This year may be the most innovative on record. Recent advances in AI are beginning to transform how we live and work. And the potential impacts of artificial intelligence (AI) on the healthcare and life sciences industries are expected to be far-reaching.
It’s essential for organizations to leverage vast amounts of structured and unstructured data for effective generative AI (gen AI) solutions that deliver a clear return on investment. However, the volume and breadth of sensitive, regulated data that healthcare and life sciences organizations collect, create and manage represents a major challenge.
The healthcare industry alone has approximately 30% of the world’s data volume. That vast amount of data comes with both tremendous responsibilities (and regulations) to maintain the highest levels of patient privacy, as well as incredible opportunities to solve tough medical and industry challenges, such as the ongoing staffing crisis.
For a glimpse into how these industries will be using AI in 2024, we sat down with our Snowflake healthcare and life sciences industry experts to discuss their predictions for the year ahead. Below are a few of their top predictions for 2024. For their full insights, read the new report, Healthcare and Life Sciences Data + AI Predictions 2024.
1. Generative AI could help improve operational efficiency for healthcare payers and providers
The immense pressures COVID-19 put on the healthcare system and its lasting impacts are requiring payers and providers to meet patients’ needs more quickly and effectively across the entire healthcare ecosystem. Introducing new technologies is integral to solving these staffing and operational inefficiency challenges.
This year, payers and providers will implement gen AI solutions to improve human efficiency, deliver improved outcome-based care, and augment operational systems and processes. For example, payers and providers can use AI-powered tools and solutions to streamline cumbersome claims processing or to write discharge summaries. These tools could also help analyze historical electronic health record data and social determinants of health data to identify the most effective interventions for specific conditions or populations.
he human touch will always drive the best patient care and patient outcomes. AI can never replace that. But it holds tremendous promise to improve efficiency across the healthcare system so more time can be spent caring for patients.”Jesse Cugliotta, Global Industry GTM Lead, Healthcare & Life Sciences at Snowflake
2. Healthcare systems will be able to shift from reactive to predictive management using AI and data
There is no shortage of challenges in healthcare today. Its staff and systems are strained beyond capacity by ongoing supply chain management issues, a continued widespread staffing crisis and significant cost pressures. Last year, the U.S. Surgeon General issued an advisory warning about a projected deficit of more than 3 million essential healthcare workers until 2027, and a projected shortage of about 140,000 physicians by 2033. This is happening while hospital supply chain overspending costs an estimated $25.4 billion annually — accounting for nearly 30% of all hospital spending in the U.S alone.
The strains on healthcare systems are also compromising timely patient care, making it difficult to adhere to value- and outcome-based care regulatory requirements worldwide. These systems must get better at predicting both near-term and long-term needs. Predictive analytics, driven by AI and machine learning, make it possible for healthcare leaders to answer tough, complex questions about their organization’s future outcomes and demands.
3. Life sciences and healthcare companies will be able to bring gen AI solutions and LLMs in-house to better maximize return on investment
The decision to develop in-house large language models (LLMs) for life sciences and healthcare organizations will be a strategic one in 2024. Data security and governance aren’t the only reasons leading organizations will take this approach. Using their own and third-party data in in-house LLM solutions will provide them with multiple competitive advantages, including cost efficiency and scalability, faster response times, customization and control, and long-term sustainability.
“Enterprises can also scale their in-house infrastructure based on their evolving needs. And they can fine-tune the models internally, allowing them to optimize performance without incurring additional costs for each improvement iteration,” says Cugliotta.
Maintaining regulatory compliance will continue to be essential and top of mind for healthcare and life sciences organizations in 2024. This year, innovative companies will begin to build their own LLMs in-house to ensure regulatory compliance with full control over data governance and security for more complex use cases across their ecosystem.
Get the full report, Healthcare and Life Sciences Data + AI Predictions 2024 or watch the webinar.