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Development of a Computerized Adverse Drug Event (ADE) Monitor in the Outpatient Setting.

Editors

In: Henriksen K1, Battles JB1, Marks ES2, Lewin DI1, editors.

Source

Advances in Patient Safety: From Research to Implementation (Volume 2: Concepts and Methodology). Rockville (MD): Agency for Healthcare Research and Quality (US); 2005 Feb.
Advances in Patient Safety.

Author information

1
Agency for Healthcare Research and Quality
2
US Department of Defense
3
Division of General Medicine, Brigham and Women's Hospital, Boston (ACS, TKG, DW, JF, DWB). Regenstrief Institute, Indianapolis (JMO, MDM, ET). Harvard Medical School, Boston (TKG, DWB). University of Mississippi Medical School, Jackson, MS (CH). Massachusetts College of Pharmacy and Health Sciences, Boston, (ACS). Indiana University School of Medicine, Indianapolis (JMO). Purdue School of Pharmacy and Pharmacal Sciences, West Lafayette, IN (MDM)

Excerpt

This paper describes the collaboration of Brigham and Women's Hospital and Regenstrief Institute to develop a computerized adverse drug event (ADE) monitor using electronic medical records from outpatient practices. We describe the steps involved in ADE monitor development and rule validation at large outpatient practices at Boston and Indianapolis. The final standard rule set adopted by both practice sites are currently being used to test the impact of basic and advanced decision support on ADE rates. The rules used by the ADE monitor derive from coded medication names and laboratory results, as well as text from clinician notes contained within the electronic medical record systems. The nontext rules are subdivided into five categories: medication, laboratory, medication-laboratory, ICD-9 codes, or miscellaneous. Rules target various diagnostic and laboratory abnormalities caused by a broad range of outpatient medications commonly used in primary care. Text-based rules were developed for certain medications by linking the medications with symptoms (ADE) that are often associated with short- or long-term. The rules were run on 4 months of data at both sites, possible ADEs were identified and validated by chart review, and the positive predictive values of each rule were calculated. We found that clinically based rule sets can be developed and implemented at different outpatient settings using distinct information systems to identify ADEs related to commonly prescribed outpatient medications.

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