Highlights
The promise of personalised medicine is coming into sharper focus as multi-omics, advanced data analytics, and artificial intelligence (AI) reshape our understanding of disease and treatment. This vision requires an evolution of healthcare models: from reactive, one-size-fits-all paradigms to proactive, patient-centric, and science-driven systems.
Advancements in genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics have ushered in a holistic understanding of diseases. These insights enable the identification of novel therapeutic targets and biomarkers, catalysing the development of innovative therapies. Technologies such as DNA editing, synthetic biology, and AI-driven drug design are accelerating the transition from traditional small molecules to complex biotherapeutics, including biologics, cell and gene therapies, and RNA-based medicines.
For holistic patient engagement, healthcare delivery should also evolve along with the advancements in therapies.
Despite scientific progress, the current healthcare delivery models remain fragmented. Life sciences organisations innovate therapies but have limited access to real-world clinical data. Care providers deliver treatment yet lack molecular-level insights about patients. Health insurers rarely utilise scientific information to shape personalised coverage or risk strategies. This disconnect impedes the effective deployment of precision medicine.
Delivering on the promise of personalised medicine necessitates a model where every patient’s molecular profile is integrated into care pathways and clinical decisions. For insurers, integrating molecular and real-world data enables precise risk stratification and prevention strategies, reducing claims and risk exposure. For caregivers, predictive modelling will support early disease diagnosis, therapy response forecasting, and complication risk prediction, especially in oncology and chronic conditions, while enabling personalised care plans. As these systems are adopted and evidence gets generated, insurers can design coverage options that incentivise healthy behaviours and preventive care. Integrating a broader group of stakeholders—payers, technology providers, regulators, and patient advocacy groups—enhances cost-effectiveness, ensures holistic personalisation and improves outcomes. This future ecosystem will blur traditional boundaries, driving collaboration across life sciences, healthcare delivery, insurers, and technology sectors.
Central to this vision is the concept of the patient scientific digital twin (PSDT),a comprehensive, evolving digital representation of an individual’s health and biology. The PSDT enables real-time simulations of disease progression, outcome prediction, and therapy personalisation. As creating a PSDT may require deeper biological interrogation capabilities, only a few care providers would have such expertise, and those would evolve into care hubs. The remaining care delivery centres may remain users of data and technology and become the spokes in the new hub to drive forward the personalised medicine model. For instance, cancer care globally lies in cities, with patients traveling across the country to obtain a diagnosis and treatment.
However, the burgeoning out-of-pocket expenses force families to move back home, with the local centres often unable to provide the same support and care as they do not have all the details about the line of treatment. Bringing together ecosystem players, the hub-and-spoke model will ensure minimal cost and dislocation to patients and their families.
The integration of AI promises to bridge gaps in linking symptoms, diagnoses, and molecular data, powering decision-support systems and translational research. Electronic health records and serial sampling further enable comprehensive data collection, supporting dynamic and adaptive models for patient care.
As healthcare advances toward personalised models powered by multi-omics and AI, safeguarding patient confidentiality becomes increasingly complex. Existing regulations like Health Insurance Portability and Accountability Act (HIPAA) and General Data Protection Regulation (GDPR) provide important foundation, but they may not fully address the challenges posed by large-scale, granular health data and technologies such as PSDTs. These digital replicas of individual health profiles require stronger standards for consent, security, and data sharing.
To support such innovation responsibly, regulatory frameworks must evolve, establishing clear protocols for digital twin use, robust privacy safeguards, and consistent mechanisms for cross-border data exchange. Only with updated, adaptive regulation can the benefits of personalised healthcare be realised without compromising ethical and legal protections.
Realising the vision of scientific and personalised healthcare requires a radical reorganisation of the health value chain, underpinned by collaboration, interoperability, and a patient-centric ethos. By embracing PSDTs, multi-omics, and AI, the healthcare ecosystem can deliver more effective, equitable, and affordable care worldwide.