Healthcare extends beyond physical illness to include mental, social and spiritual wellbeing. While genetics play a role, they account for only a small proportion of overall health outcomes. Far more influential are personal behaviours, social conditions and the environments in which people live, the wider ecosystem that truly shapes health.

I love new learning so am excited by the use of technology to create new ways of thinking about our health.

Our SMART devices create consumer led health information and positive words create nudges for change and artificial intelligence creating an online presence beyond what can be imagined.

Cloud computing, blockchain, telehealth and health apps are now part of everyday practice. Yet with every technological shift comes discomfort, as medicine expands beyond traditional clinical boundaries into psychology, neuroscience, linguistics, computer science, artificial intelligence and genomics — all shaped by culture, history and environment.

Care itself is becoming more complex, with multiple organisations involved and increasing levels of polytherapy and polypharmacy. Navigating this landscape will require innovation and, above all, a commitment to building shared understanding across disciplines and systems.

Digital systems now capture physical data, lived experience and even social narratives, creating unprecedented opportunities for personalised care, population health strategy and real-time learning.

But most chronic disease management and prevention happens at home. If we want better outcomes, we must meet people in their own contexts while addressing barriers such as distance, digital access, time off work, language, literacy, numeracy, culture and cost. Financial pressure often forces impossible choices and can lead to unfair judgement of how people use services.

Health is shaped far more by social determinants, the conditions in which people are born, live, work and age than by healthcare alone. The new 10 Year Health Plan for England published in July 2025, sets the decade-long vision for how health and care should evolve, including shifting care from hospital to community, expanding digital services and emphasising prevention and personalised care.

Personalised Medicine

Personalised medicine (PM) has emerged through advances in clinical pharmacology, genetics and neural networks, alongside growing recognition that behaviour and the social determinants of health shape outcomes for individuals and their families. We are moving away from a model in which diagnosis leads to treatments based on average results from traditional randomised controlled trials and meta-analyses, towards one in which decisions are tailored to the individual.

Although we share common risk factors for disease, such as age, exercise, cholesterol, weight and smoking, our genetic differences influence how these risks affect us, why some people develop illness while others do not, how disease progresses and how individuals respond to treatment. The genomics revolution has deepened our understanding of these variations, opening opportunities to minimise side effects, improve outcomes and even predict or prevent illness. Crucially, personalised medicine is not simply about more precise prescribing; it is a person-centred approach that integrates biological insight with behavioural, social and environmental context.

Pharmacogenetics

Pharmacogenetics has described how people may metabolise drugs differently due to the genes and our unique biochemical processes which influence our drug response. This has led to the concept of a “personalised prescription” by “tailoring drugs to a patient’s genetic makeup”.

Artificial Intelligence

Utilising collections of data which arise from electronic health records and other sources, means that any aspect of medical practice such as patient characteristics, symptoms of specific diseases, diagnostic criteria, medication doses and abnormal signs on radiographs or other technology can be reviewed and aligned to decisions on diagnosis and treatment. This data can be used to construct algorithms to create action.

AI can interpret visual information such as images and videos, to which it can then react based on its algorithms. Natural language processing (NLP) is how AI can understand and interpret human language, whether spoken or written.

Currently AI and NLP cannot mimic the human connection with others, our resilience and flexibility in response to experiences rather it acts as a diagnostic decision support, often in a specific clinical domain such as radiology and pathology, using algorithms that learn to classify. Examples include diagnosis of malignancy from photographs of skin lesions or from radiography, prediction of sight-threatening eye disease from tomography scans and prediction of impending sepsis from a set of clinical observations and test results.

Subcutaneous insulin pumps are driven by information from wearable sensors, and equipment like ventilator control is driven by physiological monitoring data.

Our clinical roles can be released to spend more time on explaining choices, discussing worries and anxieties and creating a shared understanding.

Digital Therapeutics and Gamification of Serious Play

Digital Therapeutics or DTx is one of the latest buzzwords in the digital health ecosystem.

DTx deliver evidence-based therapeutic interventions that are driven by high-quality software programmes to prevent, manage, or treat a medical disorder or disease. They are used independently or in concert with medications, devices, or other therapies to optimise patient care and health outcomes.

Chronic pain management, oncology support programmes, substance misuse interventions and lifestyle changes including health coaching, meditation and health behaviours such as exercise and diet all feature in our technological world.

SMART devices

Healthcare professionals are very familiar with blood sugar technology and substantial improvements in diabetic management, such as normalising blood sugars through SMART technologies however we need to upskill ourselves in understanding SMART data and their link to health outcomes.

Our devices, smart watches and phones measure our heart rates, count our steps, understand our sleep patterns and are part of Microsoft and other platforms.

This march of health sensors and wearables is expanding exponentially and now is interwoven with our clothes, can appear as digital tattoos and possibly digestibles or in our blood vessels as nanobots.

Digital health technologies can identify environmental factors, including air pollution and UV light, pollen and may lead to new ways of management of asthma or risk of malignancy and population health strategies. Food scanners could alert users to one of the constituents in their meal ensuring those allergic to certain foodstuffs are informed.

Facilitating Off-Site Patient Management through Telemedicine

COVID-19 drove many health consultations to a virtual technology and we then have been increasingly utilising the model of hospital at home, virtual wards and virtual consultation utilising remote devices to share information and provide services in a more effective and often more acceptable manner.

Robotics

Robotics, now are part of many surgical procedures and support simulation and learning, however the technology exists for robots that could undertake phlebotomy and other technical skills, disinfect environments and even be social companions. Toy robots have been utilised as educational resources for children with ASD and other disabilities and artificial limbs have become unique and admired.

However with every great opportunity comes unintended consequence

Algorithm bias has already commenced with the data collection process already mirroring certain population groups. We therefore need diverse and well balanced study populations, paying particular attention to racial and ethnic diversity, gender balance, socioeconomic equity, and other social determinants of health including ability to access a service.

Data needs to be accuracy, have identity matching capability, and privacy protections as part of the governance requirements for successful technology transformation.

The big technology giants such as Apple, Microsoft and Amazon are able to mine their health data creating more data points than traditional health services and may use AI models to create market opportunities.

Other challenges in technology include automation bias describes the phenomenon where we accept the guidance of an automated system and cease searching for confirmatory evidence, transferring responsibility for the decision to the machine. This is already seen in automation of blood pressure and oxygen saturations, believing the machine and not the presentation of the patient.

Cybersecurity and privacy concerns are major obstacles to digital health adoption and interoperability between systems continue to create problems. Cybersecurity requires special attention to avoid intentional corruption of training datasets (training data poisoning), use of AI by attackers, or anti-privacy designs in digital health.

An immediate priority is to ensure access to digital solutions as digital exclusion can reinforce inequity so ownership of devices, understanding of technology and broadband access is essential across all economic groups and all regions of the country and as a clinician this should now be part of our medical history.

Quality control questions that we need to be able to ask are:

• Has the system been tested in diverse locations and populations?
• How can we be sure the training data matches what we expect to see in real life and does not contain bias?
• How can we be confident of the quality of the ‘labels’ the system is trained on?
• Do the ‘labels’ represent a concrete outcome or a clinical opinion?
• How has imbalance in the training set been addressed?
• How is the system going to be monitored and maintained over time?
• Does the system adjust its behaviour (‘err on the side of caution’) where there are high impact negative outcomes?
• Does it produce an estimate of confidence?
• How is the certainty of prediction communicated to clinicians to avoid automation bias?
• How can it accommodate changes to clinical practice?
• What aspects of existing clinical practice does this system reinforce?

The transition from ‘outcomes that matter to the industry’ to ‘outcomes that matter to patients’ has the capacity to transform EBM and will be driven by the technology companies and our own interactions with apps, SMART devices and consumer led initiatives however it is also dependent on a highly-skilled digital health workforce, and the training challenge for leveraging digital health is our next learning journey.

So I will continue to enjoy learning about technology and its opportunities, alongside the responsibility to ensure that the limitations are understood. I look forward to the time saved through AI, leaving me the ability to communicate and create shared understanding with others on how to navigate the complex eco-system of healthcare.