title: The use of an automated interactive voice response system to manage medication identification calls to a poison center.
abstract: BACKGROUND
  In 2007, medication identification requests (MIRs) accounted for 26.2% of all calls to U.S. poison centers. MIRs are documented with minimal information, but they still require an inordinate amount of work by specialists in poison information (SPI). An analysis was undertaken to identify options to reduce the impact of MIRs on both human and financial resources.
  
  
  METHODS
  All MIRs (2003-2007) to a certified regional poison information center were analyzed to determine call patterns and staffing. The data were used to justify an efficient and cost-effective solution.
  
  
  RESULTS
  MIRs represented 42.3% of the 2007 call volume. Optimal staffing would require hiring an additional four full-time equivalent SPI. An interactive voice response (IVR) system was developed to respond to the MIRs.
  
  
  CONCLUSIONS
  The IVR was used to develop the Medication Identification System that allowed the diversion of up to 50% of the MIRs, enhancing surge capacity and allowing specialists to address the more emergent poison exposure calls. This technology is an entirely voice-activated response call management system that collects zip code, age, gender and drug data and stores all responses as .csv files for reporting purposes. The query bank includes the 200 most common MIRs, and the system features text-to-voice synthesis that allows easy modification of the drug identification menu. Callers always have the option of engaging a SPI at any time during the IVR call flow.
  
  
  CONCLUSIONS
  The IVR is an efficient and effective alternative that creates better staff utilization.
authors.affiliation: Pittsburgh Poison Center, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA. krenzelokep@upmc.edu
authors.forename: Edward P
authors.initials: EP
authors.lastname: Krenzelok
authors.forename: Rita
authors.initials: R
authors.lastname: Mrvos
mesh_terms: D001331:Automation; D003362:Cost-Benefit Analysis; D017598:Efficiency, Organizational; D006801:Humans; D010561:Personnel Staffing and Scheduling / Q000458:organization & administration; D011039:Poison Control Centers / Q000191:economics / Q000458:organization & administration*; D011041:Poisoning / Q000209:etiology*; D049250:Speech Recognition Software / Q000191:economics; D013689:Telephone; D013997:Time Factors; D000078329:Workforce