A randomized controlled trial of screening for maternal depression with a clinical decision support system

Aaron E. Carroll, Paul Biondich, Vibha Anand, T. M. Dugan, Stephen M. Downs

Research output: Contribution to journalArticle

38 Scopus citations

Abstract

Objective To determine if automated screening and just in time delivery of testing and referral materials at the point of care promotes universal screening referral rates for maternal depression. Methods The Child Health Improvement through Computer Automation (CHICA) system is a decision support and electronic medical record system used in our pediatric clinics. All families of patients up to 15 months of age seen between October 2007 and July 2009 were randomized to one of three groups: (1) screening questions printed on prescreener forms (PSF) completed by mothers in the waiting room with physician alerts for positive screens, (2) everything in (1) plus 'just in time' (JIT) printed materials to aid physicians, and (3) a control group where physicians were simply reminded to screen on printed physician worksheets. Results The main outcome of interest was whether physicians suspected a diagnosis of maternal depression and referred a mother for assistance. This occurred significantly more often in both the PSF (2.4%) and JIT groups (2.4%) than in the control group (1.2%) (OR 2.06, 95% CI 1.08 to 3.93). Compared to the control group, more mothers were noted to have depressed mood in the PSF (OR 7.93, 95% CI 4.51 to 13.96) and JIT groups (OR 8.10, 95% CI 4.61 to 14.25). Similarly, compared to the control group, more mothers had signs of anhedonia in the PSF (OR 12.58, 95% CI 5.03 to 31.46) and JIT groups (OR 13.03, 95% CI 5.21 to 32.54). Conclusions Clinical decision support systems like CHICA can improve the screening of maternal depression.

Original languageEnglish (US)
Pages (from-to)311-316
Number of pages6
JournalJournal of the American Medical Informatics Association
Volume20
Issue number2
DOIs
StatePublished - Mar 13 2013

ASJC Scopus subject areas

  • Health Informatics

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