title: Using a snowflake data model and autocompletion to support diagnostic coding in acute care hospitals.
abstract: OBJECTIVE
  Efficient and adequate coding is essential for all hospitals to optimize funding, follow activity, and perform epidemiological studies.
  
  
  OBJECTIVE
  We propose an autocompletion method for optimizing diagnostic coding in acute care hospitals.
  
  
  METHODS
  Using a terminology snowflake model integrating SNOMED 3.5 and ICD-10 codes, autocompletion algorithms generate a list of diagnostic expressions from partial input concepts.
  
  
  RESULTS
  A general autocompletion component has been developed and tested on a set of inpatient summary reports. Concepts expressed as strings of three or four characters return a noisy list of diagnostic labels or codes. Concepts expressed as groups of strings return lists that are semantically close to the labels present in hospital reports. The most pertinent information lies in the length of the expressions entered.
  
  
  CONCLUSIONS
  Autocompletion can be a complementary tool to existing coding support systems.
authors.affiliation: INSERM, UMR_S 1138, CRC, Team 22, Paris, France.
authors.forename: Joseph
authors.initials: J
authors.lastname: Noussa-Yao
authors.affiliation: INSERM, UMR_S 1138, CRC, Team 22, Paris, France.
authors.forename: Abdelali
authors.initials: A
authors.lastname: Boussadi
authors.affiliation: Hôpital Européen Georges Pompidou (HEGP), Paris, France.
authors.forename: Monique
authors.initials: M
authors.lastname: Richard
authors.affiliation: INSERM, UMR_S 1138, CRC, Team 22, Paris, France.
authors.forename: Didier
authors.initials: D
authors.lastname: Heudes
authors.affiliation: INSERM, UMR_S 1138, CRC, Team 22, Paris, France.
authors.forename: Patrice
authors.initials: P
authors.lastname: Degoulet
mesh_terms: D000465:Algorithms; D003422:Critical Care; D057286:Electronic Health Records / Q000458:organization & administration*; D016247:Information Storage and Retrieval / Q000379:methods; D038801:International Classification of Diseases; D000069550:Machine Learning; D017146:Models, Organizational; D063886:Patient Discharge Summaries; D010363:Pattern Recognition, Automated / Q000379:methods; D039061:Systematized Nomenclature of Medicine; D014584:User-Computer Interface