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Top 5 Applications of NLP in Healthcare – 2023

The healthcare industry is undergoing a technological revolution in recent years, with digitalization at the forefront of this transformation. The increasing use of emerging technologies has substantially changed how healthcare services are delivered, driving a significant shift towards more efficient, accessible, and personalized healthcare services.

We expect even more cutting-edge applications in the healthcare sector in 2023 as technology continues to play a pivotal role in improving patient care and outcomes. Enabling healthcare providers to understand better and respond to patient needs, enhance the accuracy of diagnoses, and improve treatment plans. Natural language processing (NLP) and predictive analytics are among the technologies set to play a more significant role. These emerging technologies promise to drive even greater efficiencies and improve healthcare outcomes for patients worldwide.

This article will delve into the cutting-edge applications of emerging technology in healthcare, highlighting the top 5 for the year 2023. From enhancing clinical documentation to deploying AI chatbots and virtual scribes, these applications are proving to be transformative in their ability to optimize healthcare processes and deliver superior care. By incorporating predictive analytics, these innovative tools are revolutionizing the healthcare industry and contributing to better patient outcomes.

 5 Best Applications of Emerging Technology in Healthcare 

1. Clinical documentation:

The application of natural language processing (NLP) in clinical documentation has significantly impacted the healthcare industry. By utilizing NLP, clinicians can extract important patient information from unstructured medical texts, reducing their dependence on cumbersome EHR systems and enabling them to devote more time to patient care. NLP can also be utilized to analyze public data sets and social media, providing insight into Social Determinants of Health (SDOH) and the effectiveness of wellness-based policies, ultimately leading to improved outcomes for clinicians.

2.AI Chatbots and Virtual Scribe:

In healthcare, AI chatbots and virtual scribe are two promising NLP applications that use natural language processing to improve patient care. Chatbots can imitate human-like conversations with patients to collect data, provide medical advice, and assist in diagnosis and treatment. Healthcare providers already use chatbots to capture symptoms, triage patients, and gather health data. Virtual scribe uses NLP to transcribe physician-patient discussions in real time, enabling healthcare providers to create accurate medical records.

3.Computational Phenotyping:

Using NLP in clinical trial matching makes it easier for doctors to study a patient’s current condition by modifying the way they identify their physical and biological traits. NLP can also assess speech patterns, with diagnostic potential for neurocognitive and cardiovascular disorders. Companies like Winterlight Labs are developing technologies to recognize vocal biomarkers and linguistic patterns in patients with these disorders.

4.Review Management & Sentiment Analysis:

NLP has the potential to assist organizations in managing online reviews by analyzing thousands of healthcare reviews on third-party listings each day. NLP can also track customer attitudes by identifying positive and negative terms within the review. Some systems can even monitor the customer’s voice in review, allowing physicians to understand better how patients discuss their care and use shared vocabulary.

5.Automated Registry Reporting:

One of the most common use cases of NLP in healthcare is extracting values needed for various use cases. This is particularly helpful for health IT systems that face regulatory reporting requirements where specific measures are not stored as discrete values. For instance, ejection fraction (EF) is a crucial measure of heart function used to diagnose and manage heart failure. Still, in many cases, EF is not stored as a discrete value in electronic health records (EHRs), making it challenging for health systems to use automated reporting tools for regulatory reporting purposes. To address this challenge, health systems can use NLP algorithms to identify when an EF value is documented as part of a note and save each deal in a form that the organization’s analytics platform can use for automated registry reporting.  

Implementing Predictive Analytics in Healthcare

Predictive analytics in healthcare is a game changer with the potential to improve patient identification and diagnosis. It involves data mining, machine learning, and other statistical techniques to identify patterns and predict future healthcare events.

Healthcare organizations can leverage predictive analytics with the increasing availability of electronic health records (EHRs) and other forms of digital healthcare data. This can aid in identifying patients at risk of developing certain medical conditions, predict the likelihood of a patient’s hospital readmission, and improve the accuracy of disease diagnoses.

One major advantage of integrating predictive analytics into healthcare is that it enables providers to make better-informed decisions regarding patient care. By identifying high-risk patients early on, providers can intervene before a condition worsens and provide more proactive and personalized care. Additionally, predictive analytics can help providers improve clinical decision-making by providing more accurate information about a patient’s health status and potential outcomes.

Organizations must first establish a strong foundation of data management and analytics capabilities to implement predictive analytics. This includes investing in advanced analytics tools and technologies, building a team of data scientists and analysts, and developing robust data governance and security protocols. Additionally, healthcare organizations must be committed to continuous improvement, regularly refining their data capabilities and incorporating new insights and learnings into their clinical practices.


In conclusion, integrating emerging technologies like natural language processing (NLP) and predictive analytics in the healthcare industry opens up a world of exciting possibilities for the future of patient care. If you’re looking to leverage the full potential of these technologies to enhance patient care and stay ahead of the curve in healthcare, consider exploring NLP Services offered by Maruti Techlabs. Their cutting-edge solutions can help your organization improve healthcare outcomes and drive operational efficiencies.


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