Role and Future of NLP in Healthcare
What do you think of when you hear “NLP”? About voice assistants, about translators, about a search engine… But the field of application for natural language processing technology is much wider! Let’s discuss what is NLP in healthcare, how NLP technology simplifies human interaction in medicine, and what the future of NLP in healthcare is.
What is Natural Language Processing?
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. It combines linguistics, computer science, and machine learning to bridge the gap between human communication and computer understanding.
Researchers and businesses are introducing NLP technology into healthcare with the following arguments:
- The treating physician does not have time to familiarize himself with modern research and often uses already outdated drugs and treatments. It would be great if a computer could review articles and provide recommendations from a text database of up-to-date research on demand.
- Doctors spend a great deal of time on record keeping and dealing with paperwork. Speech-to-text algorithms would prevent the specialist from being distracted by filling out a patient’s chart. In the future, the model could even suggest previously used treatments to the specialist by analyzing case histories with similar symptoms.
- Sometimes, a patient receives a prescription with medications containing incompatible ingredients. A special program can alert the health care provider to possible problems by comparing the instructions in the texts. In addition, if a drug is contraindicated for a certain group of people or has side effects, such as lowering blood sugar levels, based on the patient’s data, the program will be able to display a warning that the patient is at risk automatically.
- NLP technologies are particularly active in the field of radiology. Modern programs learn to predict a diagnosis based on the description of an X-ray image and assign an international disease code (ICD-10).
The modern quality of healthcare natural language processing can indeed solve all these problems! However, the list of NLP tasks in medicine does not end there.
NLP Use Cases in Healthcare
Doctors, patients, medical administrators, and medical researchers can benefit from applying natural language processing in healthcare. The latter are trying to divide people into groups according to the degree of exposure to drugs so that in the future, they can predict the effectiveness of treatment for each individual patient and develop drugs based on the susceptibility of the group.
Clinical Decision Support
NLP systems assist healthcare professionals in making informed clinical decisions by analyzing vast amounts of medical literature and patient data. These systems can flag potential drug interactions, suggest relevant diagnostic tests, and provide evidence-based treatment recommendations. When a doctor enters a patient’s symptoms, the NLP system can quickly search through medical databases and present relevant case studies, research papers, and treatment guidelines.
Electronic Health Records Analysis
We all remember when the pandemic began: no one understood how to treat the virus, standard techniques did not work, and antibiotics did not work on the virus. When scientists and clinicians are faced with a problem of this magnitude, one of the most important steps toward a solution is to gather as much information about the disease as possible.
Information about the biological properties of SARS-CoV2, COVID-19, patient demographics and comorbidities, global or regional distribution, and possible drugs that can address the symptoms and effects of the disease are just the tip of the iceberg of data that had to be processed to produce a solution. Much of this data is unstructured text:
- Scientific articles;
- Preprints;
- Clinical trial records;
- Side effect reports;
- Electronic medical records.
Even news feeds and social media can provide relevant information, such as epidemiologic factors, radius of spread, and disease symptoms.
At the height of the first wave of the pandemic, one of the largest health systems in the United States used NLP to analyze incoming emails and patient support chat messages. In this way, the organization identified a set of COVID-19 symptoms. Researchers then ran analytics to categorize patients by their likelihood of being infected with COVID-19. All of this turned into an automated, near real-time process that allowed healthcare professionals to deal more efficiently with incoming patients.
Medical Image Analysis Documentation
In radiology and pathology, NLP helps bridge the gap between visual data and written reports. It can automatically generate preliminary reports from medical images, extract key findings from radiologist narratives, and standardize reporting formats. This integration of NLP with medical imaging improves workflow efficiency and helps maintain consistency in medical documentation.
Disease Surveillance and Epidemic Monitoring
In 2022, a pharmaceutical company decided to analyze social media to assess the spread of COVID-19 and risk factors for infection. Specialists compiled a set of queries to categorize patients according to various attributes, such as mentions of the medical facilities they visit. In addition, the analysis of social media posts helped to analyze the status and risks of COVID-19 disease for different professions.
Researchers developing therapeutic drugs against COVID-19 use NLP to track new publications, especially those evaluating the safety of drugs and vaccines. Publications have opened free access to critical literature: for example, the CORD-19 dataset compiled by the Allen Institute for AI, the Elsevier dataset, and the Copyright Clearance Center’s COVID-19 collection of texts. These vast resources were used to understand the efficacy or safety of drugs, predict the presence of comorbidities and the natural course of the virus, as well as determine which populations are most susceptible to the disease.
Patient Engagement and Communication
NLP powers chatbots and virtual assistants that provide personalized healthcare information to patients. These systems can answer common medical questions, remind patients about medications, schedule appointments, and provide post-care instructions in natural language. This application helps improve patient education, compliance with treatment plans, and overall healthcare accessibility while reducing the burden on healthcare staff.
Benefits of NLP in Healthcare
So, let’s discuss 4 main benefits of NLP healthcare.
Reduced Costs
NLP significantly decreases healthcare operational expenses by automating time-consuming documentation tasks. Medical professionals spend less time on administrative work, allowing them to focus more on patient care. NLP automates medical coding, billing processes, and record management, reducing the need for manual data entry and minimizing costly errors. Healthcare facilities have reported substantial cost savings through reduced staffing needs for administrative tasks and fewer documentation-related errors.
Improved Patient Engagement
NLP technology enables more meaningful and efficient patient interactions through intelligent chatbots, personalized communication, and automated follow-up systems. Patients can receive immediate responses to common questions, medication reminders, and appointment scheduling assistance in natural language. This continuous engagement helps patients:
- Better understand health conditions;
- Adhere to treatment plans;
- Maintain regular communication with healthcare providers.
NLP in healthcare leads to better health outcomes and increased patient satisfaction.
Enhanced Insights into Patient Health
By analyzing vast amounts of unstructured medical data, NLP provides healthcare providers with deeper insights into patient health patterns and trends. The technology can identify subtle connections between symptoms, treatments, and outcomes across large patient populations. This comprehensive analysis helps doctors:
- Understand disease progression;
- Predict potential complications;
- Identify risk factors that might otherwise go unnoticed.
These insights enable more proactive and preventive healthcare approaches, ultimately leading to better patient outcomes.
Improved Quality of Care
NLP technology enhances the overall quality of healthcare delivery by providing real-time clinical decision support and reducing medical errors. By analyzing patient records, medical literature, and clinical guidelines simultaneously, NLP helps healthcare providers make more informed decisions about patient care. The technology can flag potential drug interactions, suggest evidence-based treatments, and ensure adherence to best practices. This results in more accurate diagnoses, more effective treatment plans, and better coordination among healthcare team members.
Future of NLP in Healthcare: Will It Transform the Industry?
Personalized medicine will advance through NLP’s capability to analyze millions of medical records, research papers, and clinical trials, identifying subtle patterns for highly individualized treatment recommendations. The integration of NLP with medical imaging analysis will create powerful diagnostic tools that simultaneously process verbal descriptions, symptoms, images, and lab results.
Predictive healthcare will evolve as NLP systems monitor data from medical records, wearable devices, and patient reports. This way, it identifies potential health issues before they become serious. Mental health care will benefit from systems analyzing speech patterns and emotional content to detect early warning signs and provide 24/7 support.
While this transformation promises significant benefits, success depends on:
- Maintaining robust data privacy;
- Preserving the human element in patient care;
- Ensuring proper validation of AI-driven decisions.
The future of NLP in healthcare lies in seamlessly integrating NLP technologies with medical expertise to enhance, rather than replace, human judgment in healthcare delivery.
Bring NLP to Your Hospital with the OS-System Team!
We specialize in developing intelligent healthcare software that harnesses the power of NLP and machine learning to streamline your medical operations. Our team works closely with healthcare providers to create customized solutions that address specific challenges while ensuring seamless integration with existing workflows.
At OS-System, we develop:
- Electronic Health Record (EHR) Systems
- Clinical Decision Support Software
- Medical Imaging Software
- Patient Engagement Platforms
- Hospital Management Systems
- Telemedicine Platforms
- Medical Data Analytics Platforms
- Laboratory Information Systems (LIS)
- Pharmacy Management Software
- Emergency Department Information Systems
- Medical Resource Planning Software
- Medical Billing Systems
- Remote Patient Monitoring Solutions
- Medical Inventory Management Systems
- Medical Staff Scheduling Software
Let’s collaborate to bring innovative technology to your hospital that enhances clinical efficiency, improves patient care, and drives better health outcomes. Contact us to explore how our expertise can help digitally transform your healthcare facility.
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