By Enbasekar D, Co-founder & CTO, MediBuddy
The healthcare industry is undergoing a significant transformation, driven by the need for personalised user experiences. Personalisation in healthtech refers to the use of technology to tailor healthcare solutions based on an individual’s unique medical history, lifestyle, and preferences. By shifting from a generic, one-size-fits-all approach to personalised care, healthtech solutions improve patient engagement, adherence and overall health outcomes.
In India, the demand for personalised healthcare is growing rapidly, driven by a large and diverse population, increasing smartphone penetration, and government initiatives like Ayushman Bharat Digital Mission (ABDM). The country’s healthtech ecosystem is leveraging Artificial Intelligence (AI), Machine Learning (ML), and data analytics to enhance diagnostics, remote patient monitoring and customised treatment plans. With India’s healthcare sector moving towards increased digitisation, personalisation is set to play a crucial role in improving accessibility and affordability for millions.
However, significant challenges remain in extending these digital health benefits to rural India. While ABDM establishes the framework for digital health infrastructure, the ‘last mile’ problem persists. Rural areas face multiple barriers, including limited digital literacy, inconsistent internet connectivity and insufficient healthcare data collection infrastructure. Personalised, AI-powered health solutions require comprehensive health data that simply does not exist yet for much of India’s population. Bridging this urban-rural digital divide is essential before AI can deliver on its promise for all Indians.
As India embraces digital health, personalisation will be the key in addressing challenges such as healthcare disparities, rural accessibility and chronic disease management.
The Need for Personalisation in Healthcare
Traditional healthcare models often adopt a one-size-fits-all approach, which fails to account for the diverse needs, medical histories and treatment responses of individuals. This limitation can lead to suboptimal outcomes, as patients may not receive the most effective care for their specific conditions. Personalisation addresses this gap by considering factors such as genetics, lifestyle, and environmental influences to create tailored treatment plans.
Moreover, personalised healthcare fosters greater user engagement and adherence, which are critical for achieving positive health outcomes. When patients feel that their unique needs are being addressed, they are more likely to follow treatment regimens and actively participate in their care.
Key Technologies Driving Personalisation
Several cutting-edge technologies are enabling the rise of personalised healthtech solutions:
1. Artificial Intelligence & Machine Learning: AI and ML algorithms analyse vast amounts of data to predict health risks, recommend treatments, and optimise care plans. For instance, predictive analytics can identify patients at risk of chronic diseases, enabling early intervention.
2. Wearables & IoT: Devices like smartwatches and fitness trackers collect real-time health data, such as heart rate, sleep patterns, and activity levels. This information can be used to provide personalised alerts and recommendations, empowering users to take proactive steps toward better health.
3. Big Data & Cloud Computing: The ability to store and process large datasets in the cloud allows healthcare providers to access and analyse personalised health information seamlessly. This facilitates more informed decision-making and enhances the overall patient experience.
4. Genomics & Precision Medicine: Advances in genomics enable treatments to be tailored to an individual’s genetic makeup. Precision medicine, for example, uses genetic information to prescribe the most effective medications and therapies, minimising adverse effects.
While AI holds tremendous potential, it is important to acknowledge that most healthcare AI models are currently trained on Western datasets, limiting their applicability to the Indian population. These models may not account for India’s genetic diversity, regional health patterns and socioeconomic determinants of health. Building AI systems that truly benefit Indian patients requires investment in local data collection, indigenous research, and models specifically designed for Indian demographics and disease patterns.
Crafting User Experiences That Matter
To truly deliver personalised healthcare, healthtech solutions must prioritise user-centric design. This involves creating intuitive, accessible, and inclusive apps and devices that cater to diverse user needs. Personalised health dashboards, for instance, provide custom interfaces for patients and healthcare providers, offering a comprehensive view of health metrics and progress. AI-powered virtual assistants, such as chatbots and symptom checkers, offer real-time support and personalised nudges, enhancing user engagement. Additionally, behavioural nudging—small, tailored interventions—can significantly improve adherence to treatment plans by encouraging positive health behaviours.
Apple Health and Fitbit have revolutionised personal health tracking by integrating wearables with AI-driven insights. These platforms provide users with personalised health recommendations, enhancing fitness tracking and chronic disease management.
Challenges & Ethical Considerations
Despite its promise, personalised healthtech faces significant challenges. Data privacy concerns are paramount when handling sensitive health information. Companies must implement robust security measures while maintaining transparency about data usage.
Algorithmic bias represents another critical concern. If the data used to train AI systems lacks diversity, personalised recommendations may perpetuate or even amplify existing healthcare disparities. Responsible development requires diverse datasets and regular bias audits.
The implementation of India’s Digital Personal Data Protection Act (DPDP) provides a regulatory framework to address these concerns. Healthtech companies must align their data practices with DPDP requirements, including obtaining valid consent, implementing data minimisation principles, and establishing transparency in data processing activities. The creation of a consent manager framework under ABDM further supports patients’ rights to control their health data. Companies that proactively embrace these regulatory standards will build greater trust with users and healthcare providers alike.
As these trends gain momentum, the focus will remain on crafting user experiences that truly matter—ensuring that healthcare is not only effective but also empathetic and inclusive.
In India, the shift toward personalised digital healthcare is already underway, with startups and established players embracing AI-driven solutions. However, addressing challenges related to data privacy, AI fairness, and regulatory compliance remains crucial. As technology evolves, the future of healthtech will continue to focus on hyper-personalisation, secure data management, and innovative digital therapeutics, ultimately improving healthcare outcomes worldwide.