The delivery of healthcare in Low-to-Medium-Income Countries (LMICs) has long posed challenges, with established models predominantly found in wealthier nations. These models are found to be either strategic or operational, and very rarely combine these two perspectives. Most importantly, these models lack a comprehensive, holistic and synchronous construct that accompanies a systems thinking approach. This research evaluates international best practices, fundamental global theories and existing systems and tools in healthcare through a systems approach. It collates these data to propose a customized systems-based, comprehensive framework for modeling and optimizing both the management and operational tiers of healthcare in LMICs. The approach is based on the adoption of digital tools, inclusive of AI, to analyze, assimilate, align and develop advanced, holistic and inclusive frameworks. The current gap in global healthcare delivery is characterized by an ongoing lack of ability to provide quality and cost-effective care, especially in the LMICs. Despite the fact that developmental challenges are unique and specific to respective countries, there are commonalities with regard to healthcare processes that present opportunities for optimization. The main challenge lies in the effective collation and synchronization of data and tools with the specific contexts of each country. This situation highlights the need for a cohesive systems approach to enhance healthcare delivery in LMICs, allowing for tailored solutions that can bridge existing gaps. This paper presents a strategic model, with initial data quantification guiding the development of the system model. The practical significance of this research lies in its potential to transform healthcare delivery in LMICs, leading to enhanced access and quality of care through optimized systems.

The government initiated social security schemes in countries such as India, target a large proportion of the population to provide various types of benefits that involve a number of stakeholders. Such schemes are executed by a large number of transactions between the Government agencies and the other stakeholders on a real time basis, thus resulting in large data sets. Current research advancements in the domain of social security schemes include analysis of sequential activities and debt occurrences for such transactions at the national level only. It has been a challenge in recent times to monitor and evaluate the performance of such gigantic schemes which also involves financial decision making at different levels. This paper proposes an innovative frame-work that combines data mining strategies with actuarial techniques to evaluate one of the popular schemes in India, namely AB-PMJAY (“Ayushman Bharat–Pradhan Mantri Jan Arogya Yojana”) launched by the Government in 2018 at family level. In the proposed framework, the scheme has been divided into a number of sub-processes for which various data mining techniques such as, clustering, classification, anomaly detection and actuarial techniques for pricing are proposed to evaluate the scheme effective at micro level.

Data Mining applications to Social Security Schemes (SSS) have been one of the most interesting research areas in the recent past. In general, the benefits of SSS are availed by the beneficiaries at different geographical locations at different points in time, thus generating sequential patterns of interest for the stakeholders such as Government bodies or financial institutions to make effective decisions. Typically, SSS launched in the domains of the health sector involves temporal data, and the research in this domain is termed social security data mining (SSDM), where techniques such as sequential pattern mining, sequential rule mining, and association rule mining are in vogue. In this regard, we have proposed a novel data mining framework called the Combined Sequential Rule Mining framework (CSRM-Framework) which is effective in bringing out the actionable knowledge through the activity sequences pertaining to the beneficiaries. The proposed framework was implemented on Ayushman Bharat-Pradahn Manthri Jan Arogya Yojana (AB-PMJAY), a flagship social security scheme launched by the Government of India. We have also proposed a new interesting measure namely Combined Cumulative Lift (CCL) which has the property of estimating the ‘Interestingness’ effectively when activity sequences are combined with characteristic beneficiary data in the context of AB-PMJAY.