Today, it is estimated that more than 80% of healthcare data is stored as free text and unstructured data. Unstructured text documents, such as clinical notes and lab reports, contain information that could lead to an understanding of treatments and outcomes, ultimately improving patient care and delivery. But free text poses challenges. Due to its inconsistent form, it cannot be transformed into a format that can be analyzed statistically.
“Free text cannot be synthesized, because it’s too unique, so many of the great things that can be done with clinical data are not relevant for most of the clinical data.””
Historically, clinicians have had to manually read through huge volumes of free text and attempt to summarize the data locked within — an immense task that could take weeks, months, or even years. But using natural language processing (NLP), this text can be quickly and easily digested and summarized, saving organizations months or years of time.
NLP is a branch of machine learning in which computers can understand text or spoken word in a similar way to humans.
Using NLP, computer programs are able to classify, extract, or summarize text that is unstructured. NLP can mine free text for key words and phrases and pull critical information out of clinical documents. Health systems and life science organizations can therefore use NLP for predictive analytics to explore patterns in patient populations and gain new insights to improve patient health outcomes.
NLP processing applications can bring order and structure to healthcare data environments. They allow users to extract specific traits from free-text documents and combine information with structured data to enrich the available healthcare data. They can extract key elements from natural language text, such as clinical, procedural, and laboratory reports, as well as physician notes and clinical trial protocols. NLP transforms how data is processed and empowers clinicians and researchers to quickly summarize their data and better understand patient treatments, leading to improved outcomes for numerous disease states.
Once NLP has processed and transformed the data into a structured format, the data can then be analyzed.
3 Ways NLP Furthers Health Outcomes
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One example of how NLP can benefit health systems is in clinical decision support. NLP can provide clinicians with an abundance of knowledge about disease patterns and outcomes, giving them the ability to make better decisions about the care of their patients.
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NLP can also be used to automate clinical trial matching, linking specific patients up with relevant clinical trials. Patient enrollment in clinical trials remains a challenge, but with NLP patients can easily be matched and enrolled in these opportunities. This can accelerate the clinical trial process so that treatments can be validated more quickly.
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NLP can also be used in the predictive analysis of medical records. NLP can determine which geographic regions, racial groups, or other population variables face different types of health disparities. Additionally, it can be used to understand the causes of poor health outcomes for specific populations.
The MDClone Difference: NLP Studio
The NLP Studio, nestled within the MDClone ADAMS Platform, allows organizations to consume large amounts of structured or unstructured data. Once data is organized and compiled into a single data warehouse, users can process and synthesize the content and then produce pertinent information for healthcare workers to digest, analyze, and spur into action.
NLP tools can rapidly structure healthcare data offering:
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A customizable and collaborative platform
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User-specific controls
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Fast time from model training to results
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Easy repair of poorly formed sentences
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Integration with any NLP model
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An application that doesn’t require NLP knowledge
“It is rewarding to see an NLP model come to life. As the data set grows with each refresh, the insights become more and more meaningful and provide leverage in our mission to transform healthcare.””
Real-world use case examples:
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Identify BRCA-positive prostate cancer patients for additional study
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Retrieve endometriosis diagnoses written only in free text to complete EMR documentation
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Pharma-specific: Identify metastatic status, tumor markers, ECT treatments
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Recruit patients for orthopedic surgery based on CT/MRI results
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Recruit patients for a new or follow-up exams based on results of previous exams
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Update lists on a daily to monthly basis, recognizing that some patients have already had surgery based on this list
Today, numerous health systems, academic medical centers, and life science organizations have used the NLP Studio to quickly impact patient care and save lives by expanding the granular level of data available to their users, which can offer breakthrough insights into patient populations. With more information at their fingertips, these organizations can identify patterns in healthcare data, determine ideal early treatment options, and make faster decisions about care delivery through continuous data enhancements.
“The user can now correlate all of this data together,” Caduri said. “They can ask questions that involve data for structured databases, together with those that were originally not structured, so it gives us a much more powerful querying tool.”
Finally, MDClone’s NLP Studio doesn’t require any special knowledge of machine learning or statistics, making it accessible for nearly any user to leverage.
“The end user doesn’t need to have any NLP knowledge. He doesn’t need to know machine learning or understand statistics, and he also doesn’t need to have a huge amount of training sets for achieving excellent results. With a simple process of tagging only a few of his documents, he can get some effective models very fast.”
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NLP Studio & ADAMS
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