title: A system for solution-orientated reporting of errors associated with the extraction of routinely collected clinical data for research and quality improvement.
abstract: BACKGROUND
  We have used routinely collected clinical data in epidemiological and quality improvement research for over 10 years. We extract, pseudonymise and link data from heterogeneous distributed databases; inevitably encountering errors and problems.
  
  
  OBJECTIVE
  To develop a solution-orientated system of error reporting which enables appropriate corrective action.
  
  
  METHODS
  Review of the 94 errors, which occurred in 2008/9. Previously we had described failures in terms of the data missing from our response files; however this provided little information about causation. We therefore developed a taxonomy based on the IT component limiting data extraction.
  
  
  RESULTS
  Our final taxonomy categorised errors as: (A) Data extraction Method and Process; (B) Translation Layer and Proxy Specification; (C) Shape and Complexity of the Original Schema; (D) Communication and System (mainly Software-based) Faults; (E) Hardware and Infrastructure; (F) Generic/Uncategorised and/or Human Errors. We found 79 distinct errors among the 94 reported; and the categories were generally predictive of the time needed to develop fixes.
  
  
  CONCLUSIONS
  A systematic approach to errors and linking them to problem solving has improved project efficiency and enabled us to better predict any associated delays.
authors.affiliation: Department of Computing, University of Surrey, Guildford, UK.
authors.forename: Georgios
authors.initials: G
authors.lastname: Michalakidis
authors.forename: Pushpa
authors.initials: P
authors.lastname: Kumarapeli
authors.forename: Andre
authors.initials: A
authors.lastname: Ring
authors.forename: Jeremy
authors.initials: J
authors.lastname: van Vlymen
authors.forename: Paul
authors.initials: P
authors.lastname: Krause
authors.forename: Simon
authors.initials: S
authors.lastname: de Lusignan
mesh_terms: D035843:Biomedical Research / Q000706:statistics & numerical data*; D057225:Data Mining / Q000379:methods*; D019300:Medical Errors / Q000145:classification* / Q000517:prevention & control / Q000706:statistics & numerical data*; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data*; D008923:Missouri; D011785:Quality Assurance, Health Care / Q000592:standards*; D012308:Risk Management / Q000458:organization & administration*