The Value of E-Learning for the Prevention of Healthcare-Associated Infections

Infect Control Hosp Epidemiol. 2016 Sep;37(9):1052-9. doi: 10.1017/ice.2016.107. Epub 2016 May 13.

Abstract

BACKGROUND Healthcare workers (HCWs) lack familiarity with evidence-based guidelines for the prevention of healthcare-associated infections (HAIs). There is good evidence that effective educational interventions help to facilitate guideline implementation, so we investigated whether e-learning could enhance HCW knowledge of HAI prevention guidelines. METHODS We developed an electronic course (e-course) and tested its usability and content validity. An international sample of voluntary learners submitted to a pretest (T0) that determined their baseline knowledge of guidelines, and they subsequently studied the e-course. Immediately after studying the course, posttest 1 (T1) assessed the immediate learning effect. After 3 months, during which participants had no access to the course, a second posttest (T2) evaluated the residual learning effect. RESULTS A total of 3,587 HCWs representing 79 nationalities enrolled: 2,590 HCWs (72%) completed T0; 1,410 HCWs (39%) completed T1; and 1,011 HCWs (28%) completed T2. The median study time was 193 minutes (interquartile range [IQR], 96-306 minutes) The median scores were 52% (IQR, 44%-62%) for T0, 80% (IQR, 68%-88%) for T1, and 74% (IQR, 64%-84%) for T2. The immediate learning effect (T0 vs T1) was +24% (IQR, 12%-34%; P300 minutes yielded the greatest residual effect (24%). CONCLUSIONS Moderate time invested in e-learning yielded significant immediate and residual learning effects. Decision makers could consider promoting e-learning as a supporting tool in HAI prevention. Infect Control Hosp Epidemiol 2016;37:1052-1059.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Cross Infection / prevention & control*
  • Education, Distance / economics
  • Education, Distance / methods*
  • Female
  • Guideline Adherence
  • Health Personnel / education*
  • Health Personnel / statistics & numerical data*
  • Humans
  • Language
  • Linear Models
  • Male
  • Middle Aged
  • Multivariate Analysis