title: Automatic Detection of Skin and Subcutaneous Tissue Infections from Primary Care Electronic Medical Records.
abstract: BACKGROUND
  Skin and subcutaneous tissue infections (SSTI) are common conditions that cause avoidable hospitalisation in New Zealand. As part of a program to improve the management of SSTI in primary care, electronic medical records (EMR) of four Auckland general practices were analysed to identify SSTI occurrences in the last three years.
  
  
  METHODS
  An ontology for SSTI risks, manifestation and treatment was created based on literature and guidelines. An SSTI identification algorithm was developed examining EMR data for skin swab tests, diagnoses (READ codes) and textual clinical notes.
  
  
  RESULTS
  High occurrence and recurrence rates in those aged 20 or younger were found. Due to low usage of READ coding and laboratory tests, 65% of SSTI occurrences were identified by notes. However, 91% of all identified SSTI occurrences were appropriately treated with oral/topical antibiotics according to prescription records in the EMR. The F1 score of the analysis algorithm is 0.76 using manual review as gold standard.
  
  
  CONCLUSIONS
  The SSTI identification algorithm shows a reasonable accuracy suggesting the feasibility of automatic detecting SSTI occurrences using clinical data that are routinely collected in healthcare delivery.
authors.affiliation: University of Auckland, New Zealand.
authors.forename: Yulong
authors.initials: Y
authors.lastname: Gu
authors.affiliation: University of Auckland, New Zealand.
authors.forename: John
authors.initials: J
authors.lastname: Kennelly
authors.affiliation: University of Auckland, New Zealand.
authors.forename: Jim
authors.initials: J
authors.lastname: Warren
authors.affiliation: University of Auckland, New Zealand.
authors.forename: Pritesh
authors.initials: P
authors.lastname: Nathani
authors.affiliation: The Fono Health & Social Services, Auckland, New Zealand.
authors.forename: Tai
authors.initials: T
authors.lastname: Boyce
mesh_terms: D057225:Data Mining / Q000379:methods; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D057286:Electronic Health Records / Q000145:classification*; D006801:Humans; D000069550:Machine Learning; D009323:Natural Language Processing; D009520:New Zealand; D011320:Primary Health Care / Q000145:classification*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017192:Skin Diseases, Bacterial / Q000175:diagnosis*; D040521:Subcutaneous Tissue / Q000473:pathology; D018875:Vocabulary, Controlled