Traditionally, treatment practices have relied on population-level statistics. This is good evidence-based practice in general, but the weakness of this premise is the lack of individualised care.
This is rapidly changing as digital technology is increasingly entering medicine. Technological developments and data analysis are enabling more personalised patient care. This is evident in both primary prevention and disease management.
Nevertheless, the clinician’s role remains central, as digital tools only augment, not replace, clinical decision-making.
“Future digital technologies are expected to integrate primary prevention more naturally into everyday life, supporting earlier risk identification and more consistent preventive behaviours beyond traditional healthcare visits.”
The Digital Health Revolution In Prevention
Although primary prevention is the most effective form of treatment, it is usually neglected. Disease management often takes up the majority of the resources allocated to treatment.
Primary prevention has traditionally been available mostly through healthcare contacts. In people’s everyday lives, primary prevention rarely comes to mind. Usually, the doctor’s advice is quickly forgotten after the appointment. However, digital devices and applications are changing this, as smart devices bring primary prevention and health awareness into people’s everyday lives.
Initial research suggests potential benefits, while more robust data are still forthcoming. For example, a comprehensive scoping review analysing 241 studies found that digital health interventions in primary care settings demonstrated meaningful improvements in clinical and nonclinical outcomes, with approximately 80% showing statistically significant positive results, though overall study quality was moderate to low [1].
Future digital technologies are expected to integrate primary prevention more naturally into everyday life, supporting earlier risk identification and more consistent preventive behaviours beyond traditional healthcare visits.
Artificial Intelligence: Personalising Prevention
Artificial intelligence is currently the most anticipated innovation in preventive medicine. AI-powered interventions are demonstrating measurable impact across multiple health domains. As preventive care has lacked scalable methods for tailoring interventions to individual needs, AI has the potential to help fill this gap by analysing patterns across large and complex health datasets.
For example, a rapid review examining 22 studies found that AI initiatives targeting lifestyle modifications overall reported positive outcomes in process measures, cognitive-behavioural outcomes, and health outcomes, though the evidence base remains relatively small and heterogeneous [2].
In my clinical practice, I’ve observed emerging AI-based risk stratification tools that aim to identify patients who might benefit from earlier intervention, such as those that analyse patterns in blood work suggesting the development of metabolic syndrome. While promising, these applications require rigorous validation and careful clinical oversight. My experiences and writing draw on both scientific research and everyday patient interactions, offering a clinician’s perspective on preventive care and digital health.
Mobile Health Applications: Expanding Reach
The effectiveness of primary prevention largely depends on a person’s ability to adopt new habits and modify existing ones.
Sustaining these changes typically requires repeated reinforcement, which is difficult to achieve within the limits of traditional healthcare resources. As a result, conventional counselling-based approaches often fall short due to limited follow-up and feedback. However, mobile applications may be a new solution to this limitation.
Mobile health applications have emerged as powerful tools for preventive care delivery. A systematic review and meta-analysis of 172 studies involving 53,331 participants found that apps designed to modify health behaviours demonstrated a positive, though modest, advantage over standard care [3]. The most common features included self-monitoring (69.8%), visual feedback (63.4%), and health education (62.2%).
These applications show particular promise in chronic disease prevention and management. Evidence demonstrates effectiveness across diabetes management, cardiovascular disease prevention, physical activity promotion, and mental health support [3].
Many effective interventions were multicomponent programs, though standalone apps also demonstrated modest benefits. However, long-term effectiveness remains uncertain, as most studies had follow-up periods of 6 months or less.
However, mobile health applications are a relatively new phenomenon, and we do not yet have reliable long-term data on their effectiveness. Moreover, mobile applications alone cannot yet take responsibility for care, so their use should always be done in cooperation with a clinician, combining digital self-management tools with periodic human follow-up.
“As preventive care has lacked scalable methods for tailoring interventions to individual needs, AI has the potential to help fill this gap by analysing patterns across large and complex health datasets.”
Remote Patient Monitoring And Telehealth
Remote patient care took a big leap during the pandemic. Remote patient monitoring has evolved from a pandemic necessity to an essential preventive care tool. And the trend continues to grow.
A systematic review of interactive remote monitoring devices found significant reductions in mortality (risk ratio 0.71) and improvements in blood pressure and glycemic control [4]. However, the same analysis found no significant improvements in quality of life and noted potential increases in hospitalisation rates, which may reflect enhanced detection of health issues requiring medical attention.
A prospective observational study (n=186) found telemedicine adoption was associated with improved patient satisfaction and reduced healthcare costs [5]. For heart failure management specifically, combined remote monitoring with clinical consultation has demonstrated reduced cardiovascular-related mortality (RR 0.83) and hospitalisations (RR 0.71), though benefits were primarily observed in short-term studies [6].
It is therefore expected that remote care will become more popular in the future. Patients are getting used to the fact that they do not always have to come to the doctor’s office for all health-related matters, but can often be consulted remotely via messaging, chat, video, or telephone.
Wearable Technology: Continuous Health Monitoring
Previously, physiological data were only used for research purposes and in limited fitness settings. Technology has made physiological data accessible to everyday people. Initially, the meters used by consumers lacked standardisation and medical validation, which is now changing.
Wearable devices have transitioned from fitness novelties to legitimate preventive care tools. These technologies enable continuous monitoring of vital signs, physical activity, sleep patterns, and other health metrics in real-world settings. A scoping review of 99 studies on wearables in heart failure management found most devices remain in feasibility testing stages, with clinical impact still uncertain due to limited robust trial evidence [7].
To ensure a successful digital transition, manufacturers and clinicians should collaborate to integrate physiological data in a clinically meaningful and useful way. In addition, consumers should be encouraged to consult clinicians when interpreting data to avoid unnecessary anxiety and misinterpretation.
The Path Forward
Preventive care has repeatedly seen promising innovations; however, a large number of them have failed due to poor clinical implementation. Trust remains a limiting factor in the application of new health technologies, although progressive standardisation and ongoing clinical validation are gradually strengthening confidence.
At the same time, the evidence base for technology-enhanced preventive care continues to grow, supported by an increasing body of research on the interpretation and clinical use of physiological data.
Despite this promising trajectory, digital health adoption still faces meaningful obstacles. Technical limitations persist, including data quality concerns that affect AI algorithm performance, and long-term effectiveness remains uncertain, as most studies have follow-up periods of six months or less [3].
Addressing these challenges will require a deliberate and clinically grounded approach to implementation. Future directions should emphasise rigorous evaluation through long-term studies, user-centred design that incorporates both patient and provider feedback, seamless integration into clinical workflows, and proactive measures to ensure equitable access for underserved populations [2][4].
Digital preventive tools should be introduced as complements to established care models, with clearly defined roles, responsibilities, and appropriate clinical oversight.
Continued standardisation, transparent validation processes, and sustained outcome monitoring will be essential to strengthen trust among clinicians and patients alike.
Ultimately, the successful integration of digital technology into preventive care will depend not only on technical capability but also on its alignment with real-world clinical practice and patient needs.
