title: Duplicate orders: an unintended consequence of computerized provider/physician order entry (CPOE) implementation: analysis and mitigation strategies.
abstract: OBJECTIVE
  Computerized provider/physician order entry (CPOE) with clinical decision support (CDS) is designed to improve patient safety. However, a number of unintended consequences which include duplicate ordering have been reported. The objective of this time-series study was to characterize duplicate orders and devise strategies to minimize them.
  
  
  METHODS
  Time series design with systematic weekly sampling for 84 weeks. Each week we queried the CPOE database, downloaded all active orders onto a spreadsheet, and highlighted duplicate orders. We noted the following details for each duplicate order: time, order details (e.g. drug, dose, route and frequency), ordering prescriber, including position and role, and whether the orders originated from a single order or from an order set (and the name of the order set). This analysis led to a number of interventions, including changes in: order sets, workflow, prescriber training, pharmacy procedures, and duplicate alerts.
  
  
  RESULTS
  Duplicates were more likely to originate from different prescribers than from same prescribers; and from order sets than from single orders. After interventions, there was an 84.8% decrease in the duplication rate from weeks 1 to 84 and a 94.6% decrease from the highest (1) to the lowest week (75). Currently, we have negligible duplicate orders.
  
  
  CONCLUSIONS
  Duplicate orders can be a significant unintended consequence of CPOE. By analyzing these orders, we were able to devise and implement generalizable strategies that significantly reduced them. The incidence of duplicate orders before CPOE implementation is unknown, and our data originate from a weekly snapshot of active orders, which serves as a sample of total active orders. Thus, it should be noted that this methodology likely under-reports duplicate orders.
authors: Hospital for Special Surgery, Quality Research Center , New York.
authors: S
authors: S
authors: Magid
authors: C
authors: C
authors: Forrer
authors: S
authors: S
authors: Shaha
mesh_terms: D016208:Databases, Factual; D011307:Drug Prescriptions / Q000706:statistics & numerical data; D050316:Medical Order Entry Systems / Q000592:standards / Q000706:statistics & numerical data*; D010820:Physicians; D013997:Time Factors
keywords: Informatics; documentation; medication errors
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