Detecting pregnancy use of non-hormonal category X medications in electronic medical records

Brian L. Strom, Rita Schinnar, Joshua Jones, Warren B. Bilker, Mark G. Weiner, Sean Hennessy, Charles E. Leonard, Peter F. Cronholm, Eric Pifer

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Objectives: 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. Design: 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. Measurements: 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.

Original languageEnglish (US)
Pages (from-to)81-86
Number of pages6
JournalJournal of the American Medical Informatics Association
Volume18
Issue numberSUPPL. 1
DOIs
StatePublished - Dec 2011

All Science Journal Classification (ASJC) codes

  • Health Informatics

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