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.
Clinical Use Cases
The initial clinical use cases for AI, including Generative AI, in healthcare are focused on supporting care delivery and include:
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.
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.”
“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
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.
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:
