Natural Language Processing and Digital Health Transformation, Health News, ET HealthWorld
By Satish Kumar
In previous years, digital technologies have been widely adopted by healthcare organizations. He has helped healthcare organizations reduce efficiency, improve access and improve quality of care. Digital Healthcare also helps prevent chronic diseases. Artificial intelligence is one of the key technologies driving this transformation. It transforms the entire healthcare delivery value chain, including critical clinical and administrative processes. Many of these exciting AI applications use natural language processing.
Natural language processing (NLP) is a sub-branch of AI that deals with algorithms and models to process language as human beings do. NLP uses deep learning algorithms to transform unstructured data into structured data. Clinical notes and physician opinions, even in electronic health record (EHR) systems, are typically in unstructured text. NLP systems can analyze this unstructured data and derive useful medical information from it. NLP used with Record Linkage and other AI technologies such as genome analysis can help deliver personalized medicine.
NLP systems can evolve through newly generated medical knowledge in articles and research papers can create useful summaries of them. The availability of these EHR-linked summaries will help physicians make more informed clinical decisions and optimize treatment plans.
Health care is shifting to value-based care and one of the critical success factors in this transition is the patient experience. A virtual medical assistant can help collect patient information such as demographics, insurance, etc. It can guide the patient to enter self-measured physiological parameters. In the form of questions and answers or chat, these visual assistants ask the user about the symptoms or the disease. Once the assistant gets the answer, it matches it with possible diagnoses and creates probabilistic outcomes for the next step – which can be another question, pass it to a human agent, or tell the patient what to do next. Building this intelligence requires the agent to use NLP to tokenize the response, build the feature set, and match the information using pre-trained deep learning models.
Training these deep learning models requires an enormous amount of data about disease causes, symptoms, and diagnoses. Most major hospitals and healthcare providers have electronic health records, which must be anonymized before using them for model training and validation. Virtual assistants have improved their accuracy dramatically over the past few years, but the best results can be achieved when paired with a human doctor.
NLP-based assistants can also be used for routine administrative tasks such as self-scheduling/rescheduling/cancellation of appointments by the patient in a healthcare facility. For better adoption, they can be integrated into mobile applications and activated by voice.
NLP and AI can also play an important role in disease diagnosis and prevention. AI systems are used for surveillance and outbreak detection. These are essential functions of public health and are essential to protect the population against infectious diseases and their variants. AI systems can help prioritize drugs or vaccination when capacity is limited.
During the pandemic, hospitals have used pre-hospital digital triage to direct the patient to an appropriate care setting like home quarantine, respiratory clinics, testing sites, surge facilities, emergency rooms, etc.
NLP and other AI technologies are playing an important role in the transition from public health to precision public health. This will help deliver the right intervention to the right population at the right time and includes consideration of the social determinants of health.
By analyzing structured/semi-structured data in EMR/EHR systems using AI, at-risk and vulnerable populations can be identified. This can help deliver personalized public health messages and other public health measures.
Using NLP for analyzing social media feeds, newspaper snippets, and other media content can aid in environmental scanning and situational awareness. Clustering and spatial analysis as well as information on public health reportable diseases will also help identify the new disease or pathogen outbreak.
These two datasets can be cleaned, processed, and combined with other datasets to train context-aware machine learning models to estimate disease prevalence and burden. The prevalence and burden of disease and their projected values in the future will help authorities plan surge capacities for medical supplies and hospital capacities.
Q&A platforms powered by NLP/chatbots can provide personalized support or advice in the relevant context. They can also help to know the social needs of patients during hospital visits.
NLP topic modeling and sentiment analysis can also help measure adherence/acceptance of public health measures and public concern.
Developing these NLP models will require different datasets from many different organizations that needed to be connected. In addition, data must be exchanged between testing laboratories and healthcare providers.
Data exchange between different healthcare organizations and public health systems should be done using HL7/ISO data exchange protocols.
Changing prevalence and burden of disease will require new clinical and public health guidelines. Deep learning models should be continuously trained with new/updated data and new/updated public health guidelines.
For NLP systems to remain relevant, this entire process, from data acquisition to deep learning model training, must be automated. Automating these will ensure that the Question & Answer virtual assistants are always up to date.
The adoption of AI in organizations has increased many times over the past two years. It is certain that AI will improve healthcare jobs. Ideas and visualizations. This will amplify the decision-making of healthcare workers. Future applications of AI will be more oriented towards Explainable AI (XAI). Bringing XAI is key to building trust in medical AI as humans, whether patients or physicians need to understand the rationale for decisions. XAI will also accelerate the adoption of AI in public health and healthcare organizations.
By Satish Kumar, CEO of Suparna Systems
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