Why Consider NLP in Healthcare?
Before we dive into the use cases, let’s first answer the question, "What is NLP in healthcare?" Natural Language Processing is a branch of artificial intelligence that enables computers to comprehend, interpret, and produce human language. By analyzing speech and text, it aids in the conversion of unstructured data into structured ones. When used in the healthcare industry, NLP makes it easier for healthcare experts to extract necessary insights from large amounts of medical data.
Challenges that NLP Solves
Since its adoption in healthcare, NLP has proven incredibly beneficial in addressing challenges with patient records, massive unstructured datasets produced from clinical notes, and documentation.
Benefits of NLP
There are numerous benefits of Natural Language Processing integration in healthcare. From enabling quick analysis of large datasets to quick decision-making, NLP helps in all areas of the industry. Besides, it also aids in improving communication between healthcare providers and patients through virtual assistants and chatbots that can respond immediately.
7 NLP Use Cases in Healthcare: An Overview
Below is an overview of the use cases we will cover in this blog.
1. Clinical Documentation Automation
The Challenge
Clinical documentation is an extremely time-consuming process. However, since it is vital to ensure the continuity of care, healthcare professionals place major emphasis on it, significantly increasing their burden.
How Can NLP Help?
NLP aids in streamlining the process by automating clinical documentation. Advanced NLP for healthcare tools can transcribe and interpret clinician-patient interactions and convert unstructured text or speech into structured data in EHR systems.
2. Disease Progression Prediction
The Challenge
Chronic diseases like Alzheimer’s, cardiovascular disease, and diabetes need continuous monitoring. Disease progression prediction is vital to customize treatment plans and prevent complications. However, conventional methods generally depend on periodic check-ups and subjective assessments. This may result in missed opportunities for early, timely intervention.
How Can NLP Help?
NLP technologies analyze large amounts of patient information, including doctor’s notes, lab results, and medical histories. This helps in predicting how the disease may progress further. By extracting the necessary data from unstructured and structured datasets, NLP tools can determine early warning signs and provide physicians with predictive data to help navigate treatment plans.
3. Medical Literature Review
The Challenge
Healthcare researchers, as well as professionals, must stay up-to-date with medical literature. However, it can be challenging with so much existing literature and new research being published daily. Manually reviewing this vast amount of information can result in suboptimal care, reduced patient satisfaction, and confusion.
How Can NLP Help?
NLP-powered virtual assistants and chatbots can help bridge the communication gap between patients and healthcare providers. They can understand patient queries, offer customized health information, and even guide them in booking appointments or managing their treatment plans.
4. Identifying Social Health Determinants
The Challenge
Social determinants of health or SDOH, such as housing, access to food, and income, largely influence people’s health outcomes. However, these are not adequately noted in usual clinical assessments. This can lead to less effective care.
How Can NLP Help?
NLP medical records can analyze unstructured data like clinical notes and patient records to determine social factors affecting health. For instance, they can flag issues like poor housing situations. Once identified, healthcare professionals can provide comprehensive care that addresses the leading causes of their health issues.
5. Drug Adherence and Medication Management
The Challenge
Non-adherence to medications according to prescriptions is a significant problem that affects the outcomes of the treatment adversely. Nearly 50% of patients do not take their medications as prescribed, which increases rates of hospital readmissions and complexities in chronic illness management.
How Can NLP Help?
NLP addresses medication non-adherence by analyzing the patient’s medical records and communications. By identifying signs and patterns of non-adherence, NLP can inform healthcare professionals about which individuals need targeted interventions like educational support or reminders.
6. Fraud Detection & Billing Accuracy
The Challenge
Billing inaccuracies and fraud are major problems in the healthcare industry, costing billions of dollars annually. They affect not only financial resources but also the integrity of the healthcare system.
How Can NLP Help?
Among the key NLP applications in healthcare is the technology’s ability to fight these challenges by analyzing Electronic Health Record data, insurance claims, and billing codes to highlight discrepancies and determine potential fraud.
7. Conclusion
Now that you know what natural language processing is in healthcare and NLP use cases in healthcare, you probably already understand how it has transformed the way we approach medical research, administrative efficiency, and patient care. Even though NLP use cases like chatbots are well-renowned, the lesser-known ones are equally effective.
From improving patient-provider communication to enhancing disease progression predictions, NLP is highly beneficial for addressing some of the key challenges of the healthcare sector. If you are part of the healthcare industry and want to integrate NLP into your current processes, consider hiring LITSLINK’s AI software engineers. Our team of professionals will provide you with tailored solutions that meet your requirements.
With natural language processing, the future of the healthcare industry is not just driven by data — it’s more personalized and smarter than ever before.
FAQs
Q: What is NLP in healthcare?
A: NLP is a branch of artificial intelligence that enables computers to comprehend, interpret, and produce human language.
Q: What are some benefits of NLP in healthcare?
A: NLP enables quick analysis of large datasets, quick decision-making, and improves communication between healthcare providers and patients.
Q: What are some use cases of NLP in healthcare?
A: Some use cases of NLP in healthcare include clinical documentation automation, disease progression prediction, medical literature review, identifying social health determinants, drug adherence and medication management, and fraud detection & billing accuracy.
Q: How can NLP be integrated into current healthcare processes?
A: NLP can be integrated into current healthcare processes by hiring AI software engineers like LITSLINK’s team of professionals, who can provide tailored solutions that meet your requirements.
