens avenues for further research into optimising care for individuals with multiple long-term conditions, ultimately aiming to enhance clinical outcomes, patient experience quality of life, and healthcare efficiency.

Tackling Sickness Absence in the NHS: The Importance of Staff Well-being on Healthcare Delivery

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. However, the pandemic era saw these rates spike to around 6%, and even after the lifting of most COVID-19 restrictions, they have hovered between 5% and 6%. In comparison, the general UK workforce has exhibited more stable sickness absence rates, with NHS employees displaying approximately double the absence rates of their counterparts in other sectors. This disparity underscores the unique pressures faced by NHS staff, including high-stress environments and demanding physical work conditions.

Mental Health at the Forefront

A significant finding from the NHS England data is the high prevalence of mental ill health, particularly anxiety and depression, as a leading cause of sickness absence among NHS staff. This contrasts with the broader employment landscape, where other illnesses predominate. The data suggests that NHS staff are substantially more likely to take leave for mental health reasons, a situation likely exacerbated by the demanding conditions of NHS work environments.

Variations and Implications for Policy

Sickness absence rates vary across different professional groups within the NHS, with doctors generally showing lower rates than other groups such as nursing, ambulance, and allied health professionals. This variance highlights the need for a nuanced approach to addressing sickness absence, considering factors such as role flexibility, work conditions, and the potential for presenteeism.

Addressing these issues requires more than reactive measures; it demands a proactive strategy that includes improving access to occupational health services, mental health resources, and implementing systemic changes to address the root causes of high sickness absence rates. The NHS workforce plan looks to the national Growing Occupational Health and Wellbeing Strategy for solutions, but there is a clear need for more comprehensive, data-driven approaches that tackle the underlying factors contributing to workforce strain.


Ultimately, understanding and mitigating the reasons behind elevated sickness absence rates – particularly those related to mental health and varying across professional groups – will be crucial for closing the gap between the NHS and the broader UK workforce. This effort will not only enhance workforce well-being but also ensure the sustainability of high-quality healthcare delivery within the NHS.

How can we make a success of Pharmacy First?

Pharmacies in England to begin treating patients for seven common conditions. How can we work successfully across the health and care system to make a success of Pharmacy First?

1. The Pharmacy First scheme aims to provide convenient access to healthcare through community pharmacies. Patients with minor ailments or common conditions can seek advice and treatment directly from their local pharmacy instead of visiting a general practice, urgent care centre or emergency department. The conditions covered by the scheme may vary depending on local funding arrangements and participation of pharmacies.

2, A potential problem with Pharmacy First is pharmacists misdiagnosing a patient’s condition. It may also lead to delays in patients seeing doctors when medical assessment is needed. To mitigate these risks, appropriate safeguards and referral pathways should be established, ensuring timely medical assessment when necessary. The scheme will also increase the workload of pharmacies, thereby reducing the time available for other areas of work.

3. To ensure the successful implementation of Pharmacy First, it is essential to develop strong partnerships between key partners in the scheme such as pharmacies, general practices, and integrated care boards. Good communication to share information, updates about the scheme and best practice among all organisations involved is also needed; as is ensuring clear roles and responsibilities for all partners in the scheme.

4. The use of guidelines and protocols that outline the specific tasks, workflows, and processes involved in the scheme will ensure that all partners are aware of their responsibilities. This will keep partners well-informed about their responsibilities and help maintain consistent standards. Comprehensive training and educational resources for community pharmacists and other pharmacy staff are also needed, including continuous professional development and regular audits of clinical practice.

5. The NHS needs to integrate IT systems between pharmacies and general practices to facilitate efficient and accurate transfer of patient information, and to ensure good continuity of care. Additionally, the use of digital technologies and telehealth solutions should be explored to enhance follow-up and patient monitoring when required.

5. As Pharmacy First is relatively new, robust performance monitoring and evaluation are needed to assess its costs, clinical effectiveness, effects on other parts of the NHS and impact on patient satisfaction. This requires the development of key performance indicators to measure the scheme’s outcomes in these areas, enabling evidence-based decision-making and continuous quality improvement.

6. Improving public awareness and engagement is crucial. Implementing media campaigns to inform the public about the scheme’s availability and benefits will help drive its adoption. Furthermore, proactive engagement with patients, community groups, and other stakeholders, particularly those from underserved groups, will ensure inclusivity and provide valuable feedback for ongoing improvement of the scheme.


1. Clinical pharmacists in primary care: a safe solution to the workforce crisis?

2. Impact of integrating pharmacists into primary care teams on health systems indicators: a systematic review.