How to get $Billion for the NHS instead of us paying $Billions!

MPs are currently deciding about a single patient record and are talking about improving GP and Hospital access via combining digital records.

There are concerns over data safety, but what they are not talking about is the value of this data (perhaps deliberately?).

The fact is that the health records of all the population (past and present) of population of 50+ million first-world people is worth an absolute FORTUNE!

We should not be paying Palantir or other companies to bring this data together, we should be selling it (obviously de-personalised) for $billions. This would solve our NHS financial difficulties in a trice.

Why is the UK Dataset Uniquely Valuable?

• “Cradle-to-Grave” Continuity: Unlike highly fragmented healthcare systems (like the US), the NHS tracks the same individuals across their entire lives. This offers companies an unbroken, longitudinal look at how illnesses progress and how different treatments perform over decades. 
• Diversity and Scale: The dataset covers a deeply diverse population of roughly 67 million people across the entire socio-economic spectrum. This scale provides the vast amount of diverse information required to train precise, unbiased algorithms. 
• Granular Depth: A standard Electronic Health Record (EHR) is worth less than £100 on its own. However, when linked with genomic data, imaging, and prescription tracking, the market value spikes to £1,000–£5,000 per patient record for highly specialised research. 

Just think how valuable all that data is to drug companies, universities, health companies, etc.

Ernst and Young have estimated that we could get an income in the range of £10 billion a year for this data.

1. Smart Recalls: When to Call You In and What Tests to Give

Instead of standard age-based checkups, AI tools like the recently developed AstraZeneca Milton AI analyze subtle patterns in routine blood tests, urine tests, and blood pressure. [1]

• Customised Diagnostics: If an AI detects a microscopic, multi-year downward trend in your kidney biomarkers or specific blood proteins, it will automatically flag your record. [1]
• Targeted Invites: The system will autonomously prompt your GP clinic to call you in for a highly specific diagnostic test—years before you ever show physical symptoms. [1]
• Smart Booking: Systems like GP Triage use AI to categorise online or phone requests, matching your history against 2,700+ symptoms to book you with the exact right specialist. This cuts morning phone queues by up to 40%. [1]

2. Precision Prescribing: What Drugs to Give or Avoid

A huge issue in medicine is "trial-and-error prescribing," especially for conditions like depression, high blood pressure, or cancer. AI eliminates this by creating a "digital twin" of your biology to simulate treatments. [1, 2]

• Adverse Drug Reaction Flags: If a GP tries to prescribe a drug, an AI background tool like CogStack crosses-references your entire medical history to instantly block prescriptions that clash with obscure past conditions or other medications. [1]
• Precision Dosing: AI calculates the perfect dosage tailored to your weight, metabolic rate, and organ function, reducing toxic side effects and hospital readmissions. [1, 2, 3]

3. Hyper-Personalised Vaccinations

Instead of blanket public health campaigns, AI can hyper-target vaccine rollouts.

• Vulnerability Mapping: AI scans records to find highly specific sub-groups of patients (e.g., individuals under 65 with a very specific combination of mild asthma and a recent course of antibiotics) who are statistically at the highest risk for respiratory complications. [1]
• Dynamic Reminders: It then sends automated, high-priority notifications via the NHS App to those exact individuals for targeted boosters, while deprioritising lower-risk groups to save resources.

4. Predicting Potential Genetic Diseases [1]

This is where the true power of AI lies. When genomic data (your DNA sequence) is linked to your electronic health record, AI can run pharmacogenomics and risk-modelling. [1, 2, 3]

• Polygenic Risk Scores: AI scans your DNA for millions of tiny variations to build a personalized risk score for complex diseases like Alzheimer’s, type 2 diabetes, or coronary artery disease. [1]
• Preventative Overhauls: If the AI finds you have a 75% higher genetic predisposition to a certain bowel cancer, it won't wait until you are 60 to scan you. It will automatically mandate regular screenings starting in your 30s.
• DNA-Based Drug Matching: The AI will look at your Cytochrome P450 genes (which govern how your liver breaks down 70% of common drugs). If your genetics show you process pain medication too quickly, the AI will tell the doctor to avoid standard doses because they won't work on you. [1]

Government creep!

I think the government are well aware of this, but they are frightened to bring it up. The way gov. works is in small steps. First let's bring the three different systems together, next start using the data for our own local uses - e.g. we could use AI to look at what drugs and combination of drugs everyone was on and what side affects they had. AI could instantly detect any allergies to drugs or other substances, identify the best drugs to prescribe, etc. 

Finally, start sanitising the data and sell it to support the NHS.

Companies like Palantir and Anthropic want to charge us for this, but we are sitting on an enormous fortune!

Personally, I am all for this. All this data and AI can markedly improve our health and prolong our lives and put £10 billion every year into the gov. coffers.

GPs get far too little time to asses a patient. They just seem too quick to dish out some pills and shove you out the door. If they get some tests done, they often don't have the time to fully assess the results and figure out possible causes from all the evidence. An AI agent could look at what drugs we are on, our test results over time, physical and mental assessments, etc. and provide the GP with several diagnostic options and suggest further tests.

What we should ensure is that the database is built in such a way as to keep our private information [name, NI number, address, birth day (just use month and year), etc.]  separate from our health data, e.g. by using an NHS patient number. We should ensure we own this data and control it. The UK government have a past track record of giving away tax payers money - this is one place where this must not happen!