Microsoft and Epic have entered into a partnership to integrate OpenAI’s Artificial Intelligence (AI) technology into Epic’s Electronic Health Records (EHRs). This partnership will help streamline inquiries within the health information system by leveraging natural language query capabilities and interactive data analysis. Moreover, the AI-based tools will be useful in drafting response messages automatically with more precision and accuracy.
Epic is a healthcare software company that provides software solutions for healthcare organizations, health information exchange, and patient engagement. The company’s SlicerDicer product offers analytics, with support for local and broader inquiries. Seth Hain from Epic confirms that AI integration from OpenAI promises to improve the power and accessibility of self-service reporting through SlicerDicer. This will make it easier for healthcare organizations to identify potential improvements and cost savings in their operations.
This isn’t the first time Microsoft and Epic have partnered together. Prior to this, a partnership between the two companies allowed Epic environments to be hosted on Azure cloud platform. This partnership was announced in September 2020, during the height of the COVID-19 pandemic, which allowed healthcare professionals to launch virtual visits directly from the EHR systems.
Tech companies have recognized the need to incorporate AI technologies in the healthcare sector, due to financial strains and labor shortages caused by the pandemic. Towards this end, Google released in 2021 a version of its generative language model to healthcare customers, competing against OpenAI’s GPT-4 model. Cerner, an EHR company, also partnership with another organization to develop AI tools for the FDA to automatically extract data from medical notes.
Adding AI technology to EHR systems has enormous potential to reduce the burden of documentation and prevent burnout in the healthcare sector. With the implementation of AI-based integrations, healthcare organizations can more efficiently handle patient data and analyze the impact of medicines on large populations.
