title: Can surveillance systems identify and avert adverse drug events? A prospective evaluation of a commercial application.
abstract: OBJECTIVE
  Computerized monitors can effectively detect and potentially prevent adverse drug events (ADEs). Most monitors have been developed in large academic hospitals and are not readily usable in other settings. We assessed the ability of a commercial program to identify and prevent ADEs in a community hospital.
  
  
  METHODS
  and Measurement We prospectively evaluated the commercial application in a community-based hospital. We examined the frequency and types of alerts produced, how often they were associated with ADEs and potential ADEs, and the potential financial impact of monitoring for ADEs.
  
  
  RESULTS
  Among 2,407 patients screened, the application generated 516 high priority alerts. We were able to review 266 alerts at the time they were generated and among these, 30 (11.3%) were considered substantially important to warrant contacting the physician caring for the patient. These 30 alerts were associated with 4 ADEs and 11 potential ADEs. In all 15 cases, the responsible physician was unaware of the event, leading to a change in clinical care in 14 cases. Overall, 23% of high priority alerts were associated with an ADE (95% confidence interval [CI] 12% to 34%) and another 15% were associated with a potential ADE (95% CI 6% to 24%). Active surveillance used approximately 1.5 hours of pharmacist time daily.
  
  
  CONCLUSIONS
  A commercially available, computer-based ADE detection tool was effective at identifying ADEs. When used as part of an active surveillance program, it can have an impact on preventing or ameliorating ADEs.
authors.affiliation: Department of Health Policy and Management, Harvard School of Public Health, Boston, MA 02115, USA. ajha@hsph.harvard.edu
authors.forename: Ashish K
authors.initials: AK
authors.lastname: Jha
authors.forename: Julia
authors.initials: J
authors.lastname: Laguette
authors.forename: Andrew
authors.initials: A
authors.lastname: Seger
authors.forename: David W
authors.initials: DW
authors.lastname: Bates
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000191:economics; D000368:Aged; D001900:Boston; D002984:Clinical Laboratory Information Systems; D016303:Clinical Pharmacy Information Systems; D003365:Costs and Cost Analysis; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data; D006764:Hospitals, Community; D006801:Humans; D008297:Male; D008331:Management Information Systems; D050316:Medical Order Entry Systems / Q000191:economics; D008498:Medical Record Linkage; D008508:Medication Errors / Q000517:prevention & control*; D008510:Medication Systems, Hospital / Q000191:economics; D017063:Outcome Assessment, Health Care; D011446:Prospective Studies