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Exploring the Potential of Natural Language Processing in Healthcare Diagnosis

    Natural Language Processing in Healthcare

    Natural Language Processing in Healthcare, NLP is the technology that can convert masses of free-text data into useful intelligence. It helps automate paperwork, organize research, and make clinical reporting easy for healthcare professionals. Huge volumes of unstructured patient data are inputted into electronic health records daily. While structured data like claims & CCDAs/FHIR APIs may help determine disease burden, more is needed to provide a complete picture.

    Improved Patient Care

    Healthcare organizations generate a wealth of information, but most data needs more structure. Using NLP, healthcare professionals can unlock meaningful insights from this data and help improve patient care. One key use case for NLP in healthcare is identifying patients with conditions that require urgent attention. This area is where NLP can help by identifying and interpreting clinical notes and enabling analytics systems to detect patterns.

    For example, a patient may describe experiencing symptoms such as headache, anxiety, and alopecia during an appointment. NLP software could then identify these symptoms as PROBLEM entities and classify them as present, conditional, or absent. The software would then alert physicians to these findings so that they could take immediate action.

    In addition, NLP software can also automate routine tasks that require human intervention and free up valuable time for healthcare professionals to focus on their patients. This includes analyzing charts, doing HCC risk adjustment coding, and performing back office functions. The NLP process converts unstructured data into a more structured format that analytics systems can analyze. It can also enhance the user experience by organizing information on an EHR interface by patient encounter, making it easier for clinicians to find critical patient data. For example, when a patient mentions that they have been experiencing fatigue, an interface can populate the page with relevant information.

    Increased Revenue

    With increased developments and continuous research with improved technologies, natural language understanding in healthcare sector can benefit patients more. This process helps arrange and manage medical documents in a sophisticated manner with ease and provides better connectivity for the data. This leads to faster and more efficient working, increasing the productivity of a hospital or any other organization.

    Natural language processing can help doctors and other medical professionals find information buried in big data that contains valuable insights. It can help them with automated paperwork, organize research, analyze disease investigations, etc. It can even assist them in identifying the best treatment options for their patients.

    During the coronavirus pandemic, natural language processing tools were used by health authorities to analyze documents and other information to support the fight against this virus. For instance, the White House and several research groups created the COVID-19 Open Research Dataset, a massive collection of full-text articles about the virus and its related coronaviruses that researchers could use to advance the understanding of the disease.

    Moreover, NLP can detect medical errors and recommend appropriate actions.

    Enhanced Patient Experience

    The healthcare industry generates trillions of pieces of data that are unstructured. NLP and AI help collect this data, organize it, and extract meaningful patterns from it to improve patient care and enhance the overall customer experience. NLP technologies recognize speech patterns and process verbal communications to enable physicians to gain access to structured data at the point of care. This allows them to focus on the needs of their patients and eliminates labor-intensive processes, such as dictating their findings into EHRs and submitting the results to their employers or regulatory bodies.

    For example, physicians use these tools to capture adenoma detection rates (ADR) during colonoscopies. The data is then fed into a system that tracks the ADR and provides an accurate and up-to-date report. Physicians can then quickly view the results to evaluate their performance and track improvements.

    NLP also enables hospitals to automate administrative systems and reduce the need for manual labor. This saves time and resources, freeing up employees to work with more complex tasks that can enhance the customer experience.

    NLP is a specialized branch of AI, and while its application in healthcare may be more complex than in other industries, it has the potential to enhance healthcare efficiency and improve patient outcomes dramatically. This is possible because NLP can process unstructured data, automate back-office functions, perform coding and analytics, and streamline healthcare workflows—all without obstructing physician communication.

    Increased Productivity Natural language processing can make it easier for healthcare workers to find relevant information within electronic health records. This will save time and allow the healthcare team to spend more of their efforts on direct patient care. Medical NLP software can scan clinical text data and identify important components of an EHR, such as a diagnosis, procedure code or quality measure. This helps to streamline workflows, improve predictive analytics and reduce administrative burdens for physicians, nurses, pharmacists, and administrators alike. For instance, physician notes documenting a patient’s condition may refer to a “symptom” like “outstanding valvular heart disease.” At the same time, an actual ICD-10-CM code would include a specific metric such as “moderate, severe aortic stenosis.” NLP can identify these nuances and provide a more accurate measurable value than a standard code. However, the results of NLP software must be valid and useful. Otherwise, users will begin to ignore the technology, and it could decrease productivity. Therefore, the software must be trained to interpret medical sublanguages to deliver the most valuable insights.

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