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Google Cloud’s AI models power DeliverHealth’s new solution to simplify clinical documentation

The solution is developed to ease the manual documentation burden on clinicians to help them focus more on patient care.

Google Cloud SummitGoogle Cloud offers a variety of AI models. (Image credit: Vivek Umashankar/The Indian Express)

On Thursday, at the Google Cloud Startup Summit held in Bengaluru, the company announced its collaboration with DeliverHealth to help them transform medical documentation using advanced Gemini 1.5 Pro multimodal AI models.

DeliverHealth, a clinical documentation management company, uses Google Cloud’s advanced Gemini 1.5 Pro multimodal AI models. These models are trained on an expansive repository of 1,50,000 hours of human-curated medical audio notes each month, allowing an AI-powered solution that lets clinicians document patient care entirely via voice. This solution also assists clinicians in summarising operative reports, lab results, radiology reads, and inpatient notes efficiently.

The company said that, with its Google Cloud-powered AI solution, it can reduce the burden on clinicians, helping them focus on patient care rather than documentation. “Our collaboration with DeliverHealth is a reflection of our shared commitment towards innovation through the transformative potential of GenAI by automating tedious tasks and improving efficiency and accuracy. Together, we aim to reimagine,” said Bikram Singh Bedi, vice president and country MD of Google Cloud India.

“With Google Cloud’s AI models and our ability to create human-curated medical documentation, clinicians can simply speak and know their words will be accurately transformed into structured, billable documentation. This is about more than innovative technology—it’s about giving caregivers more time and peace of mind to focus on what they do best, caring for patients,” said Sasanka Yellamanchali, CEO, DeliverHealth.

The new solution co-developed by DeliverHealth and Google Cloud uses the medical vocabulary for speech recognition using natural language processing to fully document each patient encounter. This is said to improve patient-physician interaction by enabling accurate documentation.

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