This problem even has a name - the EHR burden. Physician productivity and motivation suffer from the glut of repetitive administrative tasks that force them to spend extra hours at the computer instead of interacting with patients. Natural language processing offers opportunities to tackle several problems physicians, patients, and clinics have.Īlleviate clinician burnout. How can NLP benefit healthcare organizations? The software can accurately transcribe physician notes and then summarize them or further classify and extract data. Language modeling - understanding spoken text and producing natural sounding text.Say, the system can tag data from patient history, discharge summary, or call center reports and then structure them in an EHR according to a schema. Information extraction - retrieving valuable information from unstructured data.For example, organizing patient application forms by urgency or pinpointing fraudulent claims. Text classification - categorizing unstructured data.They’re smart enough to independently perform different NLP processes. But what you need to know here is that deep learning or deep neural networks can understand and analyze data with minimum preprocessing. You can read more about the NLP types and approaches in our dedicated article. Most modern NLP applications use state-of-the-art deep learning methods. The many healthcare factors hidden in unstructured data It creates barriers in already bloated administrative tasks and in case of emergency, can lead to medical hitches and delays. So, whenever physicians need information from textual forms, they must manually rummage through stacks of documents. It can be manually transformed into structured data by hospital staff, but it’s never a priority in the medical setting. Unstructured data is unavoidable, yet extremely valuable. This structure allows physicians and other software systems to easily locate needed data, share it, and analyze it, basically - make use of it.īut a lot of data (by different estimations, 70 or 80 percent of all clinical data) remains unstructured, kept in textual reports, clinical notes, observations, and other narrative text. For example, a patient’s name, age, and gender, their lab values, or financial information are stored in a database according to a predefined schema. Some of it is structured or organized into specific EHR fields. Healthcare organizations generate a lot of text data. This allows machines to extract value even from unstructured data. NLP-powered systems can derive meaning from what’s said or written, with all the complexities and nuances of natural narrative text. Natural language processing or NLP is a branch of AI that uses linguistics, statistics, and machine learning to give computers the ability to understand human speech. And since natural language processing is one of the fastest-growing AI fields in medicine, we wanted to talk about available applications, emerging trends, and ways to prepare for NLP adoption. Evolving research is set to fill any remaining gaps. In the US, clinics boast almost 100 percent EHR adoption numbers along with strict interoperability policies in place. Indeed, AI provides tons of life-saving opportunities, and healthcare organizations are prepared to accept them. Microsoft’s move tells a lot about the company’s (and the healthcare industry’s) priorities. Its deep learning natural language processing algorithm is best in class for alleviating clinical documentation burnout, which is one of the main problems of healthcare technology. Nuance, acquired for $19.7 billion (Microsoft’s biggest purchase since LinkedIn), provides niche AI products for clinical voice transcription, used in 77 percent of US hospitals. “AI is technology’s most important priority, and health care is its most urgent application,” said Microsoft’s CEO Satya Nadella announcing the company’s new acquisition. Build custom NLP healthcare tool Reading time: 12 minutes.Healthcare NLP challenges and how to prepare for adoption.How can NLP benefit healthcare organizations?.
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