Paragon Tackles AI and Two Upcoming Events

Paragon has just released a new report by visiting fellow Kev Coleman, Lowering Health Care Costs Through AI: The Possibilities and Barriers. Today’s newsletter contains information about this report, but first I wanted to alert you to two upcoming Paragon events.
 
In a virtual event on July 17^th at 2pm, Theo Merkel and I will be discussing our new paper, Follow the Money: How Tax Policy Shapes Health Care with Alye Mlinar. You can register for that event here.
 
And on Capitol Hill on July 29^th at 10am, Paragon will co-host an event on Medicare site neutral payments that features former Secretaries of Health and Human Services Alex Azar and Kathleen Sebelius, who recently coauthored a STAT piece arguing for Medicare site neutral payments. You can register for that event here.

Lowering Health Care Costs Through AI

While artificial intelligence (AI) in health care has attracted considerable press, attention has focused on its advancements in diagnosis and treatment. This is unsurprising given AI’s recent achievements in the field. For example, researchers have developed a simple blood test that uses AI to predict Parkinson’s disease up to seven years before a person is symptomatic. In drug development, manufacturers are using generative AI and machine learning to design new medications quickly and predict which patients will benefit or not benefit from these medications. Unfortunately, far too little attention has been paid to how AI might alleviate a major health care problem: high and increasing costs that threaten family and government budgets.  
 
AI is already bringing cost-efficiencies to front office administrative activities like patient check-ins and back-office activities such as billing. Through its capacity to simulate human understanding and reasoning, AI has much greater potential to reduce health care expenditures. Specifically, the savings potential of autonomous care—the self-service delivery of a medical service via an AI system without clinician assistance—are substantial. Every time a health care service can be performed independently of a clinician, a sizable expense can be eliminated. There should also be savings from efficient scaling by autonomous self-service applications. As a computing technology, AI can scale at lower marginal costs than is the case for increasing human labor.

Regrettably, the dominant health care financing models, characterized by bureaucracy, inertia, and lobbying power — not economic efficiency — could negate AI’s cost-cutting potential. However, forward-looking approaches to regulation and intellectual property in AI can foster an environment where AI is better positioned to disrupt the existing health care paradigm and promote market forces and price competition.
 
In his paper, Kev investigates the savings that may be realized through AI, particularly through autonomous systems. There are legitimate issues about the safety and quality of autonomous AI systems. However, these concerns should not paralyze progress toward adopting systems that, in addition to reducing costs, have the potential to improve quality.
 
Kev makes key recommendations regarding the proper regulation of AI in health care. His recommendations intend to protect self-service AI from rules that would impair consumer cost-savings without materially benefiting those consumers with enhanced safety or quality. Kev also cautions that while intellectual property (IP) protections can preserve the incentives for R&D investments, if misapplied, they can inhibit innovation as well as encourage the use of patents as legal weapons to bleed funds away from AI start-ups. Against this backdrop, Kev proposes to reasonably balance those competing concerns.
 
This week’s Paragon Pic demonstrates the design of artificial neural networks, a subset of machine learning. This write-up is not for the faint of heart, but it provides an elementary explanation of the connections and the key elements involved with the progressive learning that characterizes AI.

All the best,

Brian Blase
President
Paragon Health Institute