One example from the United Kingdom comes from the University College London (UCL) and King\u2019s College London. They created an AI model called Foresight. This model was trained using data from 57 million people in the NHS England Secure Data Environment (SDE). Although this data is from the UK, it teaches lessons for the U.S. The U.S. has diverse populations and different health needs that AI must address.<\/p>\n
Foresight uses common health information like hospital visits and Covid-19 vaccination rates. It predicts things such as hospital stays, new illnesses, or problems like heart attacks. The data covers the whole population, including rare diseases and groups often left out of healthcare studies.<\/p>\n
Dr. Chris Tomlinson from UCL said AI works only as well as the data it gets. He emphasized that having varied patient data is very important. For U.S. medical offices, this means AI systems should use data that represents many races, ethnic groups, and income levels. This helps avoid unfair results and allows fair care for everyone.<\/p>\n
Big data sets help make general AI models. The next step is to add more detailed clinical data. This includes doctor notes, lab test results, imaging reports, and patient histories. Professor Richard Dobson, who worked on Foresight, said this would help AI understand health better and make more accurate predictions. It moves from general ideas to care tailored to each person.<\/p>\n
For U.S. medical offices, adding detailed clinical data to AI can change preventive care. Models could find signs of disease early. For example, linking lab tests over time and imaging with patient information could spot at-risk patients sooner and more reliably.<\/p>\n
There are challenges in using detailed data. These include protecting patient privacy, making different data systems work together, and keeping data accurate. The NHS Secure Data Environment is a good example of how to handle these issues safely. U.S. health systems will need similar secure setups.<\/p>\n
Healthcare in the U.S. faces special problems. The system is split between many payers and has varied care quality. Also, minority and poor communities face health gaps. AI trained on wide and varied data can help close these gaps.<\/p>\n
Using data from sources like Medicaid, Medicare, private insurers, and community health centers can build strong training data for AI. This would help AI predict patient outcomes, measure risks, and offer personalized care plans for individuals and communities.<\/p>\n
Including patients and public groups in review and planning, like in the U.K., improves trust and openness. U.S. healthcare workers should involve patient advocates and community leaders when using AI for prevention.<\/p>\n
Preventive healthcare means catching problems early before they get worse. AI models like Foresight look at past patient records to predict hospital visits and health events. These predictions help doctors find patients who need more care and personalized plans.<\/p>\n
Dr. Vin Diwakar said AI advances and secure data storage help move healthcare from reacting to problems to preventing them. This approach can reduce emergency visits and hospital stays, improve people\u2019s lives, and use resources better.<\/p>\n
In the U.S., using AI well could lead to earlier care for diseases like diabetes, high blood pressure, and heart failure. AI can help doctors know who needs follow-ups and advice on healthy living. This supports better health results and meets value-based care goals.<\/p>\n
Besides predictions, AI can make healthcare work flows better, especially in front desk and administrative jobs. Automated phone systems and AI answering services help handle patient calls more easily.<\/p>\n
Front desk automation helps preventive care by:<\/p>\n
Healthcare administrators who invest in AI for work flow automation can save money and improve how patients stay involved. As healthcare focuses more on population health, good patient communication with AI will be very important.<\/p>\n
Using AI for preventive care means linking it with current clinical and administrative systems. Electronic health records (EHRs) must work well with AI for smooth data sharing and real-time help in decisions.<\/p>\n
IT managers in medical facilities must ensure AI follows healthcare rules like HIPAA. AI systems should work in safe environments like the NHS Secure Data Environment. Cloud services like AWS provide the computing power but need careful control to keep data safe.<\/p>\n
Successfully using AI needs teamwork between clinical, admin, and IT staff to:<\/p>\n
Working together this way will help AI support preventive care better.<\/p>\n
Current AI models like Foresight focus on pandemic-related health issues like Covid-19. Future versions will include more types of data such as doctor notes, lab tests, images, and full patient histories.<\/p>\n
In the U.S., AI could soon help with precise preventive care for many conditions. These include heart disease, cancer risk, and mental health. AI predictions can guide how resources are used in community clinics or special medical offices.<\/p>\n
The drug industry also uses AI to speed up developing medicines and testing. They combine complex biological data with computing power. Healthcare providers can do something similar. AI can help plan care better, test new preventive treatments, and find what works best sooner.<\/p>\n
Issues like sharing data, making sure data formats match, and protecting patient privacy are still challenges in U.S. healthcare. But work between researchers, health systems, and tech companies shows progress is being made.<\/p>\n
By learning about these new AI uses in preventive care, and building the right systems and workflows, U.S. medical practices can improve patient health, reduce health differences, and work more smoothly in today\u2019s data-focused world.<\/p>\n The significance lies in the scale and diversity, enabling the AI model to learn from the entire population of England, including minority groups and rare diseases. This helps create accurate, inclusive predictions for a wide range of health outcomes, enhancing the potential to improve patient care and address healthcare inequalities.<\/p>\n<\/p><\/div>\n<\/details>\n Foresight is a generative AI model that predicts future health events by analyzing previous medical events. It works similarly to language models like ChatGPT but instead predicts medical outcomes such as hospitalisation or new diagnoses based on historical NHS data, allowing for early intervention opportunities.<\/p>\n<\/p><\/div>\n<\/details>\n The model is trained on routinely collected, de-identified NHS data like hospital admissions and vaccination rates. Privacy is maintained by using the NHS England Secure Data Environment (SDE), where data remains under strict NHS control and AI computations occur within a secure platform, preventing unauthorized access to personal information.<\/p>\n<\/p><\/div>\n<\/details>\n Including minority groups and rare diseases ensures the AI model reflects the full demographic and medical diversity of the population. This improves the model\u2019s ability to generate accurate predictions for all patients and avoids bias which can exclude groups from benefiting from AI-driven healthcare improvements.<\/p>\n<\/p><\/div>\n<\/details>\n The NHS SDE provides a controlled and secure platform enabling researchers to access and process de-identified health data at a national scale. It ensures patient data privacy, keeps all data and AI models under NHS oversight, and supports safe, compliant use of sensitive healthcare data for AI development.<\/p>\n<\/p><\/div>\n<\/details>\n By accurately predicting probable future health events, Foresight enables early identification of high-risk patient groups, allowing interventions before conditions worsen. This shifts healthcare towards prevention and reduces hospital admissions, improving patient outcomes and resource allocation within the NHS.<\/p>\n<\/p><\/div>\n<\/details>\n Challenges include ensuring data privacy, managing computational resources, maintaining data security, and addressing the complexity of healthcare records. The project overcomes these by operating within the NHS SDE, utilizing secure computing infrastructure, and following strict governance and approval processes.<\/p>\n<\/p><\/div>\n<\/details>\n Members of the public contribute to reviewing ethical considerations, ensuring transparency, and shaping research to align with patient interests. This involvement promotes trust, accountability, and ensures that AI applications prioritize public benefit while safeguarding patient data privacy.<\/p>\n<\/p><\/div>\n<\/details>\n Researchers aim to include richer data sources such as clinician notes, blood test results, and historical data extending further back in time. This will deepen the model\u2019s medical understanding, enhance prediction accuracy, and broaden its applicability beyond current Covid-19 related research.<\/p>\n<\/p><\/div>\n<\/details>\n Industry partners like AWS and Databricks provide computational resources but have no access to NHS data, AI model internals, or outputs. They have no control over research decisions or findings, ensuring patient data confidentiality and maintaining strict separation between data management and infrastructure support.<\/p>\n<\/p><\/div>\n<\/details><\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":" One example from the United Kingdom comes from the University College London (UCL) and King\u2019s College London. They created an AI model called Foresight. This model was trained using data from 57 million people in the NHS England Secure Data Environment (SDE). Although this data is from the UK, it teaches lessons for the U.S. […]<\/p>\n","protected":false},"author":6,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[],"tags":[],"class_list":["post-146116","post","type-post","status-publish","format-standard","hentry"],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/posts\/146116","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/comments?post=146116"}],"version-history":[{"count":0,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/posts\/146116\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/media?parent=146116"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/categories?post=146116"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.simbo.ai\/blog\/wp-json\/wp\/v2\/tags?post=146116"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Frequently Asked Questions<\/h2>\n
What is the significance of training AI models on de-identified NHS data from 57 million people?<\/summary>\n
How does the Foresight AI model function in predicting health outcomes?<\/summary>\n
What type of data is used for training the Foresight model and how is privacy maintained?<\/summary>\n
Why is it critical to represent minority groups and rare diseases in the training data?<\/summary>\n
What role does the NHS England Secure Data Environment play in this project?<\/summary>\n
How can predictive AI models like Foresight contribute to preventive healthcare?<\/summary>\n
What are the challenges addressed by combining AI and NHS data at this scale?<\/summary>\n
How does public involvement influence the development and approval of AI health research?<\/summary>\n
What future enhancements are planned for the Foresight model?<\/summary>\n
How do industry partners support the AI project without compromising data privacy?<\/summary>\n