Technology Corner: Natural Language Processing (NLP) in Clinical Documentation Improvement (CDI) and Coding Software.
How NLP is Being Used in Coding:
Impact on Coding Professionals:
While NLP tools offer significant benefits in terms of efficiency and accuracy, they are not intended to replace human coders. Instead, they act as powerful assistants, helping coders focus on more complex cases and critical decision-making. Coders still need a strong understanding of anatomy, physiology, medical terminology, and coding guidelines to review and validate the NLP-generated suggestions and ensure the final coded data is accurate and compliant.
In conclusion, NLP is a rapidly evolving technology that is transforming the landscape of clinical documentation improvement and medical coding. Its ability to analyze and interpret clinical language offers the potential for increased efficiency, improved accuracy, and better capture of the patient's clinical picture. As NLP continues to advance, it will likely become an even more integral part of the daily workflow for coding professionals.
How NLP is Being Used in Coding:
- Automated Chart Review and Pre-Coding Assistance: NLP can scan patient charts and identify key clinical terms, phrases, and relationships that are relevant for coding. It can highlight potential diagnoses, procedures, and quality indicators, assisting coders in quickly identifying pertinent information. For example, NLP might flag mentions of "acute hypoxic respiratory failure," "ventilator support," and "sepsis" within a discharge summary, suggesting potential codes related to respiratory failure, mechanical ventilation, and severe sepsis.
- CDI Query Suggestions: NLP can analyze physician documentation in real-time or retrospectively and identify instances where clarification or additional information might be needed for accurate coding. It can suggest targeted CDI queries to physicians, such as prompting for the underlying cause of anemia or the specific type of heart failure. This helps improve documentation quality before coding even begins.
- Automated Code Assignment (with Human Oversight): While fully automated coding is still evolving and requires careful validation, NLP is being used to suggest potential ICD-10-CM, CPT, and HCPCS codes based on the analyzed documentation. Coders then review these suggestions for accuracy and make the final coding decisions. This can significantly increase coding efficiency.
- Identification of High-Risk and HCC-Relevant Conditions: NLP algorithms can be trained to identify documentation patterns and keywords associated with high-risk conditions and Hierarchical Condition Categories (HCCs). This helps ensure that all relevant diagnoses impacting risk adjustment are captured. For instance, NLP can identify combinations of diagnoses and medications that might indicate poorly controlled diabetes or chronic kidney disease with complications.
- Quality Measure Identification: NLP can extract information relevant to various quality measures, such as identifying documentation of specific medications, procedures, or patient outcomes. This can streamline the quality reporting process.
Impact on Coding Professionals:
While NLP tools offer significant benefits in terms of efficiency and accuracy, they are not intended to replace human coders. Instead, they act as powerful assistants, helping coders focus on more complex cases and critical decision-making. Coders still need a strong understanding of anatomy, physiology, medical terminology, and coding guidelines to review and validate the NLP-generated suggestions and ensure the final coded data is accurate and compliant.
In conclusion, NLP is a rapidly evolving technology that is transforming the landscape of clinical documentation improvement and medical coding. Its ability to analyze and interpret clinical language offers the potential for increased efficiency, improved accuracy, and better capture of the patient's clinical picture. As NLP continues to advance, it will likely become an even more integral part of the daily workflow for coding professionals.