How generative AI can impact and influence prices in healthcare

Generative AI is all the rage, through ChatGPT and large language models (LLMs). But can it be the lynchpin to improve surging prices and looming affordability crisis within the US healthcare system?

Healthcare services, health insurance, and medications have, for many, become too expensive and financially out of reach. This barrier, often unspoken, impacts patient engagement, care gaps for many with chronic disease, medication non-adherence, out-of-pocket expense levels, population health, and more. 

The reality and scale of unaffordability could be considered an epidemic in America today. Causative factors include prices and the level of out-of-pocket responsibility, which have skyrocketed in the last decade.  And since many top healthcare policy conversations, solutions, and strategies today include social determinants of health, health equity, and care access, we must ask:

Is unaffordability one of the top social determinants in healthcare? And if so, can it be improved or fixed?

Here are related statistics of note:

•  A 2022 poll from Kaiser Family Foundation found that 43% OF ADULTS REPORT that they or a family member in their household put off or postponed needed health care due to cost. 

•  A report from the KFF estimates 41% of the population—or 100 MILLION ADULTS—have debt related to healthcare.

•  Since the Affordable Care Act passed in 2010, insurance premiums for families COVERED BY employer-sponsored plans increased 47 percent—to an average of $22,221 per year.

•  A recent Gallup poll noted 44% of respondents, who represent nearly 112 million Americans, give healthcare affordability a rating of a D or an F.


Healthcare prices: A disconnect overcome by Generative AI

Though hospitals have improved efforts and compliance in price transparency mandates, there haven’t been widespread benefits for patients and consumers in healthcare. One reason is that machine files, with prices for all care services, has not been effectively transformed into information that is easily understood, standardized, and actionable for patients and self-insured employers.

Enter generative artificial intelligence (AI) technology, and its large language models (LLMs). It offers signficant potential for empowering individuals and employers—as consumers in healthcare—to better work with prices and more clearly embrace their impact throughout the healthcare system.  

The following are nine ways that LLMs and generative AI can impact price and price transparency in healthcare. Also included are current industry trends that qualify generative AI as an effective tool to help stakeholders achieve better outcomes and management solution for more cost-efficient management in order to help achieve better outcomes across all stakeholders involved in medical care delivery.


Train price data from payers

A big step is this needed transformation is to train large language models (LLMs) with price transparency data from machine-readable files held by payers. It’s at 630 Terabytes (TB) or 78 billion health records—an estimated 20,000% larger than hospital transparency data. Payers themselves admit the size as a tremendous obstacle to their ability to comply with CMS price transparency regulations—rolling out in three separate waves of comprehensiveness by the end of 2024.


Data analysis and extraction:

LLMs will analyze and extract relevant pricing information from vast amounts of unstructured data, such as medical bills, insurance claims, and provider contracts, making it easier to compare service-level pricing across health systems and the underlying facilities across their networks. If you’re a self-insured employer or patient with high deductible or sizeable percentage co-insurance OOP payment, imagine being able to get the exact same services—at different covered care facilities—for the least expensive contracted rate, or cash fee. 


Normalizing and categorizing data

LLMs will help standardize and categorize healthcare pricing data, with CPT codes and groupings matched to commonly linked complaints, common phrases, and diagnoses. This will begin to make it easier for patients, employers, and policymakers to understand costs of various services and treatments.


Applications for “consumers”

LLMs will help create applications and digital tools to help individuals and employers navigate complex healthcare pricing information. These apps could include chatbots, search engines, or comparison websites that provide clear, easy-to-understand cost estimates. This could not only be used for care services, but also for coverage options and at-the-counter drug pricing.


Identifying patterns and anomalies:

Through analyzing large datasets, LLMs will be able to identify patterns, trends, and anomalies in healthcare pricing—potentially uncovering instances of price gouging, collusion, and anti-competitive practices that can be addressed by employers, regulators, or policymakers.


Educating patients and providers:

LLMs can create educational materials, such as articles, infographics, or explainer videos, to help patients, employers, and providers better understand healthcare pricing and the factors that contribute to cost variations. For providers, this can be a big part of patient acquisition, reactivation, filling care gaps, population health, and retention. For payers, it can help improve care management, medication adherence, and more.


Automated Negotiation

LLMs will be used to develop AI-powered tools that automatically negotiate healthcare prices on behalf of patients, employers, or insurance companies, catalyzing cost cutting through increased competition and more efficient price-setting processes.


Policy analysis & recommendations

LLMs will help analyze the impact of various healthcare policies on price transparency, offering insights into which strategies are most effective and recommending potential improvements. 


Public awareness campaigns

LLMs will generate content for public awareness campaigns that drive awareness, trust, and inform consumers on pricing. Financial experience will become a greater part of overall patient experience and positively impact health outcomes, medication adherence, and care journeys.


Price transparency alone will not make healthcare affordable for the masses. It’s a systemic challenge that will require change and contribution from many different healthcare stakeholders. Keep in mind that apart from providers and care services, there are also muddy waters around financials with drug companies and payers—as well as PBMs. Greater transparency with those stakeholders, delivered to employers and individuals through generative AI, could also open up substantial change in the dynamics of the US healthcare market.

Ultimately, generative AI is a powerful tool that has the ability to break down, organize, and make data actionable to levels not attainable by manual processes and current technology. How it will unfold, integrate, and drive change within healthcare will be of major interest moving forward.

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