Drug Safety

, Volume 30, Issue 9, pp 817–824 | Cite as

Adverse Drug Event Detection in a Community Hospital Utilising Computerised Medication and Laboratory Data

  • Andrew C. SegerEmail author
  • Ashish K. Jha
  • David W. Bates
Original Research Article


Objective: Computerised monitors can detect and, with clinical intervention, often prevent or ameliorate adverse drug events (ADEs). We evaluated whether a computer-based alerting system was useful in a community hospital setting.

Methods: We evaluated 6 months of retrospectively collected medication and laboratory data from a 140-bed community hospital, and applied the rules from a computerised knowledge base to determine if the resulting alerts might have allowed a clinician to prevent or lessen harm related to medication toxicity. We randomly selected 11% (n = 58, of which 56 were available) of charts deemed to be high- or critical-priority alerts, based on the likelihood of the alerts being associated with injury, to determine the frequencies of ADEs and preventable ADEs.

Results: In 6 months, there were 8829 activations of the rule set, generating a total of 3547 alerts. Of these, 528 were of high or critical priority, 664 were of medium priority and 2355 were of low priority. Chart review among the sample (56 charts) of high- or critical-priority alerts found five non-preventable and two preventable ADEs, suggesting that among the total high- or critical-priority alerts alone, there would be approximately 94 non-preventable ADEs and 37 preventable ADEs annually in this hospital that could be detected using this method.

Conclusions: Computer-based rules engines have the potential to identify and, with clinical intervention, mitigate preventable ADEs, and implementation is feasible in community hospitals without an advanced information technology application.


Metformin Glibenclamide Abciximab Computerise Physician Order Entry Critical Priority 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



No sources of funding were used to assist in the preparation of this study. Vigilanz Corporation provided the software for evaluation free of charge. Dr Bates has received honoraria from Vigilanz for speaking about drug-laboratory checking. Drs Seger and Jha have no conflicts of interest that are directly relevant to the content of this study.


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Copyright information

© Adis Data Information BV 2007

Authors and Affiliations

  • Andrew C. Seger
    • 1
    • 2
    Email author
  • Ashish K. Jha
    • 1
    • 3
    • 4
    • 5
  • David W. Bates
    • 1
  1. 1.Division of General Internal MedicineBrigham and Women’s HospitalBostonUSA
  2. 2.Massachusetts College of Pharmacy and Health SciencesBostonUSA
  3. 3.Department of Health Policy and ManagementHarvard School of Public HealthBostonUSA
  4. 4.Boston VA Healthcare SystemBostonUSA
  5. 5.Harvard Medical SchoolBostonUSA

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