title: Use of the Generating Options for Active Risk Control (GO-ARC) Technique can lead to more robust risk control options.
abstract: BACKGROUND
  Risk assessment is widely used to improve patient safety, but healthcare workers are not trained to design robust solutions to the risks they uncover. This leads to an overreliance on the weakest category of risk control recommendations: administrative controls. Increasing the proportion of non-administrative risk control options (NARCOs) generated would enable (though not ensure) the adoption of more robust solutions.
  
  
  OBJECTIVE
  Experimentally assess a method for generating stronger risk controls: The Generating Options for Active Risk Control (GO-ARC) Technique.
  
  
  METHODS
  Participants generated risk control options in response to two patient safety scenarios. Scenario 1 (baseline): All participants used current practice (unstructured brainstorming). Scenario 2: Control group used current practice; intervention group used the GO-ARC Technique. To control for individual differences between participants, analysis focused on the change in the proportion of NARCOs for each group.
  
  
  RESULTS
  
  
  
  METHODS
  Proportion of NARCOs decreased from 0.18 at baseline to 0.12. Intervention group: Proportion increased from 0.10 at baseline to 0.29 using the GO-ARC Technique. Results were statistically significant. There was no decrease in the number of administrative controls generated by the intervention group.
  
  
  CONCLUSIONS
  The Generating Options for Active Risk Control (GO-ARC) Technique appears to lead to more robust risk control options.
authors.affiliation: Evidence-Based Health Solutions, LLC, Notre Dame, IN, USA.
authors.forename: Alan J
authors.initials: AJ
authors.lastname: Card
authors.affiliation: Engineering Design Centre, Department of Engineering, University of Cambridge, Cambridge, UK.
authors.forename: Mecit Can Emre
authors.initials: MC
authors.lastname: Simsekler
authors.affiliation: Center for Social Research, University of Notre Dame, Notre Dame, IN, USA.
authors.forename: Michael
authors.initials: M
authors.lastname: Clark
authors.affiliation: Engineering Design Centre, Department of Engineering, University of Cambridge, Cambridge, UK.
authors.forename: James R
authors.initials: JR
authors.lastname: Ward
authors.affiliation: Engineering Design Centre, Department of Engineering, University of Cambridge, Cambridge, UK.
authors.forename: P John
authors.initials: PJ
authors.lastname: Clarkson
mesh_terms: D003659:Decision Making, Organizational; D006801:Humans; D016014:Linear Models; D008508:Medication Errors / Q000517:prevention & control; D010043:Outcome and Process Assessment, Health Care; D061214:Patient Safety; D011897:Random Allocation; D018570:Risk Assessment / Q000379:methods*; D012308:Risk Management / Q000379:methods*; D000092864:Suicide Prevention
keywords: Healthcare risk management; occupational health; patient safety; risk control