Healthcare Technology Featured Article

May 26, 2023

Leveraging Generative AI for Health Literacy

Despite the incredible benefits of modern communications tools, they have also fostered an era where misinformation can spread like wildfire.  At the same time, access to accurate, understandable, and timely health information has never been more crucial.  

It put the healthcare industry in a bind, considering a shortage of healthcare professionals combined with an increased demand for personalized healthcare services from an aging population.  About 46 percent of the U.S. population was born in 1980 or later, and the percentage of total population aged 65 or more is expected to grow from 16 to 23 percent by 2060 (from 53 million to 96 million).

Naturally, the healthcare ecosystem is looking to technology to help bridge the gap between healthcare supply and demand, aided by the digitization of health information. Digital transformation, the increased demand, and exorbitantly high costs of healthcare services have accelerated adoption of AI in healthcare to improve efficiency and care quality to a system that most would describe as troubled, at best.

The global AI in healthcare market size was valued at $15.4 billion in 2022 and is expected to grow at a CAGR of 37.5 percent from 2023 to 2030 – putting the market value at more than $210 billion.  This growth shouldn’t be surprising, given how widely AI is being adopted across verticals, and the surge in its use in healthcare a few years ago, when the market grew at an astounding rate of 167.1 percent from 2019 to 2021.  There’s no question the COVID-19 pandemic drive much of that acceleration as it demonstrated the potential of AI in rapid diagnosis, patient management, and claims settlement.

The latest AI craze, of course, is Generative AI, which is being integrated into a range of tools and services to, including many customer-facing technologies, like chatbots, because of its ability to generate quick, accurate responses and engage in human-like conversations with users.

Healthcare certainly seems like a logical industry for GenAI innovation, given the struggles patients face in getting timely information from their providers – including everyone from healthcare practitioners to insurance providers.   If GenAI can supplement physician interactions with automation, including analyzing patient health information and interacting with patients, it can provide a much-needed efficiency boost to the industry.

Tell Health believes AI is, indeed, the path forward.   the creator of the health-focused social media app Tell, has announced the launch of a Generative AI feature for physicians, integrating OpenAI's ChatGPT into its health-focused social media app.  According to Tell Health, the idea is to bridge the health literacy gap by facilitating the translation of complex medical jargon into straightforward language to make it simpler for patients to understand.

“Complex medical jargon can deter people from seeking health information from actual physicians and researchers, and instead turn to influencers with no medical training,” explained Tell Co-Founder and practicing physician Alan Gaffney, M.D., Ph.D.  

Tell believes its ChatGPT integration solves the problem, giving healthcare practitioners a tool to easily translate their technical medical language into something the average patient can understand and react to appropriately. 

“As we continue to realize the potential of AI in the medical community, we must remain vigilant in curbing the spread of medical misinformation,” Gaffney added.

Tell’s AI feature could help address healthcare disparities and break down barriers to accessing health information.  This, in turn, would lead to more inclusive access to critical health information and trending medical topics, in essence, democratizing health information.

The benefits of AI in healthcare are numerous.  AI can be trained to analyze patient health information, enabling care providers to diagnose conditions quickly and devise accurate treatment plans.  AI algorithms have already proven effective in detecting conditions, including COVID-19 cases that were initially misdiagnosed by human professionals.

However, AI is not without it risks.  As the most recent episodes of the TV drama “Chicago Med” depicted, AI is not infallible and can only work with the information it is trained on.  When it is asked to do something beyond the scope of its training, the results can be inaccurate – even deadly in the healthcare world.   

There’s also the issue of data privacy.  AI requires large amounts of data for training and operation, raising concerns about the confidentiality and security of sensitive health information.  In addition, some point to the potential for bias in AI algorithms, which may inadvertently favor or discriminate against certain patient populations based on training data. 

If (when) mistakes happen, there’s also a question of accountability and transparency.  When a physician makes a mistake, the answer is generally straightforward.   When AI makes a mistake, who is at fault?  The developers, the healthcare provider, or the AI itself? 

As AI takes on more responsibilities in healthcare, the potential for errors that could negatively impact health outcomes cannot be ignored. 

These kinds of issues have some questioning whether healthcare is the right industry for AI experiments.  The argument might be made that, in some instances, especially when there appear to be no positive outcomes based on human experience, AI may bring value. 

Despite these potential risks, AI integration into healthcare processes holds immense potential for improving patient outcomes, reducing healthcare costs, and addressing workforce shortages.  The key will be to carefully navigate these challenges, ensuring the benefits of AI are realized without compromising patient safety or privacy.

Tell’s integration of GenAI feels like a reasonable way to introduce Generative AI into healthcare in a way that can improve the patient experience and, importantly, should make it easy to evaluate its effectiveness and accuracy.  

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