title: Patient-specific electronic decision support reduces prescription of excessive doses.
abstract: OBJECTIVE
  Prescription of excessive doses is the most common prescription error, provoking dose-dependent adverse drug reactions. Clinical decision support systems (CDSS) can prevent prescription errors especially when mainly clinically relevant warnings are issued. We have built and evaluated a CDSS providing upper dose limits personalised to individual patient characteristics thus guaranteeing for specific warnings.
  
  
  METHODS
  For 170 compounds, detailed information on upper dose limits (according to the drug label) was compiled. A comprehensive software-algorithm extracted relevant patient information from the electronic chart (eg, age, renal function, comedication). The CDSS was integrated into the local prescribing platform for outpatients and patients at discharge, providing immediate dosage feedback. Its impact was evaluated in a 90-day intervention study (phase 1: baseline; phase 2: intervention). Outcome measures were frequency of excessive doses before and after intervention considering potential induction of new medication errors. Moreover, predictors for alert adherence were analysed.
  
  
  RESULTS
  In phase 1, 552 of 12,197 (4.5%) prescriptions exceeded upper dose limits. In phase 2, initially 559 warnings were triggered (4.8%, p=0.37). Physicians were responsive to one in four warnings mostly adjusting dosages. Thus, the final prescription rate of excessive doses was reduced to 3.6%, with 20% less excessive doses compared with baseline (p<0.001). No new manifest prescription errors were induced. Physicians' alert adherence correlated with patients' age, prescribed drug class, and reason for the alert.
  
  
  CONCLUSIONS
  During the 90-day study, implementation of a highly specific algorithm-based CDSS substantially improved prescribing quality with a high acceptance rate compared with previous studies.
authors.affiliation: Department of Internal Medicine VI, Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg, Im Neuenheimer Feld 410, Heidelberg 69120, Germany.
authors.forename: H M
authors.initials: HM
authors.lastname: Seidling
authors.forename: S P W
authors.initials: SP
authors.lastname: Schmitt
authors.forename: T
authors.initials: T
authors.lastname: Bruckner
authors.forename: J
authors.initials: J
authors.lastname: Kaltschmidt
authors.forename: M G
authors.initials: MG
authors.lastname: Pruszydlo
authors.forename: C
authors.initials: C
authors.lastname: Senger
authors.forename: T
authors.initials: T
authors.lastname: Bertsche
authors.forename: I
authors.initials: I
authors.lastname: Walter-Sack
authors.forename: W E
authors.initials: WE
authors.lastname: Haefeli
mesh_terms: D020000:Decision Support Systems, Clinical / Q000592:standards*; D062787:Drug Overdose / Q000517:prevention & control*; D005260:Female; D006801:Humans; D008297:Male; D050316:Medical Order Entry Systems / Q000592:standards; D008508:Medication Errors / Q000517:prevention & control; D008875:Middle Aged; D018802:Patient-Centered Care; D010607:Pharmacy Service, Hospital; D012984:Software