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Top 14 Applications of Natural Language Processing in Healthcare Industry

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03 Dec 2024

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Healthcare is an industry that has immense responsibility for the public. People need a good healthcare system to live a long life. However, the fact that doctors, nurses, and other people working in the field need exceptional skill sets as they are dealing with life-and-death situations every day and is necessary to ensure that they are not swamped with tedious work. 

Artificial intelligence services are now here, and they are helping professionals free up time and improve so they can focus on their work.

Natural Language Processing Services  

 

The Sub-set of Artificial Intelligence Services that Makes Healthcare Smoother

In this post, we will be focusing on natural language processing services and its various applications in the healthcare industry. However, before starting on that, let us first understand: 

What is NLP and How Does it Work? 

Natural language processing services include the use of artificial intelligence policies that collect and assess unstructured data from human language to extract patterns. It understands the meaning and then creates feedback.  

How can NLP Services Support the Healthcare Industry? 

In the United States alone, over 100 million clinical records are reviewed every year. These records include a huge range of information, including encounter data, pharmacy data, patient histories, and social determinants of health data, painting the complete picture of a patient’s health journey. Given the fact that natural language processing uses information to drive results, it is well aligned to provide quick and efficient insights to healthcare professionals so that they can offer the best care to patients while lowering the cost of care.

 

What are the Benefits of Using Natural Language Processing Services in Healthcare?

When it comes to the use of artificial intelligence services in any industry, the primary cause of mass adoption is the range of benefits it has to offer. The utilization of natural language processing services in healthcare services is no different.  

The fact is that hospitals and other corresponding entities related to healthcare benefit immensely from NLP. So, to give you a brief overview of the benefits this technology has on the healthcare industry. 

 

How can Doctors Benefit by Implementing NLP in Healthcare Projects? 

Here are the advantages of using AI in healthcare services for the doctors: 

  • Simplify Medical Jargon: Translate complex medical terms into everyday language for patients and caregivers. 

  • Easier Medical Documentation: Automates the process of documenting patient information, reducing paperwork and errors. 

  • Improves Diagnostic Accuracy: Analyze medical images and data more accurately than humans, leading to earlier and more precise diagnoses. 

  • Identifies Critical Care Needs: Quickly identify patients who require urgent attention, allowing for timely interventions. 

  • Better Patient Experience: AI-powered chatbots and virtual assistants can provide personalized support and answer patient questions 24/7. 

  • Lowers Healthcare Cost: Streamlines processes, reduce administrative overhead, and optimize resource allocation, leading to lower healthcare costs. 

  • Improves Care Quality: Identifies patterns and trends in patient data, enabling healthcare providers to make more informed decisions and deliver better care. 

  • Bespoke Solutions for Specific Needs: Natural language processing technology can be tailored to the unique needs of different healthcare organizations, ensuring optimal performance and outcomes. 

 

What Immediate Benefits Can Healthcare Organizations Get by Leveraging NLP?

There are several reasons for healthcare organizations to leverage NLP services, including but not limited to: 

  • Improved Clinical Documentation: NLP automates the process of extracting and structuring clinical information from unstructured text data.

  • Better Clinical Decision Assistance: Machine learning services tools analyze patient records to provide evidence-based recommendations for diagnosis and treatment. 

  • Streamlined Revenue Cycle Management: NLP extracts key information from medical claims, reducing manual processing and improving reimbursement rates.

  • Successful Clinical Research: NLP analyzes large volumes of medical literature to identify relevant studies and extract valuable insights. 

  • Enhanced Patient Engagement: NLP-powered chatbots and virtual assistants provide personalized support and answer patient questions 24/7. 

These are only a few of the several benefits that natural language processing services have to offer. The actual potential of artificial intelligence services truly is limitless. It will take you as far as your imagination permits. 

 

8 Factors to Evaluate Natural Language Processing Technology in Healthcare 

To determine the use and value of natural language processing technology in healthcare, these are the factors to consider: 

  1. Seamless Integration: The level of integration with existing tools to save time and resources. 

  2. Unique Capability: NLP can process vast amounts of text faster than humans, identifying key data. 

  3. Team Integration: NLP tools should enhance workflow and productivity. 

  4. Performance Metrics: Precision and recall measure NLP model accuracy. 

  5. Decision Transparency: Understanding model decision-making is crucial. 

  6. Model Maintenance: Batch training is safer for healthcare applications. 

  7. User-Friendly Interface: NLP tools should streamline medical record review. 

  8. Diverse Applications: NLP has various potential uses in healthcare and biomedicine.

Natural Language Processing Applications in Healthcare Industry 

Now, let us look at the 14 use cases associated with Natural Language Processing in Healthcare: 

  • Clinical Documentation: Automates the extraction of structured data from unstructured clinical notes. This improves efficiency, reduces errors, and facilitates data analysis.

  • Speech Recognition Technologies: Doctors and clinicians can dictate notes directly into EHRs, saving time and effort. NLP accurately transcribes spoken language into text, enabling efficient documentation.

  • Computer-Assisted Coding (CAC): Helps automate the coding process by analyzing clinical documentation to assign appropriate codes. This improves accuracy, reduces manual effort, and streamlines reimbursement.

  • Data Mining and Research: Enables the analysis of large datasets to identify patterns, trends, and insights. This facilitates medical research, drug discovery, and personalized medicine.

  • Clinical Decision Support: NLP-powered systems can analyze patient data and evidence-based guidelines to provide real-time recommendations to clinicians, aiding in decision-making and improving patient outcomes.

  • AI Chatbots and Virtual Scribe: AI chatbot development can create solutions that answer patient queries, schedule appointments, and provide basic health information. Virtual scribes can automate documentation, reducing the administrative burden on healthcare providers.

  • Computational Phenotyping: Identifies patient phenotypes based on their medical records, enabling more precise diagnosis, treatment planning, and clinical trial recruitment.

  • Pharmacovigilance: Can analyze adverse drug reaction reports to identify potential safety issues, leading to faster detection and response.

  • Patient Engagement: NLP-powered chatbots can provide personalized health information, answer patient questions, and offer support, improving patient engagement and adherence to treatment plans.

  • Mental Health: Analyzes social media and online forums to identify individuals at risk of mental health issues, enabling early intervention and support.

  • Medical Image Analysis: Examines medical images like X-rays and MRIs to extract relevant information, aiding in diagnosis and treatment planning.

  • Literature Review: Efficiently review vast amounts of medical literature to identify relevant research, accelerating the discovery process.

  • Predictive Analytics: Used for healthcare analytics, using the patient data to predict disease risk, identify high-risk patients, and recommend preventive measures.

  • Healthcare Fraud Detection: Investigates claims data to identify patterns of fraudulent activity, helping to reduce healthcare costs and improve the integrity of the system.

 

Artificial Intelligence Services – Healthcare Industry Saviour 

 

AI in healthcare has been nothing short of a miracle. The time that artificial intelligence services, natural language processing services, and more picked up pace was right around the time when healthcare around the world was overburdened.  

Technology has allowed this industry to stay afloat at the time of catastrophe and upgrade all its elements to ensure solutions that truly change the world. With people care being their goal, these organizations should be encouraged to adopt all these advancements to allow doctors and nurses to focus on giving the best care, while AI takes care of the rest. 

Hire an AI software development company to assist you in finding the most appropriate solution to target your biggest pain points and bring your biggest goals to life.

NLP ensures patient privacy by using techniques like de-identification and anonymization to remove sensitive information from medical records before analysis. This helps protect patient confidentiality and comply with data privacy regulations.

The future of NLP in healthcare is promising, with potential applications in personalized medicine, drug discovery, clinical decision support, and patient engagement. NLP can help analyze large amounts of medical data to identify patterns and insights that can improve patient care.

NLP can be used to detect medical errors by analyzing electronic health records (EHRs) for inconsistencies, contradictions, or deviations from standard protocols. This can help identify potential errors early on and prevent adverse patient outcomes.

NLP can be used to develop new drugs by analyzing large amounts of biomedical literature to identify potential drug targets and repurpose existing drugs for new indications. This can accelerate the drug discovery process and reduce costs.
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Anil Rana

03 Dec 2024

Anil Rana, a self-proclaimed tech evangelist, thrives on untangling IT complexities. This analytical mastermind brings a wealth of knowledge across various tech domains, constantly seeking new advancements to stay at the forefront. Anil doesn't just identify problems; he leverages his logic and deep understanding to craft effective solutions, actively contributing valuable insights to the MoogleLabs community.

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