The healthcare market has been inundated with new AI solutions, and to be sure, some of these solutions apply AI meaningfully and effectively; however, there are just as many solutions that promise to revolutionize care delivery and administration. In reality, many of these solutions amount to little more than using AI-powered chatbots to marginally reduce manual workflows and overhead, while charging customers exorbitant sums for the service.
The trend of AI companies moving into healthcare and healthcare companies moving into AI – fueled by large tech investments – is clear evidence of AI’s massive potential. Yet even the most well-established AI companies have struggled to adapt their solutions to the complexities of healthcare. ChatGPT is widely regarded as the most advanced generative AI available to the public, yet when researchers for JAMA Pediatrics recently put ChatGPT to the test, the program incorrectly diagnosed an astonishing 83% of pediatric cases.
These low-ROI applications make it harder for legitimate AI solutions from established healthcare AI organizations to gain a foothold in the market. And this pattern of overpromising and underdelivering will inevitably engender skepticism among healthcare leaders, which in turn will hamper adoption of AI-enabled solutions across the industry.
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To effectively utilize AI in healthcare, healthcare leaders first need to understand what AI is capable of and, just as important, what it isn’t. A more complete understanding of AI is essential to help the healthcare industry separate truth from hype. Provider organizations in particular need to be very careful in how they apply AI, especially in regard to patient care. But there is a sweet spot for AI in the healthcare continuum: to address the overwhelming administrative burden many organizations are wrangling with. AI has tremendous potential to help with administrative simplification, risk adjustment, and value-based care.
Much of the buzz surrounding healthcare AI is centered on the potential of generative models. Boston Consulting Group claims that generative AI can read and analyze MRIs, diagnose conditions, create personalized treatment plans for patients, improve healthcare data interoperability, and even support population health initiatives.
The potential for these applications certainly exists, but there are some significant hurdles to be cleared before the healthcare industry can meaningfully apply generative AI with little to no human input. Generative AI is expected to make mistakes as it learns; these mistakes are, in fact, how the AI learns. Other industries can afford to take a trial-and-error approach, but that isn’t the case in healthcare, where the stakes are much higher, and failure can be fatal. Care providers have a legal and moral duty to protect the lives of their patients, and there is simply no room for error.
Despite these challenges, the imperative to adopt AI in healthcare is clear. The industry’s move towards value-based care requires massive amounts of patient/member and population data to be effective. The more data healthcare organizations collect, the more administrative work is needed to manage it, and the more the industry spends on administrative tasks. A recent report from the Council for Affordable Quality Healthcare (CAQH) found that the healthcare industry spent $60 billion on administrative tasks in 2022, an $18 billion increase from 2021.
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Abarca executives see plenty of potential for new technologies but note the need to strike the right balance between tech and human intervention.
Most patient data is unstructured (images, chart notes, non-OCR PDFs and faxes, etc.) and is not easily organized or pulled into healthcare systems for further analysis. As a result, valuable insights about patients/members and populations often remain inaccessible to healthcare organizations. Administrative overhead can significantly be reduced by applying AI to help make human workers more efficient. Generative AI can deliver contextual data to provide details and assistance to dramatically decrease administrative tasks.
Healthcare organizations also operate across disparate systems, which exacerbates the challenge of data sharing. The insights providers and health plans exchange with one another are often fragmented and incomplete, which in turn creates friction between partner organizations. Overcoming these obstacles is vital for success in value-based care.
Effectively harnessing and applying data-derived intelligence is one major hurdle for the healthcare industry. Another is the need to ensure the fidelity and accuracy of that data. AI models are only as accurate as the data they are trained on; healthcare organizations must prioritize data fidelity to optimize the accuracy of AI-driven insights.
The road to meaningful AI adoption in healthcare is a long one. Nevertheless, the healthcare industry’s data problem underscores the urgent need for AI support. By prioritizing data accuracy and interoperability, stakeholders can begin to unlock the potential of AI in healthcare. As it always has, the industry will continue to evolve. Reshaping the future of healthcare delivery and administration will require a responsible and judicious application of AI solutions. And while AI can augment the work humans do, it is important to remember that AI alone is not an acceptable substitute for human interaction and clinical evaluation.
Photo: Sylverarts, Getty Images
As Chief Technology Officer, Sundar Shenbagam is responsible for setting Edifecs’ technology direction and strategy. He has extensive experience in Agile process, quality-first development, and converting on-premise products to cloud services. Sundar joined Edifecs from Oracle where he spent 24 years building multiple enterprise products and cloud services. Most recently he led the Oracle AI Voice Digital Assistant cloud service, Oracle AI automation cloud service and Oracle BPM suite of products. Sundar has a Master’s degree in Computer Science from IIT Bombay.
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