A Beginner’s Guide to Text Analytics for Health

By / Healthcare Technology

Key Takeaways

  • Named entity recognition (NER), a text analytics technique, harnesses natural language processing methods to extract information from text. It categorizes useful information and identifies nouns in a text.
  • Text classification is a popular text analytics technique that assigns categories to a text. It is used to structure, organize, and categorize any text: medical studies, research papers, patient documents, physician’s notes, etc.
  • Use cases of text analytics for health include healthcare organization management, fraud detection, clinical decision-making, interpreting medical documents, and analyzing patient feedback.

The purpose of text analytics for health is to process unstructured data with the help of artificial intelligence technology and gain data insights, trends, statistics, and patterns. It is one of the most talked about features of the Azure AI language because of its ability to summarize documents, classify text, and conduct sentiment analysis. In addition, text analytics for health is available in multiple languages, such as German, Spanish, and Italian.

Along with text analytics, the term text mining also pops up. These terms can sound similar, but they serve different purposes. One of the major differences between the two is that text mining is used to extract qualitative insights from unstructured data, whereas text analytics offers quantitative results.

Moving on, this article offers a comprehensive guide for providers who are looking to use text analytics in their daily practice, and for artificial intelligence developers who are seeking to include new techniques in their text analytics products.

A Comprehensive Review of Text Analytics Approaches

1. Topic Modeling

Topic modeling technique is used to detect major themes in a large volume of text or files. For example, topic modeling can identify whether a document is a lab result, medical imaging scan, insurance claim, or patient feedback form. Two key topic modeling methods are latent Dirichlet analysis and latent semantic analysis.

  • Latent Dirichlet Analysis (LDA): LDA is one of the most popular used models. This technique aims to locate topics that a document belongs to based on the words found in the document. Let’s take an example of segregating pictures in a gallery. Every picture (document in this case) has a caption (words), and it needs to be segregated as per themes (topics). So, the LDA assigns every picture under varied themes like city, nature, sports, etc., by comprehending the captions.
  • Latent Semantic Analysis (LSA): LSA is based on the idea that words with similar meanings tend to be used in the same context. It links words semantically by word frequency and context. For example, patients with similar diseases like cardiovascular or diabetes are put in one basket.

2. Named Entity Recognition (NER)

Named entity recognition harnesses natural language processing methods to extract information from text. It categorizes and detects useful information in a text format called ‘named entities.’ It identifies nouns in a text.

NER is also known as entity extraction and identification. It is used in the fields of deep learning, AI, chatbots, sentiment analysis, medical documentation, and more. It deciphers key information quickly, automates the extraction process, and provides users with patterns and statistics.

3. Word Frequency

Word frequency counts the most frequently occurring words in a text. It uses the numerical statistic ‘term frequency-inverse document frequency (TF-IDF).’ This technique is applied to analyze patient feedback. The most frequently occurring words in a bunch of feedback forms can be picked out to gain insights into whether patients are satisfied with their treatment or not.

For example, words such as poor doctor-patient communication, long wait time, or ineffective treatment can be spotted through text analytics for health and worked on to improve these issues.

4. Text Classification

Text classification is a text analytics technique that makes use of machine learning to assign certain categories to text. It is used to structure, organize, and categorize any type of text, ranging from medical studies, research papers, patient documents, physician’s notes, etc. Furthermore, text classification has several applications such as topic labeling, sentiment analysis, intent detection, and spam detection.

In addition, the text classification technique is scalable to any healthcare organization because it can analyze tons of files and documents in a few minutes. Also, there’s little room for errors as the text classification tool is trained to perform its task accurately.

5. Text Extraction

The text extraction technique pulls out pieces of data such as patient names, medical terms, keywords (trending or frequently appearing terms), treatment plans, drugs, disease diagnoses, etc. This technique is often used to gain insights into what patients are looking for, common issues, population health management, chronic disease management, and more.

6. Clustering

Clustering groups vast amounts of data together. It is less accurate than text classification but clustering algorithms are faster as they don’t require prior training. They just mine data and make predictions without training information (also known as unsupervised machine learning). Google is the best example of clustering. When you search something on Google it pulls out everything related to that word or phrase and groups it.

Use Cases of Text Analytics for Health

  • Fraud Control: Text analytics for health makes use of techniques such as word frequency and semantic analysis to spot fraudulent words and phrases. It extracts these words and identifies patterns in them, so they can be tracked down by a security system to prevent fraud. Text analytics can detect unfitting prescriptions, emails, problematic referrals, deceitful medical claims, unissued payments, etc.
  • Interpreting Medical Documents: A key advantage of text analytics for health is that it can go through multiple documents in one go. So, for doctors who can’t read several pages in a day, text analytics does it for them. It summarizes reports and interprets them, thus allowing doctors to make accurate decisions and save time.
  • Clinical Decision-Making: Text analytics for health segregates documents of every patient under different themes such as medical imaging, prescriptions, medical history, surgeries, and treatment. This allows physicians to get a complete picture of the patient in no time and make timely decisions, especially during emergencies.
  • Patient Feedback Analysis: Patient retention is the key to running hospitals and clinics successfully. So, paying close attention to patient experience can help to improve health facilities and provide them with what they need. Text analytics help to extract valuable insights from patient feedback forms, so health facilities can improve care.
  • Healthcare Facility Management: With text analytics for health, hospital management staff can assess readmissions, resources used, emergency care, deaths, births, patient retention rate, feedback, and much more. This data helps to comprehend where to focus on, what are the issues, and what action is required. Text analytics also helps to find high-risk and chronic disease patients, so they can receive affordable and effective care from the hospital.

Getting Started with Text Analytics for Health

1. Data Gathering

Physician notes and patient information aren’t the only sources of data in healthcare organizations. Other sources that contribute to text analytics are:

  • Insurance claims
  • Electronic health records/electronic medical records (EHR/EMR)
  • Internet of Medical Things (IoMT)
  • Medical journals, news feed, emergency care data
  • Lab results and medical imaging

Data is scattered either in the internal database or found externally. Every data is put together to extract meaningful insights at the end of the text analytics process.

2. Data Preparation

Once unstructured data is collected from various sources, it goes through a few preparatory stages before the analysis of AI algorithms. The data preparation step encompasses certain steps which are as follows:

  • Tokenization: Here, the algorithm breaks down a continuous thread of data into tokens (smaller units) that makeup phrases or words. For example, a character token can be an individual letter in the word: C-A-T-C-H. Tokens also discard white spaces and break up sub-word tokens: CATCH-ING.
  • Parsing: Parsing includes breaking down a sentence into smaller components, so the sentence’s meaning is understood. It establishes meaningful connections between tokens and helps text analytics software visualize words in a sentence.
  • Part-of-Speech-Tagging: Here, every token or word is assigned parts of speech like nouns, adjectives, verbs, or adverbs.
  • Stop Word Removal: Stop words are all the tokens that have appeared frequently, but have no value to text analytics. They can be removed by text analytics, depending on the use case and sentence formation. Stop word examples include for, or, such as, etc.
  • Lemmatization and Stemming: These two processes remove affixes and suffixes associated with the token and retain its lemma or the dictionary form.

3. Analysis of Text

Post-data preparation, the text is analyzed to derive insights from the data. Two of the prominent techniques used are:

  • Text Classification: This is text analytics for health technique that assigns categories or tags to text. Natural language processing is also used to sort text as per topic, sentiment, and customer intent, faster than humans. It is a handy technique to organize, categorize, and structure any type of text, ranging from medical documents to research papers, and healthcare website data. Example of how it works: ‘Healthcare app is easy to use.’ The text classifier takes the input and assigns tags like ‘healthcare app’ and ‘easy to use.’
  • Text Extraction: In this step, structured data is extracted from the unstructured format. Two methods used for text extraction are CRF (Conditional Random Fields) and regular expressions. The former is a sophisticated way of extracting data, and the latter is simple but is complicated to maintain when the amount of data increases.

4. Visualization

Visualization is all about converting text into a simple readable and understanding format. Usually, tables, graphs, and charts are used for text analytics. Visuals help to understand patterns, statistics, and trends, based on which strategies can be devised. Words such as negative, not good, unfit, abnormalities, unhealthy, etc. are in one table. So, it is easier to pick the issues and start working on them one after the other.

Artificial intelligence has given rise to various tools and techniques, text analytics being one of them. AI in healthcare has tremendous scope and opens up lucrative opportunities for healthcare software developers. Arkenea is one of the leading healthcare software development companies that also specializes in AI. We offer a range of solutions from chatbots to predictive modeling and deliver top-notch products to our clients. If you’re looking for something similar for your healthcare organization, then connect with Arkenea.