Today at HIMSS24, Google Cloud (Booth 2512) announced new advancements to help healthcare and life sciences organizations enable interoperability, build a better data foundation for their businesses, and deploy generative AI tools to improve patient outcomes.

First up is enhancing its Vertex AI Search technology. Healthcare administrative costs were up by 30% in 2022, to reach $60 billion annually, physician burnout increased to 53% in 2022, and there is a shortage of more than 13 million nurses in the world.

This presents a clear opportunity for healthcare providers, payers, electronic health records companies and life science companies to build gen AI-powered systems that enable healthcare workers and other employees to work more efficiently and effectively, Google Cloud said.

Building better assistive technology

Launched today, Google Cloud's Vertex AI Search for Healthcare helps developers build better assistive technology for clinicians and other health system workers to alleviate administrative burden. Specifically, it enables medically tuned, genAI search on a broad spectrum of data, including FHIR data and clinical notes.

These search and question-answering capabilities now integrate 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 next-generation health systems, the company contended.

"Not all generative AI is created equal, and in healthcare, the stakes are particularly high," said Aashima Gupta, global director for healthcare strategy and solutions at Google Cloud. "Healthcare organizations require enterprise-grade genAI solutions, grounded in real data.

"Vertex AI Search for Healthcare is already making a difference for healthcare organizations by helping ensure clinicians have the right information and insights at the right time to inform decisions and improve the overall quality of patient care."

Highmark Health constantly is seeking ways to harness the power of data and technology to transform the healthcare ecosystem, said Richard Clarke, chief data and analytics officer at the health system. "Google Cloud's Vertex AI Search integration with Healthcare Data Engine will enable us to provide even more personalized and proactive care to our members."

Data for the 'AI era'

On another front, Google Cloud announced today enhancements to its Healthcare Data Engine, saying it's now a "healthcare data platform for the AI era."

To help healthcare organizations build an interoperable, high-quality data platform, the foundation to take advantage of genAI, Google Cloud has debuted a new consumption-priced managed service of Healthcare Data Engine, and expanded availability internationally with new features. Key updates include:

• Simplified management and streamlined pricing. Customers can now deploy HDE as a consumption-priced, pay-as-you-go managed service, which opens up the product to more healthcare organizations, helping them to deploy, build and manage a near-real-time healthcare data platform in the cloud.
• Low-code graphical data mapping IDE. By introducing HDE Data Mapper, a new low-code graphical integrated development environment (IDE), purpose-built by Google Research for healthcare, customers can transform their data to build high-quality, longitudinal patient records in FHIR format to power genAI applications.
• Foundation for AI and analytics systems. By integrating HDE with Vertex AI Search for Healthcare, clinicians can look across multiple systems and formats in one search, saving valuable time that can instead be focused on patient engagement and improving the overall patient experience. Integration with MedLM will allow customers to answer complex questions grounded in the patient's data.

X-rays and condition summaries

And finally, the company announced new capabilities for MedLM, a family of foundation models fine-tuned for healthcare industry use cases. There are two new capabilities for Google Cloud customers to explore and test.

First, MedLM for Chest X-ray can help with classification of chest X-rays for operational, screening and diagnostics use cases. It is a domain-specialized model, launched as an application programming interface that converts chest X-ray images into embeddings. App developers and data scientists can use those embeddings 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 that aims to provide a chronological list of patient conditions, along with AI-generated briefs about each condition, with citations from original text.

In the coming months, Google Cloud is planning to bring additional functionality to the MedLM suite to offer even more capabilities.

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