The vastness of data present in healthcare institutions today is growing tremendously. Though data is considered the most significant asset in today’s digital world, healthcare doesn’t seem to fully benefit from it. Some studies suggest that over 80% of healthcare data remains unstructured and unused after its creation.
One of the biggest reasons for it is the majority of healthcare data sources like EHR, patient data, diagnostic summaries, progress records, medical imaging, prescriptions, etc., are not yet machine-readable. This significantly increases the time to process and segregate the data into a structured format.
However, by leveraging NLP, this unstructured healthcare data can be analyzed more efficiently, and valuable insights can be derived from it. So let us study more about the use of NLP in transforming medical data.
Unstructured Healthcare Data: Why is it there in Abundance?
The fundamental reason for the enormous amount of healthcare data in unstructured format is that most healthcare software used in the industry is not designed to structure the data categorically in the database.
Another vital reason for the abundance of unused data is cross-referencing in medical data. Unlike other industries, healthcare heavily relies on different patient data, such as prescriptions, X Rays, MRIs, etc., to provide optimal patient outcomes.
Unfortunately, substantial medical organizations that use healthcare software today do not offer machine readability. Furthermore, they cannot co-relate different medical aspects and generate accurate results.
However, these challenges are now successfully overcome using advanced machines and innovative healthcare NLP solutions.
Healthcare NLP Assurance for Improved Medical Outcomes
Healthcare NLP leverages NLP applications to read and analyze large volumes of healthcare data quickly and precisely. By accurately examining the patient’s data, medical providers quickly identify areas of concern and make informed decisions to improve patient care.
Additionally, Healthcare NLP can help reduce errors and improve the accuracy of diagnoses, treatments, and healthcare costs. The three major concerns of Healthcare NLP are:
- To provide insights into patient health.
- To alert patients about potential health risks.
- To identify patterns of care for the patients.
Healthcare NLP is an effective way to convert free-form textual data to a more structured format that can be used for better health reporting and patient analysis.
The four major ways NLP healthcare facilitates efficient solutions for effective patient outcomes are:
- Predictive Analysis: NLP enables physicians to process unstructured data using various predictive models to gain insights into patient behavior and health outcomes. With demographic information, medical history, and clinical notes, NLP solutions can effectively derive predictions on contagious viruses and contain them before mass spreading.
- Effective EHR Data Usability: Healthcare NLP allows physicians to make better searches and enhance their reporting capabilities using NLP. By arranging the data in a more intelligent form, NLP solutions help physicians to access information more swiftly and easily.
- NLP-Based Phenotyping: AI extracts meaningful patient data from unstructured medical documents. NLP-based Phenotyping can identify patterns and trends in medical records, which further helps uncover valuable insights from patient data. Using such solutions, physicians can improve diagnosis accuracy, reduce costs, and improve patient outcomes.
- Improvement in Overall Healthcare: NLP Healthcare is an effective solution to boost the quality of healthcare systems and processes. With in-depth reporting and analytics, NLP-based solutions offer qualitative real-time outcomes to improve people’s health.
Exploring the Diverse Use Cases of Healthcare NLP
Healthcare NLP is an important technology with numerous use cases. Some of them are listed below.
Glancing at the Challenges & Limitations of Healthcare NLPHealthcare NLP offers a terrific solution to healthcare institutions. However, there are still a few drawbacks in the process that you must learn about.
Variation in Healthcare DataHealthcare data is abundant but in an unstructured format and in various languages. This makes it extremely hard to deduce healthcare data’s intent, contextuality, and vocabulary. This is certainly one of the biggest challenges of NLP Healthcare that needs to be looked at and worked upon.
Rigid Data StructuresMost medical data is unstructured; hence, NLP-based AI models offer solutions to consolidate important medical data from imaging, X-Rays, and other unstructured data sources. This data can be used for analysis and generating valuable insights.
Structuring Vast Healthcare Data into Data TablesMost medical data across thousands of ERPs and data warehouses remain siloed for years. Structuring the data correctly into data tables and creating a relational database for it can help derive insightful information from data. Hence, structuring the current data into databases is a challenge for NLP healthcare that needs to be rightly addressed.
Need an Efficient Healthcare NLP Solution?Healthcare NLP is surely the way to move forward for healthcare enterprises. With advancing technology and higher attention to achieving improved patient outcomes, NLP is the underlying solution for healthcare. If you, too, are looking for innovative, reliable, and scalable solutions in AI for your healthcare organization, you may reach out to our Shaip experts.
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