Industry
Healthcare
Patient-centered Care: Health and Wellness Redefined
Highlights
- With the advancement of multi-omics technologies, sophisticated data analytics, and artificial intelligence, the potential for personalised medicine is becoming more bright, allowing for a deeper understanding of individual diseases and the development of tailored treatment approaches.
- To deliver on the promise of personalised medicine, each person’s molecular profile must be integrated into care decisions.
- Artificial intelligence (AI) is unlocking the future of personalised healthcare by integrating and analysing the vast complex datasets from electronic health records, serial sampling, and molecular profiles, redefining the idea of patient engagement.
On this page
Personalised care promise
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.
Predictive modelling
Delivering on the promise of personalised medicine necessitates a model where every patient’s molecular profile is integrated into care decisions. Including insurers and other stakeholders in the personalised medicine ecosystem presents distinct advantages. For insurers, the integration of comprehensive molecular and real-world data into patient care enables more precise risk stratification and prevention strategies. This can lead to a measurable reduction in claims and overall risk exposure. As predictive modelling improves the accuracy of diagnoses and the effectiveness of treatments, insurers are better positioned to design innovative coverage options that incentivise preventive care and reward healthy behaviours.
This ultimately creates a more sustainable insurance model. Furthermore, involving a broader group of stakeholders—payers, technology providers, regulators, and patient advocacy groups—enhances cost-effectiveness and ensures holistic personalization. This future ecosystem will blur traditional boundaries, driving collaboration among life sciences, healthcare delivery, insurers, payers, and technology sectors.
PSDT, a new concept
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.
AI, the change enabler
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.