Large language models, other GenAI options stimulating tech purchasing in healthcare
Budgeting for generative AI in healthcare has skyrocketed, albeit in pockets, by as much as 300% year over year, according to a survey of technology decision-makers spanning providers, vendors and other employers in the healthcare sector.
The survey takers heard from 304 respondents, most of whom work for organizations or companies that are “actively engaged in evaluating, utilizing or deploying Generative AI (GenAI) technologies.”
Provider people made up the bulk of the field, 46%, followed by healthtech executives (11%), pharma professionals (9%), digital health representatives (7.5%) and smaller samplings of individuals from academia, health insurance, biotech/medical devices and public health.
John Snow Labs ran the project with a hands-on assist from Gradient Flow. Here are some key findings from their survey report.
2024 GenAI budget as compared with 2023:
- Increased by more than 300%—8% of respondents
- Increased by 100% to 300%—13%
- Increased by 50% to 100%—22%
- Increased by 10% to 50%—34%
- Remained roughly the same—23%
Top use cases for large language models (LLMs):
- Answering patient questions—21% of respondents
- Medical chatbots—20%
- Information extraction/data abstraction—19%
- Biomedical research—18%
- Clinical coding/Chart audit—17%
Currently used LLM models:
- Healthcare- and task-specific models (“small”)—36% of respondents
- Open-source—24%
- Open-source (“small”)—21%
- Proprietary, through a SaaS API—18%
- Org’s own custom model/s—11%
- Proprietary, as a single tenant or on-premises—7%
Importance of criteria for evaluating large language models (1 to 5 scale, mean response):
- Accuracy—4.14
- Security & privacy risk—4.12
- Healthcare-specific—4.03
- Reproducible & consistent outputs—3.91
- Legal & reputation risk—3.89
- Explainability & transparency—3.83
- Cost—3.8
Steps taken to test and improve large language models:
- Human in the loop—55%
- Supervised fine-tuning—32%
- Interpretability tools & techniques—25%
- Adversarial testing—23%
- De-biasing tools & techniques—22%
- Guardrails—22%
- Quantization and/or pruning—20%
- Red-teaming—20%
- Reinforcement learning from human feedback—18%
Offering closing thoughts, the authors note the wide range of use cases to which end-users are applying GenAI in healthcare.
“The shared belief that LLMs will have the most transformative impact on patient-facing applications—such as transcribing conversations, providing medical chatbots and answering patient questions—aligns with the growing need for accessible and efficient healthcare,” they comment. More:
‘With continued investment, collaboration, and thoughtful implementation, GenAI stands to redefine healthcare in ways we are only beginning to imagine.’
Dave P. has worked in journalism, marketing and public relations for more than 30 years, frequently concentrating on hospitals, healthcare technology and Catholic communications. He has also specialized in fundraising communications, ghostwriting for CEOs of local, national and global charities, nonprofits and foundations.