Connected Intelligence Qorus-NTT DATA Innovation in Insurance Awards 2026

THE GLOBAL CHALLENGE

Healthcare systems face rising chronic disease prevalence, increasing care complexity and rapidly expanding data ecosystems. Clinicians, care teams and insurers must interpret fragmented information spread across multiple platforms while managing growing demand and constrained time.

The result is a structural gap: the data needed to act exists, but it is rarely assembled, interpreted and delivered in a way that supports fast, high-quality decisions. Connected Intelligence addresses this gap by integrating distributed health and behavioural signals into unified, decision-ready intelligence embedded directly in operational workflows.

PRECISION THROUGH INTEGRATED INTELLIGENCE

Connected Intelligence transforms longitudinal clinical, utilisation and behavioural data into role-specific outputs that support action.

It identifies emerging risk, surfaces deterioration signals, contextualises engagement patterns and aligns care teams around shared evidence. By sequencing interventions according to both clinical risk and behavioural readiness, it helps Discovery Health move from retrospective review to earlier, more targeted intervention.

This is particularly valuable in an insurance setting, where better timing and targeting of care support can improve member outcomes while also improving operational efficiency and resource allocation.

CONNECTED INTELLIGENCE AS THE ORCHESTRATION PLATFORM

Connected Intelligence ingests structured and semi-structured health data and applies governed GenAI-based synthesis to generate outputs for clinicians, multidisciplinary teams, health coaches and member-facing platforms.

Its longitudinal synthesis engine:

• consolidates claims, pathology, pharmacy, utilisation and diagnostic history into coherent timelines

• integrates lifestyle, activity, nutrition and engagement signals alongside clinical context

• generates structured problem lists, medication summaries and longitudinal risk overviews

• interprets pathology and physiological trends over time

• detects disengagement and emerging deterioration patterns

• identifies compound risks across clinical and behavioural domains

• produces prioritised care and coaching recommendations aligned to member risk and engagement profile

This reduces cognitive load, accelerates orientation to complex cases and supports coordinated intervention across care settings. Complex multidisciplinary team case review preparations were shortened from 3 hours reduced to 5 minutes in a pilot study.

Unlike a traditional analytics layer that returns fragmented outputs, Connected Intelligence performs structured reasoning across domains simultaneously. It can detect interactions and compounding effects that are often invisible when each domain is reviewed in isolation.

DUAL DELIVERY: PROVIDER AND MEMBER INTELLIGENCE

Connected Intelligence delivers intelligence in two complementary forms:

Provider and team-facing outputs that surface risks, highlight interactions, summarise key history and prioritise actions

Member-facing outputs that translate complex information into plain-language guidance that supports self-management, behaviour change and engagement

This dual delivery model strengthens coordination across the care continuum. Teams act with a shared evidence base, while members receive personalised guidance that makes health action more understandable and achievable.

INNOVATIVE INTEGRATION OF GEN AI AND ADVANCED ANALYTICS

Connected Intelligence embeds GenAI-driven reasoning within a regulated, population-scale healthcare and insurance ecosystem as an operational capability rather than a standalone experiment.

Its distinctiveness lies in five features:

1. Governed clinical GenAI

AI governance is embedded into the platform architecture. Structured schemas, evidence-grounded retrieval, evaluation frameworks, traceability and formal review pathways are built into the design, making outputs auditable and clinically defensible.

2. Cross-domain longitudinal reasoning

The platform reasons across clinical history, utilisation patterns and behavioural trajectories over time, allowing it to detect compound risks and deterioration signals that only emerge when domains are integrated.

3. Workflow-embedded delivery

Connected Intelligence operates inside clinical, coordination and engagement workflows rather than in a separate analytics environment. Insights appear where decisions are made.

4. Hybrid predictive + generative architecture

Predictive models quantify risk and prioritisation, while generative AI synthesises these signals into interpretable, role-specific outputs. This combines analytical rigour with practical usability.

5. Direct connection to behavioural activation

Connected Intelligence is linked to Personal Health Pathways and related engagement mechanisms, enabling risk identification to trigger personalised behavioural support at scale.