Natural language processing (NLP) allows systems to understand and generate human-like text, which creates contextually relevant messages. An article published in IBM titled ‘What is NLP?’ notes that NLP is useful for fully or partially automating tasks like customer support and data entry. According to the article, “For example, NLP-powered chatbots can handle routine customer queries, freeing up human agents for more complex issues.” This helps craft automated text messages with a personalized tone based on comprehensive profiles and patient responses to previous messaging.
NLP empowers automated messaging platforms to classify messages into specific topics like medical, social, and logistical issues. For example, during the COVID-19 pandemic, NLP played a crucial role in analyzing SMS conversations between patients and healthcare providers. This helped guide public health response and laid the groundwork for more effective strategies in future health crises. When integrated with electronic health records (EHRs), NLPs can further help automate clinical documentation by extracting patient data from formats like HIPAA compliant email. In turn, it can use the data from EHRs to enable virtual assistants to handle routine inquiries and improve patient understanding by simplifying complex medical terminology.
Sentiment analysis uses NLP and machine learning algorithms to classify messages as positive, negative, or neutral, enabling automated systems to respond appropriately. In customer service scenarios, sentiment analysis can detect negative feedback and trigger immediate, empathetic responses, potentially turning a dissatisfied customer into a satisfied one. When combined with HIPAA compliant text messaging systems to personalize responses based on the emotional context of patient-provider interactions, it creates a more empathetic and human-like communication experience while still providing secure communication.
NLP plays a part in the extraction, structuring, and analysis functioning of predictive modeling. Predictive models can identify patterns in data that help anticipate and manage administrative burdens more effectively. It can forecast which insurance claims are likely to be denied, allowing staff to prioritize and address these claims proactively, thereby reducing the time spent on manual review and follow-up.
Predictive modeling can also optimize appointment scheduling by predicting patient no-show rates and adjusting schedules accordingly, minimizing wasted time and resources. Automating tasks like claims processing, medical records management, and billing, predictive modeling frees up administrative staff to focus on more strategic and patient-centric activities.
NLP primarily helps in extracting meaningful information from unstructured data within EHRs.
Common NLP techniques include NER, sentiment analysis, text classification, and dependency parsing.
NLP automates the extraction of critical information from sources like handwritten clinical notes, reducing errors and speeding up documentation processes.