Enhancing Patient-Provider Matching using AI: Revolutionizing Healthcare Delivery

The U.S. healthcare system faces significant cost management and efficiency challenges, with patient-provider matching emerging as a crucial factor in optimizing healthcare delivery and reducing unnecessary expenditures. In 2022, the spending on healthcare in the U.S. increased by 4.1%, amounting to $4.5 trillion. This expenditure represented 17.3% of the nation's Gross Domestic Product [1]. It is broadly acknowledged that up to 5% of the GDP is squandered by the U.S. healthcare system [2]. The United States allocates more funds to healthcare than any other nation, with expenditures nearing 18% of its Gross Domestic Product (GDP). Previous research has suggested that around 30% of healthcare expenditures are wasteful. Despite initiatives aimed at minimizing unnecessary treatments, enhancing the quality of care, and tackling overpayment issues, significant inefficiencies in U.S. healthcare spending are likely to persist [3]. The total yearly costs attributed to waste were estimated to be between $760 billion and $935 billion [4]. According to multiple studies, the estimated annual costs of waste in healthcare are broken down as follows: between $102.4 billion and $165.7 billion is attributed to failures in care delivery; failures in care coordination cost between $27.2 billion and $78.2 billion; and overtreatment or low-value care amounts to between $75.7 billion and $101.2 billion [3]. Historically, discrepancies between what patients need and what providers specialize in have often resulted in less-than-optimal health outcomes, higher healthcare expenses, and diminished satisfaction among patients. Given the high stakes of healthcare costs in the U.S., employing artificial intelligence to refine how patients are matched with providers stands to enhance efficiency and lower costs greatly. In the United States, where healthcare costs are a significant concern, leveraging A.I. to improve the patient-provider matching process can significantly contribute to cost reduction and efficiency improvements. Artificial Intelligence (A.I.) presents a transformative solution, potentially strengthening the patient-provider matching process, improving healthcare outcomes, and reducing costs. This paper explores the application of A.I. in patient-provider matching as a strategy for cost reduction, detailing the mechanisms through which A.I. can achieve these goals, the challenges to be overcome, and the implications for the U.S. healthcare system.