Harnessing AI in healthcare to address health inequities, enhance accessibility, and improve patient care

Guest blog from Jonathan Hinchliffe and Larry Murphy at St Vincent's Consulting as part of our #PuttingAIIntoAction campaign week 2024.

The National Health Service (NHS) is a huge asset to society in the UK, yet it faces significant challenges that demand innovative solutions. Among the most pressing issues are health inequalities, operational inefficiencies, accessibility and the varying quality of patient care across different communities. As technology advances, AI stands poised to tackle these issues head-on, and immediate action is crucial to harness its full potential.

Addressing Health Inequalities with AI

Health inequalities in the NHS contribute to poor outcomes, particularly for vulnerable populations. Patients discharged from acute care often face challenges in accessing community or mental health services due to resource shortages and poor care coordination, straining hospital resources and perpetuating poor health. Additionally, disparities manifest in access to preventive care, treatment quality, and health literacy, with lower socioeconomic groups and rural communities facing significant barriers to essential services and specialist care.

AI can play a crucial role in addressing these disparities by:

Predictive Analytics for Preventive Care: AI identifies at-risk individuals, enabling targeted interventions and reducing care disparities.

Optimising Resource Allocation: AI predicts provider shortages and deploys resources to underserved areas, improving service access.

Enhancing Care Coordination: AI integrates data across care settings, ensuring continuity and reducing gaps in transitions.

Boosting Health Literacy: AI offers tailored health education, empowering patients to manage their health and make informed decisions.

Promoting Digital Inclusion and Accessibility

Digital inclusion is crucial in ensuring that all patients benefit from AI-driven healthcare innovations. However, many patients face barriers to accessing digital health services, such as telemedicine, due to language barriers, digital poverty or a lack of digital literacy. This can exacerbate health inequalities, as those who are unable to engage with digital services may miss out on timely care.

To address this, AI can be used to create more accessible and user-friendly digital health platforms. For instance, AI can tailor digital interfaces to accommodate different levels of digital literacy, ensuring that patients can easily navigate telemedicine platforms or hospital apps. Additionally, AI-driven tools can assist in providing translation services in real-time, breaking down language barriers and ensuring that non-English speaking patients receive the same level of care as others.

AI can enhance accessibility within healthcare facilities through digital wayfinding tools. These tools assist patients by guiding them through every step of their visit, from finding the best parking spots to choosing the most convenient entrance. They provide door-to-door navigation through complex hospital layouts, ensuring patients reach their appointments punctually. This is especially valuable for patients with disabilities or those unfamiliar with the hospital, as it minimises stress, improves their overall experience and streamlines patient flow.

Moreover, AI can support chronic disease management through digital platforms. For example, several NHS Trusts in the UK have deployed AI-driven virtual health assistants to support patients with chronic conditions such as diabetes. These virtual assistants can provide personalised reminders for medication, offer dietary advice, and monitor patient glucose levels through connected devices. This has the potential to improve chronic disease management, leading to fewer hospital admissions and reducing strain on the health service.

Enhancing Operational Efficiency for Better Patient Care

Operational inefficiencies are another critical issue that impacts both patient care and hospital revenue. Elective procedures and outpatient clinics often fall behind schedule, leading to delayed care and financial losses for Trusts. For instance, the concept of elective recovery—bringing elective care activities back to pre-COVID levels—is currently hindered by staff fatigue, patient backlog, and poor scheduling practices.

AI can enhance booking and scheduling by analysing patterns in patient no-shows, wait times, and clinic delays, enabling real-time adjustments. For example, if a patient arrives early, AI can assess whether to move their appointment up or adjust schedules based on other patient delays, ensuring clinics start and end on time and that procedures proceed as planned. By automating tasks like updating patient notes and managing prescriptions, AI reduces the administrative burden on clinicians, leading to smoother patient journeys and shorter wait times. This increased efficiency not only improves hospital operations but also boosts revenue through better resource utilisation and allows clinicians to spend more time on direct patient care.

Moreover, AI can optimise theatre utilisation through better prediction of patient flows and surgical schedules, improving patient experiences, and maximising hospital capacity for elective surgeries. Additionally, AI can play a transformative role in diagnostics; for example, at several NHS Trusts, AI has improved breast cancer detection in mammograms, leading to earlier diagnoses and better patient outcomes. These advancements demonstrate AI's potential to enhance both operational efficiency and patient care.

Integrating AI Across Care Pathways for Better Outcomes

One of the most promising applications of AI in healthcare is its ability to integrate across care pathways, ensuring continuity of care from acute settings to the community. For example, in an acute setting like an NHS Trust, AI can be integrated into Electronic Patient Records (EPR) systems to ensure that all relevant information is available across different care settings—whether it’s a GP practice, community care facility, or social care service.

This integration can improve the coordination of care, as healthcare professionals across different settings have access to the same patient data. It allows for better planning and execution of care plans, reducing the likelihood of errors or omissions that could negatively impact patient outcomes. Furthermore, AI can automate the creation of discharge summaries, ensuring that they are completed accurately and promptly. This automation is crucial for seamless transitions from hospital to home or community care and improves patient flow across care settings.

AI’s Role in Future-Proofing Healthcare

As the NHS evolves, integrating AI into healthcare is essential for tackling current challenges and preparing for the future. AI enhances operational efficiency, boosts accessibility, and promotes health equity, enabling the NHS to deliver high-quality care to all patients.

To fully realise AI’s full potential, the NHS must shift from traditional IT systems to digitally-enabled workflows. This transformation will optimise patient care and outcomes, making AI a core component of the healthcare ecosystem. By embracing AI, we can create a more equitable, accessible, and effective healthcare system that benefits everyone in society.

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