title: Checking coding completeness by mining discharge summaries.
abstract: Incomplete coding is a known problem in hospital information systems. In order to detect non-coded secondary diseases we developed a text classification system which scans discharge summaries for drug names. Using a drug knowledge base in which drug names are linked to sets of ICD-10 codes, the system selects those documents in which a drug name occurs that is not justified by any ICD-10 code within the corresponding record in the patient database. Treatment episodes with missing codes for diabetes mellitus, Parkinson's disease, and asthma/COPD were subject to investigation in a large German university hospital. The precision of the method was 79%, 14%, and 45% respectively, roughly estimated recall values amounted to 43%, 70%, and 36%. Based on these data we predict roughly 716 non-coded diabetes cases, 13 non-coded Parkinson cases, and 420 non-coded asthma/COPD cases among 34,865 treatment episodes.
authors: University Medical Center Freiburg, Germany. xstefan.schulz@medunigraz.at
authors: Stefan
authors: S
authors: Schulz
authors: Thorsten
authors: T
authors: Seddig
authors: Susanne
authors: S
authors: Hanser
authors: Albrecht
authors: A
authors: Zaiss
authors: Philipp
authors: P
authors: Daumke
mesh_terms: D000465:Algorithms; D001249:Asthma / Q000145:classification; D059019:Clinical Coding / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual; D003920:Diabetes Mellitus / Q000145:classification; D003953:Diagnosis-Related Groups; D057286:Electronic Health Records; D006761:Hospitals; D006801:Humans; D007256:Information Systems / Q000458:organization & administration; D010300:Parkinson Disease / Q000145:classification; D010351:Patient Discharge; D029424:Pulmonary Disease, Chronic Obstructive / Q000145:classification; D009626:Terminology as Topic