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INSIGHTS

GENERATIVE AI IN HEALTHCARE

How to Leverage the Potential of Generative AI to Transform the Business of Healthcare  
Generative AI and the Future of Healthcare

Generative AI is a distinct type of artificial intelligence that generates text, images, and other modalities in response to prompts. With each use, the technology grows in value as it discerns specific patterns and applies deep-learning algorithms based on existing data to create new content.

In healthcare, Generative AI presents tremendous potential and challenges current workflows and processes. From generating rich clinical notes after an encounter, to faster and more accurate disease diagnoses and better outcomes, providing accessible explanations of complex topics, Generative AI holds great promise for advancing healthcare and patient experiences in ways never imagined just a few years ago.

Generative AI also offers significant potential for reducing administrative complexities by automating manual, error-prone processes that cost payers, providers, and the industry millions of dollars each year. For example, Generative AI can analyze vast amounts of healthcare data to summarize complex information into easily understandable insights. It can explain medical processes, treatment options, and research findings in a simplified manner, aiding healthcare professionals and patients in decision-making and improving adherence.

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In The News
A survey by Bain & Company found that “75% of health system executives believe generative AI has reached a turning point in its ability to reshape the industry.” Yet just six percent have developed a Generative AI strategy.
The Top Use Cases for AI in Healthcare

Clinical Use Cases

The initial clinical use cases for AI, including Generative AI, in healthcare are focused on supporting care delivery and include:

  • Automating clinical note generation
  • Summarizing treatment options
  • Better risk prediction for disease stratification
  • Faster clinical trials
  • Improved disease diagnoses

A recent article published by the American Hospital Association (AHA) states that two top clinical applications of AI are clinical decision-making and diagnostics and imaging. The technology’s benefit comes from its ability to perceive and process large amounts of both structured and unstructured data, giving clinicians more insightful data for more timely, accurate diagnostics and more effective care plans. Enhanced patient safety is another top application due to AI’s abilities in error detection, patient stratification, and drug delivery.

Administrative Use Cases

One of the most significant administrative tasks ripe for Generative AI transformation is clinical documentation, a burden that has fueled our current epidemic of clinician burnout. AI, combined with its subset of technologies like machine learning (ML) and natural language processing (NLP), can assist in documentation and medical coding workflows. One such example is the recording and transcription of exam room encounters, a process patients must agree to beforehand. Large language models (LLM) like GPT can interpret and summarize the recording transcriptions and generate data directly into the EHR—data that would typically have been created by the clinician and entered manually. Clinicians can review the data, edit when necessary, and approve. In this way, clinicians spend minutes instead of hours each day on data entry. More importantly, they have more time for direct patient interaction.

AI-generated documentation can determine the appropriate diagnosis codes based on data assigned during an encounter and automatically submit billing information, significantly reducing administrative burdens for clinicians.
Overcoming the Risks to Reap the Rewards

Security and Privacy Risks

Industry experts estimate that there are over 3,300 healthcare AI startups in the U.S., many promising to revolutionize healthcare. The sheer number of new AI solutions hitting the market exacerbates critical issues like data security, patient privacy, and ethical considerations. One of the most significant concerns is how patient data is collected and stored. This is especially problematic considering that data typically exists in multiple silos across the continuum, meaning it needs to be aggregated from numerous sources before being used. Thus, AI solutions require access to a multitude of systems to capture the data they need, creating significant vulnerabilities for healthcare systems.

Another challenge is how, when, and by whom data is used. Research published by the National Library of Medicine warns of the potential misuse of the data, suggesting that without appropriate privacy protections and penalties in place, organizations may decide to “monetize the data or otherwise gain from them.” There is also the potential risk of “re-identifying previously de-identified data.”

Data Integrity Risks

Another significant risk of AI in healthcare is the propensity for bias in the data used to train AI software, which comes primarily from the lack of appropriate population representation. This happens when teams choose biased data for AI and ML algorithms. The implications for socioeconomic, ethical, and clinical inequities are vast and dangerous. The challenge is that it is difficult to identify these biases in healthcare where data exists in different formats, is distributed across multiple locations, and is often incomplete or inaccurate. According to integrate.ai, “Particularly in large organizations or those that have a history of mergers, legacy systems and distributed data architecture makes it costly to centralize and maintain governance over data for machine learning.”

In The News

“One of the key advantages of Generative AI is using it to simplify and explain the complexities of the healthcare industry and its various processes. Helping members better understand where they are in their care journey, and providing them with accessible and customized information, is so valuable.” 

Stuart Hanson
CEO Avaneer Health

Roadblocks to Generative AI Adoption in Healthcare 

Along with Generative AI’s potential to reinvent healthcare as we know it, there exist roadblocks to deploying this new technology in a way that is cost efficient, timely, and easy to scale—all essential elements for broad adoption. 

One of the most significant roadblocks—perhaps the biggest—is that healthcare organizations have invested heavily in legacy systems that typically do not play well with new technologies. Add-on solutions and multiple data interfaces are often required to share information with other providers and payers. Managing these systems takes a significant investment of time, talent, and financial resources that eat into IT budgets and leave little left over for implementing Generative AI solutions.

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In The News
While Generative AI holds great promise for transforming healthcare, we have to remember that we’re talking about an industry where “75% of all medical communication is still transmitted by fax.” Making the leap from being an industry that can’t seem to quit a technology developed in the 1800s to an industry that embraces cutting edge solutions like Generative AI is going to be an uphill battle, but it’s a battle we must win. Identifying and removing roadblocks is the first step.
Unlocking the full potential of Generative AI in healthcare

Most healthcare leaders agree that Generative AI has the potential to genuinely transform healthcare by improving outcomes and significantly reducing clinical and administrative inefficiencies. But fully realizing these benefits requires a new kind of network through which these innovative technologies can be leveraged in a way that is safe and addresses the issues of integrity and bias. This network is here today.

The Avaneer Network is a secure, permissioned, decentralized network and platform built for healthcare. Once a participant—payer or provider—connects to the network, they never have to establish a direct connection to any other participant. Once the connection is established, data can flow in real time, eliminating interoperability barriers and allowing true data fluidity.

The Avaneer Network enables Generative AI scalability, security, and data integrity without huge IT investments by enabling the following:

  • Permissioned access to large, distributed, real-time FHIR data sets from network participants
  • Stringent data governance to determine which data can be used for what purpose (ex., Which data elements can be utilized for training AI)
  • Network traceability and auditing to create a detailed understanding of the data and data transformations used in the modeling process.
  • Deployment of applications where the data resides via a private, secure, cloud-hosted environment where payers, providers, and other network participants can collaborate and transact directly with each other, allowing Gen AI solutions to be trained to generate responses based on custom data sets
  • Cloud GPU-based environments that can be configured to power Gen AI solutions at the edge
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The Avaneer Health Network enables Generative AI scalability, security, and data integrity without huge investments in time, finances, or resources—and it’s available today.

“While most healthcare leaders agree that Generative AI has the potential to transform the industry, few have developed a strategy for using the technology within their organizations or to collaborate across the continuum. It is important that these concerns be addressed before implementing solutions that put data, security, and patients at risk. Strategy is key to sensible Gen AI adoption that removes barriers and creates administrative efficiencies without wasting time, money, or putting patient privacy at risk.” 

Matt Blake
Senior Product Manager, Avaneer Health

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