Safeguarding Vulnerable Care Access: AI-Powered Risk Detection and Microfinance Linking for Community Health Small Businesses (American Journal of Engineering , Mechanics and Architecture (2993-2637), 2023)
The provision of affordable, consistent and non-discriminatory health care remains a major challenge to vulnerable groups especially in low resource areas where the community health small business entities are the main source of contact points in delivery of care. Even though they play a critical role, these providers experience challenges continuously such as financial fraud, lack of efficiency in their operations and resource utilization and poor access to funding which will compromise their sustainability in the long-term. This study will explore how artificial intelligence (AI) based risk detection mechanisms can combine with microfinance solutions to make community healthcare ventures more resistant, inclusive, and steady. This study uses machine learning to perform an exploratory analysis of the healthcare and financial datasets to reveal hidden patterns in financial and operation risks and provide predictive information that can be used to help implement logical measures in advance. The results have shown that AI-based predictive analytics does more than just identify early warning indicators of problems that might arise; it also helps maximize resource utilization and transform data-informed decision-making practices. The microfinance systems are essential in addressing the financial frailty by making capital readily available to small-time healthcare providers that otherwise have no access to the conventional sources of funds. The combination of AI risk detection and microfinance support is, therefore, a complex system by which entrepreneurs in the healthcare industry can acquire the necessary tools and resources both in technological and financial terms, to survive in the long run. The result of this integrated approach is the heightened business resilience, the service provision resilience, and the prospective prolonged access of more vulnerable communities to the essential healthcare services. This study recognizes the following limitations: incompleteness of its data, possible situation dependence of the service delivery on the algorithmic bias, and the necessity of a supportive policy framework guiding the adoption and implementation. The data covered broadly emphasize the revolutionary optimism of promoting the combination of technological innovation and inclusive financial patterns to establish sustainable healthcare environments. Through resilience and equity, this integrated structure can propel system changes to the process of care delivery among underserved groups. The future directions of research should be devoted to contextual precision of AI algorithms, extending the boundaries of the microfinance by means of novel digital tools, and cross-sector partnerships to create scalable and sustainable effect. This study valuable knowledge on advancing healthcare systems that are dynamic, inclusive, and resistant to the changing environment, yet it is providing sensitive care through equal access.