title: Detecting adverse events using information technology.
abstract: BACKGROUND
  Although patient safety is a major problem, most health care organizations rely on spontaneous reporting, which detects only a small minority of adverse events. As a result, problems with safety have remained hidden. Chart review can detect adverse events in research settings, but it is too expensive for routine use. Information technology techniques can detect some adverse events in a timely and cost-effective way, in some cases early enough to prevent patient harm.
  
  
  OBJECTIVE
  To review methodologies of detecting adverse events using information technology, reports of studies that used these techniques to detect adverse events, and study results for specific types of adverse events.
  
  
  METHODS
  Structured review.
  
  
  METHODS
  English-language studies that reported using information technology to detect adverse events were identified using standard techniques. Only studies that contained original data were included.
  
  
  METHODS
  Adverse events, with specific focus on nosocomial infections, adverse drug events, and injurious falls.
  
  
  RESULTS
  Tools such as event monitoring and natural language processing can inexpensively detect certain types of adverse events in clinical databases. These approaches already work well for some types of adverse events, including adverse drug events and nosocomial infections, and are in routine use in a few hospitals. In addition, it appears likely that these techniques will be adaptable in ways that allow detection of a broad array of adverse events, especially as more medical information becomes computerized.
  
  
  CONCLUSIONS
  Computerized detection of adverse events will soon be practical on a widespread basis.
authors.affiliation: Division of General Medicine, Department of Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA. dbates@partners.org
authors.forename: David W
authors.initials: DW
authors.lastname: Bates
authors.forename: R Scott
authors.initials: RS
authors.lastname: Evans
authors.forename: Harvey
authors.initials: H
authors.lastname: Murff
authors.forename: Peter D
authors.initials: PD
authors.lastname: Stetson
authors.forename: Lisa
authors.initials: L
authors.lastname: Pizziferri
authors.forename: George
authors.initials: G
authors.lastname: Hripcsak
mesh_terms: D000058:Accidental Falls / Q000706:statistics & numerical data*; D016907:Adverse Drug Reaction Reporting Systems; D003428:Cross Infection / Q000175:diagnosis / Q000453:epidemiology*; D064420:Drug-Related Side Effects and Adverse Reactions; D006751:Hospital Information Systems; D006801:Humans; D038801:International Classification of Diseases; D019300:Medical Errors / Q000706:statistics & numerical data; D008491:Medical Informatics Applications; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D011159:Population Surveillance / Q000379:methods; D012449:Safety