Today at HIMSS24, Google Cloud (Booth 2512) announced new developments to help healthcare and life sciences organizations enable interoperability, build a better data foundation for their businesses, and leverage generative AI tools to improve patient outcomes .
First is to improve the Vertex AI Search technology. Healthcare administrative costs will increase by 30% in 2022, reaching $60 billion annuallyburnout among doctors increased to 53% in 2022, and there is a shortage of it more than 13 million nurses in the world.
This presents a clear opportunity for healthcare providers, payers, electronic health record companies and life science companies to build generational AI-powered systems that help healthcare professionals and other employees work more efficiently and effectively, according to Google Cloud.
Building better assistive technology
Launched today, Google Cloud’s Vertex AI Search for Healthcare helps developers build better assistive technology for physicians and other healthcare workers to reduce administrative burdens. In particular, it enables medically tailored genAI searches on a broad spectrum of data, including FHIR data and clinical notes.
These search and query response capabilities are now integrated with MedLM, Healthcare Data Engine and Cloud Healthcare FHIR APIs, making it easier for healthcare and life science organizations to build the data analytics and AI systems needed for the next generation healthcare systems, the company said.
“Not all generative AI is created equal, and in healthcare the stakes are especially high,” said Aashima Gupta, Global Director for Healthcare Strategy and Solutions at Google Cloud. “Healthcare organizations need enterprise-level genAI solutions based on real data.
“Vertex AI Search for Healthcare is already making a difference for healthcare organizations by ensuring physicians have the right information and insights at the right time to inform decisions and improve the overall quality of patient care.”
Highmark Health is continually looking for ways to leverage the power of data and technology to transform the healthcare ecosystem, said Richard Clarke, chief data and analytics officer at the healthcare system. “Google Cloud’s Vertex AI Search integration with Healthcare Data Engine allows us to provide even more personalized and proactive care to our members.”
Data for the ‘AI era’
On another front, Google Cloud today announced improvements to its Healthcare Data Engine, saying it is now a “healthcare data platform for the AI age.”
To help healthcare organizations build a high-quality, interoperable data platform, the foundation for taking advantage of genAI, Google Cloud has introduced a new consumer-priced managed service of Healthcare Data Engine and expanded its availability internationally with new features. Major updates include:
- Simplified management and streamlined pricing. Customers can now deploy HDE as a managed service at a consumer price, opening the product to more healthcare organizations and helping them deploy, build and manage a near real-time healthcare data platform. cloud.
- Low-code graphical data mapping IDE. By introducing HDE Data Mapper, a new low-code graphically integrated development environment (IDE) built by Google Research specifically for healthcare, customers can transform their data to build high-quality, longitudinal patient records in FHIR format to power genAI applications to drive.
- Foundations for AI and analytics systems. By integrating HDE with Vertex AI Search for Healthcare, clinicians can look across multiple systems and formats in a single search, saving valuable time that can instead be spent on patient engagement and improving the overall patient experience. Integration with MedLM allows customers to answer complex questions based on patient data.
X-rays and condition summaries
And finally, the company announced new capabilities for MedLM, a set of core models tailored to healthcare use cases. There are two new capabilities for Google Cloud customers to explore and test.
First, MedLM for chest radiographs can assist in the classification of chest radiographs for operational, screening, and diagnostic applications. It is a domain-special model, launched as an application programming interface that converts chest X-rays into embeddings. App developers and data scientists can use this embedding along with ground-truth labels to train a simple classification model in Vertex AI.
The second new capability is a task-specific API called Condition Summary, which aims to provide a chronological list of patient conditions, along with AI-generated instructions about each condition, with quotes from the original text.
Google Cloud plans to add additional functionality to the MedLM suite in the coming months to provide even more capabilities.
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