Recent Developments in Computer-Assisted Diagnosis of Putative Adverse Drug Reactions

Naranjo, Claudio A.; Lanctôt, Krista L.

doi:10.2165/00002018-199106050-00001

Cited by 14 publications

(3 citation statements)

References 28 publications

(60 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The alerts were based on pre-defined algorithms and rules that considered all relevant and coded patient data, such as drug orders, laboratory results, nurse charting and physiologic changes from the integrated clinical database [3,[19][20][21]. The pharmacist verified each ADE alert by determining which drug may have been associated with the clinical manifestation experienced by the patient [22]. All ADE monitor alerts were relayed to the prescribing physicians to ensure timely therapeutic action and the documentation was stored in a database within the Clinical Information System [3,4,[19][20][21][23][24][25].…”

Section: Ade Monitor Adesmentioning

confidence: 99%

Comparing the Effectiveness of Computerized Adverse Drug Event Monitoring Systems to Enhance Clinical Decision Support for Hospitalized Patients

Petratos

Kim²,

Evans³

et al. 2010

Appl Clin Inform

View full text Add to dashboard Cite

KeywordsClinical decision support, pharmacy information systems, error management and prevention, data collection, system improvement SummaryObjective: 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.

show abstract

Section: Ade Monitor Adesmentioning

confidence: 99%

Comparing the Effectiveness of Computerized Adverse Drug Event Monitoring Systems to Enhance Clinical Decision Support for Hospitalized Patients

Petratos

Kim²,

Evans³

et al. 2010

Appl Clin Inform

View full text Add to dashboard Cite

show abstract

“…In assessing the causality of veterinary drug reactions, the approach described above is built intuitively into the interpretation prepared by the reporter. For human medicines however, there are a number of rather more formal approaches involving differential diagnosis, probability ratings, decision trees, and methods involving computer algorithms, Bayesian calculations and fuzzy logic reasoning (Naranjo et al, 1990(Naranjo et al, ,1992Naranjo & Lanctôt, 1991;Lanctôt & Naranjo, 1994,1995Stephens, 2000;Sproule et al, 2002).…”

Section: Algorithms For Causalitymentioning

confidence: 99%

“…For human medicines however, there are a number of rather more formal approaches involving differential diagnosis, probability ratings, decision trees, and methods involving computer algorithms, Bayesian calculations and fuzzy logic reasoning (Naranjo et al. , 1990,1992; Naranjo & Lanctôt, 1991; Lanctôt & Naranjo, 1994,1995; Stephens, 2000; Sproule et al. , 2002).…”

Section: Algorithms For Causalitymentioning

confidence: 99%

Veterinary pharmacovigilance. Part 5. Causality and expectedness

Woodward¹

2005

Vet Pharm & Therapeutics

View full text Add to dashboard Cite

In the European Union and elsewhere there is a requirement to ascribe causality to adverse drug reactions which occur in treated animals. In the EU, the ABON system of causality assessment is used but the assignment of causality assigned is not always self evident, and it may be complicated for a variety of reasons. In this paper, the approaches to causality assessment, based on a number of key criteria which examine the administration of the drug in relation to the sequence of ensuing events and the presence of biological plausibility are examined, along with the utility of using algorithms to facilitate this process. Unexpected adverse drug reactions usually require expedited reporting, depending on national or regional regulatory requirements. Again, deciding on what might constitute an expected (or unexpected) adverse reaction, particularly when a product may be intended for use in a number of species, and when within any one species a number of breeds may be treated, is not necessarily a straightforward issue. However, an approach to facilitate the decision- making process, based on a similar approach used in the pharmacovigilance of human medicinal products is discussed.

show abstract

Causality in Pharmacovigilance and Expectedness of Adverse Reactions

Woodward¹

2009

Veterinary Pharmacovigilance

View full text Add to dashboard Cite

Recent Developments in Computer-Assisted Diagnosis of Putative Adverse Drug Reactions

Cited by 14 publications

References 28 publications

Comparing the Effectiveness of Computerized Adverse Drug Event Monitoring Systems to Enhance Clinical Decision Support for Hospitalized Patients

Comparing the Effectiveness of Computerized Adverse Drug Event Monitoring Systems to Enhance Clinical Decision Support for Hospitalized Patients

Veterinary pharmacovigilance. Part 5. Causality and expectedness

Causality in Pharmacovigilance and Expectedness of Adverse Reactions

Contact Info

Product

Resources

About