The earliest data processing (hospital information systems) systems were introduced in 1960 to store and manage clinical data. The system drew the attention of the healthcare industry and efforts were put to develop systems capable of storing medical information of patients.
The first electronic medical record system was developed by the Regenstrief Institute. In 1991, the Institute of Medicine made it essential for physicians to use computers to record all patient data digitally to improve healthcare. While the healthcare industry managed to centralized patient data and made it accessible whenever needed, making sense of that large volume of data was not yet possible.
Fast-forward to today, the introduction of RFID, smart systems, and the internet of things (IoT) in healthcare has made it possible to extract and store more patient data. Further, the increasing rate at which major hospitals and pharmaceutical companies are adopting text analysis systems to mine medical data has opened doors to new possibilities and opportunities. All of the stored medical data can be analyzed to provide better patient care and treatment while also reducing the chances of errors.
At the beginning of 1990, computers were becoming increasingly affordable and the introduction of the internet further boosted its adoption. The Institute of Medicine even made it mandatory for all physicians to start using computers to improve patient care. The drastic budget increase in the healthcare IT projects during President George W Bush’s term in office further boosted the widescale adoption of EMRs. EMRs helped in store all patient data in a centralized system that every doctor and physician can access when needed. But efficient technologies to leverage this data at scale for enhancing patient care were still not available.
The National Cancer Institute defines EMRs as, “an electronic (digital) collection of medical information about a person stored on a computer”. The electronic medical records include information about the patient’s health history, such as allergies, immunization, diagnosis, treatment, medicines, etc. Healthcare professionals or providers often use this data to examine patients and to recommend the right treatment. These records are also known as EHRs (Electronic health records).
Over 80 of medical data contained in the EHRs is unstructured text, most of it consists of physicians’ medical notes written or recorded digitally. As most of this data is unstructured, it makes it difficult for machines to interpret and analyze data. However, knowing how valuable the data in the EMR is, only makes the challenge worth exploring.
Further, the introduction of NLP (Natural Language Processing) and machine learning has enabled us to develop effective text analysis solutions to analyze this data. NLP helps computers understand human language, whereas machine learning helps the computer system to learn from experience. Combining both technologies gave rise to advanced text analysis solutions that can interpret and analyze unstructured text with ease and great accuracy.
EHR (Electronic Health Records)
Electronic health records contain a lot of useful patient information that can contribute towards providing better care to patients. Some of the useful information found in the EHRs are-
- patient registration
- patient encounters/interaction documentation
- prescriptions (medication)
- document management
- requesting and receiving labs/imaging reports
- clinical decision support
- between offices communications
The rapid adoption of IoT services and systems has further increased the data that is collected from patients such as vital signs and more. There are also a few emerging data types such as bio-sample data, genetic information, geospatial data, and more that piqued the interest of healthcare professionals.
The EMR database is created through the following process-
All the details of each patient are compiled and integrated with the patient data management systems. These details can only be accessed by doctors, physicians, and healthcare professionals authorized to do so.
Text Analysis for Mining Healthcare Data (EMR)
The EMR is a treasure chest full of valuable patient data that has various beneficial applications. But making sense of this tremendous volume of data is a challenge that cannot be completed manually. A lot of this data is unstructured and the very first task comes to structuring this data so it can be interpreted and analyzed. Text analysis solutions can break down large volumes of unstructured data and transform it into structured text.
Text Analysis: Unstructured Data Pre-Processing
Text analysis solutions use NLP to break-down and make sense of large volumes of unstructured text data. The unstructured data pre-processing involves the following steps,
Unstructured text data often includes errors, unwanted symbols, or colloquialisms that can diminish the quality of your data. This unwanted information also makes the data too noisy and difficult for computers to interpret this data. Noise reduction, this step detects and eliminates any information that is difficult to interpret.
Tokenization is the process of breaking down the data into machine-readable tokens. Tokenization can also be used to secure sensitive information by changing the data with an unrelated value of the same length and format. To put it in simple words, it’s the data that acts as a proxy for a more valuable piece of information. Most businesses keep sensitive data such as financial or personal details. Tokenization can help you keep that sensitive data safe.
Part of Speech Tagging
As the name suggests, this process involves tagging every word with the right part of speech based on both the definition and context of each word. It is different from than list of words as the same word might be tagged with multiple parts-of-speech. The part of speech taught to school-age-children is a much-simplified form of part of speech tagging.
Analyzing Electronic Medical Records (EMRs) with Text Analysis
Extract Data with Named Entity Recognition
The named entity recognition text analysis model can extract all information related to any medical term. In simple terms, named entity recognition is the process of identifying complex medical terms. Using text analysis, you can extract all data related to any disease, medicine, specific surgery, and much more. It can be really helpful for pharmaceutical industries and researchers. They can easily access and interpret all relevant data such as clinical findings, previously recommended treatment, medication, etc.
Pharmaceutical companies can also use this to monitor the symptoms and progression of a disease or track the effects of new medicines. It can make it easier to record, maintain, and access medical research data. Using named entity extraction, you can also find similar cases and compare the symptoms or progression of the disease with previous cases to decide the right treatment. This can significantly decrease the time required for examining the patient and decision-making.
Knowledge Discovery with Keyword Extraction
NLP makes it easier for machines to understand human language. Text analysis solutions use NLP to interpret the unstructured text and derive valuable information. This can be utilized to analyze unstructured text for research purposes. Healthcare professions often have to go through a ton of medical data to develop vaccines for new diseases or viruses. Analyzing this data requires a lot of time and effort. Keyword extraction can help you extract all important information from any piece of text including EMRs and medical research papers.
Example: The spread of a new disease or virus can be devastating, moreover, gathering enough research data can be difficult. You can use keyword extraction techniques to scrape important information or findings from news and research papers shared all over the internet. You can easily information such as symptoms, disease progression rate, suggested treatment, medication, etc. This can significantly speed up the research and make it easier for researchers to find relevant data.
Text Summarization with Feature Extraction
A patient’s medical record contains his entire his/her entire medical history accumulated from the various visits to a clinic or hospital. A healthcare professional has to factor in all the information accumulated over time to suggest the proper treatment. This can take a lot of time and could delay the treatment depending on the time required the summarize the data.
You can use the text analysis feature extraction model instead. It can extract important information from EMRs such as diseases, symptoms, diagnosis, previously recommended treatment, medication doses, etc. This can help summarize the data and make it easier to analyze. The physician or doctor doesn’t have to spend hours studying a case.
Find Similar Cases with Semantic Similarities
While feature extraction helps summarize textual data and makes it easier to analyze, sematic similarities can assist in finding information sources with similar or same data. This text analysis model analyzes textual data to extract a set of documents or terms based on the likeliness of them having the same meaning or content.
This text analysis model can be a great help to doctors or physicians. You can use the patient’s EMR and use it to find patients with very similar medical conditions. Finding a recorded case with similar symptoms and disease progression rate can enhance the examination process and decrease the chances of errors.
The last decade has shown us many technological advancements that are very promising. Although, it only makes it more difficult to choose the technologies to adopt. Text analysis used to be on the same page, but in recent years, more medical institutions and pharmaceutical companies have integrated text analysis solutions to enhance patient care.
The healthcare industry creates a lot of data that is often not accessible but rather locked away in physicians’ notes. The introduction of NLP and machine learning has opened new opportunities to improve healthcare.
BytesView is a cutting-edge text analysis tool that can compile and analyze large volumes of unstructured textual data from various sources. The various text analysis solutions can help you extract relevant information with guaranteed accuracy. You can also train custom models with data specific to your business to further increase their accuracy. BytesView’s text analysis solutions can help you lift the barriers in data governance to enhance patient care.