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Journal of Emerging Technologies and Innovation Management

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Predictive Analytics and AI Decision Science in Indian Healthcare: A PRISMA-Guided Synthesis on Scaling Frameworks

  • Anuj Tripathi* Anuj Tripathi Corresponding author Associate Professor Decision Science and AI School of Management, IILM University Gurugram, Haryana, India. India Anuj Tripathi ORCID Id ,  
  • Rajesh Walia Rajesh Walia Delivery Project Manager HealthEdge Pvt. Ltd., Hyderabad, Telangana, India India
Received: November 18, 2025
Accepted: December 08, 2025
Published: December 10, 2025
Volume: 1 (2) | Page: 34-49

Abstract

In India, 2.8 million new tuberculosis cases are recorded every year, managed by a health system running at 0.74 physicians per 1,000 people. Algorithmic Decision Support Systems (ADSS) for clinical triage, breast cancer screening, and population risk management have produced documented results in Indian field deployments. Systematic adoption across the country's primary health centres has not followed. Three gaps explain the disconnect: no published synthesis addresses the transition from pilot to programme; rural and urban adoption conditions are treated as interchangeable when they are not; and implementation barriers have not been mapped to sequenced, actionable prerequisites.

This PRISMA-guided integrative review of 36 sources, drawn from an initial corpus of 360 records, addresses all three gaps. Guided by sociotechnical systems theory and the NASSS framework, the synthesis identifies five ADSS opportunity domains, five interdependent barrier clusters, and a three-tier sequenced framework for equitable scale-up. Infrastructure, data governance, and regulatory clarity must precede deployment. The concept of context-aware institutionalisation is introduced as a practical planning tool for health system managers in low-to-middle-income settings.

Keywords: predictive analytics; AI decision science; algorithmic decision support systems; Indian healthcare; ADSS implementation; digital health scale-up; PRISMA synthesis; health system barriers; context-aware institutionalisation

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