The EHR vendor has been partnered with Google Cloud for about five years. Over the past year, Meditech has deepened its relationship with the tech giant by exploring ways to embed Google’s generative AI into its EHR. But Meditech is not adding generative AI to its EHR capabilities just because that seems like the hot thing to do right now — the company is approaching its generative AI efforts in a steady, intentional way, COO Helen Waters said in an interview this week.
Given that the digital health field is in the midst of a generative AI hype cycle, it’s imperative that companies in this space don’t fall into the trap of implementing new technologies just for the sake of adopting something new and exciting, Waters noted. Meditech is avoiding this by focusing its generative AI efforts on specific use cases that the company thinks will have serious potential to alleviate clinicians’ burnout, she said.
For one of its generative AI projects, Meditech is using Google’s large language models (LLMs) to power the search and summarization experience within its EHR. Meditech is relying on Google’s LLMs for data harmonization so that clinicians can quickly access a longitudinal view of their patient. In other words, the effort is seeking to ensure clinicians have quick and easy access to all relevant information about a patient — including health data from the Meditech Expanse EHR, health data from legacy technology platforms, scanned handwritten notes and medical images.
Having swift access to a comprehensive view of their patient accelerates physicians’ ability to make sound, informed decisions about treatment, Waters pointed out.
Meditech is also exploring how to layer Google’s Med-PaLM 2 into its EHR’s search and summarization capabilities. Unveiled in April, Med-PaLM 2 is a medical AI system that harnesses the power of Google’s LLMS. The tool is currently being piloted at Mayo Clinic and other health systems — they are testing its ability to answer medical questions, summarize unstructured texts and organize health data.
Once multiple LLMs are layered together into the EHR, clinicians may soon be able to ask the EHR more intelligent questions about the patient data that’s being summarized, said Rachel Wilkes, Meditech’s director of marketing.
As it works to integrate Google’s generative AI into its technology, another use case that Meditech is focusing on is the auto-generation of clinical documentation. Specifically, the company is developing an EHR functionality that will generate “hospital course narratives” — predictive summaries of what a patient’s stay might look like at the time of admission.
When a patient has an acute inpatient hospital stay, the length of the stay and the complexity of the care provided can make the documentation process quite arduous at the time of discharge. Providers tell Meditech that this documentation process can take 30 minutes of a clinician’s time each time a patient leaves the hospital, Wilkes pointed out.
She said Meditech is currently working with Google to determine the best way to leverage its LLMs to generate hospital course narratives in the EHR when patients are admitted. These summaries of what a patient’s stay could potentially look like will be presented to clinicians in their Meditech Expanse workflow, and they will have the option to edit the summary or any drafted pieces of documentation included within it.
“We’re not doing this just to do it. We’re looking to see how we can use this technology to make sure we can help our organizations deliver safe, efficient, impactful care. We’re doing this in a thoughtful, deliberate way. We’re doing a very careful review of the use cases that we’re pursuing, how they impact existing workflows and how we can embed this into Expanse for the betterment of the experience for our users,” Wilkes declared.
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