In the corridors of NHS hospitals, I’ve watched healthcare professionals scramble between patients, their attention stretched to breaking point. Patient satisfaction with NHS services is at an all-time low, with only 24% of the public currently satisfied with the NHS and only 13% satisfied with social care.
Meanwhile, vital information that could improve patient outcomes sits untapped in mountainous paper files and disjointed digital systems. This represents perhaps the greatest missed opportunity in modern British healthcare. I remember having to wade through piles of patients’ paper hospital files to assess their medical history, realizing there must be a much better way.
Despite the NHS Patient Safety Strategy’s aim to save almost 1,000 extra lives and £100 million in care costs annually, implementation has been woefully inadequate. The initiative has been live since 2019 and is still far from reaching its intended milestones. The UK ranks a disappointing 21st out of 38 OECD countries in patient safety rankings, according to Imperial University’s National State of Patient Safety Research.
The Disconnect Between Collection and Analysis
Healthcare organizations enthusiastically embrace data collection but consistently fail to extract meaningful insights. This gap between gathering information and using it effectively has created a dangerous chasm in patient care.
Patient data in the NHS is seen as an individual vector and rarely considered on a larger scale. While audits monitor adherence to guidelines, they rarely connect outcomes to patient demographics and individual factors – the key to more efficient, personalized care.
The disconnect between primary and secondary care exemplifies this problem. When patients are discharged from the hospital, vital information often arrives weeks later in discharge letters—if at all. Throughout my career, I’ve seen how this fragmentation means that patterns across patient populations remain invisible, and opportunities to personalize treatment based on similar patient profiles are missed. This ignores a huge potential for maximizing health outcomes both on an individual and large population scale.
A Cultural Barrier to Innovation
The NHS’s resistance to change runs deeper than resource limitations. Healthcare in the UK approaches system changes much like the aerospace industry: they avoid altering anything for fear of introducing unknown problems. This “if it’s working, don’t fix it” mindset persists despite clear evidence that the system isn’t working optimally. This is a mindset that we need to shift in order to focus on innovation and improving our healthcare system on a larger scale.
While the NHS fixates on broad metrics like reducing hospital stays, it neglects patient satisfaction and quality of experience, which have an equally important long-term impact on the healthcare system. This stands in contrast to American healthcare systems, where feedback collection is ingrained in the care model and patient satisfaction is put at the forefront.
There, providers routinely send follow-up emails and make calls asking about side effects and treatment outcomes. This information feeds directly into treatment adjustments and future care planning, creating a responsive loop that’s largely absent in the NHS. Many private UK healthcare providers, such as dental surgeries, are already following suit, and the NHS needs to do the same. Multifaceted patient feedback is a crucial element in continuous service improvement in all sectors of healthcare.
The Human Cost of Data Mismanagement
The consequences of poor data utilization are often tragically tangible.
Something I’ve seen time and again in my work in the NHS is ignorance of drug allergies. For example, a patient allergic to or who has had adverse reactions to amoxicillin is being prescribed this again. This extreme example extends to clinicians not taking into account more subtle negative outcomes from certain drugs or drug classes.
More routine scenarios highlight similar problems. Consider a patient with acid reflux who receives a medication that causes side effects without effectively resolving their condition. Rather than analyzing why the treatment failed and tailoring a different approach, the same ineffective medication is often prescribed again because it worked “well enough.”
For elderly patients, the stakes are particularly high. Data utilization is particularly important in older patients because of the much higher incidence of polypharmacy. Older patients also have a lower threshold for negative outcomes, which means we have to be extra careful to ensure they get the right treatments and doses. I have personally conducted medication reviews on many elderly patients and would estimate that in almost half of the cases, I found some intervention in their medication that would lead to better clinical outcomes. That is a staggering number of patients having to put up with substandard care and often unnecessary side effects.
AI: The Data Analyst We Need
While the NHS struggles with staff shortages that make manual data analysis impractical, artificial intelligence offers a solution. Rather than hiring more analysts—an expensive proposition in a cash-strapped system—AI could identify patterns humans would miss.
AI can effectively play the role of a data analyst, processing countless variables simultaneously—age, gender, ethnic background, medical history—to understand how these factors influence treatment outcomes. This capability could transform healthcare from reactive to proactive.
There are obstacles, of course. Patient data privacy remains paramount, requiring contained ecosystems that prevent sensitive information from leaking into general AI models. More fundamentally, the NHS must overcome its deep-seated resistance to technological change.
A Vision for Patient-Centred Data Utilization
Transforming the NHS’s approach to data requires three immediate actions:
- Deploy AI to analyze existing data, finding patterns that time-constrained human analysts would miss
- Digitise and centralize all data inputs into a “single source of truth” that bridges primary and secondary care
- Establish systematic post-treatment feedback collection capturing both clinical outcomes and patient experiences
The NHS should encourage patients to report their progress and side effects regularly through digital apps, comparing results with demographic data to determine optimal treatments. AI and data analysts can then use this information to drive clinical decisions for patients in similar circumstances.
This personalized approach to healthcare is an achievable reality requiring primarily a shift in mindset. The NHS doesn’t necessarily need more data; it needs to use existing data more intelligently. Both medical staff and patients’ lives can be significantly improved by closing this gap. This approach would be an easy win for an already struggling healthcare system and go a long way in improving patient satisfaction and tangible health outcomes.
