Andrei Kasyanau, cofounder and CEO at Glorium Technologies. Startup advisor and an expert in health and real estate tech.
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The stories of Blockbuster, Kodak and Borders serve as reminders of what happens when companies fail to adapt to technological advancements, leading to their decline. In the healthcare sector, we're at a similar crossroads with AI.
The big question for healthcare leaders is whether to start using AI now or wait. Nevertheless, recent reports about AI usage show enormous potential for significant cost cuts. Private payers could see impressive annual savings of $80 billion to $110 billion over the next five years (pg. 22). Similarly, physician groups stand to save between 3% and 8% of their costs, which could mean an additional $20 billion to $60 billion in savings (pg. 23).
Generative AI In Healthcare
Here, generative AI is taking a leading role. The newest GPT-based LLMs serving as chatbots or virtual assistants make tasks such as scheduling, diagnostics, visit summarization and handling claims faster, letting staff focus on more complex problems. As a result, it saves money by cutting down on manual work and expanding service hours beyond regular times.
Despite the swift advancement of generative AI, traditional predictive AI remains crucial and is expected to be around for a while. While generative AI in healthcare excels at creating new data and simulations, aiding in drug discovery and personalized treatment plans, predictive AI focuses on analyzing existing data to forecast health outcomes and risks, enhancing preventive care and diagnosis accuracy. Both technologies revolutionize healthcare but cater to different needs: generative AI for innovation and personalization and predictive AI for risk assessment and early intervention.
Notable cases like the Cleveland Clinic and UPMC stand out in demonstrating the ability to implement AI. These institutions have successfully integrated predictive AI into their systems to streamline processes and enhance patient care.
Another example is a concierge management system, which uses AI to find high-risk patients and create personalized care plans. Such an approach has cut readmissions and saved money. These examples demonstrate the cost-efficiency benefits of AI in healthcare. However, numbers only show part of the impact.
Beyond Cutting Costs
AI wearables monitor health constantly, catching problems early to prevent serious health issues. They help patients manage their health with custom reminders, leading to healthier habits. As a result, fewer doctor visits and hospital stays save healthcare resources.
AI's transformative role in healthcare delivery extends beyond hospital and clinical settings; pharmaceutical companies and scientists have always been at the forefront of AI use. The rise of AI in life sciences could enhance health by speeding up drug discovery, allowing us to treat diseases faster and focus more on underserved areas.
Another benefit of implementing AI involves using medical imaging, such as X-rays and MRIs. Generative AI models help scientists find new disease markers, leading to better treatments and shorter clinical trials. The result is a 10% higher success rate for trials, 20% lower costs and time and quicker approval by up to two years, amplifying the value of medical projects.
Barriers To Adoption
If AI in healthcare can be so cost-efficient, why is the adoption of AI so slow? AI's limited uptake can be partly explained by the difficulty of addressing skill gaps, regulatory compliances, data privacy and security concerns.
Additionally, despite the potential for cost savings in the long run, the initial investment for implementing AI in healthcare can be high. This includes the cost of the technology, staff training and workflow adaptation. Special consideration should also be given to the digital literacy of older patients as they resist using new technologies. Healthcare leaders need to take care of these potential obstacles when implementing AI.
It is the beginning of the AI revolution, and all these challenges are continuously being solved. As one of the most efficient approaches to handling these matters, healthcare leaders should consider finding trusted and experienced partners. It can significantly reduce the time-to-market and cost of implementation by avoiding wasteful steps. Bringing on strategic resources, such as a fractional Chief AI Officer, is also very important.
Moreover, the significant challenge of high costs during the initial implementation and deployment phases can be readily addressed through, for example, open-source models instead of proprietary ones. Understanding data flow costs will help as well. Conversely, using high-capacity models, such as Gemini 1.5, which supports up to 1 million tokens, can deliver results without additional costly infrastructures and solutions.
The usage of AI technologies naturally leads to the context of AI regulation. Last year marked a significant shift as the U.S. and EU governments initiated the AI regulation documents. For example, U.S. senators introduced the Artificial Intelligence Research, Innovation, and Accountability Act of 2023. This act is part of the U.S.'s ongoing efforts to create a secure, innovation-friendly AI development and use environment.
The latest example of such efforts is the UN General Assembly resolution adopted on March 21, 2024. It's about making AI systems safe, secure and trustworthy, aiming to benefit sustainable development universally. Consequently, these acts set the stage for more strategic and widespread adoption of AI. This changing scene shows a bright, safer future where AI boosts efficiency and saves money, making early investments worthwhile.
Looking Ahead
This year, I expect further innovations, savings, efficiency and transparent regulations, making AI essential in healthcare and other fields. More companies and organizations are adopting AI technologies at a faster pace. This landscape is perfect for the early adoption of AI technologies by healthcare organizations and startups. It will give them a competitive advantage and streamline operations.
However, the transition will cause multiple changes in technologies and regulations. One might assume that waiting for stable conditions might mean fewer risks associated with AI technology. But it should be understood that such an approach will sharply increase the probability of falling behind the competition and missing out on ways to save costs and, more importantly, increase revenue.
Ultimately, the choice hinges on a company's risk appetite, market position and agility to adapt to new technologies. This decision will significantly influence their competitive edge, innovation capacity and long-term sustainability in an ever-evolving digital landscape.
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