in Language Technology

Amid tales of language AI that can write its own poetry, crack its own jokes, and spin up a plausible, if not realistic, response to prompts that we usually expect people to struggle with, it is easy to wonder what practical good there is in such intensive development of natural language processing (NLP) systems while a global pandemic rages on. As it turns out, though, healthcare systems are shaping to be leading deployment for just this type of algorithm in 2022, following broad and focused plans from tech companies to develop innovative AI-based solutions to further automate workflows and scale communications in an industry that is undergoing transformational change in resource management.

Recently, in Google’s AI plan for 2022 and beyond, the AI leader separately emphasized assistive machine learning (ML) for clinical practices in healthcare and the expansion of NLP input functions. Simultaneous news, however, suggests these two seemingly disjointed directives actually go hand-in-hand with the general rollout of language AI in the medical community. Though this sector is in range for Google moving forward, the widespread deployment of NLP AI that is currently underway is now already being driven in large part by smaller projects of interest, reflecting a demand to develop and integrate automated language-based solutions, reduce costs, and streamline time-consuming documentation and analysis practices.

Globally, healthcare systems are becoming more reliant on automated solutions in the form of AI models and NLP algorithms to meet the needs of resource management and personnel distribution, especially if the software functions faster and to a higher degree of accuracy. To meet this challenge, many recent advancements in the healthcare AI community are being spearheaded by smaller tech companies focused on deploying customized NLP AI to areas of customer service and data collection. One of these companies, DigitalOwl, recently secured funds to expand the deployment of its NLP-based software for insurance companies that aims to help automate analysis of medical records, which is currently operational in several countries. By leveraging cutting-edge language processing technology, DigitalOwl’s software is designed to read and process thousands of electronic health records (EHRs) and streamline a time consuming and costly documentation practice that is also prone to human error. A similar development is being led by BirchAI, a company that develops customer-support based platforms for the medical industry, using its NLP technology to automate calls and digitize medical documents. BirchAI’s intelligent automation platform also addresses another leading challenge in healthcare: the allotment of manpower and resources to staff customer service departments and accurately document, collect, and assess medical data.

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In both subsectors of the healthcare system – insurance and customer service – the deployment of NLP to further automate, digitize, and analyze vast amounts of documents is coming at a time when many hospitals and healthcare networks are facing an unprecedented demand to better allocate resources and maximize information extraction and analysis. While it is impressive that the rollout of this technology serves to decrease the lengthy human-operated documentation practices and further reduce the costs involved in this process, DigitalOwl claims that NLP can analyze and collect information to a higher degree of accuracy, thus reducing the chance of hospital readmission on the basis of human error. Regardless, it remains clear that the healthcare and medical sectors are becoming an important market in the 2022 landscape as AI companies of all sizes continue to leverage customized language solutions and deliver sophisticated localization practices.

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