title: Tailoring "best-of-breed" safety classification for patient fall voluntary reporting.
abstract: OBJECTIVE
  Voluntary safety event reporting often produces poorly defined data points, which complicate data analyses across health care settings. Such data should be restructured into a standard patient safety language translatable within and outside health care organizations. We designed and implemented a "best-of-breed" patient safety classification for data created by the Duke University Health System Safety Reporting System.
  
  
  METHODS
  We report our approach for patient fall classification. Our strategy was to deploy the International Classification for Patient Safety Framework of the World Health Organization augmented with additional data points of interest, thereby allowing for data translatability while maintaining local practices. System interface redesign using the "best-of-breed" fall classification was mindful of workflows and known reporting barriers. Custom aggregate reports were also developed.
  
  
  RESULTS
  We estimated the impact of the redesigned portal on Safety Reporting System usage before and after classification through comparisons of fall report volume and report completion time. When normalized as falls per day, the rate of falls only changed slightly, indicating that the enhancement had little effect on reporting desire. Report completion time increased modestly but not significantly from a practical standpoint. The presence of structured data eliminated substantial hours dedicated to manual data management and enabled evaluation of quality improvement interventions within and outside our organization.
  
  
  CONCLUSIONS
  Creation and implementation of a "best-of-breed" patient safety classification for voluntary reporting requires multidisciplinary collaboration between clinical experts, frontline clinicians, and functional and technical analysts. Formal usability evaluations of reporting systems are needed to ensure design facilitates effective data collection.
authors.affiliation: Duke Health Technology Solutions, Duke University Health System, 2424 Erwin Road, Durham, NC 27705, USA. julie.whitehurst@duke.edu
authors.forename: Julie M
authors.initials: JM
authors.lastname: Whitehurst
authors.forename: Heidi
authors.initials: H
authors.lastname: Cozart
authors.forename: Dave
authors.initials: D
authors.lastname: Leonard
authors.forename: John
authors.initials: J
authors.lastname: Schroder
authors.forename: Monica
authors.initials: M
authors.lastname: Horvath
authors.forename: Susan
authors.initials: S
authors.lastname: Avent
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Ferranti
mesh_terms: D000058:Accidental Falls; D004282:Documentation / Q000379:methods*; D006801:Humans; D020407:Internet; D009657:North Carolina; D010361:Patients; D017751:Safety Management / Q000145:classification*; D014584:User-Computer Interface