BACKGROUND

This disclosure relates generally to clinical documentation, and specifically to improvements in medical coding. In particular, the disclosure relates to ontologically driven medical coding systems and related methods, including compositional natural language processing (NLP) techniques for ICD type (International Classification of Diseases) coding standards, and other medical coding systems. Applications include computer assisted coding (CAC) for medical conditions and treatments, for example procedural coding systems for billing, diagnostic, statistical and analytic purposes.

The ICD coding system was developed in order to provide a standard tool for epidemiology and health care management, including clinical and hospital services. ICD classifications encompass a range of medical conditions including diseases and other disorders, as well as other, more nuanced factors including symptoms, complaints, abnormal findings, external causes of injury or disease, and social circumstances. Different ICD classification systems are also utilized to document patient treatment and reimbursement, including inpatient hospital care, emergency room care, and outpatient services including clinical and professional services.

The application of natural language processing tools to ICD classification systems provides substantial opportunities for increased efficiency and improved clinical documentation. In addition, ontologically driven NLP processing techniques can be utilized to smooth the transition to newer, more complete coding standards, including implementations of the ICD-10-CM (Clinical Modification) and ICD-10-PCS (Procedure Coding System) standards.

SUMMARY OF THE INVENTION

This application is directed to computer based, ontologically driven systems and methods for procedure coding, including automated ICD and PCS type coding suggestions based on compositional natural language processing (NLP) engines applied to narrative text in the clinical documentation. Software embodiments are also encompassed, including standalone software modules and programs delivered within a computer assisted coding (CAC) platform.

Ontologically driven, compositional NLP analysis can be applied to leverage the properties of multi-axial coding, providing a more comprehensive approach to medical and billing code generation. These techniques can also be designed to support individual coders during the transition from one standard to another (e.g., from ICD-9 to ICD-10-PCS), softening the learning curve and helping to mitigate financial impacts by providing specific coding suggestions based on natural language processing of narrative text and other unstructured data extracted from clinical documentation in medical records.

Semantic objects representing medical codes, rather than medical codes, may be treated as the primitive concepts in the natural language processing system. Thus, partial output (that is, some but not all character slots) may be returned for output, when a full code has not been identified. A full code may not be identified when, for example, at least one slot is provided with a wildcard or other indication that a particular medical concept necessary to specify one or more of the characters was not identified in the narrative text.

