title: A classification of hospital-acquired diagnoses for use with routine hospital data.
abstract: OBJECTIVE
  To develop a tool to allow Australian hospitals to monitor the range of hospital-acquired diagnoses coded in routine data in support of quality improvement efforts.
  
  
  METHODS
  Secondary analysis of abstracted inpatient records for all episodes in acute care hospitals in Victoria for the financial year 2005-06 (n=2.032 million) to develop a classification system for hospital-acquired diagnoses; each record contains up to 40 diagnosis fields coded with the ICD-10-AM (International Classification of Diseases, 10th revision, Australian modification).
  
  
  METHODS
  The Classification of Hospital Acquired Diagnoses (CHADx) was developed by: analysing codes with a "complications" flag to identify high-volume code groups; assessing their salience through an iterative review by health information managers, patient safety researchers and clinicians; and developing principles to reduce double counting arising from coding standards.
  
  
  RESULTS
  The dataset included 126,940 inpatient episodes with any hospital-acquired diagnosis (complication rate, 6.25%). Records had a mean of three flagged diagnoses; including unflagged obstetric and neonatal codes, 514,371 diagnoses were available for analysis. Of these, 2.9% (14,898) were removed as comorbidities rather than complications, and another 118,640 were removed as redundant codes, leaving 380,833 diagnoses for grouping into CHADx classes. We used 4345 unique codes to characterise hospital-acquired conditions; in the final CHADx these were grouped into 144 detailed subclasses and 17 "roll-up" groups.
  
  
  CONCLUSIONS
  Monitoring quality improvement requires timely hospital-onset data, regardless of causation or "preventability" of each complication. The CHADx uses routinely abstracted hospital diagnosis and condition-onset information about in-hospital complications. Use of this classification will allow hospitals to track monthly performance for any of the CHADx indicators, or to evaluate specific quality improvement projects.
authors.affiliation: Australian Centre for Economic Research on Health, University of Queensland, Brisbane, QLD, Australia. t.jackson@uq.edu.au
authors.forename: Terri J
authors.initials: TJ
authors.lastname: Jackson
authors.forename: Jude L
authors.initials: JL
authors.lastname: Michel
authors.forename: Rosemary F
authors.initials: RF
authors.lastname: Roberts
authors.forename: Christine M
authors.initials: CM
authors.lastname: Jorm
authors.forename: John G
authors.initials: JG
authors.lastname: Wakefield
mesh_terms: D005260:Female; D005568:Forms and Records Control / Q000145:classification; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007049:Iatrogenic Disease; D038801:International Classification of Diseases / Q000145:classification*; D008297:Male; D019300:Medical Errors / Q000145:classification; D008499:Medical Records / Q000145:classification*; D011183:Postoperative Complications / Q000145:classification; D011247:Pregnancy; D011248:Pregnancy Complications / Q000145:classification; D019984:Quality Indicators, Health Care / Q000458:organization & administration*; D012189:Retrospective Studies; D014739:Victoria