Aligned with the complex language of healthcare, Clinical Natural Language Processing (cNLP) enables healthcare organizations to unlock the value of their data.
An important goal of cNLP is to provide structure to highly unstructured data sources. In the first blog of our four-part series on cNLP, we reviewed how extracting clinical information can improve the risk adjustment process. In this second installment, we uncover the opportunities and challenges healthcare providers and health plans face when extracting data from free text for quality measures reporting.
Providers, health plans, and patients all benefit from clinical records that are thorough, accurate, understandable, and interoperable. Having quality data available is foundational for providing quality care to patients and ultimately improving outcomes. However, the vast amount of data paired with the varied types of data available in electronic health records creates challenges when attempting to review information meaningfully, extract it from the records, and share it effectively.
Clinical documentation and unstructured data
An essential component of quality clinical documentation is accurately depicting the patient story. To accomplish this, clinicians often combine the art of storytelling with the science of medicine to create their notes. Many different types of documents can be found in patient records and the content within varies by document type. To construct clinical notes, a variety of documentation methodologies are used to input data such as point and click entry of data elements from templates, narrative free-text type, speech recognition, and dictation. Often, more than one of these techniques are utilized.
Notes are typically then augmented by incorporating free form data entry that provides detail to the record that may not be available in the EHR templates, or not easily documented using a templated format. Common areas of the chart where unstructured data can be found are the history of present illness, assessment and plan, progress notes, procedure notes, and entries from comment fields scattered throughout the electronic health record.
Extracting meaningful information from unstructured data
The information contained in patient records is complex. In healthcare, we have our own unique vocabulary. There is a large lexicon of medical terms and there are many ways we can describe a medical concept. While training providers to use speech recognition software to augment notes, I quickly realized the importance of using a program that included a medical dictionary. While there are many benefits to having multiple ways to input data, including speech to text and typing, we are often left with a myriad of medical terms, synonyms, acronyms, approved or unapproved abbreviations, and misspelled words present in the clinical notes. Patient records include a mix of structured and unstructured data which leads to a more complete patient story, but at the same time presents us challenges in the review of records, and the extraction of meaningful information for patient care, analytics, research, and reporting.