ASGE International Sampler (On-Demand)-9

Pdf Summary

In this document, Jeffrey Potkul of Medtronic Endoscopy discusses various topics related to payment and coverage in the healthcare industry. The document highlights lessons learned in the current state of AI payment and classification, with a positive trend towards the establishment of criteria for reimbursement. The document also emphasizes the need for clear criteria to differentiate AI algorithms and the challenges in differentiating AI from other computer and software-based technologies.<br /><br />The future state of AI payment and coverage is discussed, with expectations for the establishment of criteria to guide innovation and funding. The document suggests that mechanisms should be in place to pay for value and capture costs across the continuum of care, addressing silos in healthcare. The importance of partnerships and collaboration in the industry is emphasized, with the potential emergence of new partnership models. The legal implications of not using AI are also mentioned.<br /><br />The document further explores the value, quality, and data in future reimbursement models. It suggests that alternative data sources can accelerate the demonstration of value in healthcare. Payors are more likely to pay for quality if it is defined, published, and measurable. The document also acknowledges the evidence gap in AI research, particularly in population health, and suggests that health technology assessment (HTA) principles can adapt to the complexities of AI.<br /><br />The document briefly touches on the differences between radiomics and gastromics in imaging, highlighting the need for defining the science, method, systematics, standardization, and measurable benefit in AI applications.<br /><br />In terms of global AI policy, the document provides insights into the reimbursement macro trends in the US, EMEA, and APAC regions. It mentions the establishment of AI codes and taxonomies in the US, while noting fragmented reimbursement pathways in the EMEA region and the impact of AI regulations on regulatory approval and payment systems. The document also briefly discusses the reimbursement landscape in Japan and Korea.<br /><br />Lastly, the document references the CMS Final Rule 2023, which includes the use of radiomics features for quantifiable analysis of medical imaging data.<br /><br />Overall, this document provides a comprehensive overview of various topics and trends related to payment and coverage in the healthcare industry, particularly in the context of AI technologies.

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Concept Artificial Intelligence

Concept Reimbursement

Concept Endoscopy AI

Concept Coverage Policy

Concept Reimbursement Model

Keywords

AI payment

healthcare reimbursement

AI classification

value-based care

partnership models

legal implications

alternative data sources

health technology assessment

global AI policy

radiomics

Artificial Intelligence

Reimbursement

Endoscopy AI

Coverage Policy

Reimbursement Model