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Study: AI Could Save 5-10% in Healthcare Spending

By HospiMedica International staff writers
Posted on 30 Jan 2023
Image: Researchers expect broader adoption of AI in healthcare in the near future (Photo courtesy of Pexels)
Image: Researchers expect broader adoption of AI in healthcare in the near future (Photo courtesy of Pexels)

A new study by McKinsey & Company (New York City, NY, USA) and Harvard University (Cambridge, MA, USA) estimates that the broader adoption of AI could lead to savings in the range of 5% to 10% in healthcare spending, or between approximately USD 200-360 billion per year in the US.

Presently, the healthcare sector has low adoption of AI-based tools despite its benefits discovered by researchers. The study's estimates are based on AI uses utilizing current technologies that are achievable within the next five years, without compromising quality or access. Hospitals could see cost savings mainly through improved clinical operations, quality and safety – such as optimizing operating rooms, or identifying adverse events. Physician groups can experience similar benefits by leveraging AI for continuity of care, such as referral management.

Health insurers could experience savings from uses that improve claims management, such as automating prior authorization, along with healthcare and provider relationship management, including preventing readmissions and provider directory management. Based on AI-driven uses, private payers could save approximately 7% to 9% of their total costs within the next five years. Physician groups could save 3% to 8% of their costs. Additionally, the report estimates that hospitals could register savings between 4% to 11% in their expenses per year.

Related Links:
McKinsey & Company
Harvard University

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Image: A preclinical rabbit model undergoing longitudinal ocular vascular monitoring before and after induced optic nerve injury (upper panel) and a clinical cohort including patients with cervical lymph nodes of different pathological types (bottom panel) were used to demonstrate the generalizability of the framework. The experimental procedures for both studies include ultrasound data acquisition and reconstruction, extraction and analysis of vascular biomarkers, and construction of diagnostic models. The hierarchical edge-bundling diagram (right) illustrates relationships among ocular biomarkers. Each circular leaf node represents a specific biomarker measured in a particular vascular structure. Node size is proportional to the magnitude of the statistically significant difference observed for that biomarker in this study; larger nodes indicate a greater statistically significant difference. (Photo courtesy of XUEJUN Lab@ShanghaiTech)

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