title: Detecting pregnancy use of non-hormonal category X medications in electronic medical records.
abstract: OBJECTIVE
  To determine whether a rule-based algorithm applied to an outpatient electronic medical record (EMR) can identify patients who are pregnant and prescribed medications proved to cause birth defects.
  
  
  METHODS
  A descriptive study using the University of Pennsylvania Health System outpatient EMR to simulate a prospective algorithm to identify exposures during pregnancy to category X medications, soon enough to intervene and potentially prevent the exposure. A subsequent post-hoc algorithm was also tested, working backwards from pregnancy endpoints, to search for possible exposures that should have been detected.
  
  
  METHODS
  Category X medications prescribed to pregnant patients.
  
  
  RESULTS
  The alert simulation identified 2201 pregnancies with 16,969 pregnancy months (excluding abortions and ectopic pregnancies). Of these, 30 appeared to have an order for a non-hormone category X medication during pregnancy. However, none of the 30 'exposed pregnancies' were confirmed as true exposures in medical records review. The post-hoc algorithm identified 5841 pregnancies with 64 exposed pregnancies in 52,569 risk months, only one of which was a confirmed case.
  
  
  CONCLUSIONS
  Category X medications may indeed be used in pregnancy, although rarely. However, most patients identified by the algorithm as exposed in pregnancy were not truly exposed. Therefore, implementing an electronic warning without evaluation would have inconvenienced prescribers, possibly hurting some patients (leading to non-use of needed drugs), with no benefit. These data demonstrate that computerized physician order entry interventions should be selected and evaluated carefully even before their use, using alert simulations such as that performed here, rather than just taken off the shelf and accepted as credible without formal evaluation.
authors.affiliation: Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6021, USA. bstrom@mail.med.upenn.edu
authors.forename: Brian L
authors.initials: BL
authors.lastname: Strom
authors.forename: Rita
authors.initials: R
authors.lastname: Schinnar
authors.forename: Joshua
authors.initials: J
authors.lastname: Jones
authors.forename: Warren B
authors.initials: WB
authors.lastname: Bilker
authors.forename: Mark G
authors.initials: MG
authors.lastname: Weiner
authors.forename: Sean
authors.initials: S
authors.lastname: Hennessy
authors.forename: Charles E
authors.initials: CE
authors.lastname: Leonard
authors.forename: Peter F
authors.initials: PF
authors.lastname: Cronholm
authors.forename: Eric
authors.initials: E
authors.lastname: Pifer
mesh_terms: D000465:Algorithms; D057286:Electronic Health Records; D005260:Female; D006801:Humans; D057970:Inappropriate Prescribing; D018811:Maternal Exposure; D050316:Medical Order Entry Systems; D011247:Pregnancy; D011248:Pregnancy Complications / Q000188:drug therapy*; D011258:Pregnancy Tests; D013723:Teratogens
chemical_list: D013723:Teratogens
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