title: Deriving dose limits for warnings in electronic prescribing systems: statistical analysis of prescription data at University Hospital Birmingham, UK.
abstract: BACKGROUND
  : Electronic decision support can reduce medication errors, and dose-range checking is one element of that support.
  
  
  OBJECTIVE
  : The aim of this study was to design an approach to setting upper dose warning limits in electronic prescribing systems where there are historical data on dosing.
  
  
  METHODS
  : We used historical data on 56 drug-form combinations for which over 100 prescriptions had been issued between 1 June 2009 and 31 May 2010 in a bespoke electronic prescribing system at University Hospital Birmingham, UK. First, two experts derived dose limits for each drug-form combination, then the drugs were randomly divided into a training set and a test set. A variation of the 'Nearest Rank' approach to estimate statistical limits was used to derive the percentile with the optimal sensitivity and specificity.
  
  
  RESULTS
  : For the 28 drug-form combinations in the test set, the 86th percentile of dose gave a mean sensitivity of 95.3% and a mean specificity of 97.9% for warning limits, representing the highest reasonable dose; the 96th percentile gave a mean sensitivity of 90.2% and mean specificity of 99.5% for disallow limits, beyond which no dose should be prescribed.
  
  
  CONCLUSIONS
  : Dosing decision support within electronic prescribing systems can be derived by statistical analysis of historical prescription data. We advocate a combined theoretical and statistical derivation of dose checking rules in order to ensure that prescribers are alerted appropriately to potentially toxic doses.
authors.affiliation: University Hospital Birmingham NHS Foundation Trust, Queen Elizabeth Hospital Birmingham, Birmingham, UK. j.j.coleman@bham.ac.uk
authors.forename: Jamie J
authors.initials: JJ
authors.lastname: Coleman
authors.forename: James
authors.initials: J
authors.lastname: Hodson
authors.forename: Robin E
authors.initials: RE
authors.lastname: Ferner
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000592:standards* / Q000706:statistics & numerical data; D020000:Decision Support Systems, Clinical / Q000592:standards* / Q000706:statistics & numerical data; D004347:Drug Interactions; D004360:Drug Therapy, Computer-Assisted / Q000379:methods* / Q000592:standards; D064420:Drug-Related Side Effects and Adverse Reactions; D055695:Electronic Prescribing / Q000592:standards* / Q000706:statistics & numerical data; D004739:England; D006785:Hospitals, University; D006801:Humans; D050316:Medical Order Entry Systems / Q000592:standards; D008508:Medication Errors / Q000517:prevention & control* / Q000706:statistics & numerical data; D012372:ROC Curve