Technology-Driven Solutions for Smarter Care: Telemedicine, Data-Driven Analytics, and AI in Healthcare Operations

Healthcare systems globally are grappling with increasing demand, ageing populations, and persistent resource constraints. In the United Kingdom, the National Health Service (NHS), a publicly funded system offering free care at the point of use, is facing mounting pressure from long outpatient waiting lists, growing inequality in access, and overstretched secondary care capacity. Addressing these challenges requires innovative, system-level solutions that enhance efficiency and equity without necessarily expanding physical infrastructure. This dissertation, developed in close collaboration with the NHS and public health researchers, investigates how digital technologies, including telemedicine, data-driven analytics, and artificial intelligence, can be leveraged to improve care coordination, demand management, and health outcomes. It comprises three interrelated parts, each applying a distinct methodological lens (game-theoretic modelling, empirical analysis, and machine learning) to address a specific healthcare operations problem. Part I examines the NHS’s Advice & Guidance (A&G) service, a telemedicine platform that enables general practitioners (GPs) to consult specialists prior to referrals. While A&G holds promise to reduce unnecessary referrals, current financial incentives often lead to suboptimal adoption. A game-theoretic model is developed to evaluate stakeholder incentives under common contracting schemes, revealing inefficiencies. The study proposes a novel COst-sharing PErformance-based (COPE) contract and its dynamic extension, which better aligns incentives and promotes system-wide efficiency. Numerical analyses show that performance-based contracts can unlock significant value, especially when diagnostic uncertainty is moderate. Part II turns to trauma care in the East of England. Using nine years of data from the regional trauma network, this empirical study evaluates rising trauma demand, geographic disparities in access to Major Trauma Centres (MTCs), and the performance of Trauma Units (TUs). Despite lower injury severity, TU-managed patients often face worse outcomes, particularly among the elderly and socioeconomically disadvantaged. A follow-up analysis employs regression, instrumental variable, and propensity score matching methods to evaluate the role of pre-hospital triage tools. Findings suggest that digital triage tools improve patients’ recovery, particularly in “trauma center deserts", regions where geographical access to MTCs is limited, and may mitigate inequality without requiring new infrastructure. Across these two parts, the dissertation provides actionable strategies to improve NHS operations by aligning incentives, leveraging empirical evidence, and deploying advanced analytics. It contributes both theoretical frameworks and practical tools aimed at fostering a more resilient, equitable, and data-driven healthcare system.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2025

Author dc:creator

• Liu, Zidong

Advisors dc:contributor.advisor

• Erhun, Feryal
• Jiang, Houyuan

• healthcare operations
• game theory
• data analytics
• telemedicine
• trauma care

Licence

• https://www.repository.cam.ac.uk/bitstreams/7abe7042-1414-4425-ad95-986a27ae9800/download
• http://purl.org/NET/rdflicense/allrightsreserved

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.126845

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/397818