Updated on 17 September 2013
Natural Language Understanding (NLU) technology is an advanced language querying technology that focuses on syntax, semantics and pragmatics to improve how data is understood and coded
In most US healthcare information technology (HIT) environments, the speed and accuracy of medical coding relies heavily on the narrative that a physician details during a patient consultation. However, the nation's transition from the ICD-9 to the robust ICD-10 coding system will pose new challenges for capturing this specificity. The complexity of this transition to ICD-10 has to do with the significant increase in the number and specificity of codes; there are 69,000 diagnostic codes in ICD-10 compared to 14,000 in ICD-9.
Take a specific diagnosis for example: the single ICD-9 code for "closed fractured femur," or thigh bone, translates to 36 distinct ICD-10 codes that describe details regarding the precise nature of fracture, which thigh was fractured, whether a delay in healing occurred, etc. Clinical documentation seems like it would naturally increase and in fact, the American Association of Professional Coders predicts this will lead to a 10-20 percent increase in documentation activities.
Natural language understanding (NLU) can help the healthcare industry meet this challenge and ensure that a patient's comprehensive clinical documentation captures the level of specificity needed for proper reimbursement. NLU is an advanced language querying technology that focuses on syntax, semantics and pragmatics (context contributing to meaning) to improve how structured data, free text and system data are understood and coded. So for instance, if we notice a radiology report has captured that the chest x-ray shows pleural effusion, and the physician has documented congestive heart failure (CHF), we may suggest or ask him if the condition the patient has is an acute exacerbation of CHF. As such, it can help identify holes and missing pieces in a physician narrative, or identify where there appears to be inaccurate information. When something is coded correctly, it is reimbursed correctly and keeps all major healthcare stakeholders happy. However, many doctors are unaware of what information is necessary downstream for proper coding and billing which leads to great inefficiencies.
NLU is getting wide-spread attention these days, and includes a range of techniques that go from rule-based systems to supervised machine learning solutions to unsupervised approaches to deep learning systems. As such, there are a number of vendors who work with similar techniques and approaches.