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Comparing the Effectiveness of Computerized Adverse Drug Event Monitoring Systems to Enhance Clinical Decision Support for Hospitalized Patients
11 November 2009
accepted: 30 July 2010
16 December 2017 (online)
Objective: Performance of computerized adverse drug event (ADE) monitoring of electronic health records through a prospective ADE Monitor and ICD9-coded clinical text review operating independently and simultaneously on the same patient population for a 10-year period are compared. Requirements are compiled for clinical decision support in pharmacy systems to enhance ADE detection.
Methods: A large tertiary care facility in Utah, with a history of quality improvement using its advanced hospital information system, was leveraged in this study. ICD9-based review of clinical charts (ICD9 System) was compared quantitatively and qualitatively to computer-assisted pharmacist-verified ADEs (ADE Monitor). The capture-recapture statistical method was applied to the data to determine an estimated prevalence of ADEs.
Results: A total estimated ADE prevalence of 5.53% (13,420/242,599) was calculated, with the ICD9 system identifying 2,604 or 19.4%, and the ADE monitor 3,386 or 25.2% of all estimated ADEs. Both methods commonly identified 4.9% of all estimated ADEs and matched 62.0% of the time, each having its strength in detecting a slightly different domain of ADEs. 70% of the ADE documentation in the clinical notes was found in the discharge summaries.
Conclusion: Coupled with spontaneous reporting, computerized methods account for approximately half of all ADEs that can currently be detected. To enhance ADE monitoring and patient safety in a hospitalized setting, pharmacy information systems should incorporate prospective structuring and coding of the text in clinical charts and using that data alongside computer-generated alerts of laboratory results and drug orders. Natural language processing can aid computerized detection by automating the coding, in real-time, of physician text from clinical charts so that decision support rules can be created and applied. New detection strategies and enhancements to existing systems should be researched to enhance the detection of ADEs since approximately half are not currently detected.
- 1 Jha AK. et al. Identifying adverse drug events: development of a computer-based monitor and comparison with chart review and stimulated voluntary report. J Am Med Inform Assoc 1998; 5: 305-314.
- 2 Cullen DJ. et al. The incident reporting system does not detect adverse drug events: a problem for quality improvement. Jt Comm J Qual Improv 1995; 21: 541-548.
- 3 Classen DC. et al. Computerized surveillance of adverse drug events in hospital patients. JAMA 1991; 266: 2847-2851.
- 4 Hougland P. et al. Performance of International Classification Of Diseases, 9th Revision, Clinical Modification codes as an adverse drug event surveillance system. Med Care 2006; 44: 629-636.
- 5 Bates DW. et al. Detecting adverse events using information technology. J Am Med Inform Assoc 2003; 10: 115-128.
- 6 AHRQ.. Reducing and Preventing Adverse Drug Events to Decrease Hospital Costs. Research in Action, Issue 1 2001; AHRQ Publication Number 01-0020.
- 7 http://imi.europa.eu/documents_en.html
- 8 http://omop.fnih.org/node/22
- 9 http://ftp.cdc.gov/pub/Health_Statistics/NCHS/Publications/ICD9-CM/2001/Guide02.RTF.ICD9CodingGuidelines(FY02) 2002
- 10 ICD9 Coding Handbook. In: Brown F. ed American Hospital Association. 2002: 400.
- 11 McCarthy EP. et al. Does clinical evidence support ICD9 diagnosis coding of complications?. Med Care 2000; 38: 868-876.
- 12 Committee UHD. Adverse Events Related to Medical Care, Utah: 1995-99. Salt Lake City: Utah Department of Health,; 2001
- 13 Gardner RM, Pryor TA, Warner HR. The HELP hospital information system: update 1998. Int J Med Inf 1999; 54: 169-182.
- 14 The ICD9 Classification of Adverse Events, Version 2002. Salt Lake City, UT: The Utah/Missouri Patient Safety Project National Expert Panel; 2002
- 15 3M Codefinder Software. Vol. 2003: 3M Health Information Systems. 2003
- 16 Pryor TA. et al. The HELP system. J Med Syst 1983; 7: 87-102.
- 17 Haug PJ. et al. Decision support in medicine: examples from the HELP system. Comput Biomed Res 1994; 27: 396-418.
- 18 Kuperman GJ, Gardner RM, Pryor TA. HELP: A Dynamic Hospital Information System. In: Orthner HF. ed. Computers and Medicine. Springer-Verlag; 1990
- 19 Evans RS. et al. Development of a computerized adverse drug event monitor. Proc Annu Symp Comput Appl Med Care 1991: 23-27.
- 20 Classen DC. et al. Description of a computerized adverse drug event monitor using a hospital information system. Hosp Pharm 1992; 27: 774 776-9, 783.
- 21 Evans RS. et al. Prevention of adverse drug events through computerized surveillance. Proc Annu Symp Comput Appl Med Care 1992: 437-441.
- 22 Naranjo CA, Lanctot KL. Recent developments in computer-assisted diagnosis of putative adverse drug reactions. Drug Saf 1991; 6: 315-322.
- 23 Classen DC. et al. Adverse drug events in hospitalized patients. Excess length of stay, extra costs, and attributable mortality. JAMA 1997; 277: 301-306.
- 24 Evans RS. et al. Using a hospital information system to assess the effects of adverse drug events. Proc Annu Symp Comput Appl Med Care 1993: 161-165.
- 25 Evans RS. et al. Preventing adverse drug events in hospitalized patients. Ann Pharmacother 1994; 28: 523-527.
- 26 Petratos GN. Masters Thesis Published by the University of Utah. 2003
- 27 Martyn CN. Capture-recapture methods in surveys of diseases of the nervous system. J Neurol Neurosurg Psychiatry 1998; 64: 2-3.
- 28 Lazarou J, Pomeranz BH, Corey PN. Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA 1998; 279: 1200-1205.
- 29 Hafner Jr JW. et al. Adverse drug events in emergency department patients. Ann Emerg Med 2002; 39: 258-267.
- 30 Rothschild JM. et al. Analysis of medication-related malpractice claims: causes, preventability, and costs. Arch Intern Med 2002; 162: 2414-2420.
- 31 Friedman C. et al. Natural language processing in an operational clinical information system. Nat Lang Eng 1995; 1: 83-108.
- 32 Jha AK. et al. Identifying hospital admissions due to adverse drug events using a computer-based monitor. Pharmacoepidemiol Drug Saf 2001; 10: 113-119.
- 33 Phansalkar S. et al. Use of verbal protocol analysis for identification of ADE signals. AMIA Annu Symp Proc 2006; 1063.
- 34 Phansalkar S. et al. Understanding pharmacist decision making for adverse drug event (ADE) detection. J Eval Clin Pract 2009; 15: 266-275.