Detecting adverse drug events through data mining

American Journal of Health-System Pharmacy

Volumn 67, Issue 4, 2010, Pages 317-320

  Glasgow, Justin M ^a,b   Kaboli, Peter J ^a,b  

^a UNIVERSITY OF IOWA   (United States)

^b VETERANS AFFAIRS MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANTIDIARRHEAL AGENT; ANTIEMETIC AGENT; DIPHENHYDRAMINE; FLUMAZENIL; GLUCOSE; NALOXONE; PHYTOMENADIONE; POLYSTYRENESULFONATE SODIUM;

ABSENCE OF SIDE EFFECTS; ADVERSE DRUG REACTION; CLINICAL PRACTICE; DATA ANALYSIS; HEALTH CARE DELIVERY; HEALTH CARE SYSTEM; HOSPITAL PATIENT; HUMAN; MEDICAL RECORD REVIEW; NOTE; PRIORITY JOURNAL; UNSPECIFIED SIDE EFFECT;

EID: 77249087630     PISSN: 10792082     EISSN: None     Source Type: Journal    
DOI: 10.2146/ajhp090115     Document Type: Note

References (14)

1. Kohn LT, Corrigan JM, Donaldson MS, eds. To err is human: building a safer health system. Washington, DC: National Academy Press; 1999.
2. Bates DW, Cullen DJ, Laird N et al. Incidence of adverse drug events and potential adverse drug events. Implications for prevention. JAMA. 1995;274:29-34.
3. Lazarou J, Pomeranz BH, Corey PN. Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA. 1998; 279:1200-5.
4. Rozich JD, Haraden CR, Resar RK. Adverse drug event trigger tool: a practical methodology for measuring medication related harm. Qual Saf Health Care. 2003; 12:194-7.
5. Kaboli PJ, Glasgow JM, Jaipaul CK et al. Identifying medication misadventures: poor agreement between chart, physician, nurse, and patient reports. Pharmacotherapy. In press.
6. Classen DC, Pestotnik SL, Evans RS et al. Computerized surveillance of adverse drug events in hospital patients. JAMA. 1991; 266:2847-51.
7. Jha AK, Laguette J, Seger A et al. Can surveillance systems identify and avert adverse drug events? A prospective evaluation of a commercial application. J Am Med Inform Assoc. 2008; 15:647-53.
8. Handles SM, Altman RL, Perera S et al. A systematic review of the performance characteristics of clinical event monitor signals used to detect adverse drug events in the hospital setting. J Am Med Inform Assoc. 2007; 14:451-7.
9. Felkey BG, Liang H, Kruger KP. Data mining for the health system pharmacist. Hosp Pharm. 2003; 38:845-50.
10. Aspden P, Wolcott JA, Bootman L et al., eds. Preventing medication errors. Washington, DC: National Academies Press; 2007.
11. Aspden P, Corrigan JM, Wolcott J et al., eds. Patient safety: achieving a new standard of care. Washington, DC: National Academies Press; 2004.
12. Hauben M, Horn S, Reich L. Potential use of data-mining algorithms for the detection of 'surprise' adverse drug reactions. Drug Saf. 2007; 30:143-55.
13. Stephenson WP, Hauben M. Data mining for signals in spontaneous reporting databases: proceed with caution. Pharmacoepidemiol Drug Saf. 2007; 16:359-65.
14. Witten 1H, Frank E. Data mining: practical machine learning tools and techniques. 2nd ed. San Francisco: Morgan Kaufmann; 2005:189-200.