The recent advances in natural language processing (NLP) have already proven its valuable capabilities in the healthcare industry. The technology largely demonstrates its potential in elucidating clinical documentation and enabling voice-to-text dictation. Thanks to its potential in interpreting complex, unstructured datasets, and in generating actionable intelligence, NLP has made its way into effective care delivery. It helps organizations to process voluminous amounts of data present in general linguistic form, and run advanced machine learning algorithms on it to attain valuable insights.
While ensuring the physicians’ ability to bestow maximum time to their patients and delivering more attention to them has become indispensable, NLP is increasingly proving to be an effective solution in this scenario. By embracing this tech, healthcare providers can focus more on developing solutions that can understand, assess, and create languages humans can understand.
NLP tools are also useful in areas such as voice-activated assistants and speech recognition platforms, creating better experiences by expanding patient access to information, trimming down transcription costs and delays, and advancing the quality of health records. The tools have also the potential to reduce stress and enable more face time during appointments. The technology can help improve the efficiency and accuracy of electronic health records (EHRs) by transforming the free text into standardized data.
The use of natural language processing tools in healthcare benefits care providers in several ways. It helps extract and interprets clinical notes precisely. As clinicians require exact information to make critical decisions concerning the treatment of their patients, they significantly use diagnostic outcomes and notes from doctors to comprehend patient profiles. That is why electronic medical records (EMR) pose undeniable importance. But information registered in EMRs is often in an unstructured form that creates challenges for practitioners while defining insights.
This is where NLP comes into the rescue, improving healthcare processes and results by effectively interpreting clinical notes. The technology hauls out details from diagnostic reports and doctors’ letters, and ensures the entirety and precision of patient health profile. While health systems are having difficulty using all the available data to its fullest potential, the use of NLP that leverages artificial intelligence can help analytics systems understand and work with unstructured data. It extracts useful data from EHRs and provides EHRs speech-recognition capabilities during clinician visits.
NLP tools can process unstructured data from various sources such as EMRs, literature, and social media, so that analytics systems can construe it. Once the tools convert the text to structured data, health systems can utilize it to classify patients, mine insights, and recapitulate information. Thus, the technology can make documentation requirements easier by enabling care providers to dictate their notes, or create tailored educational materials for patients ready for discharge. Considering a report, NLP in Healthcare is forecast to grow from US$1030.2 million in 2016 to US$2650.2 million in 2021, at a CAGR of 20.8 percent during the forecast period.