Building Resilient Public Healthcare Systems Using Digital Twins

Vinay Kulkarni (), Shrinivas Darak (), Ritu Parchure (), Aditya Paranjape () and Souvik Barat ()
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Vinay Kulkarni: Prayas Health
Shrinivas Darak: Prayas Health
Ritu Parchure: Prayas Health
Aditya Paranjape: Monash University
Souvik Barat: Tata Consultancy Services Research

Chapter Chapter 9 in Digital Twins for Simulation-Based Decision-Making, 2025, pp 195-222 from Springer

Abstract: Abstract The global healthcare sector is facing a number of challenges in the face of a growing range of infectious and lifestyle diseases, ageing populations in several countries, as well as climate change. The challenge is particularly exacerbated in low- and middle-income countries. The diverse demands on the healthcare system demonstrate the importance of ensuring sustainability and resilience: the healthcare infrastructure must be prepared to manage sudden outbreaks of infectious diseases while providing long-term care for chronic and age-related conditions. In order to achieve this, global healthcare systems must continuously adapt to evolving situations, plan strategically, and optimize the use of available resources to manage acute stressors and chronic illnesses effectively. However, building a sustainable and resilient healthcare system that meets the growing public demand is a complex task. Even as the demand for healthcare services is increasing, with individuals becoming ever more reliant on healthcare systems to improve their quality of life and longevity, economic constraints have limited the growth of healthcare infrastructure. As a result, there is a clear need to optimize the medical infrastructure holistically through improved planning, smarter resource utilization, and more efficient workflows. This chapter explores the design and use of digital twins to provide a comprehensive perspective of the public healthcare system, identify operational gaps, and facilitate in silico experimentation to develop effective strategies for intelligent real-time as well as for long-term planning and resource management.

Date: 2025
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DOI: 10.1007/978-3-031-89654-5_9

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